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Analysis of family- and population-based samples in cohort genome-wide association studies

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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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Acknowledgments

MESA and the MESA SHARe project are conducted and supported by contracts N01-HC-95159 through N01-HC-95169 and RR-024156 from the National Heart, Lung, and Blood Institute (NHLBI). Funding for MESA SHARe genotyping was provided by NHLBI Contract N02‐HL‐6‐4278. MESA Family is conducted and supported in collaboration with MESA investigators; support is provided by grants and contracts R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071252, R01HL071258, R01HL071259. The authors thank the participants of the MESA study, the Coordinating Center, MESA investigators, and study staff for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.

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Authors and Affiliations

  1. Center for Public Health Genomics, University of Virginia, West Complex, 6th Fl, Suite 6111, P.O. Box 800717, Charlottesville, VA, 22908, USA

    Ani Manichaikul, Wei-Min Chen, Michèle M. Sale, Stephen S. Rich & Josyf C. Mychaleckyj

  2. Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, USA

    Ani Manichaikul & Wei-Min Chen

  3. Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, USA

    Kayleen Williams & Quenna Wong

  4. Department of Medicine, University of Virginia, Charlottesville, VA, USA

    Michèle M. Sale

  5. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA

    James S. Pankow

  6. Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA

    Michael Y. Tsai

  7. Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jerome I. Rotter

Authors
  1. Ani Manichaikul
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  2. Wei-Min Chen
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  3. Kayleen Williams
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  4. Quenna Wong
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  5. Michèle M. Sale
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  6. James S. Pankow
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  7. Michael Y. Tsai
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  8. Jerome I. Rotter
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  9. Stephen S. Rich
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  10. Josyf C. Mychaleckyj
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Corresponding author

Correspondence to Josyf C. Mychaleckyj.

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Manichaikul, A., Chen, WM., Williams, K. et al. Analysis of family- and population-based samples in cohort genome-wide association studies. Hum Genet 131, 275–287 (2012). https://doi.org/10.1007/s00439-011-1071-0

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  • DOI: https://doi.org/10.1007/s00439-011-1071-0

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