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Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits.

ABSTRACT: Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem-when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes' expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these 'genetic confounders', resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.

SUBMITTER: Zhao S

PROVIDER: S-EPMC10864181 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits.

Zhao Siming S Crouse Wesley W Qian Sheng S Luo Kaixuan K Stephens Matthew M He Xin X

Nature genetics 20240126 2

Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem-when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes' expression may be correlated with these eQTLs and have d ...[more]

PMID: 38279041

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Project description:Meat rabbits are a small herbivorous livestock and have been popularly raised in China for producing high-quality meat. Therefore, it is economically important to genetically improve both carcass performance and meat quality in meat rabbits. However, we still know less about the underlying candidate genes that may determine phenotypic variation on carcass and meat traits of meat rabbits. The main objective of this study was to identify candidate genes whose mRNA expression levels may be significantly involved in regulating carcass and meat traits of meat rabbits based on the transcriptome-wide association studies (TWAS). Five carcass traits of the carcass weight (CW), dressing out percentage (DP), cut weight of hind legs (LW), weight ratio of cut hind legs to carcass (RLW), and weight of visceral and interscapular fat (WF), as well as two meat traits of the drip loss (DL) and cooking loss (CL) were phenotyped in a F1 crossbred population (N = 119) between Zika rabbits and Sichuan White rabbits. The effects of mRNA expression levels of a total of 10,288 genome-wide genes on these seven traits were statistically estimated using the mixed linear model, in which the polygenic background effects were accounted for. Our results revealed two candidate genes (RDH5 and MTARC2) that were statistically significantly associated with LW trait (the adjusted p values <0.05), whereas no gene reached the statistically significant threshold for all the remaining six traits. Because of the relatively small sample size analyzed, we alternatively selected 20 candidate genes with the lowest p values for every trait and subjected them to functional enrichment analyses, which identified three Gene Ontology (GO) terms that were significantly enriched by the candidate genes of CW and RLW traits. In conclusion, this study used TWAS approach to successfully reveal several candidate genes whose mRNA expression levels may be involved in regulating carcass and meat traits in meat rabbits, which are helpful to explore the underlying molecular mechanism in the future studies.

Project description: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.

Dataset Information

Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits.

Publications

Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits.

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