Analysis of family- and population-based samples in cohort genome-wide association studies |
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Authors: | Ani?Manichaikul Wei-Min?Chen Kayleen?Williams Quenna?Wong Michèle?M?Sale James?S?Pankow Michael?Y?Tsai Jerome?I?Rotter Stephen?S?Rich Email author" target="_blank">Josyf?C?MychaleckyjEmail author |
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Institution: | (1) Center for Public Health Genomics, University of Virginia, West Complex, 6th Fl, Suite 6111, P.O. Box 800717, Charlottesville, VA 22908, USA;(2) Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, USA;(3) Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, USA;(4) Department of Medicine, University of Virginia, Charlottesville, VA, USA;(5) Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA;(6) Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA;(7) Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA |
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Abstract: | Cohort studies typically sample unrelated individuals from a population, although family members of index cases may also be
recruited to investigate shared familial risk factors. Recruitment of family members may be incomplete or ancillary to the
main cohort, resulting in a mixed sample of independent family units, including unrelated singletons and multiplex families.
Multiple methods are available to perform genome-wide association (GWA) analysis of binary or continuous traits in families,
but it is unclear whether methods known to perform well on ascertained pedigrees, sibships, or trios are appropriate in analysis
of a mixed unrelated cohort and family sample. We present simulation studies based on Multi-Ethnic Study of Atherosclerosis
(MESA) pedigree structures to compare the performance of several popular methods of GWA analysis for both quantitative and
dichotomous traits in cohort studies. We evaluate approaches suitable for analysis of families, and combined the best performing
methods with population-based samples either by meta-analysis, or by pooled analysis of family- and population-based samples
(mega-analysis), comparing type 1 error and power. We further assess practical considerations, such as availability of software
and ability to incorporate covariates in statistical modeling, and demonstrate our recommended approaches through quantitative
and binary trait analysis of HDL cholesterol (HDL-C) in 2,553 MESA family- and population-based African-American samples.
Our results suggest linear modeling approaches that accommodate family-induced phenotypic correlation (e.g., variance-component
model for quantitative traits or generalized estimating equations for dichotomous traits) perform best in the context of combined
family- and population-based cohort GWAS. |
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