NDLI: Coal-Miner: A Statistical Method for GWA Studies of Quantitative Traits with Complex Evolutionary Origins

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Coal-Miner: A Statistical Method for GWA Studies of Quantitative Traits with Complex Evolutionary Origins

Content Provider	ACM Digital Library
Author	Liu, Kevin J. Hejase, Hussein A. Pol, Natalie Vande Bonito, Gregory M. Edger, Patrick P.
Abstract	Association mapping (AM) methods are used in genome-wide association (GWA) studies to test for statistically significant associations between genotypic and phenotypic data. The genotypic and phenotypic data share common evolutionary origins -- namely, the evolutionary history of sampled organisms -- introducing covariance which must be distinguished from the covariance due to biological function that is of primary interest in GWA studies. A variety of methods have been introduced to perform AM while accounting for sample relatedness. However, the state of the art predominantly utilizes the simplifying assumption that sample relatedness is effectively fixed across the genome. In contrast, population genetic theory and empirical studies have shown that sample relatedness can vary greatly across different loci within a genome. This phenomenon -- referred to as local genealogical variation -- is commonly encountered in many genomic datasets. New AM methods are needed to better account for local variation in sample relatedness within genomes. We address this gap by introducing Coal-Miner, a new statistical AM method. The Coal-Miner algorithm takes the form of a methodological pipeline. The initial stages of Coal-Miner seek to detect candidate loci, or loci which contain putatively associated markers. Subsequent stages of Coal-Miner perform test for association using a linear mixed model with multiple effects which account for sample relatedness locally within candidate loci and globally across the entire genome. Using synthetic and empirical datasets, we compare the statistical power and type I error control of Coal-Miner against state-of-the-art AM methods. The simulation conditions reflect a variety of genomic architectures for complex traits and incorporate a range of evolutionary scenarios, each with different evolutionary processes that can generate local genealogical variation. Across the datasets in our study, we find that Coal-Miner consistently offers comparable or typically better statistical power and type I error control compared to the state-of-the-art methods.
Starting Page	107
Ending Page	114
Page Count	8
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3107490
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Gwas Genealogy Genome wide association study Arabidopsis Population stratification Coalescent
Content Type	Text
Resource Type	Article

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