scispace - formally typeset
Search or ask a question
Posted ContentDOI

A large-scale transcriptome-wide association study (TWAS) of ten blood cell phenotypes reveals complexities of TWAS fine-mapping

TL;DR: In this paper, the authors performed a transcriptome-wide association study (TWAS) using PrediXcan to systematically investigate the association between genetically-predicted gene expression and hematological measures in 54,542 individuals of European ancestry from the Genetic Epidemiology Research on Adult Health and Aging cohort.
Abstract: Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases. Hematological measures are highly heritable, and although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) using PrediXcan to systematically investigate the association between genetically-predicted gene expression and hematological measures in 54,542 individuals of European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We found 239 significant gene-trait associations with hematological measures. Among this set of 239 associations, we replicated 71 at p < 0.05 with same direction of effect for the blood cell trait in a meta-analysis of TWAS results consisting of up to 35,900 European ancestry individuals from the Womens Health Initiative (WHI), the Atherosclerosis Risk in Communities Study (ARIC), and BioMe Biobank. We further attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with these hematological measures, and performed further fine-mapping of TWAS loci. To assist with the interpretation of TWAS findings, we designed an R Shiny application to interactively visualize TWAS results, one genomic locus at a time, by integrating our TWAS results with additional genetic data sources (GWAS, TWAS from other gene expression reference panels, conditional analyses, known GWAS variants, etc.). Our results and R Shiny application highlight frequently overlooked challenges with TWAS and illustrate the complexity of TWAS fine-mapping efforts. Author SummaryTranscriptome-wide association studies (TWAS) have shown great promise in furthering our understanding of the genetic regulatory mechanisms underlying complex trait variation. However, interpreting TWAS results can be incredibly complex, especially in large-scale analyses where hundreds of signals appear throughout the genome, with multiple genes often identified in a single chromosomal region. Our research demonstrates this complexity through real data examples from our analysis of hematological traits, and we provide a useful web application to visualize TWAS results in a broadly approachable format. Together, our results and web application illustrate the importance of interpreting TWAS studies in context and highlight the need to carefully examine results in a region-wide context to draw reasonable conclusions and formulate mechanistic hypotheses.

Summary (2 min read)

Jump to: [Introduction][Results][Discussion][Materials and Methods] and [FOCUS:]

Introduction

  • Hematological measures (red cell, white cell, and platelet traits) have a critical role in oxygen transport, immunity, infection, thrombosis, and hemostasis and are associated with many acute and chronic diseases, including autoimmunity, asthma, cardiovascular disease, and COVID-19 [1-5].
  • Unfortunately, these individual SNP-based GWAS make it difficult to identify regulatory variants with small effect sizes which in aggregate impact the same gene, even in very large sample sizes, and they identify regions of associated variants whose biological function is often not clear [6].
  • Hematological phenotypes are particularly good candidates for TWAS analysis due to the availability of large RNA-sequencing datasets in a relevant tissue type, high heritability across traits, and the large number of known genetic associations, most with poorly understood mechanisms and target genes.

Results

  • The authors applied the PrediXcan method to identify expression-trait associations using individual level genotype and phenotype data from the GERA non-Hispanic white ethnic group.
  • CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprintthis version posted February 23, 2021.
  • Marginal TWAS result displayed in (A), with Black colored genes and variants denoting those previously reported by GWAS, blue variants denote those not previously reported as GWAS sentinel variants.
  • (B) is a mirrored-Manhattan locus-zoom plot displaying genes connected to their predictive model variants with TWAS results (top panel) and GWAS results .

Discussion

  • The authors performed a large-scale TWAS using PrediXcan on 54,542 GERA individuals of European ancestry and present compelling evidence that results from marginal TWAS analyses alone cannot illuminate causal genes at loci for complex traits.
  • The copyright holder for this preprintthis version posted February 23, 2021.
  • CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • Additionally, results for these two genes differ slightly by reference panel.
  • While the authors show that TWAS may help in some cases to pinpoint likely causal genes, they emphasize the need for investigators to carefully interpret TWAS results alone, out of context.

Materials and Methods

  • CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • Genotyping was performed using the Illumina GSA array (~640.000 variants) and genotype data were imputed using the “1000G Phase 3 v5” reference panel.
  • In total, 8,455 European ancestry participants with hematological phenotypes were included in the analysis.
  • In order to replicate the conditionally significant gene-trait association, the authors tested each association via a meta-analysis of the ARIC, WHI, and BioMe cohorts.

FOCUS:

  • The authors used the Fine-mapping Of CaUsal gene Sets [15] software to fine-map TWAS statistics at genomic risk regions.
  • CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • BioRxiv preprint 28 has not yet been assigned to a locus and continue in this fashion until all statistically significant TWAS genes have been assigned to a locus.
  • The authors excluded those without a valid date of blood cell count measurement, with age < 18 years, or with discordant genotypic and phenotypic sex, as well as .
  • Specifically, the authors computed the eigenvalue decomposition on the GERA sample outside of the cpgen script (for each phenotype), and then subsequently loaded the appropriate eigenvectors and eigenvalues into the program, modifying the script so that it could take these eigenvectors and eigenvalues as input.

Did you find this useful? Give us your feedback

Content maybe subject to copyright    Report

1
Full title: A large-scale transcriptome-wide association study (TWAS) of ten blood cell
phenotypes reveals complexities of TWAS fine-mapping
Short title: TWAS fine-mapping of blood cell phenotypes
Authors
Amanda L Tapia MS
1
, Bryce T Rowland BS
1
, Jonathan D Rosen MS
1
, Michael Preuss PhD
2
, Kris
Young PhD
3
, Misa Graff PhD
3
, Hélène Choquet PhD
4
, David J Couper PhD
1
, Steve Buyske PhD
5
,
Stephanie A Bien PhD
6
, Eric Jorgenson PhD
4
, Charles Kooperberg PhD
6
, Ruth J.F. Loos PhD
2
,
Alanna C Morrison PhD
7
, Kari E North PhD
3
, Bing Yu PhD
7
, Alexander P Reiner MD
8
, Yun Li
PhD
9,1,10*
, Laura M Raffield PhD
9*
*Contributed equally to this work
1
Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA,
2
The Charles
Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New
York, NY, USA,
3
Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA,
4
Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA,
5
Department
of Statistics, Rutgers University, Piscataway, NJ, USA,
6
Division of Public Health Sciences, Fred
Hutchinson Cancer Research Center, Seattle, WA, USA,
7
Human Genetics Center, Department of
Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The
University of Texas Health Science Center at Houston, Houston, TX, USA,
8
Department of
Epidemiology, University of Washington, Seattle, WA, USA,
9
Department of Genetics, University
of North Carolina, Chapel Hill, NC, USA,
10
Department of Computer Science, University of North
Carolina at Chapel Hill, Chapel Hill, NC, USA
Correspondence: Laura M. Raffield, PhD
Assistant Professor, Department of Genetics
University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599
laura_raffield@unc.edu
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.23.432444doi: bioRxiv preprint

2
Abstract
Hematological measures are important intermediate clinical phenotypes for many acute
and chronic diseases. Hematological measures are highly heritable, and although genome-wide
association studies (GWAS) have identified thousands of loci containing trait-associated
variants, the causal genes underlying these associations are often uncertain. To better
understand the underlying genetic regulatory mechanisms, we performed a transcriptome-
wide association study (TWAS) using PrediXcan to systematically investigate the association
between genetically-predicted gene expression and hematological measures in 54,542
individuals of European ancestry from the Genetic Epidemiology Research on Adult Health and
Aging (GERA) cohort. We found 239 significant gene-trait associations with hematological
measures. Among this set of 239 associations, we replicated 71 at p < 0.05 with same direction
of effect for the blood cell trait in a meta-analysis of TWAS results consisting of up to 35,900
European ancestry individuals from the Women’s Health Initiative (WHI), the Atherosclerosis
Risk in Communities Study (ARIC), and BioMe Biobank. We further attempted to refine this list
of candidate genes by performing conditional analyses, adjusting for individual variants
previously associated with these hematological measures, and performed further fine-mapping
of TWAS loci. To assist with the interpretation of TWAS findings, we designed an R Shiny
application to interactively visualize TWAS results, one genomic locus at a time, by integrating
our TWAS results with additional genetic data sources (GWAS, TWAS from other gene
expression reference panels, conditional analyses, known GWAS variants, etc.). Our results and
R Shiny application highlight frequently overlooked challenges with TWAS and illustrate the
complexity of TWAS fine-mapping efforts.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.23.432444doi: bioRxiv preprint

3
Author Summary
Transcriptome-wide association studies (TWAS) have shown great promise in furthering our
understanding of the genetic regulatory mechanisms underlying complex trait variation.
However, interpreting TWAS results can be incredibly complex, especially in large-scale
analyses where hundreds of signals appear throughout the genome, with multiple genes often
identified in a single chromosomal region. Our research demonstrates this complexity through
real data examples from our analysis of hematological traits, and we provide a useful web
application to visualize TWAS results in a broadly approachable format. Together, our results
and web application illustrate the importance of interpreting TWAS studies in context and
highlight the need to carefully examine results in a region-wide context to draw reasonable
conclusions and formulate mechanistic hypotheses.
Introduction
Hematological measures (red cell, white cell, and platelet traits) have a critical role in
oxygen transport, immunity, infection, thrombosis, and hemostasis and are associated with
many acute and chronic diseases, including autoimmunity, asthma, cardiovascular disease, and
COVID-19 [1-5]. Genome-wide association studies (GWAS) have identified thousands of loci
containing such trait-associated variants, and previous Mendelian randomization and
phenome-wide association study analyses have highlighted the likely causal role of blood cell
trait-associated genetic variants in a variety of disorders, including autoimmune conditions and
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.23.432444doi: bioRxiv preprint

4
coronary heart disease [1-3]. Unfortunately, these individual SNP-based GWAS make it difficult
to identify regulatory variants with small effect sizes which in aggregate impact the same gene,
even in very large sample sizes, and they identify regions of associated variants whose
biological function is often not clear [6]. Thus, utilizing a gene-based method to aggregate the
effect of multiple regulatory variants may increase the study power to identify novel trait-
associated loci and elucidate mechanisms of biological function.
A transcriptome-wide association study (TWAS) is one gene-based method which
systematically investigates the association between genetically predicted gene expression and
phenotypes of interest [6-9]. Here, we report results from a large TWAS of hematological
measures using the PrediXcan method [6] to analyze data from 54,542 individuals of European
ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort
(our discovery data set) [10] [11]. Hematological phenotypes are particularly good candidates
for TWAS analysis due to the availability of large RNA-sequencing datasets in a relevant tissue
type, high heritability across traits, and the large number of known genetic associations, most
with poorly understood mechanisms and target genes. We perform this analysis using whole
blood RNA-sequencing in 922 individuals from the Depression Genes and Networks (DGN) [12]
study as our primary reference panel. After association analysis of imputed gene transcript
levels with hematological indices in GERA, we performed conditional analyses, adjusting for
variants previously identified to affect hematological measures, to evaluate if TWAS-identified
genes represented novel statistical signals or were primarily driven by variants known from
GWAS [3]. These direct conditional analyses represent a major advantage of the use of
individual level data for our TWAS analyses, since these conditional tests could not be
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.23.432444doi: bioRxiv preprint

5
performed as easily or accurately using summary statistic-based methods. We replicated our
significant set of gene-trait associations in a meta-analyzed sample of TWAS results containing
18,100 individuals from the Women’s Health Initiative (WHI), 9,345 individuals from the
Atherosclerosis Risk in Communities Study (ARIC), and 8,455 individuals from Mount Sinai
BioMe Biobank (BioMe), all of European ancestry (Supplementary Table 1). We also compared
the TWAS results from the primary DGN reference panel to three additional reference panels
(whole blood and Epstein-Barr virus (EBV) transformed lymphocytes from the Genotype-Tissue
Expression (GTEx) Project [13], and monocytes from the Multi-Ethnic Study of Atherosclerosis
(MESA)[14]); these are considered secondary reference panels due to their smaller sample
sizes. These additional analyses helped us to determine if relevant tissues with smaller sample
sizes support our primary TWAS findings with DGN.
We employ several strategies to improve our understanding and interpretation of
complex genomic regions containing multiple TWAS-identified genes. First, we used FOCUS
(fine-mapping of causal gene sets [15]) to seek to identify a set of causal genes within genomic
loci containing multiple significant TWAS gene-trait associations. FOCUS is a software used to
fine-map TWAS statistics at genomic risk regions, while accounting for linkage disequilibrium
(LD) among variants and predicted expression correlation among genes at those risk regions.
Second, we present a novel web-based tool for integrating and visualizing TWAS and GWAS
results, as well as results from multiple expression reference datasets. Additionally, we discuss
frequently overlooked challenges of TWAS interpretation, such as failure to consider the
number of proximal genes which cannot be accurately imputed with a given reference panel,
but which may still be influenced by variants identified in GWAS studies. Our results illustrate
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted February 23, 2021. ; https://doi.org/10.1101/2021.02.23.432444doi: bioRxiv preprint

Citations
More filters
Posted ContentDOI
05 Aug 2021-bioRxiv
TL;DR: This paper performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399,835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals.
Abstract: Previous genome-wide association studies (GWAS) of hematological traits have identified over 10,000 distinct trait-specific risk loci, but the underlying causal mechanisms at these loci remain incompletely characterized. We performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399,835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 TWAS signals associated with hematological traits distinct from previously discovered GWAS variants, including 10 completely novel gene-trait pairs corresponding to 9 unique genes. Among the 557 associations, 301 were available for replication in a cohort of 141,286 participants of European ancestry from the Million Veteran Program (MVP). Of these 301 associations, 199 replicated at a nominal threshold (α = 0.05) and 108 replicated at a strict Bonferroni adjusted threshold (α = 0.05/301). Using our TWAS results, we systematically assigned 4,261 out of 16,900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity to assign variants to target genes.
References
More filters
Journal ArticleDOI
TL;DR: A rapid algorithm for relationship inference using high-throughput genotype data typical of GWAS that allows the presence of unknown population substructure and performs relationship inference on millions of pairs of individuals in a matter of minutes, dozens of times faster than the most efficient existing algorithm.
Abstract: Motivation: Genome-wide association studies (GWASs) have been widely used to map loci contributing to variation in complex traits and risk of diseases in humans. Accurate specification of familial relationships is crucial for family-based GWAS, as well as in population-based GWAS with unknown (or unrecognized) family structure. The family structure in a GWAS should be routinely investigated using the SNP data prior to the analysis of population structure or phenotype. Existing algorithms for relationship inference have a major weakness of estimating allele frequencies at each SNP from the entire sample, under a strong assumption of homogeneous population structure. This assumption is often untenable. Results: Here, we present a rapid algorithm for relationship inference using high-throughput genotype data typical of GWAS that allows the presence of unknown population substructure. The relationship of any pair of individuals can be precisely inferred by robust estimation of their kinship coefficient, independent of sample composition or population structure (sample invariance). We present simulation experiments to demonstrate that the algorithm has sufficient power to provide reliable inference on millions of unrelated pairs and thousands of relative pairs (up to 3rd-degree relationships). Application of our robust algorithm to HapMap and GWAS datasets demonstrates that it performs properly even under extreme population stratification, while algorithms assuming a homogeneous population give systematically biased results. Our extremely efficient implementation performs relationship inference on millions of pairs of individuals in a matter of minutes, dozens of times faster than the most efficient existing algorithm known to us. Availability: Our robust relationship inference algorithm is implemented in a freely available software package, KING, available for download at http://people.virginia.edu/~wc9c/KING. Contact: wmchen@virginia.edu Supplementary information:Supplementary data are available at Bioinformatics online.

2,147 citations

Journal ArticleDOI
TL;DR: A powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits is introduced.
Abstract: Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼ 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

1,473 citations

Journal ArticleDOI
TL;DR: The results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.
Abstract: Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.

1,372 citations

Journal ArticleDOI
William J. Astle, Heather Elding1, Heather Elding2, Tao Jiang3, Dave Allen4, Dace Ruklisa4, Dace Ruklisa3, Alice L. Mann1, Daniel Mead1, Heleen J. Bouman1, Fernando Riveros-Mckay1, Myrto Kostadima4, Myrto Kostadima5, Myrto Kostadima3, John J. Lambourne4, John J. Lambourne3, Suthesh Sivapalaratnam3, Suthesh Sivapalaratnam6, Kate Downes3, Kate Downes4, Kousik Kundu1, Kousik Kundu3, Lorenzo Bomba1, Kim Berentsen7, John Bradley3, John Bradley2, Louise C. Daugherty3, Louise C. Daugherty4, Olivier Delaneau8, Kathleen Freson9, Stephen F. Garner4, Stephen F. Garner3, Luigi Grassi4, Luigi Grassi3, Jose A. Guerrero3, Jose A. Guerrero4, Matthias Haimel3, Eva M. Janssen-Megens7, Anita Kaan7, Mihir A Kamat3, Bowon Kim7, Amit Mandoli7, Jonathan Marchini10, Jonathan Marchini11, Joost H.A. Martens7, Stuart Meacham3, Stuart Meacham4, Karyn Megy4, Karyn Megy3, Jared O'Connell10, Jared O'Connell11, Romina Petersen3, Romina Petersen4, Nilofar Sharifi7, S.M. Sheard, James R Staley3, Salih Tuna3, Martijn van der Ent7, Klaudia Walter1, Shuang-Yin Wang7, Eleanor Wheeler1, Steven P. Wilder5, Valentina Iotchkova5, Valentina Iotchkova1, Carmel Moore3, Jennifer G. Sambrook4, Jennifer G. Sambrook3, Hendrik G. Stunnenberg7, Emanuele Di Angelantonio12, Emanuele Di Angelantonio3, Emanuele Di Angelantonio2, Stephen Kaptoge3, Stephen Kaptoge2, Taco W. Kuijpers13, Enrique Carrillo-de-Santa-Pau, David Juan, Daniel Rico14, Alfonso Valencia, Lu Chen1, Lu Chen3, Bing Ge15, Louella Vasquez1, Tony Kwan15, Diego Garrido-Martín16, Stephen Watt1, Ying Yang1, Roderic Guigó16, Stephan Beck17, Dirk S. Paul17, Dirk S. Paul3, Tomi Pastinen15, David Bujold15, Guillaume Bourque15, Mattia Frontini3, Mattia Frontini12, Mattia Frontini4, John Danesh, David J. Roberts18, David J. Roberts19, Willem H. Ouwehand, Adam S. Butterworth2, Adam S. Butterworth12, Adam S. Butterworth3, Nicole Soranzo 
17 Nov 2016-Cell
TL;DR: A genome-wide association analysis in the UK Biobank and INTERVAL studies is performed, providing evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations betweenBlood cell indices and cardiovascular disease may be non-causal.

982 citations

Journal ArticleDOI
Genevieve L. Wojcik1, Mariaelisa Graff2, Katherine K. Nishimura3, Ran Tao4, Jeffrey Haessler3, Christopher R. Gignoux1, Christopher R. Gignoux5, Heather M. Highland2, Yesha Patel6, Elena P. Sorokin1, Christy L. Avery2, Gillian M. Belbin7, Stephanie A. Bien3, Iona Cheng8, Sinead Cullina7, Chani J. Hodonsky2, Yao Hu3, Laura M. Huckins7, Janina M. Jeff7, Anne E. Justice2, Jonathan M. Kocarnik3, Unhee Lim9, Bridget M Lin2, Yingchang Lu7, Sarah C. Nelson10, Sungshim L. Park6, Hannah Poisner7, Michael Preuss7, Melissa A. Richard11, Claudia Schurmann7, Claudia Schurmann12, Veronica Wendy Setiawan6, Alexandra Sockell1, Karan Vahi6, Marie Verbanck7, Abhishek Vishnu7, Ryan W. Walker7, Kristin L. Young2, Niha Zubair3, Victor Acuña-Alonso, José Luis Ambite6, Kathleen C. Barnes5, Eric Boerwinkle11, Erwin P. Bottinger7, Erwin P. Bottinger12, Carlos Bustamante1, Christian Caberto9, Samuel Canizales-Quinteros, Matthew P. Conomos10, Ewa Deelman6, Ron Do7, Kimberly F. Doheny13, Lindsay Fernández-Rhodes2, Lindsay Fernández-Rhodes14, Myriam Fornage11, Benyam Hailu15, Gerardo Heiss2, Brenna M. Henn16, Lucia A. Hindorff15, Rebecca D. Jackson17, Cecelia A. Laurie10, Cathy C. Laurie10, Yuqing Li8, Yuqing Li18, Danyu Lin2, Andrés Moreno-Estrada, Girish N. Nadkarni7, Paul Norman5, Loreall Pooler6, Alexander P. Reiner10, Jane Romm13, Chiara Sabatti1, Karla Sandoval, Xin Sheng6, Eli A. Stahl7, Daniel O. Stram6, Timothy A. Thornton10, Christina L. Wassel19, Lynne R. Wilkens9, Cheryl A. Winkler, Sachi Yoneyama2, Steven Buyske20, Christopher A. Haiman6, Charles Kooperberg3, Loic Le Marchand9, Ruth J. F. Loos7, Tara C. Matise20, Kari E. North2, Ulrike Peters3, Eimear E. Kenny7, Christopher S. Carlson3 
27 Jun 2019-Nature
TL;DR: The value of diverse, multi-ethnic participants in large-scale genomic studies is demonstrated and evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications are shown.
Abstract: Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

591 citations

Frequently Asked Questions (2)
Q1. What are the contributions in "Full title: a large-scale transcriptome-wide association study (twas) of ten blood cell phenotypes reveals complexities of twas fine-mapping short title: twas fine-mapping of blood cell phenotypes authors" ?

Authors Amanda L Tapia MS1, Bryce T Rowland BS1, Jonathan D Rosen MS1, Michael Preuss PhD2, Kris Young PhD3, Misa Graff PhD3, Hélène Choquet PhD4, David J Couper PhD1, Steve Buyske PhD5, Stephanie A Bien PhD6, Eric Jorgenson PhD4, Charles Kooperberg PhD6, Ruth J. F. Loos PhD2, Alanna C Morrison PhD7, Kari E North PhD3, Bing Yu PhD7, Alexander P Reiner MD8, Yun Li PhD9,1,10 *, Laura M Raffield PhD9 * * Contributed equally to this work 

Joint/multiple tissue TWAS approaches such as UTMOST [ 9 ] and MR-JTI [ 8 ] could be employed in the future to assess the relevance of other tissues at blood-cell related loci.