Nature Genetics37, 683 - 691 (2005)
Published online: 5 June 2005; | doi:10.1038/ng1582
Genomic screening and replication using the same data set in family-based association testing
Kristel Van Steen1, Matthew B McQueen2, Alan Herbert3, Benjamin Raby4, Helen Lyon4, 5, Dawn L DeMeo4, Amy Murphy1, Jessica Su2, Soma Datta4, Carsten Rosenow6, Michael Christman3, Edwin K Silverman4, Nan M Laird1, Scott T Weiss4
& Christoph Lange1, 4
1
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts
02115, USA.
2
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
02115, USA.
3
Department of Genetics and Genomics, Boston University School of Medicine, Boston, Massachusetts
02115, USA.
4
Channing Laboratory, Harvard Medical School, Boston, Massachusetts
02115, USA.
5
Division of Genetics, Children's Hospital, Boston, Massachusetts
02115, USA.
6
Genomics Collaboration Genotyping, Affymetrix, Inc., Santa Clara, California
95051, USA.
The Human Genome Project and its spin-offs are making it increasingly feasible to determine the genetic basis of complex traits using genome-wide association studies. The statistical challenge of analyzing such studies stems from the severe multiple-comparison problem resulting from the analysis of thousands of SNPs. Our methodology for genome-wide family-based association studies, using single SNPs or haplotypes, can identify associations that achieve genome-wide significance. In relation to developing guidelines for our screening tools, we determined lower bounds for the estimated power to detect the gene underlying the disease-susceptibility locus, which hold regardless of the linkage disequilibrium structure present in the data. We also assessed the power of our approach in the presence of multiple disease-susceptibility loci. Our screening tools accommodate genomic control and use the concept of haplotype-tagging SNPs. Our methods use the entire sample and do not require separate screening and validation samples to establish genome-wide significance, as population-based designs do.
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