Family-Based Tests of Association in the Presence of Linkage
Stephen L. Lakea b✉, Deborah Blackerc d, Nan M. Lairdb
- aDepartment of Biostatistics, Harvard School of Public Health, Harvard University, Boston, MA 02115, USA. slake@hsph.harvard.edu
- bDepartments of Biostatistics Harvard University, Boston
- cDepartment of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston
- dDepartments of Epidemiology, Harvard School of Public Health Harvard University, Boston
- ✉Corresponding author
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Abstract
Linkage analysis may not provide the necessary resolution for identification of the genes underlying phenotypic variation. This is especially true for gene-mapping studies that focus on complex diseases that do not exhibit Mendelian inheritance patterns. One positional genomic strategy involves application of association methodology to areas of identified linkage. Detection of association in the presence of linkage localizes the gene(s) of interest to more-refined regions in the genome than is possible through linkage analysis alone. This strategy introduces a statistical complexity when family-based association tests are used: the marker genotypes among siblings are correlated in linked regions. Ignoring this correlation will compromise the size of the statistical hypothesis test, thus clouding the interpretation of test results. We present a method for computing the expectation of a wide range of association test statistics under the null hypothesis that there is linkage but no association. To standardize the test statistic, an empirical variance-covariance estimator that is robust to the sibling marker-genotype correlation is used. This method is widely applicable: any type of phenotypic measure or family configuration can be used. For example, we analyze a deletion in the A2M gene at the 5' splice site of "exon II" of the bait region in Alzheimer disease (AD) discordant sibships. Since the A2M gene lies in a chromosomal region (chromosome 12p) that consistently has been linked to AD, association tests should be conducted under the null hypothesis that there is linkage but no association.
TL;DRThis work presents a method for computing the expectation of a wide range of association test statistics under the null hypothesis that there is linkage but no association, and uses an empirical variance-covariance estimator that is robust to the sibling marker-genotype correlation. (Semantic Scholar)