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Zhou JJ, Yip WK, Cho MH, et al. A comparative analysis of family-based and population-based association tests using whole genome sequence data. BMC Proc. 2014;8:S33doi: 10.1186/1753-6561-8-S1-S33.
Zhou, J. J., Yip, W. K., Cho, M. H., Qiao, D., McDonald, M. L., & Laird, N. M. (2014). A comparative analysis of family-based and population-based association tests using whole genome sequence data. BMC proceedings, 8S33. https://doi.org/10.1186/1753-6561-8-S1-S33
Zhou, Jin J, et al. "A comparative analysis of family-based and population-based association tests using whole genome sequence data." BMC proceedings vol. 8 (2014): S33. doi: https://doi.org/10.1186/1753-6561-8-S1-S33
Zhou JJ, Yip WK, Cho MH, Qiao D, McDonald ML, Laird NM. A comparative analysis of family-based and population-based association tests using whole genome sequence data. BMC Proc. 2014 Jun 17;8:S33. doi: 10.1186/1753-6561-8-S1-S33. eCollection 2014. PMID: 25519381; PMCID: PMC4143682.
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BMC Proc. 2014 Jun 17;8:S33. doi: 10.1186/1753-6561-8-S1-S33. eCollection 2014.

A comparative analysis of family-based and population-based association tests using whole genome sequence data.

BMC proceedings

Jin J Zhou, Wai-Ki Yip, Michael H Cho, Dandi Qiao, Merry-Lynn N McDonald, Nan M Laird

Affiliations

  1. Biostatistics Department, Harvard School of Public Health, Boston, MA 02115 USA ; Division of Epidemiology and Biostatistics, College of Public Health, University of Arizona, Tucson, AZ 85724, USA.
  2. Biostatistics Department, Harvard School of Public Health, Boston, MA 02115 USA.
  3. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA ; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
  4. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.

PMID: 25519381 PMCID: PMC4143682 DOI: 10.1186/1753-6561-8-S1-S33

Abstract

The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework--FBAT-v, FBAT-lmm, FBAT-m, and FBAT-l--were used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both set-based and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis. The updated FBAT package is available at: http://www.hsph.harvard.edu/fbat/.

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