Phenotypic extremes in rare variant study designs.
Currently, next-generation sequencing studies aim to identify rare and low-frequency variation that may contribute to disease. For a given effect size, as the allele frequency decreases, the power to detect genes or variants of interest also decreases. Although many methods have been proposed for the analysis of such data, study design and analytic issues still persist in data interpretation. In this study we present sequencing data for ABCA1 that has known rare variants associated with high-density lipoprotein cholesterol (HDL-C). We contrast empirical findings from two study designs: a phenotypic extreme sample and a population-based random sample. We found differing strengths of association with HDL-C across the two study designs (P=0.0006 with n=701 phenotypic extremes vs P=0.03 with n=1600 randomly sampled individuals). To explore this apparent difference in evidence for association, we performed a simulation study focused on the impact of phenotypic selection on power. We demonstrate that the power gain for an extreme phenotypic selection study design is much greater in rare variant studies than for studies of common variants. Our study confirms that studying phenotypic extremes is critical in rare variant studies because it boosts power in two ways: the typical increases from extreme sampling and increasing the proportion of relevant functional variants ascertained and thereby tested for association. Furthermore, we show that when combining statistical evidence through meta-analysis from an extreme-selected sample and a second separate population-based random sample, power is lower when a traditional sample size weighting is used compared with weighting by the noncentrality parameter.
Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA.|Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.|Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
Gina M
GM
Peloso
Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA.
Daniel J
DJ
Rader
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Stacey
S
Gabriel
Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA.|Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.|Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.|Department of Medicine, Harvard Medical School, Boston, MA, USA.
Sekar
S
Kathiresan
Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA.|Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.|Department of Medicine, Analytical and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.|Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Mark J
MJ
Daly
Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA.|Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.|Department of Medicine, Analytical and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.|Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Benjamin M
BM
Neale
D064286:ATP Binding Cassette Transporter 1 / Q000235:genetics; D015982:Bias; D055106:Genome-Wide Association Study / Q000379:methods* / Q000592:standards; D006801:Humans; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D009154:Mutation; D010641:Phenotype; D018401:Sample Size
C577836:ABCA1 protein, human; D064286:ATP Binding Cassette Transporter 1