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Veller C, Przeworski M, Coop G. Causal interpretations of family GWAS in the presence of heterogeneous effects. Proc Natl Acad Sci U S A 2024; 121:e2401379121. [PMID: 39269774 DOI: 10.1073/pnas.2401379121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 07/26/2024] [Indexed: 09/15/2024] Open
Abstract
Family-based genome-wide association studies (GWASs) are often claimed to provide an unbiased estimate of the average causal effects (or average treatment effects; ATEs) of alleles, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. We show that this claim does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. This feature will matter if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in linkage disequilibrium patterns. At a single locus, family-based GWAS can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (i.e., a local average treatment effect; LATE). This interpretation does not extend to polygenic scores (PGSs), however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate of the LATE for any subset or weighted average of families. In practice, the potential biases of a family-based GWAS are likely smaller than those that can arise from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, their causal interpretation is less straightforward than has been widely appreciated.
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Affiliation(s)
- Carl Veller
- Department of Ecology & Evolution, University of Chicago, Chicago, IL 60637
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University, New York, NY 10027
- Department of Systems Biology, Columbia University, New York, NY 10032
| | - Graham Coop
- Center for Population Biology and Department of Evolution and Ecology, University of California, Davis, CA 95616
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Veller C, Coop GM. Interpreting population- and family-based genome-wide association studies in the presence of confounding. PLoS Biol 2024; 22:e3002511. [PMID: 38603516 PMCID: PMC11008796 DOI: 10.1371/journal.pbio.3002511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 01/19/2024] [Indexed: 04/13/2024] Open
Abstract
A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.
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Affiliation(s)
- Carl Veller
- Department of Ecology & Evolution, University of Chicago, Chicago, Illinois, United States of America
| | - Graham M. Coop
- Department of Evolution and Ecology, and Center for Population Biology, University of California, Davis, California, United States of America
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Brugger M, Lutz M, Müller-Nurasyid M, Lichtner P, Slater EP, Matthäi E, Bartsch DK, Strauch K. Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer. Hum Hered 2024; 89:8-31. [PMID: 38198765 DOI: 10.1159/000535840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/07/2023] [Indexed: 01/12/2024] Open
Abstract
INTRODUCTION Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. METHODS In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). RESULTS Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. CONCLUSION Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.
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Affiliation(s)
- Markus Brugger
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Institute of Medical Information Processing, Biometry and Epidemiology - IBE, LMU Munich, Munich, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Manuel Lutz
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Institute of Medical Information Processing, Biometry and Epidemiology - IBE, LMU Munich, Munich, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Institute of Medical Information Processing, Biometry and Epidemiology - IBE, LMU Munich, Munich, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Peter Lichtner
- Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Emily P Slater
- Department of Visceral, Thoracic and Vascular Surgery, Philipps University, Marburg, Germany
| | - Elvira Matthäi
- Department of Visceral, Thoracic and Vascular Surgery, Philipps University, Marburg, Germany
| | - Detlef K Bartsch
- Department of Visceral, Thoracic and Vascular Surgery, Philipps University, Marburg, Germany
| | - Konstantin Strauch
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Institute of Medical Information Processing, Biometry and Epidemiology - IBE, LMU Munich, Munich, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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Veller C, Przeworski M, Coop G. Causal interpretations of family GWAS in the presence of heterogeneous effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566950. [PMID: 38014124 PMCID: PMC10680648 DOI: 10.1101/2023.11.13.566950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Family-based genome-wide association studies (GWAS) have emerged as a gold standard for assessing causal effects of alleles and polygenic scores. Notably, family studies are often claimed to provide an unbiased estimate of the average causal effect (or average treatment effect; ATE) of an allele, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. Here, we show that this interpretation does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. Consequently, if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in LD patterns, family studies provide a biased estimate of the average effect in the sample. At a single locus, family-based association studies can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (i.e., a local average treatment effect; LATE). This interpretation does not extend to polygenic scores, however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate for any subset or weighted average of families. Instead, family-based studies can be reinterpreted as enabling an unbiased estimate of the extent to which Mendelian segregation at loci in the PGS contributes to the population-level variance in the trait. Because this estimate does not include the between-family variance, however, this interpretation applies to only (roughly) half of the sample PGS variance. In practice, the potential biases of a family-based GWAS are likely smaller than those arising from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, the causal interpretation of family-based GWAS estimates is less straightforward than has been widely appreciated.
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Affiliation(s)
- Carl Veller
- Department of Ecology and Evolution, University of Chicago
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University
- Department of Systems Biology, Columbia University
| | - Graham Coop
- Center for Population Biology and Department of Evolution and Ecology, University of California, Davis
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Cretin J, Adjemout M, Dieppois C, Gallardo F, Torres M, Merard Z, Sawadogo SA, Picard C, Rihet P, Paul P. A Non-Coding Fc Gamma Receptor Cis-Regulatory Variant within the 1q23 Gene Cluster Is Associated with Plasmodium falciparum Infection in Children Residing in Burkina Faso. Int J Mol Sci 2023; 24:15711. [PMID: 37958695 PMCID: PMC10650193 DOI: 10.3390/ijms242115711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/27/2023] [Accepted: 08/31/2023] [Indexed: 11/15/2023] Open
Abstract
Antibodies play a crucial role in activating protective immunity against malaria by interacting with Fc-gamma receptors (FcγRs). Genetic variations in genes encoding FcγRs can affect immune cell responses to the parasite. In this study, our aim was to investigate whether non-coding variants that regulate FcγR expression could influence the prevalence of Plasmodium falciparum infection. Through bioinformatics approaches, we selected expression quantitative trait loci (eQTL) for FCGR2A, FCGR2B, FCGR2C, FCGR3A, and FCGR3B genes encoding FcγRs (FCGR), in whole blood. We prioritized two regulatory variants, rs2099684 and rs1771575, located in open genomic regions. These variants were identified using RegVar, ImmuNexUT, and transcription factor annotations specific to immune cells. In addition to these, we genotyped the coding variants FCGR2A/rs1801274 and FCGR2B/rs1050501 in 234 individuals from a malaria-endemic area in Burkina Faso. We conducted age and family-based analyses to evaluate associations with the prevalence of malarial infection in both children and adults. The analysis revealed that the regulatory rs1771575-CC genotype was predicted to influence FCGR2B/FCGR2C/FCGR3A transcripts in immune cells and was the sole variant associated with a higher prevalence of malarial infection in children. In conclusion, this study identifies the rs1771575 cis-regulatory variant affecting several FcγRs in myeloid and neutrophil cells and associates it with the inter-individual capacity of children living in Burkina Faso to control malarial infection.
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Affiliation(s)
- Jules Cretin
- INSERM 1090, TAGC Theories and Approaches of Genomic Complexity, Campus de Luminy, Aix Marseille University, 13288 Marseille, France (M.A.); (C.D.); (F.G.); (M.T.)
- Institut MarMaRa, 13288 Marseille, France
| | - Mathieu Adjemout
- INSERM 1090, TAGC Theories and Approaches of Genomic Complexity, Campus de Luminy, Aix Marseille University, 13288 Marseille, France (M.A.); (C.D.); (F.G.); (M.T.)
- Institut MarMaRa, 13288 Marseille, France
| | - Christelle Dieppois
- INSERM 1090, TAGC Theories and Approaches of Genomic Complexity, Campus de Luminy, Aix Marseille University, 13288 Marseille, France (M.A.); (C.D.); (F.G.); (M.T.)
| | - Frederic Gallardo
- INSERM 1090, TAGC Theories and Approaches of Genomic Complexity, Campus de Luminy, Aix Marseille University, 13288 Marseille, France (M.A.); (C.D.); (F.G.); (M.T.)
| | - Magali Torres
- INSERM 1090, TAGC Theories and Approaches of Genomic Complexity, Campus de Luminy, Aix Marseille University, 13288 Marseille, France (M.A.); (C.D.); (F.G.); (M.T.)
| | - Zachary Merard
- ADES UMR, Aix Marseille University, 13288 Marseille, France (C.P.)
| | - Serge Aimé Sawadogo
- Unité de Formation en Sciences de la Santé (UFR/SDS), Université Joseph KI-ZERBO, Ouagadougou 03 BP 7021, Burkina Faso;
- Centre PrïmO-Nelson Mandela, 84 rue Sao Tomé et Principe, Ouagadougou 09 BP 706, Burkina Faso
| | - Christophe Picard
- ADES UMR, Aix Marseille University, 13288 Marseille, France (C.P.)
- Immunogenetics Laboratory, Etablissement Français du Sang PACA-Corse, 13001 Marseille, France
| | - Pascal Rihet
- INSERM 1090, TAGC Theories and Approaches of Genomic Complexity, Campus de Luminy, Aix Marseille University, 13288 Marseille, France (M.A.); (C.D.); (F.G.); (M.T.)
| | - Pascale Paul
- INSERM 1090, TAGC Theories and Approaches of Genomic Complexity, Campus de Luminy, Aix Marseille University, 13288 Marseille, France (M.A.); (C.D.); (F.G.); (M.T.)
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Veddum L, Greve AN, Gregersen M, Andreassen AK, Knudsen CB, Brandt JM, Krantz MF, Søndergaard A, Burton BK, Jepsen JRM, Hemager N, Werge T, Thorup AAE, Nordentoft M, Mors O, Nudel R. A study of the genetic architecture of social responsiveness in families with parental schizophrenia or bipolar disorder and population-based controls. Psychiatry Res 2023; 326:115280. [PMID: 37339530 DOI: 10.1016/j.psychres.2023.115280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/10/2023] [Accepted: 05/30/2023] [Indexed: 06/22/2023]
Abstract
Twin-studies of social responsiveness have reported moderate to high heritabilities, but studies using parent-child data are lacking. Additionally, social impairments have been suggested as a vulnerability marker for schizophrenia and bipolar disorder, but the heritability of social responsiveness in this context is unknown. This study is part of the Danish High Risk and Resilience Study - VIA, comprising families with one parent with schizophrenia (n = 202) or bipolar disorder (n = 120) and population-based controls (PBC, n = 200). Social responsiveness was assessed with The Social Responsiveness Scale, Second Edition (SRS-2). Heritability was estimated from variance components, and a polygenic risk score (PRS) for autism spectrum disorder (ASD) was calculated to assess the genetic relationship between ASD and SRS-2. SRS-2 heritability was moderate to high and significantly different from zero in all groups when the children were rated by the primary caregiver. With teacher ratings, the heritability was lower and only significant in the full cohort and PBC. We found no significant association between SRS-2 and PRS for ASD. Our study confirms that social responsiveness is heritable, but that heritability estimates are affected by the child-respondent relation and familial risk of mental illness. This has implications for clinical practice and research using SRS-2 and provides insight on the familial transmission of mental illness.
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Affiliation(s)
- Lotte Veddum
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, Aarhus University, Denmark; The Psychosis Research Unit, Aarhus University Hospital Skejby, Psychiatry, Denmark; iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark.
| | - Aja Neergaard Greve
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, Aarhus University, Denmark; The Psychosis Research Unit, Aarhus University Hospital Skejby, Psychiatry, Denmark; iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark
| | - Maja Gregersen
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; CORE - Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen, Denmark
| | - Anna Krogh Andreassen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, Aarhus University, Denmark; The Psychosis Research Unit, Aarhus University Hospital Skejby, Psychiatry, Denmark; iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark
| | - Christina Bruun Knudsen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, Aarhus University, Denmark; The Psychosis Research Unit, Aarhus University Hospital Skejby, Psychiatry, Denmark; iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark
| | - Julie Marie Brandt
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; CORE - Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen, Denmark; University of Copenhagen - Faculty of Health and Medical Sciences, Institute of Clinical Medicine, Denmark
| | - Mette Falkenberg Krantz
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; CORE - Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen, Denmark; Child and Adolescent Mental Health Center, Research Unit, Capital Region of Copenhagen, Denmark
| | - Anne Søndergaard
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; CORE - Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen, Denmark; University of Copenhagen - Faculty of Health and Medical Sciences, Institute of Clinical Medicine, Denmark
| | - Birgitte Klee Burton
- Child and Adolescent Mental Health Center, Research Unit, Capital Region of Copenhagen, Denmark; University of Copenhagen - Faculty of Health and Medical Sciences, Institute of Clinical Medicine, Denmark; Department of Child and Adolescent Psychiatry, Copenhagen University Hospital, Psychiatry Region Zealand, Denmark
| | - Jens Richardt Møllegaard Jepsen
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; CORE - Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen, Denmark; Child and Adolescent Mental Health Center, Research Unit, Capital Region of Copenhagen, Denmark; Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Center, Copenhagen University Hospital, Mental Health Services Copenhagen, Denmark
| | - Nicoline Hemager
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; CORE - Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen, Denmark; Child and Adolescent Mental Health Center, Research Unit, Capital Region of Copenhagen, Denmark
| | - Thomas Werge
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Denmark
| | - Anne Amalie Elgaard Thorup
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; Child and Adolescent Mental Health Center, Research Unit, Capital Region of Copenhagen, Denmark; University of Copenhagen - Faculty of Health and Medical Sciences, Institute of Clinical Medicine, Denmark
| | - Merete Nordentoft
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; CORE - Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen, Denmark; University of Copenhagen - Faculty of Health and Medical Sciences, Institute of Clinical Medicine, Denmark
| | - Ole Mors
- The Psychosis Research Unit, Aarhus University Hospital Skejby, Psychiatry, Denmark; iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark
| | - Ron Nudel
- iPSYCH - The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark; CORE - Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen, Denmark.
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Beck JJ, Ahmed T, Finnicum CT, Zwinderman K, Ehli EA, Boomsma DI, Hottenga JJ. Genetic Ancestry Estimates within Dutch Family Units and Across Genotyping Arrays: Insights from Empirical Analysis Using Two Estimation Methods. Genes (Basel) 2023; 14:1497. [PMID: 37510400 PMCID: PMC10379078 DOI: 10.3390/genes14071497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Accurate inference of genetic ancestry is crucial for population-based association studies, accounting for population heterogeneity and structure. This study analyzes genome-wide SNP data from the Netherlands Twin Register to compare genetic ancestry estimates. The focus is on the comparison of ancestry estimates between family members and individuals genotyped on multiple arrays (Affymetrix 6.0, Affymetrix Axiom, and Illumina GSA). Two conventional methods, principal component analysis and ADMIXTURE, were implemented to estimate ancestry, each serving its specific purpose, rather than for direct comparison. The results reveal that as the degree of genetic relatedness decreases, the Euclidean distances of genetic ancestry estimates between family members significantly increase (empirical p < 0.001), regardless of the estimation method and genotyping array. Ancestry estimates among individuals genotyped on multiple arrays also show statistically significant differences (empirical p < 0.001). Additionally, this study investigates the relationship between the ancestry estimates of non-identical twin offspring with ancestrally diverse parents and those with ancestrally similar parents. The results indicate a statistically significant weak correlation between the variation in ancestry estimates among offspring and differences in ancestry estimates among parents (Spearman's rho: 0.07, p = 0.005). This study highlights the utility of current methods in inferring genetic ancestry, emphasizing the importance of reference population composition in determining ancestry estimates.
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Affiliation(s)
- Jeffrey J Beck
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
| | - Talitha Ahmed
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Casey T Finnicum
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
| | - Koos Zwinderman
- Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Erik A Ehli
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
| | - Dorret I Boomsma
- Avera Institute for Human Genetics, Avera McKennan Hospital and University Health Center, Sioux Falls, SD 57105, USA
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
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LaPierre N, Fu B, Turnbull S, Eskin E, Sankararaman S. Leveraging family data to design Mendelian randomization that is provably robust to population stratification. Genome Res 2023; 33:1032-1041. [PMID: 37197991 PMCID: PMC10538495 DOI: 10.1101/gr.277664.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/16/2023] [Indexed: 05/19/2023]
Abstract
Mendelian randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases owing to weak instruments, as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We show in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, whereas standard MR methods yield inflated false positive rates. We then conduct an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank data set. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, whereas MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated owing to confounding from population stratification.
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Affiliation(s)
- Nathan LaPierre
- Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA;
| | - Boyang Fu
- Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Steven Turnbull
- Department of Statistics, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Eleazar Eskin
- Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, California 90095, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California 90095, USA
| | - Sriram Sankararaman
- Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA;
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, California 90095, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California 90095, USA
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Hill T, Cassibba V, Joukhadar I, Tonnessen B, Havlik C, Ortega F, Sripolcharoen S, Visser BJ, Stoffel K, Thammapichai P, Garcia-Llanos A, Chen S, Hulse-Kemp A, Walker S, Van Deynze A. Genetics of destemming in pepper: A step towards mechanical harvesting. Front Genet 2023; 14:1114832. [PMID: 37007971 PMCID: PMC10064014 DOI: 10.3389/fgene.2023.1114832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 01/31/2023] [Indexed: 03/19/2023] Open
Abstract
Introduction: The majority of peppers in the US for fresh market and processing are handpicked, and harvesting can account for 20-50% of production costs. Innovation in mechanical harvesting would increase availability; lower the costs of local, healthy vegetable products; and perhaps improve food safety and expand markets. Most processed peppers require removal of pedicels (stem and calyx) from the fruit, but lack of an efficient mechanical process for this operation has hindered adoption of mechanical harvest. In this paper, we present characterization and advancements in breeding green chile peppers for mechanical harvesting. Specifically, we describe inheritance and expression of an easy-destemming trait derived from the landrace UCD-14 that facilitates machine harvest of green chiles. Methods: A torque gauge was used for measuring bending forces similar to those of a harvester and applied to two biparental populations segregating for destemming force and rate. Genotyping by sequencing was used to generate genetic maps for quantitative trait locus (QTL) analyses. Results: A major destemming QTL was found on chromosome 10 across populations and environments. Eight additional population and/or environment-specific QTL were also identified. Chromosome 10 QTL markers were used to help introgress the destemming trait into jalapeño-type peppers. Low destemming force lines combined with improvements in transplant production enabled mechanical harvest of destemmed fruit at a rate of 41% versus 2% with a commercial jalapeńo hybrid. Staining for the presence of lignin at the pedicel/fruit boundary indicated the presence of an abscission zone and homologs of genes known to affect organ abscission were found under several QTL, suggesting that the easy-destemming trait may be due to the presence and activation of a pedicel/fruit abscission zone. Conclusion: Presented here are tools to measure the easy-destemming trait, its physiological basis, possible molecular pathways, and expression of the trait in various genetic backgrounds. Mechanical harvest of destemmed mature green chile fruits was achieved by combining easy-destemming with transplant management.
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Affiliation(s)
- Theresa Hill
- Seed Biotechnology Center, University of California, Davis, Davis, CA, United States
| | - Vincenzo Cassibba
- Seed Biotechnology Center, University of California, Davis, Davis, CA, United States
| | - Israel Joukhadar
- Department of Extension Plant Sciences, New Mexico State University, Las Cruces, NM, United States
| | - Bradley Tonnessen
- Department of Extension Plant Sciences, New Mexico State University, Las Cruces, NM, United States
| | - Charles Havlik
- Los Lunas Agricultural Science Center, Los Lunas, NM, United States
| | - Franchesca Ortega
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
| | | | | | - Kevin Stoffel
- Seed Biotechnology Center, University of California, Davis, Davis, CA, United States
| | - Paradee Thammapichai
- Seed Biotechnology Center, University of California, Davis, Davis, CA, United States
| | - Armando Garcia-Llanos
- Seed Biotechnology Center, University of California, Davis, Davis, CA, United States
| | - Shiyu Chen
- Seed Biotechnology Center, University of California, Davis, Davis, CA, United States
| | - Amanda Hulse-Kemp
- Seed Biotechnology Center, University of California, Davis, Davis, CA, United States
| | - Stephanie Walker
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
| | - Allen Van Deynze
- Seed Biotechnology Center, University of California, Davis, Davis, CA, United States
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10
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Veller C, Coop G. Interpreting population and family-based genome-wide association studies in the presence of confounding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.26.530052. [PMID: 36909521 PMCID: PMC10002712 DOI: 10.1101/2023.02.26.530052] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding, and can also absorb the 'indirect' genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of Mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect size estimates are used in polygenic scores. We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. In addition to known biases that can arise in family-based GWASs when interactions between family members are ignored, we show that biases can also arise from gene-by-environment (G×E) interactions when parental genotypes are not distributed identically across interacting environmental and genetic backgrounds. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding and interactions.
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Affiliation(s)
- Carl Veller
- Department of Evolution and Ecology, and Center for Population Biology, University of California, Davis, CA 95616
| | - Graham Coop
- Department of Evolution and Ecology, and Center for Population Biology, University of California, Davis, CA 95616
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11
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LaPierre N, Fu B, Turnbull S, Eskin E, Sankararaman S. Leveraging family data to design Mendelian Randomization that is provably robust to population stratification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522936. [PMID: 36711635 PMCID: PMC9881984 DOI: 10.1101/2023.01.05.522936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Mendelian Randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We applied MR-Twin to 121 trait pairs in the UK Biobank dataset and found that MR-Twin identifies likely causal trait pairs and does not identify trait pairs that are unlikely to be causal. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding.
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Affiliation(s)
| | - Boyang Fu
- Department of Computer Science, UCLA, Los Angeles CA
| | | | - Eleazar Eskin
- Department of Computer Science, UCLA, Los Angeles CA
- Department of Computational Medicine, UCLA, Los Angeles CA
- Department of Human Genetics, UCLA, Los Angeles CA
| | - Sriram Sankararaman
- Department of Computer Science, UCLA, Los Angeles CA
- Department of Computational Medicine, UCLA, Los Angeles CA
- Department of Human Genetics, UCLA, Los Angeles CA
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12
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A family-based study of genetic and epigenetic effects across multiple neurocognitive, motor, social-cognitive and social-behavioral functions. Behav Brain Funct 2022; 18:14. [PMID: 36457050 PMCID: PMC9714039 DOI: 10.1186/s12993-022-00198-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 09/09/2022] [Indexed: 12/03/2022] Open
Abstract
Many psychiatric and neurodevelopmental disorders are known to be heritable, but studies trying to elucidate the genetic architecture of such traits often lag behind studies of somatic traits and diseases. The reasons as to why relatively few genome-wide significant associations have been reported for such traits have to do with the sample sizes needed for the detection of small effects, the difficulty in defining and characterizing the phenotypes, partially due to overlaps in affected underlying domains (which is especially true for cognitive phenotypes), and the complex genetic architectures of the phenotypes, which are not wholly captured in traditional case-control GWAS designs. We aimed to tackle the last two issues by performing GWASs of eight quantitative neurocognitive, motor, social-cognitive and social-behavioral traits, which may be considered endophenotypes for a variety of psychiatric and neurodevelopmental conditions, and for which we employed models capturing both general genetic association and parent-of-origin effects, in a family-based sample comprising 402 children and their parents (mostly family trios). We identified 48 genome-wide significant associations across several traits, of which 3 also survived our strict study-wide quality criteria. We additionally performed a functional annotation of implicated genes, as most of the 48 associations were with variants within protein-coding genes. In total, our study highlighted associations with five genes (TGM3, CACNB4, ANKS1B, CSMD1 and SYNE1) associated with measures of working memory, processing speed and social behavior. Our results thus identify novel associations, including previously unreported parent-of-origin associations with relevant genes, and our top results illustrate new potential gene → endophenotype → disorder pathways.
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13
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Alamin M, Sultana MH, Lou X, Jin W, Xu H. Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS. PLANTS (BASEL, SWITZERLAND) 2022; 11:3277. [PMID: 36501317 PMCID: PMC9739826 DOI: 10.3390/plants11233277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.
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Affiliation(s)
- Md. Alamin
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | | | - Xiangyang Lou
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Wenfei Jin
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haiming Xu
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
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14
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Demange PA, Hottenga JJ, Abdellaoui A, Eilertsen EM, Malanchini M, Domingue BW, Armstrong-Carter E, de Zeeuw EL, Rimfeld K, Boomsma DI, van Bergen E, Breen G, Nivard MG, Cheesman R. Estimating effects of parents' cognitive and non-cognitive skills on offspring education using polygenic scores. Nat Commun 2022; 13:4801. [PMID: 35999215 PMCID: PMC9399113 DOI: 10.1038/s41467-022-32003-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 07/12/2022] [Indexed: 12/12/2022] Open
Abstract
Understanding how parents' cognitive and non-cognitive skills influence offspring education is essential for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings (N = 47,459), adoptees (N = 6407), and parent-offspring trios (N = 2534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36-40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify potential causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents' skills, facilitating future mechanistic work.
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Affiliation(s)
- Perline A Demange
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands.
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Espen Moen Eilertsen
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Margherita Malanchini
- Department of Biological and Experimental Psychology, School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Benjamin W Domingue
- Graduate School of Education, Stanford University, Stanford, CA, USA
- Center for Population Health Sciences, Stanford University, Stanford, CA, USA
- Center for Education Policy Analysis, Stanford University, Stanford, CA, USA
| | - Emma Armstrong-Carter
- Graduate School of Education, Stanford University, Stanford, CA, USA
- Center for Education Policy Analysis, Stanford University, Stanford, CA, USA
| | - Eveline L de Zeeuw
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Kaili Rimfeld
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychology, Royal Holloway University of London, London, UK
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gerome Breen
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway.
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
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15
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McGurk KA, Farrell L, Kendall AC, Keavney BD, Nicolaou A. Genetic analyses of circulating PUFA-derived mediators identifies heritable dihydroxyeicosatrienoic acid species. Prostaglandins Other Lipid Mediat 2022; 160:106638. [PMID: 35472599 DOI: 10.1016/j.prostaglandins.2022.106638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/30/2022] [Accepted: 04/20/2022] [Indexed: 10/18/2022]
Abstract
Estimates of heritability are the first step in identifying a trait with substantial variation due to genetic factors. Large-scale genetic analyses can identify the DNA variants that influence the levels of circulating lipid species and the statistical technique Mendelian randomisation can use these DNA variants to address potential causality of these lipids in disease. We estimated the heritability of plasma eicosanoids, octadecanoids and docosanoids to identify those lipid species with substantial heritability. We analysed plasma lipid mediators in 31 White British families (196 participants) ascertained for high blood pressure and deeply clinically and biochemically phenotyped over a 25-year period. We found that the dihydroxyeicosatrienoic acid (DHET) species, 11,12-DHET and 14,15-DHET, products of arachidonic acid metabolism by cytochrome P450 (CYP) monooxygenase and soluble epoxide hydrolase (sEH), exhibited substantial heritability (h2 = 33%-37%; Padj<0.05). Identification of these two heritable bioactive lipid species allows for future large-scale, targeted, lipidomics-genomics analyses to address causality in cardiovascular and other diseases.
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Affiliation(s)
- Kathryn A McGurk
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; Laboratory for Lipidomics and Lipid Research, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Laura Farrell
- Laboratory for Lipidomics and Lipid Research, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Alexandra C Kendall
- Laboratory for Lipidomics and Lipid Research, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Bernard D Keavney
- Manchester Heart Centre, Manchester University NHS Foundation Trust, UK
| | - Anna Nicolaou
- Laboratory for Lipidomics and Lipid Research, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
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16
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Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, Chittoor G, Ahlskog R, Lind PA, Palviainen T, van der Zee MD, Cheesman R, Mangino M, Wang Y, Li S, Klaric L, Ratliff SM, Bielak LF, Nygaard M, Giannelis A, Willoughby EA, Reynolds CA, Balbona JV, Andreassen OA, Ask H, Baras A, Bauer CR, Boomsma DI, Campbell A, Campbell H, Chen Z, Christofidou P, Corfield E, Dahm CC, Dokuru DR, Evans LM, de Geus EJC, Giddaluru S, Gordon SD, Harden KP, Hill WD, Hughes A, Kerr SM, Kim Y, Kweon H, Latvala A, Lawlor DA, Li L, Lin K, Magnus P, Magnusson PKE, Mallard TT, Martikainen P, Mills MC, Njølstad PR, Overton JD, Pedersen NL, Porteous DJ, Reid J, Silventoinen K, Southey MC, Stoltenberg C, Tucker-Drob EM, Wright MJ, Hewitt JK, Keller MC, Stallings MC, Lee JJ, Christensen K, Kardia SLR, Peyser PA, Smith JA, Wilson JF, Hopper JL, Hägg S, Spector TD, Pingault JB, Plomin R, Havdahl A, Bartels M, Martin NG, Oskarsson S, Justice AE, Millwood IY, Hveem K, Naess Ø, Willer CJ, Åsvold BO, Koellinger PD, Kaprio J, Medland SE, Walters RG, Benjamin DJ, Turley P, Evans DM, Davey Smith G, Hayward C, Brumpton B, Hemani G, Davies NM. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet 2022; 54:581-592. [PMID: 35534559 PMCID: PMC9110300 DOI: 10.1038/s41588-022-01062-7] [Citation(s) in RCA: 124] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 03/25/2022] [Indexed: 02/01/2023]
Abstract
Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.
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Affiliation(s)
- Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Michel G Nivard
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ailin F Hansen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Humaira Rasheed
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yoonsu Cho
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Geetha Chittoor
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Rafael Ahlskog
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Teemu Palviainen
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Matthijs D van der Zee
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Yunzhang Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Scott M Ratliff
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Marianne Nygaard
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | | | | | - Chandra A Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Jared V Balbona
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Christopher R Bauer
- BioMarin Pharmaceutical Inc., Novato, CA, USA
- Biomedical and Translational Informatics, Geisinger Health, Danville, PA, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health (APH) and Amsterdam Reproduction and Development (AR&D), Amsterdam, the Netherlands
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | | | - Elizabeth Corfield
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | | | - Deepika R Dokuru
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
- Department of Ecology & Evolutionary Biology, University of Colorado at Boulder, Boulder, CO, USA
| | - Eco J C de Geus
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
| | - Sudheer Giddaluru
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Scott D Gordon
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - K Paige Harden
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - W David Hill
- Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Shona M Kerr
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Yongkang Kim
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Hyeokmoon Kweon
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Antti Latvala
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
- Institute of Criminology and Legal Policy, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Skøyen, Oslo, Norway
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Travis T Mallard
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Pekka Martikainen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
- The Max Planck Institute for Demographic Research, Rostock, Germany
- Department of Public Health Sciences, Stockholm University, Stockholm, Sweden
| | - Melinda C Mills
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Pål Rasmus Njølstad
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | | | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | | | - Karri Silventoinen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Camilla Stoltenberg
- Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - John K Hewitt
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Matthew C Keller
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Michael C Stallings
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - James J Lee
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Kaare Christensen
- The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jean-Baptiste Pingault
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Robert Plomin
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alexandra Havdahl
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Meike Bartels
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, the Netherlands
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Sven Oskarsson
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health, Danville, PA, USA
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Øyvind Naess
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Norwegian Institute of Public Health, Oslo, Norway
| | - Cristen J Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Philipp D Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Daniel J Benjamin
- UCLA Anderson School of Management, Los Angeles, CA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Gonda (Goldschmied) Neuroscience and Genetics Research Center, Los Angeles, CA, USA
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Ben Brumpton
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway.
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
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Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P. Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:1. [PMID: 37309486 PMCID: PMC10248672 DOI: 10.1007/s11032-021-01272-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.
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Affiliation(s)
- Dinesh K. Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Jagmohan Singh
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
| | - Anand Kumar
- Department of Genetics and Plant Breeding, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 202002 India
| | - Sumandeep Bazzer
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211 USA
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
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Howe LJ, Battram T, Morris TT, Hartwig FP, Hemani G, Davies NM, Smith GD. Assortative mating and within-spouse pair comparisons. PLoS Genet 2021; 17:e1009883. [PMID: 34735433 PMCID: PMC8594845 DOI: 10.1371/journal.pgen.1009883] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 11/16/2021] [Accepted: 10/15/2021] [Indexed: 12/20/2022] Open
Abstract
Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias.
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Affiliation(s)
- Laurence J. Howe
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Thomas Battram
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tim T. Morris
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Fernando P. Hartwig
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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19
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McGurk KA, Williams SG, Guo H, Watkins H, Farrall M, Cordell HJ, Nicolaou A, Keavney BD. Heritability and family-based GWAS analyses of the N-acyl ethanolamine and ceramide plasma lipidome. Hum Mol Genet 2021; 30:500-513. [PMID: 33437986 PMCID: PMC8101358 DOI: 10.1093/hmg/ddab002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/25/2020] [Accepted: 12/23/2020] [Indexed: 12/11/2022] Open
Abstract
Signalling lipids of the N-acyl ethanolamine (NAE) and ceramide (CER) classes have emerged as potential biomarkers of cardiovascular disease (CVD). We sought to establish the heritability of plasma NAEs (including the endocannabinoid anandamide) and CERs, to identify common DNA variants influencing the circulating concentrations of the heritable lipids, and assess causality of these lipids in CVD using 2-sample Mendelian randomization (2SMR). Nine NAEs and 16 CERs were analyzed in plasma samples from 999 members of 196 British Caucasian families, using targeted ultra-performance liquid chromatography with tandem mass spectrometry. All lipids were significantly heritable (h2 = 36-62%). A missense variant (rs324420) in the gene encoding the enzyme fatty acid amide hydrolase (FAAH), which degrades NAEs, associated at genome-wide association study (GWAS) significance (P < 5 × 10-8) with four NAEs (DHEA, PEA, LEA and VEA). For CERs, rs680379 in the SPTLC3 gene, which encodes a subunit of the rate-limiting enzyme in CER biosynthesis, associated with a range of species (e.g. CER[N(24)S(19)]; P = 4.82 × 10-27). We observed three novel associations between SNPs at the CD83, SGPP1 and DEGS1 loci, and plasma CER traits (P < 5 × 10-8). 2SMR in the CARDIoGRAMplusC4D cohorts (60 801 cases; 123 504 controls) and in the DIAGRAM cohort (26 488 cases; 83 964 controls), using the genetic instruments from our family-based GWAS, did not reveal association between genetically determined differences in CER levels and CVD or diabetes. Two of the novel GWAS loci, SGPP1 and DEGS1, suggested a casual association between CERs and a range of haematological phenotypes, through 2SMR in the UK Biobank, INTERVAL and UKBiLEVE cohorts (n = 110 000-350 000).
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Affiliation(s)
- Kathryn A McGurk
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9NT, UK
- Laboratory for Lipidomics and Lipid Biology, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PG, UK
| | - Simon G Williams
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9NT, UK
| | - Hui Guo
- Division of Population Health, Health Services Research & Primary Care, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PL, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Heather J Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Anna Nicolaou
- Laboratory for Lipidomics and Lipid Biology, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PG, UK
| | - Bernard D Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9NT, UK
- Manchester Heart Centre, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
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Variants in regulatory elements of PDE4D associate with major mental illness in the Finnish population. Mol Psychiatry 2021; 26:816-824. [PMID: 31138891 DOI: 10.1038/s41380-019-0429-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 04/04/2019] [Accepted: 04/11/2019] [Indexed: 01/29/2023]
Abstract
We have previously reported a replicable association between variants at the PDE4D gene and familial schizophrenia in a Finnish cohort. In order to identify the potential functional mutations underlying these previous findings, we sequenced 1.5 Mb of the PDE4D genomic locus in 20 families (consisting of 96 individuals and 79 independent chromosomes), followed by two stages of genotyping across 6668 individuals from multiple Finnish cohorts for major mental illnesses. We identified 4570 SNPs across the PDE4D gene, with 380 associated to schizophrenia (p ≤ 0.05). Importantly, two of these variants, rs35278 and rs165940, are located at transcription factor-binding sites, and displayed replicable association in the two-stage enlargement of the familial schizophrenia cohort (combined statistics for rs35278 p = 0.0012; OR = 1.18, 95% CI: 1.06-1.32; and rs165940 p = 0.0016; OR = 1.27, 95% CI: 1.13-1.41). Further analysis using additional cohorts and endophenotypes revealed that rs165940 principally associates within the psychosis (p = 0.025, OR = 1.18, 95% CI: 1.07-1.30) and cognitive domains of major mental illnesses (g-score p = 0.044, β = -0.033). Specifically, the cognitive domains represented verbal learning and memory (p = 0.0091, β = -0.044) and verbal working memory (p = 0.0062, β = -0.036). Moreover, expression data from the GTEx database demonstrated that rs165940 significantly correlates with the mRNA expression levels of PDE4D in the cerebellum (p-value = 0.04; m-value = 0.9), demonstrating a potential functional consequence for this variant. Thus, rs165940 represents the most likely functional variant for major mental illness at the PDE4D locus in the Finnish population, increasing risk broadly to psychotic disorders.
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21
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Lou XY, Hou TT, Liu SY, Xu HM, Lin F, Tang X, MacLeod SL, Cleves MA, Hobbs CA. Innovative approach to identify multigenomic and environmental interactions associated with birth defects in family-based hybrid designs. Genet Epidemiol 2021; 45:171-189. [PMID: 32996630 PMCID: PMC8495752 DOI: 10.1002/gepi.22363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 09/08/2020] [Accepted: 09/11/2020] [Indexed: 11/09/2022]
Abstract
Genes, including those with transgenerational effects, work in concert with behavioral, environmental, and social factors via complex biological networks to determine human health. Understanding complex relationships between causal factors underlying human health is an essential step towards deciphering biological mechanisms. We propose a new analytical framework to investigate the interactions between maternal and offspring genetic variants or their surrogate single nucleotide polymorphisms (SNPs) and environmental factors using family-based hybrid study design. The proposed approach can analyze diverse genetic and environmental factors and accommodate samples from a variety of family units, including case/control-parental triads, and case/control-parental dyads, while minimizing potential bias introduced by population admixture. Comprehensive simulations demonstrated that our innovative approach outperformed the log-linear approach, the best available method for case-control family data. The proposed approach had greater statistical power and was capable to unbiasedly estimate the maternal and child genetic effects and the effects of environmental factors, while controlling the Type I error rate against population stratification. Using our newly developed approach, we analyzed the associations between maternal and fetal SNPs and obstructive and conotruncal heart defects, with adjustment for demographic and lifestyle factors and dietary supplements. Fourteen and 11 fetal SNPs were associated with obstructive and conotruncal heart defects, respectively. Twenty-seven and 17 maternal SNPs were associated with obstructive and conotruncal heart defects, respectively. In addition, maternal body mass index was a significant risk factor for obstructive defects. The proposed approach is a powerful tool for interrogating the etiological mechanism underlying complex traits.
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Affiliation(s)
- Xiang-Yang Lou
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Ting-Ting Hou
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Shou-Ye Liu
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Hai-Ming Xu
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Feng Lin
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Xinyu Tang
- The US Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Mario A. Cleves
- Department of Pediatrics, Morsani College of Medicine, Health Informatics Institute, University of South Florida, Tampa, Florida, USA
| | - Charlotte A. Hobbs
- Rady Children’s Institute for Genomic Medicine, San Diego, California, USA
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22
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Maes HH. Notes on Three Decades of Methodology Workshops. Behav Genet 2021; 51:170-180. [PMID: 33585974 DOI: 10.1007/s10519-021-10049-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 01/27/2021] [Indexed: 01/20/2023]
Abstract
Since 1987, a group of behavior geneticists have been teaching an annual methodology workshop on how to use state-of-the-art methods to analyze genetically informative data. In the early years, the focus was on analyzing twin and family data, using information of their known genetic relatedness to infer the role of genetic and environmental factors on phenotypic variation. With the rapid evolution of genotyping and sequencing technology and availability of measured genetic data, new methods to detect genetic variants associated with human traits were developed and became the focus of workshop teaching in alternate years. Over the years, many of the methodological advances in the field of statistical genetics have been direct outgrowths of the workshop, as evidence by the software and methodological publications authored by workshop faculty. We provide data and demographics of workshop attendees and evaluate the impact of the methodology workshops on scientific output in the field by evaluating the number of papers applying specific statistical genetic methodologies authored by individuals who have attended workshops.
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Affiliation(s)
- Hermine H Maes
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA, 23298-0033, USA. .,Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA. .,Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA. .,Department of Kinesiology, Katholieke Universiteit Leuven, Leuven, Belgium.
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23
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Xu TT, Li YJ, Afshari NA, Aleff RA, Rinkoski TA, Patel SV, Maguire LJ, Edwards AO, Brown WL, Fautsch MP, Wieben ED, Baratz KH. Disease Expression and Familial Transmission of Fuchs Endothelial Corneal Dystrophy With and Without CTG18.1 Expansion. Invest Ophthalmol Vis Sci 2021; 62:17. [PMID: 33444430 PMCID: PMC7814354 DOI: 10.1167/iovs.62.1.17] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 12/22/2020] [Indexed: 12/14/2022] Open
Abstract
Purpose To characterize inheritance, penetrance, and trinucleotide repeat expansion stability in Fuchs endothelial corneal dystrophy (FECD). Methods One thousand unrelated and related subjects with and without FECD were prospectively recruited. CTG18.1 repeat length (CTG18.1L) was determined via short tandem repeat assay and Southern blotting of leukocyte DNA. Multivariable logistic regression and generalized estimating equation models were employed. Results There were 546 unrelated FECD cases (67.6% female; 70 ± 10 years) and 235 controls (63.8% female; 73 ± 8 years; all ≥ 50 years). CTG18.1 expansion (CTG18.1exp+) was observed in 424 (77.7%) cases and 18 (7.7%) controls (P = 2.48 × 10-44). CTG18.1 expansion was associated with FECD severity (P = 5.62 × 10-7). The family arm of the study included 331 members from 112 FECD-affected families; 87 families were CTG18.1exp+. Autosomal dominant inheritance with variable expression of FECD was observed, regardless of expansion status. FECD penetrance of CTG18.1 expansion increased with age, ranging from 44.4% in the youngest (19-46 years) to 86.2% in the oldest (64-91 years) age quartiles. Among 62 parent-offspring transmissions of CTG18.1exp+, 48 (77.4%) had a change in CTG18.1L ≤ 10 repeats, and eight (12.9%) were ≥50 repeats, including five large expansions (∼1000-2000 repeats) that contracted. Among 44 offspring who did not inherit the CTG18.1exp+ allele, eight (18.2%) exhibited FECD. Conclusions CTG18.1 expansion was highly associated with FECD but demonstrated incomplete penetrance. CTG18.1L instability occurred in a minority of parent-offspring transmissions, with large expansions exhibiting contraction. The observation of FECD without CTG18.1 expansion among family members in CTG18.1exp+ families highlights the complexity of the relationship between the FECD phenotype and CTG18.1 expansion.
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Affiliation(s)
- Timothy T. Xu
- Alix School of Medicine, Mayo Clinic, Rochester, Minnesota, United States
| | - Yi-Ju Li
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina, United States
| | - Natalie A. Afshari
- Shiley Eye Institute, University of California, San Diego, La Jolla, California, United States
| | - Ross A. Aleff
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, United States
| | - Tommy A. Rinkoski
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
| | - Sanjay V. Patel
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
| | - Leo J. Maguire
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
| | - Albert O. Edwards
- Oregon Retina Division, Sterling Vision, Eugene, Oregon, United States
- Casey Eye Institute, Oregon Health Sciences University, Portland, Oregon, United States
| | - William L. Brown
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
| | - Michael P. Fautsch
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
| | - Eric D. Wieben
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, United States
| | - Keith H. Baratz
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
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Shi SQ, Li SS, Zhang XY, Wei Z, Fu WZ, He JW, Hu YQ, Li M, Zheng LL, Zhang ZL. LGR4 Gene Polymorphisms Are Associated With Bone and Obesity Phenotypes in Chinese Female Nuclear Families. Front Endocrinol (Lausanne) 2021; 12:656077. [PMID: 34707566 PMCID: PMC8544421 DOI: 10.3389/fendo.2021.656077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 06/14/2021] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVE The current study was conducted to determine whether peak bone mineral density (BMD) and obesity phenotypes are associated with certain LGR4 gene polymorphisms found in Chinese nuclear families with female children. METHODS A total of 22 single nucleotide polymorphisms (SNPs) located in and around the LGR4 gene were identified in 1,300 subjects who were members of 390 Chinese nuclear families with female children. Then, BMD readings of the femoral neck, total hip, and lumbar spine as well as measurements of the total lean mass (TLM), total fat mass (TFM), and trunk fat mass were obtained via dual-energy X-ray absorptiometry. The quantitative transmission disequilibrium test was used to analyze the associations between specific SNPs and LGR4 haplotypes and peak BMD as well as between LGR4 haplotypes and TLM, percent lean mass, TFM, percent fat mass, trunk fat mass, and body mass index (BMI). RESULTS Here, rs7936621 was significantly associated with the BMD values for the total hip and lumbar spine, while rs10835171 and rs6484295 were associated with the trunk fat mass and BMI, respectively. Regarding the haplotypes, we found significant associations between GAA in block 2 and trunk fat mass and BMI, between AGCGT in block 3 and total hip BMD, between TGCTCC in block 5 and femoral neck BMD, and between TACTTC in block 5 and both lumbar spine and femoral neck BMD (all P-values < 0.05). CONCLUSION Genetic variations of the LGR4 gene are related to peak BMD, BMI, and trunk fat mass.
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Affiliation(s)
- Su-qin Shi
- Department of Endocrinology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Shan-shan Li
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Xiao-ya Zhang
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Zhe Wei
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Wen-zhen Fu
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Jin-wei He
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yun-qiu Hu
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Miao Li
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Li-li Zheng
- Department of Endocrinology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Zhen-lin Zhang, ; Li-li Zheng,
| | - Zhen-lin Zhang
- Metabolic Bone Disease and Genetic Research Unit, Department of Osteoporosis and Bone Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- *Correspondence: Zhen-lin Zhang, ; Li-li Zheng,
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Qi L, Liu L, Li L, Hu W, Fu W, Hu J, Xu Y, Zhang Z. The rs1634330 Polymorphisms in the SOST Gene Are Associated with Body Composition in Chinese Nuclear Families with Male Offspring. Int J Endocrinol 2021; 2021:6698822. [PMID: 34054948 PMCID: PMC8123982 DOI: 10.1155/2021/6698822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 04/21/2021] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE The purpose of this study was to explore the effect of the SOST gene polymorphisms on body composition in Chinese nuclear families with male offspring. METHODS 1,016 individuals were recruited from 335 Chinese nuclear families with male offspring. The nuclear families consist of at least one male offspring aged 18 to 44. We genotyped the 10 tagged single-nucleotide polymorphisms (SNPs) in SOST gene (rs7220711, rs865429, rs851057, rs1708635, rs2023794, rs1234612, rs74252774, rs1634330, rs851058, and rs1513670) in all the above people. We used dual-energy X-ray absorptiometry to measure the composition of the human body. The quantitative transmission disequilibrium test (QTDT) was used to analyze the associations of the SNPs with the body composition. RESULTS QTDT analysis showed that rs1634330 was significantly associated with trunk LM (P < 0.05). However, haplotypes were not found to be significantly associated with the body composition in the within-family association. The 1000 permutations were consistent with these within-family association results. CONCLUSIONS Our results showed that the genetic variation in the SOST gene may contribute to variations in the body composition of Chinese male offspring.
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Affiliation(s)
- Luyue Qi
- Shanghai Clinical Research Center of Bone Disease, Department of Osteoporosis and Bone Diseases, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai 200233, China
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Lianyong Liu
- Department of Endocrinology, Punan Hospital of Pudong New District, No.279 Linyi Road, Pudong, Shanghai 200125, China
| | - Li Li
- Shanghai Clinical Research Center of Bone Disease, Department of Osteoporosis and Bone Diseases, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai 200233, China
| | - Weiwei Hu
- Shanghai Clinical Research Center of Bone Disease, Department of Osteoporosis and Bone Diseases, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai 200233, China
| | - Wenzhen Fu
- Shanghai Clinical Research Center of Bone Disease, Department of Osteoporosis and Bone Diseases, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai 200233, China
| | - Ji Hu
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Youjia Xu
- Department of Orthopaedics Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Zhenlin Zhang
- Shanghai Clinical Research Center of Bone Disease, Department of Osteoporosis and Bone Diseases, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai 200233, China
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Abstract
The International Statistical Genetics Workshop (commonly referred to as the "Boulder Workshop") has been held annually in Boulder, Colorado almost every year since 1990. A staple feature of each workshop has been the presence of a "question box" (either a physical box or an online virtual one) where workshop participants are given the opportunity of asking questions to the faculty. In this manuscript, we have compiled a list of ten "classic" questions that have appeared in one form or another across multiple workshops and our attempts at answering them.
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Diao G, Lin DY. Statistically efficient association analysis of quantitative traits with haplotypes and untyped SNPs in family studies. BMC Genet 2020; 21:99. [PMID: 32894040 PMCID: PMC7487716 DOI: 10.1186/s12863-020-00902-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 08/17/2020] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Associations between haplotypes and quantitative traits provide valuable information about the genetic basis of complex human diseases. Haplotypes also provide an effective way to deal with untyped SNPs. Two major challenges arise in haplotype-based association analysis of family data. First, haplotypes may not be inferred with certainty from genotype data. Second, the trait values within a family tend to be correlated because of common genetic and environmental factors. RESULTS To address these challenges, we present an efficient likelihood-based approach to analyzing associations of quantitative traits with haplotypes or untyped SNPs. This approach properly accounts for within-family trait correlations and can handle general pedigrees with arbitrary patterns of missing genotypes. We characterize the genetic effects on the quantitative trait by a linear regression model with random effects and develop efficient likelihood-based inference procedures. Extensive simulation studies are conducted to examine the performance of the proposed methods. An application to family data from the Childhood Asthma Management Program Ancillary Genetic Study is provided. A computer program is freely available. CONCLUSIONS Results from extensive simulation studies show that the proposed methods for testing the haplotype effects on quantitative traits have correct type I error rates and are more powerful than some existing methods.
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Affiliation(s)
- Guoqing Diao
- Department of Biostatistics and Bioinformatics, The George Washington University, Washington, District of Columbia, USA.
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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28
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McGurk KA, Owen B, Watson WD, Nethononda RM, Cordell HJ, Farrall M, Rider OJ, Watkins H, Revell A, Keavney BD. Heritability of haemodynamics in the ascending aorta. Sci Rep 2020; 10:14356. [PMID: 32873833 PMCID: PMC7463029 DOI: 10.1038/s41598-020-71354-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 06/25/2020] [Indexed: 01/27/2023] Open
Abstract
Blood flow in the vasculature can be characterised by dimensionless numbers commonly used to define the level of instabilities in the flow, for example the Reynolds number, Re. Haemodynamics play a key role in cardiovascular disease (CVD) progression. Genetic studies have identified mechanosensitive genes with causal roles in CVD. Given that CVD is highly heritable and abnormal blood flow may increase risk, we investigated the heritability of fluid metrics in the ascending aorta calculated using patient-specific data from cardiac magnetic resonance (CMR) imaging. 341 participants from 108 British Caucasian families were phenotyped by CMR and genotyped for 557,124 SNPs. Flow metrics were derived from the CMR images to provide some local information about blood flow in the ascending aorta, based on maximum values at systole at a single location, denoted max, and a 'peak mean' value averaged over the area of the cross section, denoted pm. Heritability was estimated using pedigree-based (QTDT) and SNP-based (GCTA-GREML) methods. Estimates of Reynolds number based on spatially averaged local flow during systole showed substantial heritability ([Formula: see text], [Formula: see text]), while the estimated heritability for Reynolds number calculated using the absolute local maximum velocity was not statistically significant (12-13%; [Formula: see text]). Heritability estimates of the geometric quantities alone; e.g. aortic diameter ([Formula: see text], [Formula: see text]), were also substantially heritable, as described previously. These findings indicate the potential for the discovery of genetic factors influencing haemodynamic traits in large-scale genotyped and phenotyped cohorts where local spatial averaging is used, rather than instantaneous values. Future Mendelian randomisation studies of aortic haemodynamic estimates, which are swift to derive in a clinical setting, will allow for the investigation of causality of abnormal blood flow in CVD.
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Affiliation(s)
- Kathryn A McGurk
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, UK.
| | - Benjamin Owen
- Department of Mechanical, Aerospace and Civil Engineering, Faculty of Science and Engineering, University of Manchester, Manchester, UK
- School of Engineering, Multiscale Thermofluids Institute, University of Edinburgh, Edinburgh, UK
| | - William D Watson
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Richard M Nethononda
- Division of Cardiology, Chris Hani Baragwanath Hospital, Soweto and the University of Witwatersrand, Johannesburg, South Africa
| | - Heather J Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, International Centre for Life, Newcastle upon Tyne, UK
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Oliver J Rider
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alistair Revell
- Department of Mechanical, Aerospace and Civil Engineering, Faculty of Science and Engineering, University of Manchester, Manchester, UK
| | - Bernard D Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
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29
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Vega-Sevey JG, Martínez-Magaña JJ, Genis-Mendoza AD, Escamilla M, Lanzagorta N, Tovilla-Zarate CA, Nicolini H. Copy number variants in siblings of Mexican origin concordant for schizophrenia or bipolar disorder. Psychiatry Res 2020; 291:113018. [PMID: 32540681 DOI: 10.1016/j.psychres.2020.113018] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 04/02/2020] [Accepted: 04/13/2020] [Indexed: 12/14/2022]
Abstract
Schizophrenia (SCZ) and bipolar disorder (BD) cause similar symptomatology. A correlation between these disorders has been found. We aimed to explore shared CNVs between SCZ and BD, in 35 sibpairs diagnosed with SCZ and 21 sibpairs diagnosed with BD. CNV calling was performed using data derived of Psycharray, by PennCNV. We did not find any shared CNVs between individuals diagnosed with BD and SCZ, neither with psychotic symptoms in individuals with BD. Nevertheless, we found a significant higher CNV burden in early-onset SCZ. This is one of the first's studies analyzing shared CNVs between SCZ and BD in Mexican population.
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Affiliation(s)
- Julissa Gabriela Vega-Sevey
- Laboratorio de Genómica de Enfermedades Psiquiátricas y Neurodegenerativas, Instituto Nacional de Medicina Genómica, CDMX, México; Facultad de Ciencias Químico-Biológicas, Universidad Autónoma de Sinaloa, Culiacán, México
| | - José Jaime Martínez-Magaña
- Laboratorio de Genómica de Enfermedades Psiquiátricas y Neurodegenerativas, Instituto Nacional de Medicina Genómica, CDMX, México; División de Ciencias de la Salud, Universidad Juárez Autónoma de Tabasco, Villahermosa, México
| | - Alma Delia Genis-Mendoza
- Laboratorio de Genómica de Enfermedades Psiquiátricas y Neurodegenerativas, Instituto Nacional de Medicina Genómica, CDMX, México; Servicios de Atención Psiquiátrica, Hospital Psiquiátrico Infantil "Juan N. Navarro", CDMX, México
| | - Michael Escamilla
- Center of Emphasis in Neurosciences, Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas, United States; Department of Psychiatry, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas, USA
| | | | | | - Humberto Nicolini
- Laboratorio de Genómica de Enfermedades Psiquiátricas y Neurodegenerativas, Instituto Nacional de Medicina Genómica, CDMX, México; Grupo de Estudios Médicos y Familiares Carracci, CDMX, México.
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30
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Turkmen AS, Lin S. Detecting X-linked common and rare variant effects in family-based sequencing studies. Genet Epidemiol 2020; 45:36-45. [PMID: 32864779 DOI: 10.1002/gepi.22352] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 06/26/2020] [Accepted: 08/03/2020] [Indexed: 11/08/2022]
Abstract
The breakthroughs in next generation sequencing have allowed us to access data consisting of both common and rare variants, and in particular to investigate the impact of rare genetic variation on complex diseases. Although rare genetic variants are thought to be important components in explaining genetic mechanisms of many diseases, discovering these variants remains challenging, and most studies are restricted to population-based designs. Further, despite the shift in the field of genome-wide association studies (GWAS) towards studying rare variants due to the "missing heritability" phenomenon, little is known about rare X-linked variants associated with complex diseases. For instance, there is evidence that X-linked genes are highly involved in brain development and cognition when compared with autosomal genes; however, like most GWAS for other complex traits, previous GWAS for mental diseases have provided poor resources to deal with identification of rare variant associations on X-chromosome. In this paper, we address the two issues described above by proposing a method that can be used to test X-linked variants using sequencing data on families. Our method is much more general than existing methods, as it can be applied to detect both common and rare variants, and is applicable to autosomes as well. Our simulation study shows that the method is efficient, and exhibits good operational characteristics. An application to the University of Miami Study on Genetics of Autism and Related Disorders also yielded encouraging results.
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Affiliation(s)
- Asuman S Turkmen
- Statistics Department, The Ohio State University, Columbus, Ohio.,Statistics Department, The Ohio State University, Newark, Ohio
| | - Shili Lin
- Statistics Department, The Ohio State University, Columbus, Ohio
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31
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Brumpton B, Sanderson E, Heilbron K, Hartwig FP, Harrison S, Vie GÅ, Cho Y, Howe LD, Hughes A, Boomsma DI, Havdahl A, Hopper J, Neale M, Nivard MG, Pedersen NL, Reynolds CA, Tucker-Drob EM, Grotzinger A, Howe L, Morris T, Li S, Auton A, Windmeijer F, Chen WM, Bjørngaard JH, Hveem K, Willer C, Evans DM, Kaprio J, Davey Smith G, Åsvold BO, Hemani G, Davies NM. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun 2020; 11:3519. [PMID: 32665587 PMCID: PMC7360778 DOI: 10.1038/s41467-020-17117-4] [Citation(s) in RCA: 190] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 06/12/2020] [Indexed: 01/24/2023] Open
Abstract
Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trøndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies.
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Affiliation(s)
- Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK.
- Clinic of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Karl Heilbron
- 23andMe, Inc., 223 N Mathilda Avenue, Sunnyvale, CA, 94086, USA
| | - Fernando Pires Hartwig
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Sean Harrison
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gunnhild Åberge Vie
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yoonsu Cho
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alexandra Havdahl
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Spångbergveien 25, 0853, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Sandakerveien 24 C, 0473, Oslo, Norway
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Michael Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Michel G Nivard
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, 108 E. Dean Keeton Stop A8000,, Austin, TX, 78712, USA
| | - Andrew Grotzinger
- Department of Psychology and Population Research Center, University of Texas at Austin, 108 E. Dean Keeton Stop A8000,, Austin, TX, 78712, USA
| | - Laurence Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tim Morris
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK
| | - Adam Auton
- 23andMe, Inc., 223 N Mathilda Avenue, Sunnyvale, CA, 94086, USA
| | - Frank Windmeijer
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Department of Economics, University of Bristol, 2 Priory Road, Bristol, BS8 1TU, UK
| | - Wei-Min Chen
- Center for public health genomics, Department of public health sciences, University of Virginia, Charlottesville, VA, USA
| | - Johan Håkon Bjørngaard
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Cristen Willer
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia
| | - Jaakko Kaprio
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Neil M Davies
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK.
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Nudel R, Christiani CAJ, Ohland J, Uddin MJ, Hemager N, Ellersgaard D, Spang KS, Burton BK, Greve AN, Gantriis DL, Bybjerg-Grauholm J, Jepsen JRM, Thorup AAE, Mors O, Werge T, Nordentoft M. Quantitative genome-wide association analyses of receptive language in the Danish High Risk and Resilience Study. BMC Neurosci 2020; 21:30. [PMID: 32635940 PMCID: PMC7341668 DOI: 10.1186/s12868-020-00581-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 06/28/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND One of the most basic human traits is language. Linguistic ability, and disability, have been shown to have a strong genetic component in family and twin studies, but molecular genetic studies of language phenotypes are scarce, relative to studies of other cognitive traits and neurodevelopmental phenotypes. Moreover, most genetic studies examining such phenotypes do not incorporate parent-of-origin effects, which could account for some of the heritability of the investigated trait. We performed a genome-wide association study of receptive language, examining both child genetic effects and parent-of-origin effects. RESULTS Using a family-based cohort with 400 children with receptive language scores, we found a genome-wide significant paternal parent-of-origin effect with a SNP, rs11787922, on chromosome 9q21.31, whereby the T allele reduced the mean receptive language score by ~ 23, constituting a reduction of more than 1.5 times the population SD (P = 1.04 × 10-8). We further confirmed that this association was not driven by broader neurodevelopmental diagnoses in the child or a family history of psychiatric diagnoses by incorporating covariates for the above and repeating the analysis. CONCLUSIONS Our study reports a genome-wide significant association for receptive language skills; to our knowledge, this is the first documented genome-wide significant association for this phenotype. Furthermore, our study illustrates the importance of considering parent-of-origin effects in association studies, particularly in the case of cognitive or neurodevelopmental traits, in which parental genetic data are not always incorporated.
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Affiliation(s)
- Ron Nudel
- Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
| | - Camilla A J Christiani
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Mental Health Centre Copenhagen, University of Copenhagen Hospital, Copenhagen, Denmark
| | - Jessica Ohland
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Mental Health Centre Copenhagen, University of Copenhagen Hospital, Copenhagen, Denmark
| | - Md Jamal Uddin
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Mental Health Centre Copenhagen, University of Copenhagen Hospital, Copenhagen, Denmark
- Section for Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Nicoline Hemager
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Mental Health Centre Copenhagen, University of Copenhagen Hospital, Copenhagen, Denmark
| | - Ditte Ellersgaard
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Mental Health Centre Copenhagen, University of Copenhagen Hospital, Copenhagen, Denmark
| | - Katrine S Spang
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Mental Health Centre for Child and Adolescent Psychiatry-Research unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Birgitte K Burton
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Mental Health Centre for Child and Adolescent Psychiatry-Research unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Aja N Greve
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - Ditte L Gantriis
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - Jonas Bybjerg-Grauholm
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Jens Richardt M Jepsen
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Mental Health Centre Copenhagen, University of Copenhagen Hospital, Copenhagen, Denmark
- Mental Health Centre for Child and Adolescent Psychiatry-Research unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Anne A E Thorup
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Mental Health Centre for Child and Adolescent Psychiatry-Research unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Ole Mors
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark
- Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark.
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Merete Nordentoft
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark.
- Mental Health Centre Copenhagen, University of Copenhagen Hospital, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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33
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Wittenburg D, Bonk S, Doschoris M, Reyer H. Design of experiments for fine-mapping quantitative trait loci in livestock populations. BMC Genet 2020; 21:66. [PMID: 32600319 PMCID: PMC7324978 DOI: 10.1186/s12863-020-00871-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 06/09/2020] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Single nucleotide polymorphisms (SNPs) which capture a significant impact on a trait can be identified with genome-wide association studies. High linkage disequilibrium (LD) among SNPs makes it difficult to identify causative variants correctly. Thus, often target regions instead of single SNPs are reported. Sample size has not only a crucial impact on the precision of parameter estimates, it also ensures that a desired level of statistical power can be reached. We study the design of experiments for fine-mapping of signals of a quantitative trait locus in such a target region. METHODS A multi-locus model allows to identify causative variants simultaneously, to state their positions more precisely and to account for existing dependencies. Based on the commonly applied SNP-BLUP approach, we determine the z-score statistic for locally testing non-zero SNP effects and investigate its distribution under the alternative hypothesis. This quantity employs the theoretical instead of observed dependence between SNPs; it can be set up as a function of paternal and maternal LD for any given population structure. RESULTS We simulated multiple paternal half-sib families and considered a target region of 1 Mbp. A bimodal distribution of estimated sample size was observed, particularly if more than two causative variants were assumed. The median of estimates constituted the final proposal of optimal sample size; it was consistently less than sample size estimated from single-SNP investigation which was used as a baseline approach. The second mode pointed to inflated sample sizes and could be explained by blocks of varying linkage phases leading to negative correlations between SNPs. Optimal sample size increased almost linearly with number of signals to be identified but depended much stronger on the assumption on heritability. For instance, three times as many samples were required if heritability was 0.1 compared to 0.3. An R package is provided that comprises all required tools. CONCLUSIONS Our approach incorporates information about the population structure into the design of experiments. Compared to a conventional method, this leads to a reduced estimate of sample size enabling the resource-saving design of future experiments for fine-mapping of candidate variants.
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Affiliation(s)
- Dörte Wittenburg
- Leibniz Institute for Farm Animal Biology, Institute of Genetics and Biometry, Dummerstorf, 18196 Germany
| | - Sarah Bonk
- University Medicine Greifswald, Department of Psychiatry and Psychotherapy, Greifswald, 17475 Germany
| | - Michael Doschoris
- Leibniz Institute for Farm Animal Biology, Institute of Genetics and Biometry, Dummerstorf, 18196 Germany
| | - Henry Reyer
- Leibniz Institute for Farm Animal Biology, Institute of Genome Biology, Dummerstorf, 18196 Germany
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34
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Davies NM, Howe LJ, Brumpton B, Havdahl A, Evans DM, Davey Smith G. Within family Mendelian randomization studies. Hum Mol Genet 2020; 28:R170-R179. [PMID: 31647093 DOI: 10.1093/hmg/ddz204] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 08/13/2019] [Accepted: 08/14/2019] [Indexed: 01/08/2023] Open
Abstract
Mendelian randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP-phenotype associations. We explain how studies of related individuals such as sibling pairs or parent-offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.
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Affiliation(s)
- Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, United Kingdom.,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, United Kingdom.,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
| | - Ben Brumpton
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, United Kingdom.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Alexandra Havdahl
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, United Kingdom.,Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway.,Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, United Kingdom.,University of Queensland Diamantina Institute, University of Queensland, Brisbane, 4102, Australia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, United Kingdom.,Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, United Kingdom
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35
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Klein N, Entwistle A, Rosenberger A, Kneib T, Bickeböller H. Candidate-gene association analysis for a continuous phenotype with a spike at zero using parent-offspring trios. J Appl Stat 2019; 47:2066-2080. [DOI: 10.1080/02664763.2019.1704226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Nadja Klein
- Humboldt University of Berlin, Berlin, Germany
| | | | | | - Thomas Kneib
- Georg-August-Universität Göttingen, Göttingen, Germany
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36
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Nersisyan L, Nikoghosyan M, Arakelyan A. WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene. Sci Rep 2019; 9:18758. [PMID: 31822713 PMCID: PMC6904582 DOI: 10.1038/s41598-019-55109-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 11/24/2019] [Indexed: 12/14/2022] Open
Abstract
Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother's, and, to a lesser extent, with father's TL having the strongest influence on the offspring. In this cohort, mother's, but not father's age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait.
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Affiliation(s)
- Lilit Nersisyan
- Bioinformatics Group, Institute of Molecular Biology NAS RA, 7 Hasratyan str., 0014, Yerevan, Armenia.
| | - Maria Nikoghosyan
- Bioinformatics Group, Institute of Molecular Biology NAS RA, 7 Hasratyan str., 0014, Yerevan, Armenia
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 123 Hovsep Emin St, 0051, Yerevan, Armenia
| | - Arsen Arakelyan
- Bioinformatics Group, Institute of Molecular Biology NAS RA, 7 Hasratyan str., 0014, Yerevan, Armenia
- Institute of Biomedicine and Pharmacy, Russian-Armenian University, 123 Hovsep Emin St, 0051, Yerevan, Armenia
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37
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Dwivedi OP, Lehtovirta M, Hastoy B, Chandra V, Krentz NAJ, Kleiner S, Jain D, Richard AM, Abaitua F, Beer NL, Grotz A, Prasad RB, Hansson O, Ahlqvist E, Krus U, Artner I, Suoranta A, Gomez D, Baras A, Champon B, Payne AJ, Moralli D, Thomsen SK, Kramer P, Spiliotis I, Ramracheya R, Chabosseau P, Theodoulou A, Cheung R, van de Bunt M, Flannick J, Trombetta M, Bonora E, Wolheim CB, Sarelin L, Bonadonna RC, Rorsman P, Davies B, Brosnan J, McCarthy MI, Otonkoski T, Lagerstedt JO, Rutter GA, Gromada J, Gloyn AL, Tuomi T, Groop L. Loss of ZnT8 function protects against diabetes by enhanced insulin secretion. Nat Genet 2019; 51:1596-1606. [PMID: 31676859 PMCID: PMC6858874 DOI: 10.1038/s41588-019-0513-9] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Accepted: 09/13/2019] [Indexed: 12/30/2022]
Abstract
A rare loss-of-function allele p.Arg138* in SLC30A8 encoding the zinc transporter 8 (ZnT8), which is enriched in Western Finland, protects against type 2 diabetes (T2D). We recruited relatives of the identified carriers and showed that protection was associated with better insulin secretion due to enhanced glucose responsiveness and proinsulin conversion, particularly when compared with individuals matched for the genotype of a common T2D-risk allele in SLC30A8, p.Arg325. In genome-edited human induced pluripotent stem cell (iPSC)-derived β-like cells, we establish that the p.Arg138* allele results in reduced SLC30A8 expression due to haploinsufficiency. In human β cells, loss of SLC30A8 leads to increased glucose responsiveness and reduced KATP channel function similar to isolated islets from carriers of the T2D-protective allele p.Trp325. These data position ZnT8 as an appealing target for treatment aimed at maintaining insulin secretion capacity in T2D.
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Affiliation(s)
- Om Prakash Dwivedi
- Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
| | - Mikko Lehtovirta
- Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
| | - Benoit Hastoy
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Vikash Chandra
- Stem Cells and Metabolism Research Program and Biomedicum Stem Cell Centre, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Nicole A J Krentz
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Deepak Jain
- Department of Experimental Medical Science, Lund University, Lund, Sweden
| | | | - Fernando Abaitua
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Nicola L Beer
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Antje Grotz
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Rashmi B Prasad
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Ola Hansson
- Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Emma Ahlqvist
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Ulrika Krus
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Isabella Artner
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Anu Suoranta
- Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
| | | | - Aris Baras
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Benoite Champon
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Anthony J Payne
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Daniela Moralli
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Soren K Thomsen
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Philipp Kramer
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Ioannis Spiliotis
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Reshma Ramracheya
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Pauline Chabosseau
- Section of Cell Biology, Department of Medicine, Imperial College London, Imperial Centre for Translational and Experimental Medicine, Hammersmith, Hospital, London, UK
| | - Andria Theodoulou
- Section of Cell Biology, Department of Medicine, Imperial College London, Imperial Centre for Translational and Experimental Medicine, Hammersmith, Hospital, London, UK
| | - Rebecca Cheung
- Section of Cell Biology, Department of Medicine, Imperial College London, Imperial Centre for Translational and Experimental Medicine, Hammersmith, Hospital, London, UK
| | - Martijn van de Bunt
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Jason Flannick
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Maddalena Trombetta
- Department of Medicine, University of Verona and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Enzo Bonora
- Department of Medicine, University of Verona and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Claes B Wolheim
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | | | - Riccardo C Bonadonna
- Department of Medicine and Surgery, University of Parma School of Medicine and Azienda Ospedaliera Universitaria of Parma, Parma, Italy
| | - Patrik Rorsman
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Benjamin Davies
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Mark I McCarthy
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Timo Otonkoski
- Stem Cells and Metabolism Research Program and Biomedicum Stem Cell Centre, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jens O Lagerstedt
- Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Guy A Rutter
- Section of Cell Biology, Department of Medicine, Imperial College London, Imperial Centre for Translational and Experimental Medicine, Hammersmith, Hospital, London, UK
| | | | - Anna L Gloyn
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
- Abdominal Center, Endocrinology, Helsinki University Central Hospital, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
| | - Leif Groop
- Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland.
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden.
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38
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Nethononda RM, McGurk KA, Whitworth P, Francis J, Mamasoula C, Cordell HJ, Neubauer S, Keavney BD, Mayosi BM, Farrall M, Watkins H. Marked variation in heritability estimates of left ventricular mass depending on modality of measurement. Sci Rep 2019; 9:13556. [PMID: 31537879 PMCID: PMC6753112 DOI: 10.1038/s41598-019-49961-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 08/29/2019] [Indexed: 11/09/2022] Open
Abstract
Left ventricular (LV) hypertrophy is a strong risk factor for heart failure and cardiovascular death. ECG measures of LV mass are estimated as heritable in twin and family-based analyses and heritability estimates of LV mass measured by echocardiography are lower. We hypothesised that CMR-derived measurements, being more precise than echocardiographic measurements, would advance our understanding of heritable LV traits. We phenotyped 116 British families (427 individuals) by CMR and ECG, and undertook heritability analyses using variance-components (QTDT) and GWAS SNP-based (GCTA-GREML) methods. ECG-based traits such as LV mass and Sokolow-Lyon duration showed substantial estimates of heritability (60%), whereas CMR-derived LV mass was only modestly heritable (20%). However, the ECG LV mass was positively correlated with the lateral diameter of the chest (rho = 0.67), and adjustment for this attenuated the heritability estimate (42%). Finally, CMR-derived right ventricular mass showed considerable heritability (44%). Heritability estimates of LV phenotypes show substantial variation depending on the modality of measurement, being greater when measured by ECG than CMR. This may reflect the differences between electrophysiological as opposed to anatomical hypertrophy. However, ECG LV hypertrophy traits are likely to be influenced by genetic association with anthropometric measures, inflating their overall measured heritability.
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Affiliation(s)
- Richard M Nethononda
- Division of Cardiology, Chris Hani Baragwanath Hospital, Soweto and the University of Witwatersrand, Johannesburg, South Africa
| | - Kathryn A McGurk
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Polly Whitworth
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.,Oxford Cardiovascular Clinical Research Facility (CCRF), John Radcliffe Hospital, Oxford, UK
| | - Jane Francis
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.,Oxford Centre for Clinical Magnetic Resonance Research (OCMR), John Radcliffe Hospital, Oxford, UK
| | | | - Heather J Cordell
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.,Oxford Centre for Clinical Magnetic Resonance Research (OCMR), John Radcliffe Hospital, Oxford, UK.,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Bernard D Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Bongani M Mayosi
- Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK. .,Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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39
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Wang W, Tan S, Luo J, Shi H, Zhou T, Yang Y, Jin Y, Wang X, Niu D, Yuan Z, Gao D, Dunham R, Liu Z. GWAS Analysis Indicated Importance of NF-κB Signaling Pathway in Host Resistance Against Motile Aeromonas Septicemia Disease in Catfish. MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2019; 21:335-347. [PMID: 30895402 DOI: 10.1007/s10126-019-09883-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 02/18/2019] [Indexed: 06/09/2023]
Abstract
Motile Aeromonas septicemia (MAS) disease caused by a bacterial pathogen, Aeromonas hydrophila, is an emerging but severe disease of catfish. Genetic enhancement of disease resistance is considered to be effective to control the disease. To provide an insight into the genomic basis of MAS disease resistance, in this study, we conducted a genome-wide association study (GWAS) to identify quantitative trait loci (QTL). A total of 1820 interspecific backcross catfish of 7 families were challenged with A. hydrophila, and 382 phenotypic extremes were selected for genotyping with the catfish 690 K SNP arrays. Three QTL on linkage group (LG) 2, 26 and 29 were identified to be significantly associated with MAS resistance. Within these regions, a total of 24 genes had known functions in immunity, 10 of which were involved in NF-κB signaling pathway, suggesting the importance of NF-κB signaling pathway in MAS resistance. In addition, three suggestively significant QTL were identified on LG 11, 17, and 20. The limited numbers of QTL involved in MAS resistance suggests that marker-assisted selection may be a viable approach for catfish breeding.
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Affiliation(s)
- Wenwen Wang
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Suxu Tan
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Jian Luo
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Huitong Shi
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Tao Zhou
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Yujia Yang
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Yulin Jin
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Xiaozhu Wang
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Donghong Niu
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Zihao Yuan
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Dongya Gao
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Rex Dunham
- The Fish Molecular Genetics and Biotechnology Laboratory, School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL, 36849, USA
| | - Zhanjiang Liu
- Department of Biology, College of Art and Sciences, Syracuse University, Syracuse, NY, 13244, USA.
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40
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Amish SJ, Ali O, Peacock M, Miller M, Robinson M, Smith S, Luikart G, Neville H. Assessing thermal adaptation using family‐based association and
F
ST
outlier tests in a threatened trout species. Mol Ecol 2019; 28:2573-2593. [DOI: 10.1111/mec.15100] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 03/15/2019] [Accepted: 04/01/2019] [Indexed: 12/31/2022]
Affiliation(s)
- Stephen J. Amish
- Conservation Genomics Group, Division of Biological Sciences University of Montana Missoula Montana
- Flathead Biological Station University of Montana Polson Montana
| | - Omar Ali
- Department of Animal Science University of California Davis California
| | - Mary Peacock
- Department of Biology University of Nevada Reno Nevada
| | - Michael Miller
- Department of Animal Science University of California Davis California
| | | | - Seth Smith
- Flathead Biological Station University of Montana Polson Montana
| | - Gordon Luikart
- Conservation Genomics Group, Division of Biological Sciences University of Montana Missoula Montana
- Flathead Biological Station University of Montana Polson Montana
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41
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High-Density Linkage Map and QTLs for Growth in Snapper ( Chrysophrys auratus). G3-GENES GENOMES GENETICS 2019; 9:1027-1035. [PMID: 30804023 PMCID: PMC6469409 DOI: 10.1534/g3.118.200905] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Characterizing the genetic variation underlying phenotypic traits is a central objective in biological research. This research has been hampered in the past by the limited genomic resources available for most non-model species. However, recent advances in sequencing technologies and related genotyping methods are rapidly changing this. Here we report the use of genome-wide SNP data from the ecologically and commercially important marine fish species Chrysophrys auratus (snapper) to 1) construct the first linkage map for this species, 2) scan for growth QTL, and 3) search for putative candidate genes in the surrounding QTL regions. The newly constructed linkage map contained ∼11K SNP markers and is one of the densest maps to date in the fish family Sparidae. Comparisons with genome scaffolds of the recently assembled snapper genome indicated that marker placement was mostly consistent between the scaffolds and linkage map (R = 0.7), but that at fine scales (< 5 cM) some precision limitations occurred. Of the 24 linkage groups, which likely reflect the 24 chromosomes of this species, three were found to contain QTL with genome-wide significance for growth-related traits. A scan of 13 candidate growth genes located the growth hormone, myogenin, and parvalbumin genes within 5.3, 9.6, and 25.0 cM of these QTL, respectively. The linkage map and QTL found in this study will advance the investigation of genome structure and aquaculture breeding efforts in this and related species.
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Sun S, Zhu J, Mozaffari S, Ober C, Chen M, Zhou X. Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies. Bioinformatics 2019; 35:487-496. [PMID: 30020412 PMCID: PMC6361238 DOI: 10.1093/bioinformatics/bty644] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 06/24/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022] Open
Abstract
Motivation Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets. Results Here, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites. Availability and implementation PQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shiquan Sun
- Department of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Jiaqiang Zhu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Sahar Mozaffari
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Mengjie Chen
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
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Qi W, Allen AS, Li YJ. Family-based association tests for rare variants with censored traits. PLoS One 2019; 14:e0210870. [PMID: 30682063 PMCID: PMC6347269 DOI: 10.1371/journal.pone.0210870] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 12/27/2018] [Indexed: 11/30/2022] Open
Abstract
We propose a set of family-based burden and kernel tests for censored traits (FamBAC and FamKAC). Here, censored traits refer to time-to-event outcomes, for instance, age-at-onset of a disease. To model censored traits in family-based designs, we used the frailty model, which incorporated not only fixed genetic effects of rare variants in a region of interest but also random polygenic effects shared within families. We first partitioned genotype scores of rare variants into orthogonal between- and within-family components, and then derived their corresponding efficient score statistics from the frailty model. Finally, FamBAC and FamKAC were constructed by aggregating the weighted efficient scores of the within-family components across rare variants and subjects. FamBAC collapsed rare variants within subject first to form a burden test that followed a chi-squared distribution; whereas FamKAC was a variant component test following a mixture of chi-squared distributions. For FamKAC, p-values can be computed by permutation tests or for computational efficiency by approximation methods. Through simulation studies, we showed that type I error was correctly controlled by FamBAC for various variant weighting schemes (0.0371 to 0.0527). However, FamKAC type I error rates based on approximation methods were deflated (max 0.0376) but improved by permutation tests. Our simulations also demonstrated that burden test FamBAC had higher power than kernel test FamKAC when high proportion (e.g. ≥ 80%) of causal variants had effects in the same direction. In contrast, when the effects of causal variants on the censored trait were in mixed directions, FamKAC outperformed FamBAC and had comparable or higher power than an existing method, RVFam. Our proposed framework has the flexibility to accommodate general nuclear families, and can be used to analyze sequence data for censored traits such as age-at-onset of a complex disease of interest.
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Affiliation(s)
- Wenjing Qi
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States of America
- Duke Molecular Physiology Institute, Duke University, Durham, NC, United States of America
| | - Andrew S. Allen
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States of America
- Center for Statistical Genetics and Genomics, Duke University, Durham, NC, United States of America
| | - Yi-Ju Li
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States of America
- Duke Molecular Physiology Institute, Duke University, Durham, NC, United States of America
- * E-mail:
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Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H, Field Y, Boyle EA, Zhang X, Racimo F, Pritchard JK, Coop G. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 2019; 8:39725. [PMID: 30895923 PMCID: PMC6428572 DOI: 10.7554/elife.39725] [Citation(s) in RCA: 210] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 01/15/2019] [Indexed: 01/27/2023] Open
Abstract
Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect estimates are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification. Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood. Editorial note This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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Affiliation(s)
- Jeremy J Berg
- Department of Biological SciencesColumbia UniversityNew YorkUnited States
| | - Arbel Harpak
- Department of Biological SciencesColumbia UniversityNew YorkUnited States,Department of BiologyStanford UniversityStanfordUnited States
| | | | - Anja Moltke Joergensen
- Lundbeck GeoGenetics Centre, Department of BiologyUniversity of CopenhagenCopenhagenDenmark
| | | | - Yair Field
- Department of GeneticsStanford UniversityStanfordUnited States
| | | | - Xinjun Zhang
- Department of AnthropologyUniversity of California, DavisDavisUnited States
| | - Fernando Racimo
- Lundbeck GeoGenetics Centre, Department of BiologyUniversity of CopenhagenCopenhagenDenmark
| | - Jonathan K Pritchard
- Department of BiologyStanford UniversityStanfordUnited States,Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Graham Coop
- Center for Population BiologyUniversity of California, DavisDavisUnited States,Department of Evolution and EcologyUniversity of California, DavisDavisUnited States
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Fang C, Luo J. Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:91-100. [PMID: 30231195 DOI: 10.1111/tpj.14097] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 09/07/2018] [Accepted: 09/11/2018] [Indexed: 05/21/2023]
Abstract
Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome-wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass-spectrometry-based analytical systems and genome sequencing technologies. Metabolic genome-wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS-based multi-dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS-based multi-dimensional analysis and emerging insights into the diversity of metabolism.
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Affiliation(s)
- Chuanying Fang
- Hainan Key Laboratory for Sustainable Utilisation of Tropical Bioresource, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, 470228, China
| | - Jie Luo
- Hainan Key Laboratory for Sustainable Utilisation of Tropical Bioresource, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, 470228, China
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
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Robust Rare-Variant Association Tests for Quantitative Traits in General Pedigrees. STATISTICS IN BIOSCIENCES 2018; 10:491-505. [DOI: 10.1007/s12561-017-9197-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Algady W, Louzada S, Carpenter D, Brajer P, Färnert A, Rooth I, Ngasala B, Yang F, Shaw MA, Hollox EJ. The Malaria-Protective Human Glycophorin Structural Variant DUP4 Shows Somatic Mosaicism and Association with Hemoglobin Levels. Am J Hum Genet 2018; 103:769-776. [PMID: 30388403 PMCID: PMC6218809 DOI: 10.1016/j.ajhg.2018.10.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 10/04/2018] [Indexed: 01/23/2023] Open
Abstract
Glycophorin A and glycophorin B are red blood cell surface proteins and are both receptors for the parasite Plasmodium falciparum, which is the principal cause of malaria in sub-Saharan Africa. DUP4 is a complex structural genomic variant that carries extra copies of a glycophorin A-glycophorin B fusion gene and has a dramatic effect on malaria risk by reducing the risk of severe malaria by up to 40%. Using fiber-FISH and Illumina sequencing, we validate the structural arrangement of the glycophorin locus in the DUP4 variant and reveal somatic variation in copy number of the glycophorin B-glycophorin A fusion gene. By developing a simple, specific, PCR-based assay for DUP4, we show that the DUP4 variant reaches a frequency of 13% in the population of a malaria-endemic village in south-eastern Tanzania. We genotype a substantial proportion of that village and demonstrate an association of DUP4 genotype with hemoglobin levels, a phenotype related to malaria, using a family-based association test. Taken together, we show that DUP4 is a complex structural variant that may be susceptible to somatic variation and show that DUP4 is associated with a malarial-related phenotype in a longitudinally followed population.
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Affiliation(s)
- Walid Algady
- Department of Genetics and Genome Biology, University of Leicester, Leicester LE1 7RH, UK
| | - Sandra Louzada
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Danielle Carpenter
- Department of Genetics and Genome Biology, University of Leicester, Leicester LE1 7RH, UK
| | - Paulina Brajer
- Department of Genetics and Genome Biology, University of Leicester, Leicester LE1 7RH, UK
| | - Anna Färnert
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, 17176 Stockholm, Sweden; Department of Infectious Diseases, Karolinska University Hospital, Stockholm 17176, Sweden
| | - Ingegerd Rooth
- Nyamisati Malaria Research, Rufiji, National Institute for Medical Research, Dar-es-Salaam, Tanzania
| | - Billy Ngasala
- Department of Parasitology and Medical Entomology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania; Department of Women's and Children's Health, International Maternal and Child Health (IMCH), Uppsala Universitet, 75185 Uppsala, Sweden
| | - Fengtang Yang
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Marie-Anne Shaw
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK
| | - Edward J Hollox
- Department of Genetics and Genome Biology, University of Leicester, Leicester LE1 7RH, UK.
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Tremblay BL, Guénard F, Lamarche B, Pérusse L, Vohl MC. Familial resemblances in human plasma metabolites are attributable to both genetic and common environmental effects. Nutr Res 2018; 61:22-30. [PMID: 30683436 DOI: 10.1016/j.nutres.2018.10.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 09/26/2018] [Accepted: 10/05/2018] [Indexed: 01/06/2023]
Abstract
Metabolites are of great importance for understanding the pathogenesis of several diseases. Understanding the genetic contribution to metabolite concentrations may provide insights into mechanisms of complex diseases. Several studies have investigated heritability of metabolites but none investigated potential influences of genetic and environmental factors on the relationship between metabolites and cardiometabolic (CM) risk factors. Thus, we tested the hypothesis that both genetic and common environmental effects contribute to the variance of plasma metabolite concentrations and that shared genetic and environmental effects explain their phenotypic correlations with CM risk factors. To test this hypothesis, variance component method and bivariate genetic analysis were performed in a family-based sample of 48 French Canadians from 16 families. Familial resemblances were computed for all 147 detected metabolites and 9 (acetylornithine, acylcarnitine C9, arginine, phosphatidylcholine acyl-alkyl C36:4, serotonin, lysophosphatidylcholine acyl C20:4, citrulline, asymmetric dimethylarginine, phosphatidylcholine acyl-alkyl C36:5) showed a significant familial effect (55.7%, 18.7%, and 37.0% for maximal heritability, genetic heritability, and common environmental effect, respectively). Citrulline, phosphatidylcholine acyl-alkyl C36:4, phosphatidylcholine acyl-alkyl C36:5, and serotonin had significant phenotypic correlations with CM risk factors. Citrulline had a positive genetic correlation with apolipoprotein B100, while phosphatidylcholine acyl-alkyl C36:5 had a positive environmental correlation with total cholesterol. In conclusion, familial resemblances in metabolite concentrations were mainly attributable to common environmental effect when considering metabolites with a significant familial effect. Common genetic and environmental factors may also influence the relationship between metabolites and CM risk factors.
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Affiliation(s)
- Bénédicte L Tremblay
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada.
| | - Frédéric Guénard
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada.
| | - Benoît Lamarche
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada.
| | - Louis Pérusse
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada; CHU de Québec Research Center - Endocrinology and Nephrology, 2705 Laurier Blvd, Quebec City, QC, G1V 4G2, Canada.
| | - Marie-Claude Vohl
- Institute of Nutrition and Functional Foods (INAF), Laval University, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada; CHU de Québec Research Center - Endocrinology and Nephrology, 2705 Laurier Blvd, Quebec City, QC, G1V 4G2, Canada.
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Sarnowski C, Lent S, Dupuis J. Investigation of parent-of-origin effects induced by fenofibrate treatment on triglycerides levels. BMC Genet 2018; 19:83. [PMID: 30255771 PMCID: PMC6156838 DOI: 10.1186/s12863-018-0640-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Background Genome-wide association studies performed on triglycerides (TGs) have not accounted for epigenetic mechanisms that may partially explain trait heritability. Results Parent-of-origin (POO) effect association analyses using an agnostic approach or a candidate approach were performed for pretreatment TG levels, posttreatment TG levels, and pre- and posttreatment TG-level differences in the real GAW20 family data set. We detected 22 genetic variants with suggestive POO effects with at least 1 phenotype (P ≤ 10− 5). We evaluated the association of these 22 significant genetic variants showing POO effects with close DNA methylation probes associated with TGs. A total of 18 DNA methylation probes located in the vicinity of the 22 SNPs were associated with at least 1 phenotype and 6 SNP-probe pairs were associated with DNA methylation probes at the nominal level of P < 0.05, among which 1 pair presented evidence of POO effect. Our analyses identified a paternal effect of SNP rs301621 on the difference between pre- and posttreatment TG levels (P = 1.2 × 10− 5). This same SNP showed evidence for a maternal effect on methylation levels of a nearby probe (cg10206250; P = 0.01). Using a causal inference test we established that the observed POO effect of rs301621 was not mediated by DNA methylation at cg10206250. Conclusions We performed POO effect association analyses of SNPs with TGs, as well as association analyses of SNPs with DNA methylation probes. These analyses, which were followed by a causal inference test, established that the paternal effect at the SNP rs301621 is induced by treatment and is not mediated by methylation level at cg10206250.
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Affiliation(s)
- Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA, 02118, USA.
| | - Samantha Lent
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA, 02118, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA, 02118, USA
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Fuady AM, Lent S, Sarnowski C, Tintle NL. Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20. BMC Genet 2018; 19:72. [PMID: 30255777 PMCID: PMC6157126 DOI: 10.1186/s12863-018-0647-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods. RESULTS Although the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups also considered multimarker versus single-marker tests. Multimarker tests had modestly improved power versus single-marker methods on simulated data, and on real data identified additional associations that were not identified with single-marker methods, including identification of a gene with a strong biological interpretation. Finally, some of the groups explored methods to combine single-nucleotide polymorphism (SNP) and DNA methylation into a single association analysis. CONCLUSIONS A causal inference method showed promise at discovering new mechanisms of SNP activity; gene-based methods of summarizing SNP and DNA methylation data also showed promise. Even though numerous questions still remain in the analysis of DNA methylation data, our discussions at GAW20 suggest some emerging best practices.
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Affiliation(s)
- Angga M. Fuady
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 Leiden, ZC Netherlands
| | - Samantha Lent
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118 USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118 USA
| | - Nathan L. Tintle
- Department of Mathematics and Statistics, Dordt College, Sioux Center, IA 51250 USA
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