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Song W, Shi Y, Lin GN. Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits. eLife 2024; 12:RP92574. [PMID: 38639992 PMCID: PMC11031082 DOI: 10.7554/elife.92574] [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] [Indexed: 04/20/2024] Open
Abstract
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.
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Affiliation(s)
- Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Bioengineering, Shanghai Jiao Tong UniversityShanghaiChina
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong UniversityShanghaiChina
- Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X12 Institutes), Qingdao UniversityQingdaoChina
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Bioengineering, Shanghai Jiao Tong UniversityShanghaiChina
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Stender S, Davey Smith G, Richardson TG. Genetic variation and elevated liver enzymes during childhood, adolescence and early adulthood. Int J Epidemiol 2023; 52:1341-1349. [PMID: 37105232 PMCID: PMC10555681 DOI: 10.1093/ije/dyad048] [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: 09/21/2022] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Genetic factors influence the risk of fatty liver disease (FLD) in adults. The aim of this study was to test if, and when, genetic risk factors known to affect FLD in adults begin to exert their deleterious effects during childhood, adolescence and early adulthood. METHODS We included up to 4018 British children and adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Three genetic variants known to associate robustly with FLD in adults (PNPLA3 rs738409, TM6SF2 rs58542926 and HSD17B13 rs72613567) were tested for association with plasma levels of alanine transaminase (ALT) and aspartate transaminase (AST) during childhood (mean age: 9.9 years), early adolescence (15.5 years), late adolescence (17.8 years), and early adulthood (24.5 years). We also tested the associations of a 17-variant score and whole-genome polygenic risk scores (PRS) derived from associations in adults with plasma ALT and AST at the same four time points. Associations with elastography-derived liver steatosis and fibrosis were tested in early adulthood. RESULTS Genetic risk factors for FLD (individually, combined into a 3-variant score, a 17-variant score and as a genome-wide PRS), were associated with higher liver enzymes, beginning in childhood and throughout adolescence and early adulthood. The ALT-increasing effects of the genetic risk variants became larger with increasing age. The ALT-PRS was associated with liver steatosis in early adulthood. No genetic associations with fibrosis were observed. CONCLUSIONS Genetic factors that promote FLD in adults associate with elevated liver enzymes already during childhood, and their effects get amplified with increasing age.
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Affiliation(s)
- Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, UK
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3
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Nguyen VT, Braun A, Kraft J, Ta TMT, Panagiotaropoulou GM, Nguyen VP, Nguyen TH, Trubetskoy V, Le CT, Le TTH, Pham XT, Heuser-Collier I, Lam NH, Böge K, Hahne IM, Bajbouj M, Zierhut MM, Hahn E, Ripke S. Increasing sample diversity in psychiatric genetics - Introducing a new cohort of patients with schizophrenia and controls from Vietnam - Results from a pilot study. World J Biol Psychiatry 2022; 23:219-227. [PMID: 34449294 DOI: 10.1080/15622975.2021.1951474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
OBJECTIVES Genome-Wide Association Studies (GWAS) of Schizophrenia (SCZ) have provided new biological insights; however, most cohorts are of European ancestry. As a result, derived polygenic risk scores (PRS) show decreased predictive power when applied to populations of different ancestries. We aimed to assess the feasibility of a large-scale data collection in Hanoi, Vietnam, contribute to international efforts to diversify ancestry in SCZ genetic research and examine the transferability of SCZ-PRS to individuals of Vietnamese Kinh ancestry. METHODS In a pilot study, 368 individuals (including 190 SCZ cases) were recruited at the Hanoi Medical University's associated psychiatric hospitals and outpatient facilities. Data collection included sociodemographic data, baseline clinical data, clinical interviews assessing symptom severity and genome-wide SNP genotyping. SCZ-PRS were generated using different training data sets: (i) European, (ii) East-Asian and (iii) trans-ancestry GWAS summary statistics from the latest SCZ GWAS meta-analysis. RESULTS SCZ-PRS significantly predicted case status in Vietnamese individuals using mixed-ancestry (R2 liability = 4.9%, p = 6.83 × 10-8), East-Asian (R2 liability = 4.5%, p = 2.73 × 10-7) and European (R2 liability = 3.8%, p = 1.79 × 10-6) discovery samples. DISCUSSION Our results corroborate previous findings of reduced PRS predictive power across populations, highlighting the importance of ancestral diversity in GWA studies.
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Affiliation(s)
- V T Nguyen
- Department of Psychiatry, Hanoi Medical University, Hà Nội, Việt Nam.,National Institute of Mental Health, Bach Mai Hospital, Hà Nội, Việt Nam
| | - A Braun
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - J Kraft
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - T M T Ta
- Berlin Institute of Health, Charité - Universitätsmedizin Berlin, BIH Academy, Clinician Scientist Program, Berlin, Germany.,Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Berlin, Germany
| | - G M Panagiotaropoulou
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - V P Nguyen
- Department of Psychiatry, Hanoi Medical University, Hà Nội, Việt Nam
| | - T H Nguyen
- Department of Psychiatry, Hanoi Medical University, Hà Nội, Việt Nam.,Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Berlin, Germany
| | - V Trubetskoy
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany
| | - C T Le
- Department of Psychiatry, Hanoi Medical University, Hà Nội, Việt Nam.,National Institute of Mental Health, Bach Mai Hospital, Hà Nội, Việt Nam
| | - T T H Le
- Department of Psychiatry, Hanoi Medical University, Hà Nội, Việt Nam.,National Institute of Mental Health, Bach Mai Hospital, Hà Nội, Việt Nam
| | - X T Pham
- Department of Psychiatry, Hanoi Medical University, Hà Nội, Việt Nam
| | - I Heuser-Collier
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Berlin, Germany
| | - N H Lam
- Hanoi Mental Hospital, Hà Nội, Việt Nam
| | - K Böge
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Berlin, Germany
| | - I M Hahne
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Berlin, Germany
| | - M Bajbouj
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Berlin, Germany
| | - M M Zierhut
- Berlin Institute of Health, Charité - Universitätsmedizin Berlin, BIH Academy, Clinician Scientist Program, Berlin, Germany.,Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Berlin, Germany
| | - E Hahn
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Berlin, Germany
| | - S Ripke
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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4
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Ji Y, Long J, Kweon SS, Kang D, Kubo M, Park B, Shu XO, Zheng W, Tao R, Li B. Incorporating European GWAS findings improve polygenic risk prediction accuracy of breast cancer among East Asians. Genet Epidemiol 2021; 45:471-484. [PMID: 33739539 PMCID: PMC8372543 DOI: 10.1002/gepi.22382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 09/22/2020] [Revised: 01/15/2021] [Accepted: 02/08/2021] [Indexed: 12/23/2022]
Abstract
Previous genome-wide association studies (GWASs) have been largely focused on European (EUR) populations. However, polygenic risk scores (PRSs) derived from EUR have been shown to perform worse in non-EURs compared with EURs. In this study, we aim to improve PRS prediction in East Asians (EASs). We introduce a rescaled meta-analysis framework to combine both EUR (N = 122,175) and EAS (N = 30,801) GWAS summary statistics. To improve PRS prediction in EASs, we use a scaling factor to up-weight the EAS data, such that the resulting effect size estimates are more relevant to EASs. We then derive PRSs for EAS from the rescaled meta-analysis results of EAS and EUR data. Evaluated in an independent EAS validation data set, this approach increases the prediction liability-adjusted Nagelkerke's pseudo R2 by 40%, 41%, and 5%, respectively, compared with PRSs derived from an EAS GWAS only, EUR GWAS only, and conventional fixed-effects meta-analysis of EAS and EUR data. The PRS derived from the rescaled meta-analysis approach achieved an area under the receiver operating characteristic curve (AUC) of 0.6059, higher than AUC = 0.5782, 0.5809, 0.6008 for EAS, EUR, and conventional meta-analysis of EAS and EUR. We further compare PRSs constructed by single-nucleotide polymorphisms that have different linkage disequilibrium (LD) scores and minor allele frequencies (MAFs) between EUR and EAS, and observe that lower LD scores or MAF in EAS correspond to poorer PRS performance (AUC = 0.5677, 0.5530, respectively) than higher LD scores or MAF (AUC = 0.589, 0.5993, respectively). We finally build a PRS stratified by LD score differences in EUR and EAS using rescaled meta-analysis, and obtain an AUC of 0.6096, with improvement over other strategies investigated.
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Affiliation(s)
- Ying Ji
- Vanderbilt Genetics Institute, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Hwasun, Korea
- Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
- Institute of Environmental Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Boyoung Park
- Department of Medicine, Hanyang University College of Medicine, Seoul, Korea
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bingshan Li
- Vanderbilt Genetics Institute, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA
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5
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Abstract
Prediction of phenotypes from genotypes is an important objective to fulfill the promises of genomics, precision medicine and agriculture. Although it's now possible to account for the majority of genetic variation through model fitting, prediction of phenotypes remains a challenge, especially across populations that have diverged in the past. In this study, we designed simulation experiments to specifically investigate the role of genetic interactions in failure of polygenic prediction. We found that non-additive genetic interactions can significantly reduce the accuracy of polygenic prediction. Our study demonstrated the importance of considering genetic interactions in genetic prediction.
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Affiliation(s)
| | - Nanye Long
- Institute for Cyber-Enabled Research, Michigan State University, East Lansing, MI 48824
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Abstract
BACKGROUND Emerging adulthood is a peak period of risk for alcohol and illicit drug use. Recent advances in psychiatric genetics suggest that the co-occurrence of substance use and psychopathology arises, in part, from a shared genetic etiology. We sought to extend this research by investigating the influence of genetic risk for schizophrenia on trajectories of four substance use behaviors as they occurred across emerging adulthood. METHOD Young adult participants of non-Hispanic European descent provided DNA samples and completed daily reports of substance use for 1 month per year across 4 years (N = 30 085 observations of N = 342 participants). A schizophrenia polygenic score was included in two-level hierarchical linear models designed to test associations between genetic risk for schizophrenia, participant age, and four substance use phenotypes. RESULTS Participants with a greater schizophrenia polygenic score experienced greater age-related increases in the likelihood of using substances across emerging adulthood (p < 0.005). Additionally, our results suggest that the polygenic score was positively associated with participants' overall likelihood to engage in illicit drug use but not alcohol-related substance use. CONCLUSIONS This study used a novel combination of polygenic prediction and intensive longitudinal methods to characterize the influence of genetic risk for schizophrenia on patterns of age-related change in substance use across emerging adulthood. Results suggest that genetic risk for schizophrenia has developmentally specific effects on substance use behaviors in a non-clinical population of young adults.
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Affiliation(s)
- Travis T Mallard
- Department of Psychology, University of Texas at Austin, 108 E. Dean Keeton Stop A8000, Austin, TX 78712, USA
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, 108 E. Dean Keeton Stop A8000, Austin, TX 78712, USA
| | - Kim Fromme
- Department of Psychology, University of Texas at Austin, 108 E. Dean Keeton Stop A8000, Austin, TX 78712, USA
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7
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Márquez-Luna C, Loh PR, Price AL. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet Epidemiol 2017; 41:811-823. [PMID: 29110330 PMCID: PMC5726434 DOI: 10.1002/gepi.22083] [Citation(s) in RCA: 172] [Impact Index Per Article: 24.6] [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: 04/12/2017] [Revised: 08/16/2017] [Accepted: 08/30/2017] [Indexed: 01/04/2023]
Abstract
Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff = 40k) and Latino training data in small sample size (Neff = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2 = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff = 40k) and South Asian (Neff = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.
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Affiliation(s)
- Carla Márquez-Luna
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Po-Ru Loh
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Alkes L Price
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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8
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McGeachie MJ, Clemmer GL, Croteau-Chonka DC, Castaldi PJ, Cho MH, Sordillo JE, Lasky-Su JA, Raby BA, Tantisira KG, Weiss ST. Whole genome prediction and heritability of childhood asthma phenotypes. Immun Inflamm Dis 2016; 4:487-496. [PMID: 27980782 PMCID: PMC5134727 DOI: 10.1002/iid3.133] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 09/01/2016] [Accepted: 09/04/2016] [Indexed: 01/19/2023]
Abstract
Introduction While whole genome prediction (WGP) methods have recently demonstrated successes in the prediction of complex genetic diseases, they have not yet been applied to asthma and related phenotypes. Longitudinal patterns of lung function differ between asthmatics, but these phenotypes have not been assessed for heritability or predictive ability. Herein, we assess the heritability and genetic predictability of asthma‐related phenotypes. Methods We applied several WGP methods to a well‐phenotyped cohort of 832 children with mild‐to‐moderate asthma from CAMP. We assessed narrow‐sense heritability and predictability for airway hyperresponsiveness, serum immunoglobulin E, blood eosinophil count, pre‐ and post‐bronchodilator forced expiratory volume in 1 sec (FEV1), bronchodilator response, steroid responsiveness, and longitudinal patterns of lung function (normal growth, reduced growth, early decline, and their combinations). Prediction accuracy was evaluated using a training/testing set split of the cohort. Results We found that longitudinal lung function phenotypes demonstrated significant narrow‐sense heritability (reduced growth, 95%; normal growth with early decline, 55%). These same phenotypes also showed significant polygenic prediction (areas under the curve [AUCs] 56% to 62%). Including additional demographic covariates in the models increased prediction 4–8%, with reduced growth increasing from 62% to 66% AUC. We found that prediction with a genomic relatedness matrix was improved by filtering available SNPs based on chromatin evidence, and this result extended across cohorts. Conclusions Longitudinal reduced lung function growth displayed extremely high heritability. All phenotypes with significant heritability showed significant polygenic prediction. Using SNP‐prioritization increased prediction across cohorts. WGP methods show promise in predicting asthma‐related heritable traits.
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Affiliation(s)
- Michael J McGeachie
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
| | - George L Clemmer
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
| | - Damien C Croteau-Chonka
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
| | - Peter J Castaldi
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
| | - Michael H Cho
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
| | - Joanne E Sordillo
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
| | - Benjamin A Raby
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
| | - Kelan G Tantisira
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
| | - Scott T Weiss
- Channing Division of Network Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts
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Weiss A, Baselmans BM, Hofer E, Yang J, Okbay A, Lind PA, Miller MB, Nolte IM, Zhao W, Hagenaars SP, Hottenga JJ, Matteson LK, Snieder H, Faul JD, Hartman CA, Boyle PA, Tiemeier H, Mosing MA, Pattie A, Davies G, Liewald DC, Schmidt R, De Jager PL, Heath AC, Jokela M, Starr JM, Oldehinkel AJ, Johannesson M, Cesarini D, Hofman A, Harris SE, Smith JA, Keltikangas-Järvinen L, Pulkki-Råback L, Schmidt H, Smith J, Iacono WG, McGue M, Bennett DA, Pedersen NL, Magnusson PK, Deary IJ, Martin NG, Boomsma DI, Bartels M, Luciano M. Personality Polygenes, Positive Affect, and Life Satisfaction. Twin Res Hum Genet 2016; 19:407-17. [PMID: 27546527 DOI: 10.1017/thg.2016.65] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [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] [Indexed: 01/01/2023]
Abstract
Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximal N = 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes <0.05%) were observed between the extraversion polygenic score and wellbeing measures, and a negative association was observed between the polygenic neuroticism score and life satisfaction. Furthermore, using GWA data, genetic correlations of −0.49 and −0.55 were estimated between neuroticism with life satisfaction and positive affect, respectively. The moderate genetic correlation between neuroticism and wellbeing is in line with twin research showing that genetic influences on wellbeing are also shared with other independent personality domains.
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10
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Trzaskowski M, Lichtenstein P, Magnusson PK, Pedersen NL, Plomin R. Application of linear mixed models to study genetic stability of height and body mass index across countries and time. Int J Epidemiol 2016; 45:417-423. [PMID: 26819444 PMCID: PMC4864877 DOI: 10.1093/ije/dyv355] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [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] [Indexed: 01/17/2023] Open
Abstract
Background:
It is now possible to estimate genetic correlations between two independent samples when there is no overlapping phenotypic information. We applied the latest bivariate genomic methods to children in the UK and older adults in Sweden to ask two questions. Are the same variants driving individual differences in anthropometric traits in these two populations, and are these variants as important in childhood as they are later in life?
Methods:
A sample of 3152 11-year-old children in the UK was compared with a sample of 6813 adults with an average age of 65 in Sweden. Genotypes were imputed from 1000 genomes with combined 9 767 136 single nucleotide polymorphisms meeting quality control criteria in both samples. Two cross-sample GCTA-GREML analyses and linkage disequilibrium (LD) score regressions were conducted to assess genetic correlations across more than 50 years: child versus adult height and child versus adult body mass index (BMI). Consistency of effects was tested using the recently proposed polygenic scoring method.
Results:
For height, GCTA-GREML and LD score indicated strong genetic stability between children and adults, 0.58 (0.16) and 1.335 (1.09), respectively. For BMI, both methods produced similarly strong estimates of genetic stability 0.75 (0.26) and 0.855 (0.49), respectively. In height, adult polygenic score explained 60% of genetic variance in childhood and 10% of variance in BMI.
Conclusions:
Here we replicated and extended previous findings of longitudinal genetic stability in anthropometric traits to cross-cultural dimensions, and showed that for height but not BMI these variants are as important in childhood as they are in adulthood.
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Affiliation(s)
- Maciej Trzaskowski
- King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK and
| | - Paul Lichtenstein
- Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden
| | - Patrik K Magnusson
- Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden
| | - Nancy L Pedersen
- Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden
| | - Robert Plomin
- King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK and
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11
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Luciano M, Marioni RE, Valdés Hernández M, Muñoz Maniega S, Hamilton IF, Royle NA, Chauhan G, Bis JC, Debette S, DeCarli C, Fornage M, Schmidt R, Ikram MA, Launer LJ, Seshadri S, Bastin ME, Porteous DJ, Wardlaw J, Deary IJ; Generation Scotland., CHARGE Consortium. Structural Brain MRI Trait Polygenic Score Prediction of Cognitive Abilities. Twin Res Hum Genet 2015; 18:738-45. [PMID: 26427786 DOI: 10.1017/thg.2015.71] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association (GWA) for brain infarcts (BI), white matter hyperintensities, intracranial, hippocampal, and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to: (1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits), and (2) predict cognitive traits in all three cohorts (in 8,115-8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure, and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r = 0.08) between the HV polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the GWA samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies.
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Chen CY, Han J, Hunter DJ, Kraft P, Price AL. Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction. Genet Epidemiol 2015; 39:427-38. [PMID: 25995153 PMCID: PMC4734143 DOI: 10.1002/gepi.21906] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [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: 11/25/2014] [Revised: 03/19/2015] [Accepted: 04/07/2015] [Indexed: 01/14/2023]
Abstract
Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R(2) for HC increased by 66% (0.0456-0.0755; P < 10(-16)), the R(2) for TA increased by 123% (0.0154 to 0.0344; P < 10(-16)), and the liability-scale R(2) for BCC increased by 68% (0.0138-0.0232; P < 10(-16)) when explicitly modeling ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction.
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Affiliation(s)
- Chia-Yen Chen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Jiali Han
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Simon Cancer Center, Indiana University, Indianapolis, Indiana 46202
| | - David J. Hunter
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
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Sapkota Y, Attia J, Gordon SD, Henders AK, Holliday EG, Rahmioglu N, MacGregor S, Martin NG, McEvoy M, Morris AP, Scott RJ, Zondervan KT, Montgomery GW, Nyholt DR. Genetic burden associated with varying degrees of disease severity in endometriosis. Mol Hum Reprod 2015; 21:594-602. [PMID: 25882541 DOI: 10.1093/molehr/gav021] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [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: 02/17/2015] [Accepted: 04/10/2015] [Indexed: 11/13/2022] Open
Abstract
Endometriosis is primarily characterized by the presence of tissue resembling endometrium outside the uterine cavity and is usually diagnosed by laparoscopy. The most commonly used classification of disease, the revised American Fertility Society (rAFS) system to grade endometriosis into different stages based on disease severity (I to IV), has been questioned as it does not correlate well with underlying symptoms, posing issues in diagnosis and choice of treatment. Using two independent European genome-wide association (GWA) datasets and top-level classification of the endometriosis cases based on rAFS [minimal or mild (Stage A) and moderate-to-severe (Stage B) disease], we previously showed that Stage B endometriosis has greater contribution of common genetic variation to its aetiology than Stage A disease. Herein, we extend our previous analysis to four endometriosis stages [minimal (Stage I), mild (Stage II), moderate (Stage III) and severe (Stage IV) disease] based on the rAFS classification system and compared the genetic burden across stages. Our results indicate that genetic burden increases from minimal to severe endometriosis. For the minimal disease, genetic factors may contribute to a lesser extent than other disease categories. Mild and moderate endometriosis appeared genetically similar, making it difficult to tease them apart. Consistent with our previous reports, moderate and severe endometriosis showed greater genetic burden than minimal or mild disease. Overall, our results provide new insights into the genetic architecture of endometriosis and further investigation in larger samples may help to understand better the aetiology of varying degrees of endometriosis, enabling improved diagnostic and treatment modalities.
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Affiliation(s)
- Yadav Sapkota
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - John Attia
- Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia Public Health Research Program, Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Anjali K Henders
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Elizabeth G Holliday
- Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia Public Health Research Program, Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Nilufer Rahmioglu
- Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Mark McEvoy
- Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia Public Health Research Program, Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Andrew P Morris
- Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rodney J Scott
- Public Health Research Program, Hunter Medical Research Institute, Newcastle, New South Wales, Australia School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, New South Wales, Australia Division of Genetics, Hunter Area Pathology Service, Newcastle, New South Wales, Australia
| | - Krina T Zondervan
- Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK Nuffield Department of Obstetrics and Gynaecology, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Grant W Montgomery
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Dale R Nyholt
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
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Wheeler HE, Aquino-Michaels K, Gamazon ER, Trubetskoy VV, Dolan ME, Huang RS, Cox NJ, Im HK. Poly-omic prediction of complex traits: OmicKriging. Genet Epidemiol 2014; 38:402-15. [PMID: 24799323 DOI: 10.1002/gepi.21808] [Citation(s) in RCA: 34] [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: 11/26/2013] [Revised: 03/11/2014] [Accepted: 03/12/2014] [Indexed: 12/23/2022]
Abstract
High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).
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Affiliation(s)
- Heather E Wheeler
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
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