Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. Highland, H. M, Wojcik, G. L, Graff, M., Nishimura, K. K, Hodonsky, C. J, Baldassari, A. R, Cote, A. C, Cheng, I., Gignoux, C. R, Tao, R., Li, Y., Boerwinkle, E., Fornage, M., Haessler, J., Hindorff, L. A, Hu, Y., Justice, A. E, Lin, B. M, Lin, D., Stram, D. O, Haiman, C. A, Kooperberg, C., Le Marchand, L., Matise, T. C, Kenny, E. E, Carlson, C. S, Stahl, E. A, Avery, C. L, North, K. E, Ambite, J. L., Buyske, S., Loos, R. J, Peters, U., Young, K. L, Bien, S. A, & Huckins, L. M Am J Hum Genet, 109(4):669-679, Apr, 2022.
Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits [link]Paper  doi  abstract   bibtex   3 downloads  
One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.
@article{Highland:2022aa,
	abstract = {One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.},
	author = {Highland, Heather M and Wojcik, Genevieve L and Graff, Mariaelisa and Nishimura, Katherine K and Hodonsky, Chani J and Baldassari, Antoine R and Cote, Alanna C and Cheng, Iona and Gignoux, Christopher R and Tao, Ran and Li, Yuqing and Boerwinkle, Eric and Fornage, Myriam and Haessler, Jeffrey and Hindorff, Lucia A and Hu, Yao and Justice, Anne E and Lin, Bridget M and Lin, Danyu and Stram, Daniel O and Haiman, Christopher A and Kooperberg, Charles and Le Marchand, Loic and Matise, Tara C and Kenny, Eimear E and Carlson, Christopher S and Stahl, Eli A and Avery, Christy L and North, Kari E and Ambite, Jose Luis and Buyske, Steven and Loos, Ruth J and Peters, Ulrike and Young, Kristin L and Bien, Stephanie A and Huckins, Laura M},
	date-added = {2022-09-26 11:25:18 -0400},
	date-modified = {2022-09-26 11:25:18 -0400},
	doi = {10.1016/j.ajhg.2022.02.013},
	journal = {Am J Hum Genet},
	journal-full = {American journal of human genetics},
	keywords = {PrediXcan, TWAS, ancestrally diverse, gene expression, cardiometabolic traits, PAGE},
	mesh = {Cardiovascular Diseases; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Life Style; Polymorphism, Single Nucleotide; Transcriptome},
	month = {Apr},
	number = {4},
	pages = {669-679},
	pmc = {PMC9069067},
	pmid = {35263625},
	url = {https://pubmed.ncbi.nlm.nih.gov/35263625/},
	pst = {ppublish},
	title = {Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits},
	volume = {109},
	year = {2022},
	bdsk-url-1 = {https://doi.org/10.1016/j.ajhg.2022.02.013}}
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