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The utility of empirically assigning ancestry groups in cross‐population genetic studies of addiction
Author(s) -
Peterson Roseann E.,
Edwards Alexis C.,
Bacanu SilviuAlin,
Dick Danielle M.,
Kendler Kenneth S.,
Webb Bradley T.
Publication year - 2017
Publication title -
the american journal on addictions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.997
H-Index - 76
eISSN - 1521-0391
pISSN - 1055-0496
DOI - 10.1111/ajad.12586
Subject(s) - population , genome wide association study , population stratification , concordance , genetic genealogy , genetic association , genetic variation , demography , biology , genetics , single nucleotide polymorphism , genotype , sociology , gene
Background and Objectives Given moderate heritability and significant heterogeneity among addiction phenotypes, successful genome‐wide association studies (GWAS) are expected to need very large samples. As sample sizes grow, so can genetic diversity leading to challenges in analyzing these data. Methods for empirically assigning individuals to genetically informed ancestry groups are needed. Methods We describe a strategy for empirically assigning ancestry groups in ethnically diverse GWAS data including extensions of principal component analysis (PCA) and population matching through minimum Mahalanobis distance. We apply these methods to data from Spit for Science (S4S): the University Student Survey , a study following college students longitudinally that includes genetic and environmental data on substance use and mental health ( n = 7,603). Results The genetic‐based population assignments for S4S were 48.7% European, 22.5% African, 10.4% Americas, 9.2% East Asian, and 9.2% South Asian descent. Self‐reported census categories “More than one race” and “Unknown”as well as “Hawaiian/Pacific Islander” and “American‐Indian/Native Alaskan” were empirically assigned representing a +9% sample retention over conventional methods. Although there was high concordance between self‐reported race and empirical population‐match (+.924), there was reduction in variance for most ancestry PCs for genetic‐based population assignments. Conclusions We were able to create more genetically homogenous groups and reduce sample and marker loss through cross‐ancestry meta‐analysis, potentially increasing power to detect etiologically relevant variation. Our approach provides a framework for empirically assigning genetic ancestry groups which can be applied to other ethnically diverse genetic studies. Scientific Significance Given the important public health impact and demonstrable gains in statistical power from studying diverse populations, empirically sound practices for genetic studies are needed. (Am J Addict 2017;26:494–501)