Combined linkage and family-based association analysis improves candidate gene detection in Genetic Analysis Workshop 18 simulation data

Adjusting for Familial Relatedness in the Analysis of GWAS Data

Relatedness within a sample can be of ancient (population stratification) or recent (familial structure) origin, and can either be known (pedigree data) or unknown (cryptic relatedness). All of these forms of familial relatedness have the potential to confound the results of genome-wide association studies. This chapter reviews the major methods available to researchers to adjust for the biases introduced by relatedness and maximize power to detect associations. The advantages and disadvantages of different methods are presented with reference to elements of study design, population characteristics, and computational requirements.

Complex pedigrees in the sequencing era: to track transmissions or decorrelate?

Next-generation sequencing (NGS) studies are becoming commonplace, and the NGS field is continuing to develop rapidly. Analytic methods aimed at testing for the various roles that genetic susceptibility plays in disease are also rapidly being developed and optimized. Studies that incorporate large, complex pedigrees are of particular importance because they provide detailed information about inheritance patterns and can be analyzed in a variety of complementary ways. The nine contributions from our Genetic Analysis Workshop 18 working group on family-based tests of association for rare variants using simulated data examined analytic methods for testing genetic association using whole-genome sequencing data from 20 large pedigrees with 200 phenotype simulation replicates. What distinguishes the approaches explored is how the complexities of analyzing familial genetic data were handled. Here, we explore the methods that either harness inheritance patterns and transmission information or attempt to adjust for the correlation between family members in order to utilize computationally and conceptually simpler statistical testing procedures. Although directly comparing these two classes of approaches across contributions is difficult, we note that the two classes balance robustness to population stratification and computational complexity (the transmission-based approaches) with simplicity and increased power, assuming no population stratification or proper adjustment for it (decorrelation approaches).