Literature DB >> 16094642

Exact family-based association tests for biallelic data.

Kady Schneiter1, Nan Laird, Chris Corcoran.   

Abstract

Family-based study designs have an important role in the search for association between disease phenotypes and genetic markers. Unlike traditional case-control methods, family-based tests use within-family data to avoid identification of spurious associations that may result from population admixture. Many family-based association tests have been proposed to accommodate a variety of ascertainment schemes and patterns of missing data. In this report, we describe exact family-based association tests for biallelic data. Specifically, we discuss test of the null hypotheses "no linkage and no association" and "linkage, but no association". These tests, which are valid under various models for inheritance and patterns of missingness, utilize the procedure proposed by Rabinowitz and Laird [2000: Hum Hered 50:211-223] that provides a unified framework for family based association testing (FBAT). The conditioning approach implemented in FBAT makes an exact test conceptually straightforward, but computationally difficult since the minimum sufficient statistics upon which we condition do not have a conventional form. An exact test may be especially critical when accurate computation of the extreme area of the FBAT statistic is needed, such as when the study design necessitates multiple comparisons adjustments. We describe the exact approach as a useful alternative to the asymptotic test and show that the exact tests for biallelic data may be most useful for the recessive disease model. Copyright 2005 Wiley-Liss, Inc.

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Year:  2005        PMID: 16094642     DOI: 10.1002/gepi.20088

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  4 in total

1.  Accurate and flexible power calculations on the spot: Applications to genomic research.

Authors:  Hemant K Tiwari; Thomas Birkner; Ankur Moondan; Shiju Zhang; Grier P Page; Amit Patki; David B Allison
Journal:  Stat Interface       Date:  2011       Impact factor: 0.582

2.  Gene-based segregation method for identifying rare variants in family-based sequencing studies.

Authors:  Dandi Qiao; Christoph Lange; Nan M Laird; Sungho Won; Craig P Hersh; Jarrett Morrow; Brian D Hobbs; Sharon M Lutz; Ingo Ruczinski; Terri H Beaty; Edwin K Silverman; Michael H Cho
Journal:  Genet Epidemiol       Date:  2017-02-13       Impact factor: 2.135

3.  Dopamine genes and nicotine dependence in treatment-seeking and community smokers.

Authors:  Andrew W Bergen; David V Conti; David Van Den Berg; Wonho Lee; Jinghua Liu; Dalin Li; Nan Guo; Huaiyu Mi; Paul D Thomas; Christina N Lessov-Schlaggar; Ruth Krasnow; Yungang He; Denise Nishita; Ruhong Jiang; Jennifer B McClure; Elizabeth Tildesley; Hyman Hops; Rachel F Tyndale; Neal L Benowitz; Caryn Lerman; Gary E Swan
Journal:  Neuropsychopharmacology       Date:  2009-06-03       Impact factor: 7.853

4.  EFBAT: exact family-based association tests.

Authors:  Kady Schneiter; James H Degnan; Christopher Corcoran; Xin Xu; Nan Laird
Journal:  BMC Genet       Date:  2007-12-20       Impact factor: 2.797

  4 in total

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