A Multi-Locus Likelihood Method for Assessing Parent-of-Origin Effects Using Case-Control Mother-Child Pairs
ABSTRACT Parent-of-origin effects have been pointed out to be one plausible source of the heritability that was unexplained by genome-wide association studies. Here, we consider a case-control mother-child pair design for studying parent-of-origin effects of offspring genes on neonatal/early-life disorders or pregnancy-related conditions. In contrast to the standard case-control design, the case-control mother-child pair design contains valuable parental information and therefore permits powerful assessment of parent-of-origin effects. Suppose the region under study is in Hardy-Weinberg equilibrium, inheritance is Mendelian at the diallelic locus under study, there is random mating in the source population, and the SNP under study is not related to risk for the phenotype under study because of linkage disequilibrium (LD) with other SNPs. Using a maximum likelihood method that simultaneously assesses likely parental sources and estimates effect sizes of the two offspring genotypes, we investigate the extent of power increase for testing parent-of-origin effects through the incorporation of genotype data for adjacent markers that are in LD with the test locus. Our method does not need to assume the outcome is rare because it exploits supplementary information on phenotype prevalence. Analysis with simulated SNP data indicates that incorporating genotype data for adjacent markers greatly help recover the parent-of-origin information. This recovery can sometimes substantially improve statistical power for detecting parent-of-origin effects. We demonstrate our method by examining parent-of-origin effects of the gene PPARGC1A on low birth weight using data from 636 mother-child pairs in the Jerusalem Perinatal Study.
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ABSTRACT: For qualitative traits and diallelic marker loci, the pedigree disequilibrium test (PDT) based on general pedigrees and its extension (Monte Carlo PDT (MCPDT)) for dealing with missing genotypes are simple and powerful tests for association. There is an increasing interest of incorporating imprinting into association analysis. However, PDT and MCPDT do not take account of the information on imprinting effects in the analysis, which may reduce their test powers when the effects are present. On the other hand, the transmission disequilibrium test with imprinting (TDTI*) combines imprinting into the mapping of association variants. However, TDTI* only accommodates two-generation nuclear families and thus is not suitable for extended pedigrees. In this article, we first extend PDT to incorporate imprinting and propose PDTI for complete pedigrees (no missing genotypes). To fully utilize pedigrees with missing genotypes, we further develop the Monte Carlo PDTI (MCPDTI) statistic based on Monte Carlo sampling and estimation. Both PDTI and MCPDTI are derived in a two-stage framework. Simulation study shows that PDTI and MCPDTI control the size well under the null hypothesis of no association and are more powerful than PDT and TDTI* (based on a sample of nuclear families randomly selecting from pedigrees) when imprinting effects exist.Journal of Human Genetics advance online publication, 18 December 2014; doi:10.1038/jhg.2014.109.Journal of Human Genetics 12/2014; 60(2). DOI:10.1038/jhg.2014.109 · 2.53 Impact Factor