@article {100,
	title = {On the cross-population generalizability of gene expression prediction models.},
	journal = {PLoS Genet},
	volume = {16},
	year = {2020},
	month = {2020 08},
	pages = {e1008927},
	abstract = {<p>The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.</p>},
	keywords = {African Americans, Gene Expression Profiling, Genome-Wide Association Study, Humans, Models, Genetic, Quantitative Trait Loci, Reference Standards, RNA-Seq, Transcriptome},
	issn = {1553-7404},
	doi = {10.1371/journal.pgen.1008927},
	author = {Keys, Kevin L and Mak, Angel C Y and White, Marquitta J and Eckalbar, Walter L and Dahl, Andrew W and Mefford, Joel and Mikhaylova, Anna V and Contreras, Mar{\'\i}a G and Elhawary, Jennifer R and Eng, Celeste and Hu, Donglei and Huntsman, Scott and Oh, Sam S and Salazar, Sandra and LeNoir, Michael A and Ye, Jimmie C and Thornton, Timothy A and Zaitlen, Noah and Burchard, Esteban G and Gignoux, Christopher R}
}
@article {59,
	title = {Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives.},
	journal = {Genetics},
	volume = {211},
	year = {2019},
	month = {2019 04},
	pages = {1179-1189},
	abstract = {<p>High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have [Formula: see text], standard two-step methods all have [Formula: see text]-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.</p>},
	keywords = {Animals, Genome-Wide Association Study, Humans, Models, Genetic, Phenotype, Principal Component Analysis, Quantitative Trait Loci, Reproducibility of Results},
	issn = {1943-2631},
	doi = {10.1534/genetics.118.301768},
	author = {Dahl, Andy and Guillemot, Vincent and Mefford, Joel and Aschard, Hugues and Zaitlen, Noah}
}
