Abstract
We derive the analytical mean and variance of the score test statistic in gene-dropping simulations and approximate the null distribution of the test statistic by a normal distribution. We provide insights into the gene-dropping test by decomposing the test statistic into two components: the first component provides information about linkage, and the second component provides information about fine mapping under the linkage peak. We demonstrate our theoretical findings by applying the gene-dropping test to the simulated data set from Genetic Analysis Workshop 18 and comparing its performance with existing population and family-based association tests.
Highlights
When testing genotype-phenotype association using individuals from extended families, one has to account for correlations in genotypes and/or phenotypes between related individuals
The gene-dropping method is straightforward to implement and applies to all pedigree structures, but it is computationally intensive and is impractical to use when dealing with millions of singlenucleotide polymorphisms (SNPs)
Preprocessing of phenotype data We focused on the analysis of the quantitative trait systolic blood pressure (SBP) in the simulated data set 1
Summary
When testing genotype-phenotype association using individuals from extended families, one has to account for correlations in genotypes and/or phenotypes between related individuals. One simple and effective method to account for genotype correlations is to simulate the null genotype distribution by gene dropping [1], which is simulating founder alleles according to estimated allele frequencies and dropping these alleles down the pedigrees according to random segregation of gametes (i.e., Mendel’s first law). We derive the analytical mean and variance of the score test statistic under the gene-dropping setting and approximate the gene-dropping null distribution of the test statistic by a normal distribution with the analytically derived mean and variance Using this normal approximation, the gene-dropping test becomes computationally efficient and can be applied to millions of SNPs. we provide insights into the genedropping test by decomposing the test statistic into two components: the first component resembles a quantity frequently used in variance-component based linkage tests and provides information for linkage, and the second component provides information for fine mapping under the linkage peak. Our decomposition provides an explicit separation of linkage and association information in a family-based study
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