Multi-population GWA mapping via multi-task regularized regression
By Kriti Puniyani, Seyoung Kim, Eric P. Xing. 2010
78 citations recorded by the source corpus.
Motivation: Population heterogeneity through admixing of different founder populations can produce spurious associations in genome- wide association studies that are linked to the population structure rather than the phenotype. Since samples from the same population generally co-evolve, different populations may or may not share the same genetic underpinnings for the seemingly common phenotype. Our goal is to develop a unified framework for detecting causal genetic markers through a joint association analysis of multiple populations. Results: Based on a multi-task regression principle, we present a multi-population group lasso algorithm using L1/L2-regularized regression for joint association analysis of multiple populations that are stratified either via population survey or computational estimation. Our algorithm combines information from genetic markers across populations, to identify causal markers. It also implicitly accounts for correlations between the genetic markers, thus enabling better control over false positive rates. Joint analysis across populations enables the detection of weak associations common to all populations with greater power than in a separate analysis of each population. At the same time, the regression-based framework allows causal alleles that are unique to a subset of the populations to be correctly identified. We demonstrate the effectiveness of our method on HapMap-simulated and lactase persistence datasets, where we significantly outperform state of the art methods, with greater power for detecting weak associations and reduced spurious associations. Availability: Software will be available at http://www.sailing.cs.cmu.edu/ Contact: epxing@cs.cmu.edu
Subfields: Genetics, Molecular Biology, Statistics and Probability, Computational Biology & Bioinformatics
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Citations in the corpus
- An Overview of Multi-task Learning (2018, background): “Zhang and Yang 39 populations [104], construction of personalized brain–computer interfaces [105], MHC-I binding prediction [106], splice-site prediction [106], protein subcellular location prediction [107], Alzheimer’s disease assessment scale cognitive subscale [108], prediction of cognitive…”
- A Survey on Multi-Task Learning (2017, methodology): “In [184], the MTFS method is used to detect causal genetic markers through a joint association analysis of multiple populations.”
- Multi-level Lasso for Sparse Multi-task Regression (2012, methodology): “These methods have been frequently used in genome-wide association studies (Puniyani et al., 2010; Zhang et al., 2010) to identify common mechanism of response.”