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:10.1371/journal.pgen.1007586

Cited by 131 publications

(143 citation statements)

References 51 publications

Supporting

Mentioning

119

Contrasting

Order By: Relevance

“…However MESA_AFA contains the models that should best reflect the genetic ancestry of African Americans in SAGE (Supplementary Table 1). We note that MESA_AFA also has the smallest training sample size among our weight sets (N = 233)[38], so the small number of predicted genes from MESA_AFA probably results from the small training sample size and not from any feature of the underlying MESA_AFA training data.…”

Section: Resultsmentioning

confidence: 99%

“…Among TWAS resources, we focused on PrediXcan as a test case with openly distributed prediction models available for multiple populations. [6, 38] Using 39 subjects from the SAGE study[39–42] we compared predicted expression values from PrediXcan models to measured gene expression on the same subjects and found that predictions matched poorly to measurements. Our investigation with the GEUVADIS dataset[43] offered us a more homogenous environment in which to train and test gene expression prediction models.…”

Section: Discussionmentioning

confidence: 99%

“…[4,43,44] The GEUVADIS dataset has been used extensively to validate PrediXcan models. [6, 38] However, recent analyses suggest that GTEx and DGN PrediXcan models behave differently on the constituent populations in GEUVADIS. [45] To our knowledge, nobody has investigated cross-population generalizability of new prediction models generated within GEUVADIS.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the cross-population generalizability of gene expression prediction models

Keys

Mak

White

et al. 2019

Preprint

View full text Add to dashboard Cite

27The genetic control of gene expression is a core component of human physiology. For the past 28 several years, transcriptome-wide association studies have leveraged large datasets of linked 29 genotype and RNA sequencing information to create a powerful gene-based test of association 30 that has been used in dozens of studies. While numerous discoveries have been made, the 31 populations in the training data are overwhelmingly of European descent, and little is known 32 about the generalizability of these models to other populations. Here, we test for cross-33 population generalizability of gene expression prediction models using a dataset of African 34American individuals with RNA-Seq data in whole blood. We find that the default models trained 35 in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction 36 in prediction accuracy when compared to European Americans. We replicate these limitations in 37 cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic 38 simulations of both populations and gene expression, we show that accurate cross-population 39 generalizability of transcriptome prediction only arises when eQTL architecture is substantially 40 shared across populations. In contrast, models with non-identical eQTLs showed patterns similar 41 to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step 42 towards multi-ethnic utility of gene expression prediction. 43 44 Author summary 47 Advances in RNA sequencing technology have reduced the cost of measuring gene expression at 48 a genome-wide level. However, sequencing enough human RNA samples for adequately-49 powered disease association studies remains prohibitively costly. To this end, modern 50 transcriptome-wide association analysis tools leverage existing paired genotype-expression 51datasets by creating models to predict gene expression using genotypes. These predictive models 52 enable researchers to perform cost-effective association tests with gene expression in 53 independently genotyped samples. However, most of these models use European reference 54 data, and the extent to which gene expression prediction models work across populations is not 55 fully resolved. We observe that these models predict gene expression worse than expected in a 56 dataset of African-Americans when derived from European-descent individuals. Using 57 simulations, we show that gene expression predictive model performance depends on both the 58 amount of shared genotype predictors as well as the genetic relatedness between populations. 59Our findings suggest a need to carefully select reference populations for prediction and point to 60 a pressing need for more genetically diverse genotype-expression datasets. 61In the last decade, large-scale genome-wide genotyping projects have enabled a revolution in our 63 understanding of complex traits. [1][2][3][4] This explosion of genome sequencing data has spurred the 64 development of new methods that int...

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the cross-population generalizability of gene expression prediction models

Keys

Mak

White

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Though most of the results were from the 44 predominantly European GTEx tissue 121 models, we also had significant PrediXcan results using the five MESA monocyte models built in various ethnicities [14]. 14/59 of the significant associations in HCHS/SoL were in MESA models, with 4 associations in CETP and 10 associations in 124 PSRC1 in the same effect direction as the other tissues (Table 2).…”

mentioning

confidence: 85%

“…MESA individuals include those that self-identified as "Black, African-American" (AFA, 295 n = 613), "White, Caucasian" (CAU = 2,243), and "Hispanic" (HIS, n = 999). Cohorts 296 underwent quality control and imputation as previously performed by our group [14]. (Fig.…”

mentioning

confidence: 99%

Genetically regulated gene expression underlies lipid traits in Hispanic cohorts

Andaleon

Mogil

Wheeler

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Plasma lipid levels are risk factors for cardiovascular disease, a leading cause of death worldwide. While many studies have been conducted in genetic variation underlying lipid levels, they mainly comprise individuals of European ancestry and thus their transferability to non-European populations is unclear. We performed genome-wide (GWAS) and imputed transcriptome-wide association studies of four lipid traits in the Hispanic Community Health Study/Study of Latinos cohort (HCHS/SoL, n = 11,103), replicated in the Multi-Ethnic Study of Atherosclerosis (MESA, n = 3,855), and compared the results to the larger, predominantly European ancestry meta-analysis by the Global Lipids Genetics Consortium (GLGC, n = 196,475). GWAS revealed SNPs in regions within or near known lipid and admixture mapping did not find significant associations between local ancestry and lipid phenotypes. In the imputed transcriptome-wide association study in multiple tissues and in different ethnicities, we found 59 significant gene-tissue-phenotype associations (P < 3.61×10 −8 ) with 14 unique significant genes, many of which occurred across multiple phenotypes, tissues, and ethnicities and replicated in MESA (45/59) and in GLGC (44/59). These include well-studied lipid genes such as SORT1, CETP, and PSRC1, as well as genes that have been implicated in cardiovascular phenotypes, such as CCL22 and ICAM1. The majority (40/59) of significant associations colocalized with expression quantitative trait loci (eQTLs), indicating a possible mechanism of gene regulation in lipid level variation.To fully characterize the genetic architecture of lipid traits in diverse populations, larger studies in non-European ancestry populations are needed.

show abstract

Evolutionary perspectives on African North American genetic diversity: Origins and prospects for future investigations

Caldwell

Jackson

2021

Evolutionary Anthropology

View full text Add to dashboard Cite

African‐descended peoples of the Americas represent an amalgamation of West, Central, and Southeast African regional and ethnic groups with modest gene flow from specific non‐African populations. Despite 16+ generations of residence in the Americas, there is a deficit of evolutionary knowledge about these populations. Focusing on Legacy African American, the African North American descendants of survivors of the transatlantic trade in enslaved Africans, we report on emic evolutionary perspectives of their self‐identity gleaned from our interviews of 600 individuals collected over 2 years. Gullah‐Geechee peoples of Carolina Coastal regions are a model case study due to their historical antiquity, substantial African retentions, relative geospatial isolation, and proposed progenitor status to other Legacy African American microethnic groups. We identify salient research questions for future studies that will begin to bridge the evolutionary gaps in our knowledge of these diverse peoples and the historical evidence for specific evolutionary processes.

show abstract

Genetic architecture of gene expression traits across diverse populations

Cited by 131 publications

References 51 publications

On the cross-population generalizability of gene expression prediction models

On the cross-population generalizability of gene expression prediction models

Genetically regulated gene expression underlies lipid traits in Hispanic cohorts

Evolutionary perspectives on African North American genetic diversity: Origins and prospects for future investigations

Contact Info

Product

Resources

About