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Delpero M, Korkuć P, Arends D, Brockmann GA, Hesse D. Identification of additional body weight QTLs in the Berlin Fat Mouse BFMI861 lines using time series data. Sci Rep 2024; 14:6159. [PMID: 38486030 PMCID: PMC10940635 DOI: 10.1038/s41598-024-56097-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/01/2024] [Indexed: 03/18/2024] Open
Abstract
The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and metabolic syndrome. The sublines BFMI861-S1 and BFMI861-S2 differ in weight despite high genetic similarity and a shared obesity-related locus. This study focused on identifying additional body weight quantitative trait loci (QTLs) by analyzing weekly weight measurements in a male population of the advanced intercross line BFMI861-S1 x BFMI861-S2. QTL analysis, utilizing 200 selectively genotyped mice (GigaMUGA) and 197 males genotyped for top SNPs, revealed a genome-wide significant QTL on Chr 15 (68.46 to 81.40 Mb) for body weight between weeks 9 to 20. Notably, this QTL disappeared (weeks 21 to 23) and reappeared (weeks 24 and 25) coinciding with a diet change. Additionally, a significant body weight QTL on Chr 16 (3.89 to 22.79 Mb) was identified from weeks 6 to 25. Candidate genes, including Gpt, Cbx6, Apol6, Apol8, Sun2 (Chr 15) and Trap1, Rrn3, Mapk1 (Chr 16), were prioritized. This study unveiled two additional body weight QTLs, one of which is novel and responsive to diet changes. These findings illuminate genomic regions influencing weight in BFMI and emphasize the utility of time series data in uncovering novel genetic factors.
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Affiliation(s)
- Manuel Delpero
- Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Paula Korkuć
- Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Danny Arends
- Department of Applied Sciences, Northumbria University, Newcastle Upon Tyne, UK
| | - Gudrun A Brockmann
- Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Deike Hesse
- Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften, Humboldt-Universität zu Berlin, Berlin, Germany.
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2
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Mize TJ, Evans LM. Examination of a novel expression-based gene-SNP annotation strategy to identify tissue-specific contributions to heritability in multiple traits. Eur J Hum Genet 2024; 32:263-269. [PMID: 36446896 PMCID: PMC10924090 DOI: 10.1038/s41431-022-01244-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/20/2022] [Accepted: 11/15/2022] [Indexed: 12/03/2022] Open
Abstract
Complex traits show clear patterns of tissue-specific expression influenced by single nucleotide polymorphisms (SNPs), yet current strategies aggregate SNP effects to genes by employing simple physical proximity-based windows. Here, we examined whether incorporating SNPs with effects on tissue-specific cis-expression would improve our ability to detect trait-relevant tissues across 31 complex traits using stratified linkage disequilibrium score regression (S-LDSC). We found that a physical proximity annotation produced more significant tissue enrichments and larger S-LDSC regression coefficients, as compared to an expression-based annotation. Furthermore, we showed that our expression-based annotation did not outperform an annotation strategy in which an equal number of randomly chosen SNPs were annotated to genes within the same genomic window, suggesting extensive redundancy among SNP effect estimates due to linkage disequilibrium. That said, current sample sizes limit estimation of cis-genetic SNP effects; therefore, we recommend reexamination of the expression-based annotation when larger tissue-specific expression datasets become available. To examine the influence of sample size, we used a large whole blood eQTL reference panel (N = 31,684) applying a similar expression-based annotation strategy. We found that significant cis-expression QTLs in whole blood did not outperform the physical proximity annotation when estimating tissue-specific SNP heritability enrichment for either high- or low-density lipoprotein phenotypes but performed similarly for inflammatory bowel disease. Finally, we report new and updated tissue enrichment estimates across 31 complex traits, such as significant heritability enrichment of the frontal cortex for cognitive performance, educational attainment, and intelligence, providing further evidence of this structure's importance in higher cognitive function.
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Affiliation(s)
- Travis J Mize
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA.
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA.
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA.
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA.
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3
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Martin L, Boutwell BB, Messerlian C, Adams CD. Mendelian randomization reveals apolipoprotein B shortens healthspan and possibly increases risk for Alzheimer's disease. Commun Biol 2024; 7:230. [PMID: 38402277 PMCID: PMC10894226 DOI: 10.1038/s42003-024-05887-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/05/2024] [Indexed: 02/26/2024] Open
Abstract
Apolipoprotein B-100 (APOB) is a component of fat- and cholesterol-transporting molecules in the bloodstream. It is the main lipoprotein in low-density lipoprotein cholesterol (LDL) and has been implicated in conditions that end healthspan (the interval between birth and onset of chronic disease). However, APOB's direct relationship with healthspan remains uncertain. With Mendelian randomization, we show that higher levels of APOB and LDL shorten healthspan in humans. Multivariable Mendelian randomization of APOB and LDL on healthspan suggests that the predominant trait accounting for the relationship is APOB. In addition, we provide preliminary evidence that APOB increases risk for Alzheimer's disease, a condition that ends healthspan. If these relationships are causal, they suggest that interventions to improve healthspan in aging populations could include strategies targeting APOB. Ultimately, given that more than 44 million people currently suffer from Alzheimer's disease worldwide, such interventions are needed.
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Affiliation(s)
- Leah Martin
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Brian B Boutwell
- School of Applied Sciences, University of Mississippi, University, Jackson, MS, USA
- John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA
| | - Carmen Messerlian
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Obstetrics and Gynecology, Massachusetts General Hospital Fertility Center, Boston, MA, USA
- Department of Environmental Health, Harvard T. H. Chan School of Public Heath, Boston, MA, USA
| | - Charleen D Adams
- Department of Environmental Health, Harvard T. H. Chan School of Public Heath, Boston, MA, USA.
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Singh TN, Joshi AK, Vikram A, Yadav N, Prashar S. Mean performances, character associations and multi-environmental evaluation of chilli landraces in north western Himalayas. Sci Rep 2024; 14:769. [PMID: 38191594 PMCID: PMC10774388 DOI: 10.1038/s41598-024-51348-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/03/2024] [Indexed: 01/10/2024] Open
Abstract
Even though many varieties have been recommended across agro-climate zones of Himachal Pradesh, yet the information on stability is lacking in this State. Hence, the present investigation was carried out to identify high yielding stable genotypes among various pre-adapted landraces. The material consists of 20 chilli landraces including check i.e. DKC-8. The experiment was laid out in a RCBD. The data were recorded and analyzed to work out mean performances and the inferences were drawn for parameters of variability, correlation coefficients, path coefficients and stability analysis. As per mean performances, CS7 and CS9 were earliest in flowering, CS13 is earliest in days to ripe maturity, CS10 had highest plant height and CS9 had highest average fruit weight and ripe fruit yield plant-1. High PCV and GCV were recorded for ripe fruit yield plant-1. Heritability and genetic advance were recorded maximum for plant height in summer seasons and were recorded maximum for number of ripe fruits plant-1 in winter season. Correlation coefficients showed that number of ripe fruits plant-1 and average ripe fruit weight were positively and significantly correlated with ripe fruit yield plant-1. Path coefficient analysis in summer and winter seasons showed that average ripe fruit weight had the highest positive direct effect on ripe fruit yield plant-1. The pooled data over environments were analyzed to estimate the interaction effects between genotypes × environment. The mean sum of squares due to genotypes, environments and genotypes × environment interaction were significant for all the characteristics. CS1, CS3, CS6, CS10, CS13, CS15 were adapted to all environments, CS7 and CS9 were specifically adapted to favourable environment and CS2 was specifically adapted to unfavorable environment for 50% flowering, landraces CS1, CS2 and CS3were well adapted to all environments for ripe maturity whereas landraces CS6, CS10 and CS19 were well adapted to all environment for number of ripe fruit and ripe fruit yield.
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Affiliation(s)
- Thakur Narender Singh
- Department of Vegetable Science, Dr. YS Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh, 173230, India.
| | - A K Joshi
- Department of Vegetable Science, Dr. YS Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh, 173230, India
| | - Amit Vikram
- Department of Vegetable Science, Dr. YS Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh, 173230, India
| | - Nitin Yadav
- Department of Vegetable Science, Dr. YS Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh, 173230, India
| | - Sakshi Prashar
- Department of Vegetable Science, Dr. YS Parmar University of Horticulture and Forestry, Nauni Solan, Himachal Pradesh, 173230, India.
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5
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Eiman MN, Kumar S, Serrano Negron YL, Tansey TR, Harbison ST. Genome-wide association in Drosophila identifies a role for Piezo and Proc-R in sleep latency. Sci Rep 2024; 14:260. [PMID: 38168575 PMCID: PMC10761942 DOI: 10.1038/s41598-023-50552-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
Sleep latency, the amount of time that it takes an individual to fall asleep, is a key indicator of sleep need. Sleep latency varies considerably both among and within species and is heritable, but lacks a comprehensive description of its underlying genetic network. Here we conduct a genome-wide association study of sleep latency. Using previously collected sleep and activity data on a wild-derived population of flies, we calculate sleep latency, confirming significant, heritable genetic variation for this complex trait. We identify 520 polymorphisms in 248 genes contributing to variability in sleep latency. Tests of mutations in 23 candidate genes and additional putative pan-neuronal knockdown of 9 of them implicated CG44153, Piezo, Proc-R and Rbp6 in sleep latency. Two large-effect mutations in the genes Proc-R and Piezo were further confirmed via genetic rescue. This work greatly enhances our understanding of the genetic factors that influence variation in sleep latency.
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Affiliation(s)
- Matthew N Eiman
- Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Drexel University College of Medicine, Philadelphia, PA, USA
| | - Shailesh Kumar
- Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Neuroscience and Behavior, National Institute On Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Yazmin L Serrano Negron
- Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Terry R Tansey
- Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Susan T Harbison
- Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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6
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Brekke C, Johnston SE, Knutsen TM, Berg P. Genetic architecture of individual meiotic crossover rate and distribution in Atlantic Salmon. Sci Rep 2023; 13:20481. [PMID: 37993527 PMCID: PMC10665409 DOI: 10.1038/s41598-023-47208-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/10/2023] [Indexed: 11/24/2023] Open
Abstract
Meiotic recombination through chromosomal crossovers ensures proper segregation of homologous chromosomes during meiosis, while also breaking down linkage disequilibrium and shuffling alleles at loci located on the same chromosome. Rates of recombination can vary between species, but also between and within individuals, sex and chromosomes within species. Indeed, the Atlantic salmon genome is known to have clear sex differences in recombination with female biased heterochiasmy and markedly different landscapes of crossovers between males and females. In male meiosis, crossovers occur strictly in the telomeric regions, whereas in female meiosis crossovers tend to occur closer to the centromeres. However, little is known about the genetic control of these patterns and how this differs at the individual level. Here, we investigate genetic variation in individual measures of recombination in > 5000 large full-sib families of a Norwegian Atlantic salmon breeding population with high-density SNP genotypes. We show that females had 1.6 × higher crossover counts (CC) than males, with autosomal linkage maps spanning a total of 2174 cM in females and 1483 cM in males. However, because of the extreme telomeric bias of male crossovers, female recombination is much more important for generation of new haplotypes with 8 × higher intra-chromosomal genetic shuffling than males. CC was heritable in females (h2 = 0.11) and males (h2 = 0.10), and shuffling was also heritable in both sex but with a lower heritability in females (h2 = 0.06) than in males (h2 = 0.11). Inter-sex genetic correlations for both traits were close to zero, suggesting that rates and distribution of crossovers are genetically distinct traits in males and females, and that there is a potential for independent genetic change in both sexes in the Atlantic Salmon. Together, these findings give novel insights into the genetic architecture of recombination in salmonids and contribute to a better understanding of how rates and distribution of recombination may evolve in eukaryotes more broadly.
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Affiliation(s)
- Cathrine Brekke
- Institute of Ecology and Evolution, School of Biology, University of Edinburgh, Edinburgh, EH9 3FL, UK.
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Post Box 5003, 1433, Ås, Norway.
| | - Susan E Johnston
- Institute of Ecology and Evolution, School of Biology, University of Edinburgh, Edinburgh, EH9 3FL, UK
| | | | - Peer Berg
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Post Box 5003, 1433, Ås, Norway
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7
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Sallam AM, Abou-Souliman I, Reyer H, Wimmers K, Rabee AE. New insights into the genetic predisposition of brucellosis and its effect on the gut and vaginal microbiota in goats. Sci Rep 2023; 13:20086. [PMID: 37973848 PMCID: PMC10654701 DOI: 10.1038/s41598-023-46997-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
Goats contribute significantly to the global food security and industry. They constitute a main supplier of meat and milk for large proportions of people in Egypt and worldwide. Brucellosis is a zoonotic infectious disease that causes a significant economic loss in animal production. A case-control genome-wide association analysis (GWAS) was conducted using the infectious status of the animal as a phenotype. The does that showed abortion during the last third period of pregnancy and which were positive to both rose bengal plate and serum tube agglutination tests, were considered as cases. Otherwise, they were considered as controls. All animals were genotyped using the Illumina 65KSNP BeadChip. Additionally, the diversity and composition of vaginal and fecal microbiota in cases and controls were investigated using PCR-amplicone sequencing of the V4 region of 16S rDNA. After applying quality control criteria, 35,818 markers and 66 does were available for the GWAS test. The GWAS revealed a significantly associated SNP (P = 5.01 × 10-7) located on Caprine chromosome 15 at 29 megabases. Four other markers surpassed the proposed threshold (P = 2.5 × 10-5). Additionally, fourteen genomic regions accounted for more than 0.1% of the variance explained by all genome windows. Corresponding markers were located within or in close vicinity to several candidate genes, such as ARRB1, RELT, ATG16L2, IGSF21, UBR4, ULK1, DCN, MAPB1, NAIP, CD26, IFIH1, NDFIP2, DOK4, MAF, IL2RB, USP18, ARID5A, ZAP70, CNTN5, PIK3AP1, DNTT, BLNK, and NHLRC3. These genes play important roles in the regulation of immune responses to the infections through several biological pathways. Similar vaginal bacterial community was observed in both cases and controls while the fecal bacterial composition and diversity differed between the groups (P < 0.05). Faeces from the control does showed a higher relative abundance of the phylum Bacteroidota compared to cases (P < 0.05), while the latter showed more Firmicutes, Spirochaetota, Planctomycetota, and Proteobacteria. On the genus level, the control does exhibited higher abundances of Rikenellaceae RC9 gut group and Christensenellaceae R-7 group (P < 0.05), while the infected does revealed higher Bacteroides, Alistipes, and Prevotellaceae UCG-003 (P < 0.05). This information increases our understanding of the genetics of the susceptibility to Brucella in goats and may be useful in breeding programs and selection schemes that aim at controlling the disease in livestock.
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Affiliation(s)
- Ahmed M Sallam
- Animal and Poultry Breeding Department, Desert Research Center, Cairo, Egypt.
| | | | - Henry Reyer
- Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Klaus Wimmers
- Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Alaa Emara Rabee
- Animal and Poultry Nutrition Department, Desert Research Center, Cairo, Egypt
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Huang G, Yin X, Lu J, Zhang L, Lin D, Xie Y, Liu H, Liu C, Zuo J, Zhang X. Dynamic QTL mapping revealed primarily the genetic structure of photosynthetic traits in castor (Ricinus communis L.). Sci Rep 2023; 13:14071. [PMID: 37640794 PMCID: PMC10462610 DOI: 10.1038/s41598-023-41241-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/23/2023] [Indexed: 08/31/2023] Open
Abstract
High photosynthetic efficiency is the basis of high biomass and high harvest index in castor (Ricinus communis L.). Understanding the genetic law of photosynthetic traits will facilitate the breeding for high photosynthetic efficiency. In this study, the dynamic QTL mapping was performed with the populations F2 and BC1 derived from 2 parents with significant difference in net photosynthetic rate (Pn) at 3 stages, in order to reveal the genetic structure of photosynthetic traits. In F2 population, 26 single-locus QTLs were identified, including 3/3/1 (the QTL number at stage I/II/III, the same below), 1/2/0, 1/2/2, 1/3/1, 0/1/1, and 1/1/2 QTLs conferring Pn, water use efficiency (Wue), transpiration rate (Tr), stomatal conductance (Gs), intercellular CO2 concentration (Ci) and chlorophyll content (Cc), with a phenotypic variation explained (PVE) of 8.40%/8.91%/6.17%, 5.36%/31.74%/0, 7.31%/12.80%/15.15%, 1.60%/6.44%/0.02%, 0/1.10%/0.70% and 2.77%/3.96%/6.50% respectively. And 53 epistatic QTLs (31 pairs) were identified, including 2/2/5, 5/6/3, 4/4/2, 6/3/2, 3/2/0 and 4/0/0 ones conferring the above 6 traits, with a PVE of 6.52%/6.47%/19.04%, 16.72%/15.67%/14.12%, 18.57%/15.58%/7.34%, 21.72%/8.52%/7.13%, 13.33%/4.94%/0 and 7.84%/0/0 respectively. The QTL mapping results in BC1 population were consistent with those in F2 population, except fewer QTLs detected. Most QTLs identified were minor-effect ones, only a few were main-effect ones (PVE > 10%), focused on 2 traits, Wue and Tr, such as qWue1.1, qWue1.2, FqTr1.1, FqTr6, BqWue1.1 and BqTr3; The epistatic effects, especially those related to the dominance effects were the main genetic component of photosynthetic traits, and all the epistatic QTLs had no single-locus effects except qPn1.2, FqGs1.2, FqCi1.2 and qCc3.2; The detected QTLs underlying each trait varied at different stages except stable QTLs qGs1.1, detected at 3 stages, qWue2, qTr1.2 and qCc3.2, detected at 2 stages; 6 co-located QTLs were identified, each of which conferring 2-5 different traits, demonstrated the gene pleiotropy between photosynthetic traits; 2 QTL clusters, located within the marker intervals RCM1842-RCM1335 and RCM523-RCM83, contained 15/5 (F2/BC1) and 4/4 (F2/BC1) QTLs conferring multiple traits, including co-located QTLs and main-effect QTLs. The above results provided new insights into the genetic structure of photosynthetic traits and important references for the high photosynthetic efficiency breeding in castor plant.
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Affiliation(s)
- Guanrong Huang
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China
| | - Xuegui Yin
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China
| | - Jiannong Lu
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China.
| | - Liuqin Zhang
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China
| | - Dantong Lin
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China
| | - Yu Xie
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China
| | - Haiyan Liu
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China
| | - Chaoyu Liu
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China
| | - Jinying Zuo
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China
| | - Xiaoxiao Zhang
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China
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9
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Raben TG, Lello L, Widen E, Hsu SDH. Biobank-scale methods and projections for sparse polygenic prediction from machine learning. Sci Rep 2023; 13:11662. [PMID: 37468507 PMCID: PMC10356957 DOI: 10.1038/s41598-023-37580-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 06/23/2023] [Indexed: 07/21/2023] Open
Abstract
In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.
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Affiliation(s)
- Timothy G Raben
- Department of Physics and Astronomy, Michigan State University, Michigan, USA.
| | - Louis Lello
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
| | - Erik Widen
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
| | - Stephen D H Hsu
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
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10
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Pepke ML, Kvalnes T, Wright J, Araya-Ajoy YG, Ranke PS, Boner W, Monaghan P, Sæther BE, Jensen H, Ringsby TH. Longitudinal telomere dynamics within natural lifespans of a wild bird. Sci Rep 2023; 13:4272. [PMID: 36922555 DOI: 10.1038/s41598-023-31435-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Telomeres, the nucleotide sequences that protect the ends of eukaryotic chromosomes, shorten with each cell division and telomere loss may be influenced by environmental factors. Telomere length (TL) decreases with age in several species, but little is known about the sources of genetic and environmental variation in the change in TL (∆TL) in wild animals. In this study, we tracked changes in TL throughout the natural lifespan (from a few months to almost 9 years) of free-living house sparrows (Passer domesticus) in two different island populations. TL was measured in nestlings and subsequently up to four times during their lifetime. TL generally decreased with age (senescence), but we also observed instances of telomere lengthening within individuals. We found some evidence for selective disappearance of individuals with shorter telomeres through life. Early-life TL positively predicted later-life TL, but the within-individual repeatability in TL was low (9.2%). Using genetic pedigrees, we found a moderate heritability of ∆TL (h2 = 0.21), which was higher than the heritabilities of early-life TL (h2 = 0.14) and later-life TL measurements (h2 = 0.15). Cohort effects explained considerable proportions of variation in early-life TL (60%), later-life TL (53%), and ∆TL (37%), which suggests persistent impacts of the early-life environment on lifelong telomere dynamics. Individual changes in TL were independent of early-life TL. Finally, there was weak evidence for population differences in ∆TL that may be linked to ecological differences in habitat types. Combined, our results show that individual telomere biology is highly dynamic and influenced by both genetic and environmental variation in natural conditions.
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11
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Sengupta D, Mukhopadhyay P, Banerjee S, Ganguly K, Mascharak P, Mukherjee N, Mitra S, Bhattacharjee S, Mitra R, Sarkar A, Chaudhuri T, Bhattacharjee G, Nath S, Roychoudhury S, Sengupta M. Identifying polymorphic cis-regulatory variants as risk markers for lung carcinogenesis and chemotherapy responses in tobacco smokers from eastern India. Sci Rep 2023; 13:4019. [PMID: 36899086 DOI: 10.1038/s41598-023-30962-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 03/03/2023] [Indexed: 03/12/2023] Open
Abstract
Aberrant expression of xenobiotic metabolism and DNA repair genes is critical to lung cancer pathogenesis. This study aims to identify the cis-regulatory variants of the genes modulating lung cancer risk among tobacco smokers and altering their chemotherapy responses. From a list of 2984 SNVs, prioritization and functional annotation revealed 22 cis-eQTLs of 14 genes within the gene expression-correlated DNase I hypersensitive sites using lung tissue-specific ENCODE, GTEx, Roadmap Epigenomics, and TCGA datasets. The 22 cis-regulatory variants predictably alter the binding of 44 transcription factors (TFs) expressed in lung tissue. Interestingly, 6 reported lung cancer-associated variants were found in linkage disequilibrium (LD) with 5 prioritized cis-eQTLs from our study. A case-control study with 3 promoter cis-eQTLs (p < 0.01) on 101 lung cancer patients and 401 healthy controls from eastern India with confirmed smoking history revealed an association of rs3764821 (ALDH3B1) (OR = 2.53, 95% CI = 1.57-4.07, p = 0.00014) and rs3748523 (RAD52) (OR = 1.69, 95% CI = 1.17-2.47, p = 0.006) with lung cancer risk. The effect of different chemotherapy regimens on the overall survival of lung cancer patients to the associated variants showed that the risk alleles of both variants significantly decreased (p < 0.05) patient survival.
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Momin MM, Shin J, Lee S, Truong B, Benyamin B, Lee SH. A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data. Nat Commun 2023; 14:722. [PMID: 36759513 PMCID: PMC9911789 DOI: 10.1038/s41467-023-36281-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/24/2023] [Indexed: 02/11/2023] Open
Abstract
Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific allelic effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to anthropometric and other complex traits from the UK Biobank data across ancestry groups. For obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestries.
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Affiliation(s)
- Md Moksedul Momin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia
- Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, 4225, Bangladesh
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia
| | - Jisu Shin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA
| | - Soohyun Lee
- Division of Animal Breeding and Genetics, National Institute of Animal Science (NIAS), Cheonan, South Korea
| | - Buu Truong
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
| | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
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13
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Clochard GJ, Mbengue A, Mettling C, Diouf B, Faurie C, Sene O, Chancerel E, Guichoux E, Hollard G, Raymond M, Willinger M. The effect of the 7R allele at the DRD4 locus on risk tolerance is independent of background risk in Senegalese fishermen. Sci Rep 2023; 13:622. [PMID: 36635358 DOI: 10.1038/s41598-022-27002-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
It has been shown that living in risky environments, as well as having a risky occupation, can moderate risk-tolerance. Despite the involvement of dopamine in the expectation of reward described by neurobiologists, a GWAS study was not able to demonstrate a genetic contribution of genes involved in the dopaminergic pathway in risk attitudes and gene candidate studies gave contrasting results. We test the possibility that a genetic effect of the DRD4-7R allele in risk-taking behavior could be modulated by environmental factors. We show that the increase in risk-tolerance due to the 7R allele is independent of the environmental risk in two populations in Northern Senegal, one of which is exposed to a very high risk due to dangerous fishing.
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14
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Vallat R, Berry SE, Tsereteli N, Capdevila J, Khatib HA, Valdes AM, Delahanty LM, Drew DA, Chan AT, Wolf J, Franks PW, Spector TD, Walker MP. How people wake up is associated with previous night's sleep together with physical activity and food intake. Nat Commun 2022; 13:7116. [PMID: 36402781 DOI: 10.1038/s41467-022-34503-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 10/27/2022] [Indexed: 11/21/2022] Open
Abstract
How people wake up and regain alertness in the hours after sleep is related to how they are sleeping, eating, and exercising. Here, in a prospective longitudinal study of 833 twins and genetically unrelated adults, we demonstrate that how effectively an individual awakens in the hours following sleep is not associated with their genetics, but instead, four independent factors: sleep quantity/quality the night before, physical activity the day prior, a breakfast rich in carbohydrate, and a lower blood glucose response following breakfast. Furthermore, an individual's set-point of daily alertness is related to the quality of their sleep, their positive emotional state, and their age. Together, these findings reveal a set of non-genetic (i.e., not fixed) factors associated with daily alertness that are modifiable.
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15
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Lam M, Chen CY, Ge T, Xia Y, Hill DW, Trampush JW, Yu J, Knowles E, Davies G, Stahl EA, Huckins L, Liewald DC, Djurovic S, Melle I, Christoforou A, Reinvang I, DeRosse P, Lundervold AJ, Steen VM, Espeseth T, Räikkönen K, Widen E, Palotie A, Eriksson JG, Giegling I, Konte B, Hartmann AM, Roussos P, Giakoumaki S, Burdick KE, Payton A, Ollier W, Chiba-Falek O, Koltai DC, Need AC, Cirulli ET, Voineskos AN, Stefanis NC, Avramopoulos D, Hatzimanolis A, Smyrnis N, Bilder RM, Freimer NB, Cannon TD, London E, Poldrack RA, Sabb FW, Congdon E, Conley ED, Scult MA, Dickinson D, Straub RE, Donohoe G, Morris D, Corvin A, Gill M, Hariri AR, Weinberger DR, Pendleton N, Bitsios P, Rujescu D, Lahti J, Le Hellard S, Keller MC, Andreassen OA, Deary IJ, Glahn DC, Huang H, Liu C, Malhotra AK, Lencz T. Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics. Neuropsychopharmacology 2021; 46:1788-1801. [PMID: 34035472 PMCID: PMC8357785 DOI: 10.1038/s41386-021-01023-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/22/2021] [Accepted: 04/12/2021] [Indexed: 02/05/2023]
Abstract
Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.
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Affiliation(s)
- Max Lam
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Institute of Mental Health, Singapore, Singapore
| | - Chia-Yen Chen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Biogen, Inc, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Tian Ge
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Yan Xia
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Psychiatry Department, SUNY Upstate Medical University, Syracuse, NY, USA
| | - David W Hill
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, Scotland, UK
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Joey W Trampush
- Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jin Yu
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Emma Knowles
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychic Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Gail Davies
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, Scotland, UK
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Eli A Stahl
- Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura Huckins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David C Liewald
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ingrid Melle
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Andrea Christoforou
- Spaulding Rehabilitation Hospital Boston, Charlestown, MA, USA
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Ivar Reinvang
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Pamela DeRosse
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Vidar M Steen
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Thomas Espeseth
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK
- Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, Finland
| | - Johan G Eriksson
- Department of General Practice, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Folkhälsan Research Center, Helsinki, Finland
| | - Ina Giegling
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, Germany
| | - Bettina Konte
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, Germany
| | - Annette M Hartmann
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, Germany
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2), James J. Peters VA Medical Center, Bronx, NY, USA
| | | | - Katherine E Burdick
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2), James J. Peters VA Medical Center, Bronx, NY, USA
- Department of Psychiatry - Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Antony Payton
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, Manchester, UK
| | - William Ollier
- Centre for Epidemiology, Division of Population Health, Health Services Research & Primary Care, The University of Manchester, Manchester, UK
- School of Healthcare Sciences, Manchester Metropolitan University, Manchester, United Kingdom
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Bryan Alzheimer's Disease Research Center, and Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, USA
| | - Deborah C Koltai
- Psychiatry and Behavioral Sciences, Division of Medical Psychology, and Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - Anna C Need
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - Aristotle N Voineskos
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Nikos C Stefanis
- 2nd Department of Psychiatry, National and Kapodistrian University of Athens Medical School, University General Hospital "ATTIKON", Athens, Greece
- University Mental Health Research Institute, Athens, Greece
- Neurobiology Research Institute, Theodor-Theohari Cozzika Foundation, Athens, Greece
| | - Dimitrios Avramopoulos
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alex Hatzimanolis
- 2nd Department of Psychiatry, National and Kapodistrian University of Athens Medical School, University General Hospital "ATTIKON", Athens, Greece
- University Mental Health Research Institute, Athens, Greece
- Neurobiology Research Institute, Theodor-Theohari Cozzika Foundation, Athens, Greece
| | - Nikolaos Smyrnis
- 2nd Department of Psychiatry, National and Kapodistrian University of Athens Medical School, University General Hospital "ATTIKON", Athens, Greece
- University Mental Health Research Institute, Athens, Greece
| | - Robert M Bilder
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Nelson B Freimer
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Edythe London
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | | | - Fred W Sabb
- Robert and Beverly Lewis Center for Neuroimaging, University of Oregon, Eugene, OR, USA
| | - Eliza Congdon
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | | | - Matthew A Scult
- Weill Cornell Psychiatry at NewYork-Presbyterian, Weill Cornell Medical Center, New York, NY, USA
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Dwight Dickinson
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA
| | - Richard E Straub
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Gary Donohoe
- Neuroimaging, Cognition & Genomics (NICOG) Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Derek Morris
- Neuroimaging, Cognition & Genomics (NICOG) Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Neil Pendleton
- Division of Neuroscience and Experimental Psychology/School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, Salford Royal NHS Foundation Trust, University of Manchester, Manchester, UK
| | - Panos Bitsios
- Department of Psychiatry and Behavioral Sciences, Faculty of Medicine, University of Crete, Heraklion, Crete, GR, Greece
| | - Dan Rujescu
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, Germany
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland
| | - Stephanie Le Hellard
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
| | - Ole A Andreassen
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, Scotland, UK
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - David C Glahn
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychic Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Chunyu Liu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Psychiatry Department, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Anil K Malhotra
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Todd Lencz
- Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY, USA.
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA.
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
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16
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Wu P, Wang K, Zhou J, Chen D, Jiang A, Jiang Y, Zhu L, Qiu X, Li X, Tang G. A combined GWAS approach reveals key loci for socially-affected traits in Yorkshire pigs. Commun Biol 2021; 4:891. [PMID: 34285319 PMCID: PMC8292486 DOI: 10.1038/s42003-021-02416-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/29/2021] [Indexed: 02/06/2023] Open
Abstract
Socially affected traits in pigs are controlled by direct genetic effects and social genetic effects, which can make elucidation of their genetic architecture challenging. We evaluated the genetic basis of direct genetic effects and social genetic effects by combining single-locus and haplotype-based GWAS on imputed whole-genome sequences. Nineteen SNPs and 25 haplotype loci are identified for direct genetic effects on four traits: average daily feed intake, average daily gain, days to 100 kg and time in feeder per day. Nineteen SNPs and 11 haplotype loci are identified for social genetic effects on average daily feed intake, average daily gain, days to 100 kg and feeding speed. Two significant SNPs from single-locus GWAS (SSC6:18,635,874 and SSC6:18,635,895) are shared by a significant haplotype locus with haplotype alleles 'GGG' for both direct genetic effects and social genetic effects in average daily feed intake. A candidate gene, MT3, which is involved in growth, nervous, and immune processes, is identified. We demonstrate the genetic differences between direct genetic effects and social genetic effects and provide an anchor for investigating the genetic architecture underlying direct genetic effects and social genetic effects on socially affected traits in pigs.
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Affiliation(s)
- Pingxian Wu
- grid.80510.3c0000 0001 0185 3134Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan China
| | - Kai Wang
- grid.80510.3c0000 0001 0185 3134Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan China
| | - Jie Zhou
- grid.80510.3c0000 0001 0185 3134Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan China
| | - Dejuan Chen
- grid.80510.3c0000 0001 0185 3134Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan China
| | - Anan Jiang
- grid.80510.3c0000 0001 0185 3134Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan China
| | - Yanzhi Jiang
- grid.80510.3c0000 0001 0185 3134College of Life Science, Sichuan Agricultural University, Yaan, Sichuan China
| | - Li Zhu
- grid.80510.3c0000 0001 0185 3134Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan China
| | - Xiaotian Qiu
- grid.410634.4National Animal Husbandry Service, Beijing, Beijing, China
| | - Xuewei Li
- grid.80510.3c0000 0001 0185 3134Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan China
| | - Guoqing Tang
- grid.80510.3c0000 0001 0185 3134Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan China
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17
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Slavskii SA, Kuznetsov IA, Shashkova TI, Bazykin GA, Axenovich TI, Kondrashov FA, Aulchenko YS. The limits of normal approximation for adult height. Eur J Hum Genet 2021; 29:1082-1091. [PMID: 33664501 PMCID: PMC8298501 DOI: 10.1038/s41431-021-00836-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 01/05/2021] [Accepted: 02/11/2021] [Indexed: 11/14/2022] Open
Abstract
Adult height inspired the first biometrical and quantitative genetic studies and is a test-case trait for understanding heritability. The studies of height led to formulation of the classical polygenic model, that has a profound influence on the way we view and analyse complex traits. An essential part of the classical model is an assumption of additivity of effects and normality of the distribution of the residuals. However, it may be expected that the normal approximation will become insufficient in bigger studies. Here, we demonstrate that when the height of hundreds of thousands of individuals is analysed, the model complexity needs to be increased to include non-additive interactions between sex, environment and genes. Alternatively, the use of log-normal approximation allowed us to still use the additive effects model. These findings are important for future genetic and methodologic studies that make use of adult height as an exemplar trait.
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Affiliation(s)
- Sergei A Slavskii
- Skolkovo Institute of Science and Technology, Moscow, Russia
- Novosibirsk State University, Novosibirsk, Russia
- Moscow Institute of Physics and Technology, Moscow, Russia
| | | | - Tatiana I Shashkova
- Novosibirsk State University, Novosibirsk, Russia
- Moscow Institute of Physics and Technology, Moscow, Russia
- Institute for Information Transmission Problems (Kharkevich Institute), Moscow, Russia
| | - Georgii A Bazykin
- Skolkovo Institute of Science and Technology, Moscow, Russia
- Institute for Information Transmission Problems (Kharkevich Institute), Moscow, Russia
| | - Tatiana I Axenovich
- Novosibirsk State University, Novosibirsk, Russia
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | | | - Yurii S Aulchenko
- Novosibirsk State University, Novosibirsk, Russia.
- Moscow Institute of Physics and Technology, Moscow, Russia.
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.
- PolyOmica, 's-Hertogenbosch, PA, The Netherlands.
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18
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Braz CU, Rowan TN, Schnabel RD, Decker JE. Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle. Sci Rep 2021; 11:13335. [PMID: 34172761 PMCID: PMC8233360 DOI: 10.1038/s41598-021-92455-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
Understanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.
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Affiliation(s)
- Camila U Braz
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Troy N Rowan
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
- Genetics Area Program, University of Missouri, Columbia, MO, 65211, USA
| | - Robert D Schnabel
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
- Genetics Area Program, University of Missouri, Columbia, MO, 65211, USA
- Informatics Institute, University of Missouri, Columbia, MO, 65211, USA
| | - Jared E Decker
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA.
- Genetics Area Program, University of Missouri, Columbia, MO, 65211, USA.
- Informatics Institute, University of Missouri, Columbia, MO, 65211, USA.
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19
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Abstract
Social group structure is highly variable and can be important for nearly every aspect of behavior and its fitness consequences. Group structure can be modeled using social network analysis, but we know little about the evolutionary factors shaping and maintaining variation in how individuals are embedded within their networks (i.e., network position). While network position is a pervasive target of selection, it remains unclear whether network position is heritable and can respond to selection. Furthermore, it is unclear how environmental factors interact with genotypic effects on network positions, or how environmental factors shape selection on heritable network structure. Here we show multiple measures of social network position are heritable, using replicate genotypes and replicate social groups of Drosophila melanogaster flies. Our results indicate genotypic differences in network position are largely robust to changes in the environment flies experience, though some measures of network position do vary across environments. We also show selection on multiple network position metrics depends on the environmental context they are expressed in, laying the groundwork for better understanding how spatio-temporal variation in selection contributes to the evolution of variable social group structure.
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20
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Henriques JF, Lacava M, Guzmán C, Gavín-Centol MP, Ruiz-Lupión D, De Mas E, Magalhães S, Moya-Laraño J. The sources of variation for individual prey-to-predator size ratios. Heredity (Edinb) 2021; 126:684-694. [PMID: 33452465 PMCID: PMC8115045 DOI: 10.1038/s41437-020-00395-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 12/03/2020] [Accepted: 12/07/2020] [Indexed: 01/29/2023] Open
Abstract
The relative body size at which predators are willing to attack prey, a key trait for predator-prey interactions, is usually considered invariant. However, this ratio can vary widely among individuals or populations. Identifying the range and origin of such variation is key to understanding the strength and constraints on selection in both predators and prey. Still, these sources of variation remain largely unknown. We filled this gap by measuring the genetic, maternal and environmental variation of the maximum prey-to-predator size ratio (PPSRmax) in juveniles of the wolf spider Lycosa fasciiventris using a paternal half-sib split-brood design, in which each male was paired with two females and the offspring reared in two food environments: poor and rich. Each juvenile spider was then sequentially offered crickets of decreasing size and the maximum prey size killed was determined. We also measured body size and body condition of spiders upon emergence and just before the trial. We found low, but significant heritability (h2 = 0.069) and dominance and common environmental variance (d2 + 4c2 = 0.056). PPSRmax was also partially explained by body condition (during trial) but there was no effect of the rearing food environment. Finally, a maternal correlation between body size early in life and PPSRmax indicated that offspring born larger were less predisposed to feed on larger prey later in life. Therefore, PPSRmax, a central trait in ecosystems, can vary widely and this variation is due to different sources, with important consequences for changes in this trait in the short and long terms.
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Affiliation(s)
- Jorge F. Henriques
- grid.9983.b0000 0001 2181 4263cE3c - Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal ,grid.466639.80000 0004 0547 1725Functional and Evolutionary Ecology, Estación Experimental de Zonas Áridas, CSIC, Carretera de Sacramento s/n, 04120-La Cañada De San Urbano, Almeria, Spain
| | - Mariángeles Lacava
- grid.11630.350000000121657640CENUR Noreste Sede Rivera, Universidad de la República, Ituzaingó, 667 Rivera Uruguay
| | - Celeste Guzmán
- grid.466639.80000 0004 0547 1725Functional and Evolutionary Ecology, Estación Experimental de Zonas Áridas, CSIC, Carretera de Sacramento s/n, 04120-La Cañada De San Urbano, Almeria, Spain
| | - Maria Pilar Gavín-Centol
- grid.466639.80000 0004 0547 1725Functional and Evolutionary Ecology, Estación Experimental de Zonas Áridas, CSIC, Carretera de Sacramento s/n, 04120-La Cañada De San Urbano, Almeria, Spain
| | - Dolores Ruiz-Lupión
- grid.466639.80000 0004 0547 1725Functional and Evolutionary Ecology, Estación Experimental de Zonas Áridas, CSIC, Carretera de Sacramento s/n, 04120-La Cañada De San Urbano, Almeria, Spain
| | - Eva De Mas
- grid.466639.80000 0004 0547 1725Functional and Evolutionary Ecology, Estación Experimental de Zonas Áridas, CSIC, Carretera de Sacramento s/n, 04120-La Cañada De San Urbano, Almeria, Spain
| | - Sara Magalhães
- grid.9983.b0000 0001 2181 4263cE3c - Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Jordi Moya-Laraño
- grid.466639.80000 0004 0547 1725Functional and Evolutionary Ecology, Estación Experimental de Zonas Áridas, CSIC, Carretera de Sacramento s/n, 04120-La Cañada De San Urbano, Almeria, Spain
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21
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Colby AE, Kimock CM, Higham JP. Endocranial volume is variable and heritable, but not related to fitness, in a free-ranging primate. Sci Rep 2021; 11:4235. [PMID: 33608572 PMCID: PMC7895985 DOI: 10.1038/s41598-021-81265-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 12/24/2020] [Indexed: 01/31/2023] Open
Abstract
Large relative brain size is a defining characteristic of the order Primates. Arguably, this can be attributed to selection for behavioral aptitudes linked to a larger brain size. In order for selection of a trait to occur, the trait must vary, that variation must be heritable, and enhance fitness. In this study, we use a quantitative genetic approach to investigate the production and maintenance of variation in endocranial volume in a population of free-ranging rhesus macaques. We measured the endocranial volume and body mass proxies of 542 rhesus macaques from Cayo Santiago. We investigated variation in endocranial volume within and between sexes. Using a genetic pedigree, we estimated heritability of absolute and relative endocranial volume, and selection gradients of both traits as well as estimated body mass in the sample. Within this population, both absolute and relative endocranial volume display variation and sexual dimorphism. Both absolute and relative endocranial volume are highly heritable, but we found no evidence of selection on absolute or relative endocranial volume. These findings suggest that endocranial volume is not undergoing selection, or that we did not detect it because selection is neither linear nor quadratic, or that we lacked sufficient sample sizes to detect it.
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Affiliation(s)
- Abigail E Colby
- Department of Anthropology, New York University, New York, NY, 10003, USA
| | - Clare M Kimock
- Department of Anthropology, New York University, New York, NY, 10003, USA
| | - James P Higham
- Department of Anthropology, New York University, New York, NY, 10003, USA.
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22
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Shi H, Gazal S, Kanai M, Koch EM, Schoech AP, Siewert KM, Kim SS, Luo Y, Amariuta T, Huang H, Okada Y, Raychaudhuri S, Sunyaev SR, Price AL. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat Commun 2021; 12:1098. [PMID: 33597505 PMCID: PMC7889654 DOI: 10.1038/s41467-021-21286-1] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 01/15/2021] [Indexed: 01/31/2023] Open
Abstract
Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.
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Affiliation(s)
- Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Evan M Koch
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Armin P Schoech
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Katherine M Siewert
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel S Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yang Luo
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tiffany Amariuta
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Soumya Raychaudhuri
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Shamil R Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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23
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Zhao L, Zhang Z, Rodriguez SMB, Vardarajan BN, Renton AE, Goate AM, Mayeux R, Wang GT, Leal SM. A quantitative trait rare variant nonparametric linkage method with application to age-at-onset of Alzheimer's disease. Eur J Hum Genet 2020; 28:1734-1742. [PMID: 32740652 PMCID: PMC7785016 DOI: 10.1038/s41431-020-0703-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 07/09/2020] [Accepted: 07/22/2020] [Indexed: 12/18/2022] Open
Abstract
To analyze pedigrees with quantitative trait (QT) and sequence data, we developed a rare variant (RV) quantitative nonparametric linkage (QNPL) method, which evaluates sharing of minor alleles. RV-QNPL has greater power than the traditional QNPL that tests for excess sharing of minor and major alleles. RV-QNPL is robust to population substructure and admixture, locus heterogeneity, and inclusion of nonpathogenic variants and can be readily applied outside of coding regions. When QNPL was used to analyze common variants, it often led to loci mapping to large intervals, e.g., >40 Mb. In contrast, when RVs are analyzed, regions are well defined, e.g., a gene. Using simulation studies, we demonstrate that RV-QNPL is substantially more powerful than applying traditional QNPL methods to analyze RVs. RV-QNPL was also applied to analyze age-at-onset (AAO) data for 107 late-onset Alzheimer's disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with whole-genome sequence data. When AAO of AD was analyzed regardless of APOE ε4 status, suggestive linkage (LOD = 2.4) was observed with RVs in KNDC1 and nominally significant linkage (p < 0.05) was observed with RVs in LOAD genes ABCA7 and IQCK. When AAO of AD was analyzed for APOE ε4 positive family members, nominally significant linkage was observed with RVs in APOE, while when AAO of AD was analyzed for APOE ε4 negative family members, nominal significance was observed for IQCK and ADAMTS1. RV-QNPL provides a powerful resource to analyze QTs in families to elucidate their genetic etiology.
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Affiliation(s)
- Linhai Zhao
- grid.39382.330000 0001 2160 926XCenter for Statistical Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Zhihui Zhang
- grid.39382.330000 0001 2160 926XCenter for Statistical Genetics, Baylor College of Medicine, Houston, TX 77030 USA ,grid.21729.3f0000000419368729Center for Statistical Genetics, Columbia University, New York, NY 10027 USA ,grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
| | - Sandra M. Barral Rodriguez
- grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
| | - Badri N. Vardarajan
- grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
| | - Alan E. Renton
- grid.59734.3c0000 0001 0670 2351Department of Neuroscience and Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Alison M. Goate
- grid.59734.3c0000 0001 0670 2351Department of Neuroscience and Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA ,grid.59734.3c0000 0001 0670 2351Department of Neuroscience and Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY 10029 USA
| | - Richard Mayeux
- grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
| | - Gao T. Wang
- grid.21729.3f0000000419368729Center for Statistical Genetics, Columbia University, New York, NY 10027 USA ,grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA ,grid.170205.10000 0004 1936 7822Department of Human Genetics, The University of Chicago, Chicago, IL 60637 USA
| | - Suzanne M. Leal
- grid.39382.330000 0001 2160 926XCenter for Statistical Genetics, Baylor College of Medicine, Houston, TX 77030 USA ,grid.21729.3f0000000419368729Center for Statistical Genetics, Columbia University, New York, NY 10027 USA ,grid.21729.3f0000000419368729Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027 USA
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24
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Nait Saada J, Kalantzis G, Shyr D, Cooper F, Robinson M, Gusev A, Palamara PF. Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations. Nat Commun 2020; 11:6130. [PMID: 33257650 PMCID: PMC7704644 DOI: 10.1038/s41467-020-19588-x] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/02/2020] [Indexed: 12/14/2022] Open
Abstract
Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of analyses. We develop FastSMC, an IBD detection algorithm that combines a fast heuristic search with accurate coalescent-based likelihood calculations. FastSMC enables biobank-scale detection and dating of IBD segments within several thousands of years in the past. We apply FastSMC to 487,409 UK Biobank samples and detect ~214 billion IBD segments transmitted by shared ancestors within the past 1500 years, obtaining a fine-grained picture of genetic relatedness in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the use of genomic data to localize a sample's birth coordinates with a median error of 45 km. We seek evidence of recent positive selection by identifying loci with unusually strong shared ancestry and detect 12 genome-wide significant signals. We devise an IBD-based test for association between phenotype and ultra-rare loss-of-function variation, identifying 29 association signals in 7 blood-related traits.
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Affiliation(s)
| | | | - Derek Shyr
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Fergus Cooper
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Alexander Gusev
- Brigham & Women's Hospital, Division of Genetics, Boston, MA, 02215, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Pier Francesco Palamara
- Department of Statistics, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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25
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Ishida S, Kato K, Tanaka M, Odamaki T, Kubo R, Mitsuyama E, Xiao JZ, Yamaguchi R, Uematsu S, Imoto S, Miyano S. Genome-wide association studies and heritability analysis reveal the involvement of host genetics in the Japanese gut microbiota. Commun Biol 2020; 3:686. [PMID: 33208821 PMCID: PMC7674416 DOI: 10.1038/s42003-020-01416-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 10/21/2020] [Indexed: 12/22/2022] Open
Abstract
Numerous host extrinsic and intrinsic factors affect the gut microbiota composition, but their cumulative effects do not sufficiently explain the variation in the microbiota, suggesting contributions of missing factors. The Japanese population possesses homogeneous genetic features suitable for genome-wide association study (GWAS). Here, we performed GWASs for human gut microbiota using 1068 healthy Japanese adults. To precisely evaluate genetic effects, we corrected for the impacts of numerous host extrinsic and demographic factors by introducing them as covariates, enabling us to discover five loci significantly associated with microbiome diversity measures: HS3ST4, C2CD2, 2p16.1, 10p15.1, and 18q12.2. Nevertheless, these five variants explain only a small fraction of the variation in the gut microbiota. We subsequently investigated the heritability of each of the 21 core genera and found that the abundances of six genera are heritable. We propose that the gut microbiota composition is affected by a highly polygenic architecture rather than several strongly associated variants in the Japanese population.
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Affiliation(s)
| | - Kumiko Kato
- Morinaga Milk Industry Co., Ltd., Kanagawa, Japan
| | | | | | | | | | | | - Rui Yamaguchi
- Laboratory of DNA Information Analysis, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Satoshi Uematsu
- Department of Immunology and Genomics, Osaka City University Graduate School of Medicine, Osaka, Japan
- Division of Innate Immune Regulation, International Research and Development Center for Mucosal Vaccines, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Division of Metagenome Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- Laboratory of DNA Information Analysis, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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26
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Lin Z, Hosoya S, Sato M, Mizuno N, Kobayashi Y, Itou T, Kikuchi K. Genomic selection for heterobothriosis resistance concurrent with body size in the tiger pufferfish, Takifugu rubripes. Sci Rep 2020; 10:19976. [PMID: 33203997 PMCID: PMC7672106 DOI: 10.1038/s41598-020-77069-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 11/05/2020] [Indexed: 01/09/2023] Open
Abstract
Parasite resistance traits in aquaculture species often have moderate heritability, indicating the potential for genetic improvements by selective breeding. However, parasite resistance is often synonymous with an undesirable negative correlation with body size. In this study, we first tested the feasibility of genomic selection (GS) on resistance to heterobothriosis, caused by the monogenean parasite Heterobothrium okamotoi, which leads to huge economic losses in aquaculture of the tiger pufferfish Takifugu rubripes. Then, using a simulation study, we tested the possibility of simultaneous improvement of parasite resistance, assessed by parasite counts on host fish (HC), and standard length (SL). Each trait showed moderate heritability (square-root transformed HC: h2 = 0.308 ± 0.123, S.E.; SL: h2 = 0.405 ± 0.131). The predictive abilities of genomic prediction among 12 models, including genomic Best Linear Unbiased Predictor (GBLUP), Bayesian regressions, and machine learning procedures, were also moderate for both transformed HC (0.248‒0.344) and SL (0.340‒0.481). These results confirmed the feasibility of GS for this trait. Although an undesirable genetic correlation was suggested between transformed HC and SL (rg = 0.228), the simulation study suggested the desired gains index can help achieve simultaneous genetic improvements in both traits.
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Affiliation(s)
- Zijie Lin
- Fisheries Laboratory, University of Tokyo, Hamamatsu, Shizuoka, 431-0214, Japan
| | - Sho Hosoya
- Fisheries Laboratory, University of Tokyo, Hamamatsu, Shizuoka, 431-0214, Japan.
| | - Mana Sato
- Fisheries Laboratory, University of Tokyo, Hamamatsu, Shizuoka, 431-0214, Japan
| | - Naoki Mizuno
- Fisheries Laboratory, University of Tokyo, Hamamatsu, Shizuoka, 431-0214, Japan
| | - Yuki Kobayashi
- Veterinary Research Center, Nihon University, Fujisawa, Kanagawa, 252-0880, Japan
| | - Takuya Itou
- Veterinary Research Center, Nihon University, Fujisawa, Kanagawa, 252-0880, Japan
| | - Kiyoshi Kikuchi
- Fisheries Laboratory, University of Tokyo, Hamamatsu, Shizuoka, 431-0214, Japan
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Pizzagalli F, Auzias G, Yang Q, Mathias SR, Faskowitz J, Boyd JD, Amini A, Rivière D, McMahon KL, de Zubicaray GI, Martin NG, Mangin JF, Glahn DC, Blangero J, Wright MJ, Thompson PM, Kochunov P, Jahanshad N. The reliability and heritability of cortical folds and their genetic correlations across hemispheres. Commun Biol 2020; 3:510. [PMID: 32934300 PMCID: PMC7493906 DOI: 10.1038/s42003-020-01163-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 07/24/2020] [Indexed: 12/22/2022] Open
Abstract
Cortical folds help drive the parcellation of the human cortex into functionally specific regions. Variations in the length, depth, width, and surface area of these sulcal landmarks have been associated with disease, and may be genetically mediated. Before estimating the heritability of sulcal variation, the extent to which these metrics can be reliably extracted from in-vivo MRI must be established. Using four independent test-retest datasets, we found high reliability across the brain (intraclass correlation interquartile range: 0.65-0.85). Heritability estimates were derived for three family-based cohorts using variance components analysis and pooled (total N > 3000); the overall sulcal heritability pattern was correlated to that derived for a large population cohort (N > 9000) calculated using genomic complex trait analysis. Overall, sulcal width was the most heritable metric, and earlier forming sulci showed higher heritability. The inter-hemispheric genetic correlations were high, yet select sulci showed incomplete pleiotropy, suggesting hemisphere-specific genetic influences.
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Grants
- P41 EB015922 NIBIB NIH HHS
- R01 EB015611 NIBIB NIH HHS
- P01 AG026276 NIA NIH HHS
- R21 NS064534 NINDS NIH HHS
- R01 MH078111 NIMH NIH HHS
- R01 HD050735 NICHD NIH HHS
- R01 NS056307 NINDS NIH HHS
- R01 MH121246 NIMH NIH HHS
- P50 MH071616 NIMH NIH HHS
- R03 EB012461 NIBIB NIH HHS
- R01 AG059874 NIA NIH HHS
- U24 RR021382 NCRR NIH HHS
- P30 AG066444 NIA NIH HHS
- P01 AG003991 NIA NIH HHS
- P50 AG005681 NIA NIH HHS
- U54 EB020403 NIBIB NIH HHS
- R01 MH117601 NIMH NIH HHS
- U54 MH091657 NIMH NIH HHS
- R01 AG021910 NIA NIH HHS
- R01 MH078143 NIMH NIH HHS
- P41 RR015241 NCRR NIH HHS
- S10 OD023696 NIH HHS
- R01 MH083824 NIMH NIH HHS
- This research was funded in part by NIH ENIGMA Center grant U54 EB020403, supported by the Big Data to Knowledge (BD2K) Centers of Excellence program funded by a cross-NIH initiative. Additional grant support was provided by: R01 AG059874, R01 MH117601, R01 MH121246, and P41 EB015922. QTIM was supported by NIH R01 HD050735, and the NHMRC 486682, Australia; GOBS: Financial support for this study was provided by the National Institute of Mental Health grants MH078143 (PI: DC Glahn), MH078111 (PI: J Blangero), and MH083824 (PI: DC Glahn & J Blangero); HCP data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University; UK Biobank: This research was conducted using the UK Biobank Resource under Application Number ‘11559’; BrainVISA’s Morphologist software development received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No 720270 & 785907 (Human Brain ProjectSGA1 & SGA2), and by the FRM DIC20161236445. OASIS: Cross-Sectional: Principal Investigators: D. Marcus, R. Buckner, J. Csernansky J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382. KKI was supported by NIH grants NCRR P41 RR015241 (Peter C.M. van Zijl), 1R01NS056307 (Jerry Prince), 1R21NS064534-01A109 (Bennett A. Landman/Jerry L. Prince), 1R03EB012461-01 (Bennett A. Landman). Neda Jahanshad and Paul Thompson are MPIs of a research project grant from Biogen, Inc. (PO 969323).
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Affiliation(s)
- Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
| | - Guillaume Auzias
- Institut de Neurosciences de la Timone, UMR7289, Aix-Marseille Université & CNRS, Marseille, France
| | - Qifan Yang
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Samuel R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Joshua Faskowitz
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Joshua D Boyd
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Armand Amini
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Denis Rivière
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, France
- CATI, Multicenter Neuroimaging Platform, Paris, France
| | - Katie L McMahon
- School of Clinical Sciences and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Greig I de Zubicaray
- Faculty of Health, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia
| | | | - Jean-François Mangin
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, France
- CATI, Multicenter Neuroimaging Platform, Paris, France
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
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28
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Bergamaschi M, Maltecca C, Schillebeeckx C, McNulty NP, Schwab C, Shull C, Fix J, Tiezzi F. Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters. Sci Rep 2020; 10:10134. [PMID: 32576852 PMCID: PMC7311463 DOI: 10.1038/s41598-020-66791-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/26/2020] [Indexed: 12/22/2022] Open
Abstract
Despite recent efforts to characterize longitudinal variation in the swine gut microbiome, the extent to which a host's genome impacts the composition of its gut microbiome is not yet well understood in pigs. The objectives of this study were: i) to identify pig gut microbiome features associated with growth and fatness, ii) to estimate the heritability of those features, and, iii) to conduct a genome-wide association study exploring the relationship between those features and single nucleotide polymorphisms (SNP) in the pig genome. A total of 1,028 pigs were characterized. Animals were genotyped with the Illumina PorcineSNP60 Beadchip. Microbiome samples from fecal swabs were obtained at weaning (Wean), at mid-test during the growth trial (MidTest), and at the end of the growth trial (OffTest). Average daily gain was calculated from birth to week 14 of the growth trial, from weaning to week 14, from week 14 to week 22, and from week 14 to harvest. Backfat and loin depth were also measured at weeks 14 and 22. Heritability estimates (±SE) of Operational Taxonomic Units ranged from 0.025 (±0.0002) to 0.139 (±0.003), from 0.029 (±0.003) to 0.289 (±0.004), and from 0.025 (±0.003) to 0.545 (±0.034) at Wean, MidTest, and OffTest, respectively. Several SNP were significantly associated with taxa at the three time points. These SNP were located in genomic regions containing a total of 68 genes. This study provides new evidence linking gut microbiome composition with growth and carcass traits in swine, while also identifying putative host genetic markers associated with significant differences in the abundance of several prevalent microbiome features.
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Affiliation(s)
- Matteo Bergamaschi
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA
| | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA
| | | | - Nathan P McNulty
- Matatu, Inc., 4340 Duncan Ave., Suite 211, St. Louis, MO, 63110, USA
| | | | | | - Justin Fix
- The Maschhoffs LLC, Carlyle, IL, 62231, USA
| | - Francesco Tiezzi
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA.
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29
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Ashbrook LH, Krystal AD, Fu YH, Ptáček LJ. Genetics of the human circadian clock and sleep homeostat. Neuropsychopharmacology 2020; 45:45-54. [PMID: 31400754 DOI: 10.1038/s41386-019-0476-7] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 07/24/2019] [Accepted: 08/01/2019] [Indexed: 01/07/2023]
Abstract
Timing and duration of sleep are controlled by the circadian system, which keeps an ~24-h internal rhythm that entrains to environmental stimuli, and the sleep homeostat, which rises as a function of time awake. There is a normal distribution across the population in how the circadian system aligns with typical day and night resulting in varying circadian preferences called chronotypes. A portion of the variation in the population is controlled by genetics as shown by the single-gene mutations that confer extreme early or late chronotypes. Similarly, there is a normal distribution across the population in sleep duration. Genetic variations have been identified that lead to a short sleep phenotype in which individuals sleep only 4-6.5 h nightly. Negative health consequences have been identified when individuals do not sleep at their ideal circadian timing or are sleep deprived relative to intrinsic sleep need. Whether familial natural short sleepers are at risk of the health consequences associated with a short sleep duration based on population data is not known. More work needs to be done to better assess for an individual's chronotype and degree of sleep deprivation to answer these questions.
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30
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Kuchenbaecker K, Telkar N, Reiker T, Walters RG, Lin K, Eriksson A, Gurdasani D, Gilly A, Southam L, Tsafantakis E, Karaleftheri M, Seeley J, Kamali A, Asiki G, Millwood IY, Holmes M, Du H, Guo Y, Kumari M, Dedoussis G, Li L, Chen Z, Sandhu MS, Zeggini E; Understanding Society Scientific Group. The transferability of lipid loci across African, Asian and European cohorts. Nat Commun 2019; 10:4330. [PMID: 31551420 DOI: 10.1038/s41467-019-12026-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 08/19/2019] [Indexed: 02/02/2023] Open
Abstract
Most genome-wide association studies are based on samples of European descent. We assess whether the genetic determinants of blood lipids, a major cardiovascular risk factor, are shared across populations. Genetic correlations for lipids between European-ancestry and Asian cohorts are not significantly different from 1. A genetic risk score based on LDL-cholesterol-associated loci has consistent effects on serum levels in samples from the UK, Uganda and Greece (r = 0.23-0.28, p < 1.9 × 10-14). Overall, there is evidence of reproducibility for ~75% of the major lipid loci from European discovery studies, except triglyceride loci in the Ugandan samples (10% of loci). Individual transferable loci are identified using trans-ethnic colocalization. Ten of fourteen loci not transferable to the Ugandan population have pleiotropic associations with BMI in Europeans; none of the transferable loci do. The non-transferable loci might affect lipids by modifying food intake in environments rich in certain nutrients, which suggests a potential role for gene-environment interactions.
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31
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Sachdev PS, Thalamuthu A, Mather KA, Ames D, Wright MJ, Wen W. White Matter Hyperintensities Are Under Strong Genetic Influence. Stroke 2016; 47:1422-8. [PMID: 27165950 DOI: 10.1161/strokeaha.116.012532] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 04/14/2016] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND PURPOSE The genetic basis of white matter hyperintensities (WMH) is still unknown. This study examines the heritability of WMH in both sexes and in different brain regions, and the influence of age. METHODS Participants from the Older Australian Twins Study were recruited (n=320; 92 monozygotic and 68 dizygotic pairs) who volunteered for magnetic resonance imaging scans and medical assessments. Heritability, that is, the ratio of the additive genetic variance to the total phenotypic variance, was estimated using the twin design. RESULTS Heritability was high for total WMH volume (0.76), and for periventricular WMH (0.64) and deep WMH (0.77), and varied from 0.18 for the cerebellum to 0.76 for the occipital lobe. The genetic correlation between deep and periventricular WMH regions was 0.85, with one additive genetics factor accounting for most of the shared variance. Heritability was consistently higher in women in the cerebral regions. Heritability in deep but not periventricular WMH declined with age, in particular after the age of 75. CONCLUSIONS WMH have a strong genetic influence but this is not uniform through the brain, being higher for deep than periventricular WMH and in the cerebral regions. The genetic influence is higher in women, and there is an age-related decline, most markedly for deep WMH. The data suggest some heterogeneity in the pathogenesis of WMH for different brain regions and for men and women.
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Affiliation(s)
- Perminder S Sachdev
- From the Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, The University of New South Wales, Australia (P.S.S., A.T., K.A.M., W.W.); Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia (P.S.S., W.W.); National Ageing Research Institute, University of Melbourne, Parkville, Victoria, Australia (D.A.); NeuroImaging Genetics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia (M.J.W.); and Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia (M.J.W.).New South WalesNew South WalesNew South WalesNew South WalesNew South WalesNew South WalesQueenslandQueenslandVictoria
| | - Anbupalam Thalamuthu
- From the Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, The University of New South Wales, Australia (P.S.S., A.T., K.A.M., W.W.); Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia (P.S.S., W.W.); National Ageing Research Institute, University of Melbourne, Parkville, Victoria, Australia (D.A.); NeuroImaging Genetics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia (M.J.W.); and Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia (M.J.W.).New South WalesNew South WalesNew South WalesNew South WalesNew South WalesNew South WalesQueenslandQueenslandVictoria
| | - Karen A Mather
- From the Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, The University of New South Wales, Australia (P.S.S., A.T., K.A.M., W.W.); Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia (P.S.S., W.W.); National Ageing Research Institute, University of Melbourne, Parkville, Victoria, Australia (D.A.); NeuroImaging Genetics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia (M.J.W.); and Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia (M.J.W.).New South WalesNew South WalesNew South WalesNew South WalesNew South WalesNew South WalesQueenslandQueenslandVictoria
| | - David Ames
- From the Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, The University of New South Wales, Australia (P.S.S., A.T., K.A.M., W.W.); Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia (P.S.S., W.W.); National Ageing Research Institute, University of Melbourne, Parkville, Victoria, Australia (D.A.); NeuroImaging Genetics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia (M.J.W.); and Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia (M.J.W.).New South WalesNew South WalesNew South WalesNew South WalesNew South WalesNew South WalesQueenslandQueenslandVictoria
| | - Margaret J Wright
- From the Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, The University of New South Wales, Australia (P.S.S., A.T., K.A.M., W.W.); Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia (P.S.S., W.W.); National Ageing Research Institute, University of Melbourne, Parkville, Victoria, Australia (D.A.); NeuroImaging Genetics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia (M.J.W.); and Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia (M.J.W.).New South WalesNew South WalesNew South WalesNew South WalesNew South WalesNew South WalesQueenslandQueenslandVictoria
| | - Wei Wen
- From the Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Medicine, The University of New South Wales, Australia (P.S.S., A.T., K.A.M., W.W.); Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia (P.S.S., W.W.); National Ageing Research Institute, University of Melbourne, Parkville, Victoria, Australia (D.A.); NeuroImaging Genetics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia (M.J.W.); and Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia (M.J.W.).New South WalesNew South WalesNew South WalesNew South WalesNew South WalesNew South WalesQueenslandQueenslandVictoria
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