Genetic architecture of gene expression traits across diverse populations

Mogil, Lauren S.; Andaleon, Angela; Badalamenti, Alexa; Dickinson, Scott; Guo, Xiuqing; Rotter, Jerome I.; Johnson, W. Craig; Im, Hae Kyung; Liu, Yongmei; Wheeler, Heather E.

doi:10.1101/245761

Cited by 50 publications

(88 citation statements)

References 56 publications

(112 reference statements)

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“…23 However, genetic architectures of gene expression differ across diverse populations. 12,56,57 Thus, SEG annotations derived from gene expression data from diverse populations may provide additional insights into population-specific causal effect sizes. Fourth, we restricted our analyses to SNPs that were relatively common (MAF>5%) in both populations, due to the lack of a large LD reference panel for East Asians.…”

Section: Discussionmentioning

confidence: 99%

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Shi

Gazal

Kanai

et al. 2019

Preprint

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30Many diseases and complex traits exhibit population-specific causal effect sizes 31 with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic 32 polygenic risk prediction. We developed a new method, S-LDXR, for stratifying 33 squared trans-ethnic genetic correlation across genomic annotations, and applied S-34 LDXR to genome-wide association summary statistics for 30 diseases and complex 35 traits in East Asians (EAS) and Europeans (EUR) (average N EAS =93K, N EUR =274K) 36 with an average trans-ethnic genetic correlation of 0.83 (s.e. 0.01). We determined 37 that squared trans-ethnic genetic correlation was 0.81¢ (s.e. 0.01) smaller than the 38 genome-wide average at SNPs in the top quintile of background selection statistic, 39 implying more population-specific causal effect sizes. Accordingly, causal effect sizes 40 were more population-specific in functionally important regions, including coding, con-41 served, and regulatory regions. In analyses of regions surrounding specifically expressed 42 genes, causal effect sizes were most population-specific for skin and immune genes and 43 least population-specific for brain genes. Our results could potentially be explained 44 by stronger gene-environment interaction at loci impacted by selection, particularly 45 positive selection. 46 2 Trans-ethnic genetic correlations are significantly less than 1 for many diseases and 48 complex traits, 1-6 implying that population-specific causal disease effect sizes contribute to 49 the incomplete portability of genome-wide association study (GWAS) findings and poly-50 genic risk scores to non-European populations. 6-12 However, current methods for estimating 51 genome-wide trans-ethnic genetic correlations assume the same trans-ethnic genetic correla-52 tion for all categories of SNPs, 2,5,13 providing little insight into why causal disease effect sizes 53 are population-specific. Understanding the biological processes contributing to population-54 specific causal disease effect sizes can help inform polygenic risk prediction in non-European 55 populations and alleviate health disparities. 6,14,15 56 Here, we introduce a new method, S-LDXR, for stratifying squared trans-ethnic ge-57 netic correlation across functional categories of SNPs using GWAS summary statistics and 58 population-matched linkage disequilibrium (LD) reference panels (e.g. the 1000 Genomes 59Project 16 ); we stratify the squared trans-ethnic genetic correlation across functional cate-60 gories to robustly handle noisy heritability estimates. We confirm that S-LDXR yields ro-61 bust estimates in extensive simulations. We apply S-LDXR to 30 diseases and complex traits 62 with GWAS summary statistics available in both East Asian (EAS) and European (EUR) 63 populations, leveraging recent large studies in East Asian populations from the CONVERGE 64 consortium and Biobank Japan; 17-19 we analyze a broad set of genomic annotations from the 65 baseline-LD model, 20-22 as well as tissue-specific annotations based o...

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Section: Discussionmentioning

confidence: 99%

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Shi

Gazal

Kanai

et al. 2019

Preprint

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“…It is not known whether these models can be informative in African American women and other groups. Recent findings have suggested that stratification by race or ancestry may be necessary to construct proper tests of association across race or ancestry [17, 18]. However, many cohorts, especially large-scale genetic cohorts, may not have a sufficient sample size in minority populations to power these tests.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Transcriptome-Wide Association Studies in Breast Cancer in Diverse Study Populations

Bhattacharya

García‐Closas

Olshan

et al. 2019

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1 Background: The relationship between germline genetic variation and breast cancer 2 survival is largely unknown, especially in understudied minority populations who often 3 have poorer survival. Genome-wide association studies (GWAS) have interrogated 4 breast cancer survival but often are underpowered due to subtype heterogeneity and 5 many clinical covariates and detect loci in non-coding regions that are difficult to interpret. 6 Transcriptome-wide association studies (TWAS) show increased power in detecting 7 functionally-relevant loci by leveraging expression quantitative trait loci (eQTLs) from 8 external reference panels in relevant tissues. However, ancestry-or race-specific 9 reference panels may be needed to draw correct inference in ancestrally-diverse cohorts. 10 Such panels for breast cancer are lacking. 12Results: We provide a framework for TWAS for breast cancer in diverse populations, 13 using data from the Carolina Breast Cancer Study (CBCS), a North Carolina population-14 based cohort that oversampled black women. We perform eQTL analysis for 406 breast 15 cancer-related genes to train race-stratified predictive models of tumor expression from 16 germline genotypes. Using these models, we impute expression in independent data from 17 CBCS and TCGA, accounting for sampling variability in assessing performance. These 18 models are not applicable across race, and their predictive performance varies across 19 tumor subtype. Within CBCS (ܰ = 3,828), at a false discovery-adjusted significance of 20 0.10 and stratifying for race, we identify associations in black women near AURKA, 21 CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. 22Conclusions: We show that carefully implemented and thoroughly validated TWAS is an 24 efficient approach for understanding the genetics underpinning breast cancer outcomes 25 in diverse populations. 26 27 Keywords: transcriptome-wide analysis (TWAS); breast cancer; expression quantitative 28 trait loci (eQTL); survival; polygenic traits 29 4 Background 30 Breast cancer remains the most common cancer among women in the world [1]. Breast 31 cancer tends to be more aggressive in young women and African American women, 32 though underlying germline determinants of poor outcomes are not well-studied. Cohorts 33 that represent understudied minority populations, like the Carolina Breast Cancer Study 34 (CBCS), have identified differences in healthcare access, socioeconomics, and 35 environmental exposures associated with disparities in outcome [2-4], but more targeted 36 genomic studies are necessary to interrogate these disparities from a biologic and genetic 37 perspective. 38 39 Few genome-wide association studies (GWAS) have studied the relationship between 40 germline variation and survival outcomes in breast cancer, with most focusing instead on 41 genetic predictors of risk [5,6]. Recently, GWAS have shown evidence of association 42 between candidate common germline variants and breast cancer survival, but these 43 studies are often underpow...

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“…To better understand the genetic architecture of schizophrenia in African Americans, we performed transcriptome-wide association studies using prediction models built in 55 tissues. In the GAIN cohort of 2,256 individuals (969 controls and 1287 cases), we predicted gene expression across 48 tissues in GTEx, six models built from monocytes across MESA, and DLPFC from CommonMind (Barbeira et al, 2018; Wheeler et al, 2016; Mogil et al, 2018; Huckins et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…Version 7 of the GTEx predictors we used contain data exclusively from individuals of European descent. These models are not optimal for predicting expression in African American cohorts (Mogil et al, 2018; Mikhaylova and Thornton, 2019). While they offer power driven by sample size, they do not include models with African ancestry-specific alleles that might affect susceptibility to neuropsychiatric traits in this population.…”

Section: Discussionmentioning

confidence: 99%

“…Six of these models were generated from monocyte transcriptomes of individuals in the Multi-Ethnic Study of Atheroscle-rosis (MESA) cohort. The MESA models, the most diverse set of published predictors to date, were built from genotypes and transcriptomes of self-identifed African American, Hispanic, and European individuals (Mogil et al, 2018). These models can be found at http://predictdb.org/.…”

Section: Methodsmentioning

confidence: 99%

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Transcriptome association studies of neuropsychiatric traits in African Americans implicatePRMT7in schizophrenia

Fiorica

Wheeler

2019

Preprint

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14In the past fifteen years, genome-wide association studies (GWAS) have provided novel insight into the genetic architecture of various complex traits; however, this insight has been primarily focused on populations of European descent. This emphasis on European populations has led to individuals of recent African descent being grossly underrepresented in the study of genetics. With African Americans making up less than two percent of participants in neuropsychiatric GWAS, this discrepancy is magnified in diseases such as schizophrenia and bipolar disorder. In this study, we performed GWAS and the gene-based association method PrediXcan for schizophrenia (n=2,256) and bipolar disorder (n=1,019) in African American cohorts. In our PrediXcan analyses, we identified PRMT7 (P = 5.5 × 10 −6 , local false sign rate = 0.12) as significantly associated with schizophrenia following an adaptive shrinkage multiple testing adjustment. This association with schizophrenia was confirmed in the much larger, predominantly European, Psychiatric Genomics Consortium. In addition to the PRMT7 association with schizophrenia, we identified rs10168049 (P = 1.0 × 10 −6 ) as a potential candidate locus for bipolar disorder with highly divergent allele frequencies across populations, highlighting the need for diversity in genetic studies.

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Genetic architecture of gene expression traits across diverse populations

Cited by 50 publications

References 56 publications

Population-specific causal disease effect sizes in functionally important regions impacted by selection

Population-specific causal disease effect sizes in functionally important regions impacted by selection

A Framework for Transcriptome-Wide Association Studies in Breast Cancer in Diverse Study Populations

Transcriptome association studies of neuropsychiatric traits in African Americans implicatePRMT7in schizophrenia

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