Browsing by Author "Jiang, Yu"

Confounding due to population substructure is always a concern in genetic association studies. While methods have been proposed to adjust for population stratification in the context of common variation, it is unclear how well these approaches will work when interrogating rare variation. Family-based association tests can be constructed that are robust to population stratification. For example, when considering a quantitative trait, a linear model can be used that decomposes genetic effects into between and within-family components and a test of the within-family component is robust to population stratification. However, this within-family test ignores between-family information potentially leading to a loss of power. Here, we propose a family-based two-stage rare-variant test for quantitative traits. We first construct a weight for each variant within a gene, or other genetic unit, based on score tests of between-family effect parameters. These weights are then used to combine variants using score tests of within-family effect parameters. Since the between-family and within-family tests are orthogonal under the null hypothesis, this two-stage approach can increase power while still maintaining validity. Using simulation, we show that this two-stage test can significantly improve power while correctly maintaining type I error. We further show that the two-stage approach maintains the robustness to population stratification of the within-family test and we illustrate this using simulations reflecting samples comprised of continental and closely related subpopulations.

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Disease-gene mapping plays an important role in improving the development of medical science. As with the development of Next Generation Sequencing technologies, mapping disease genes through rare genetic variants become economic and reliable. De novo mutations as the most extreme form of rare variants played an important role in the occurrence of complex diseases. To detect de novo mutations, case-parent trios are used to perform the sequencing studies. This case-parent design provides the chance to detect disease-causal genes from both de novo mutations and inherited mutations. We proposed three novel methods to map disease genes according to de novo mutation load (fitDNM), allele transmission rate (rvTDT) and compound heterozygous and recessive genes (coreTDT) separately to maximize the statistical power of analysis in case-parent trios. These three methods are then applied to analyze neurodevelopmental/neuropsychiatric disorders. The analysis with fitDNM provides strong statistical evidence supporting two potentially causal genes: SUV420H1 in autism spectral disorder and TRIO in a combined analysis of the four neurodevelopmental/neuropsychiatric disorders investigated. The application of rvTDT on epileptic encephalopathy (EE) trios find that dominant (or additive) inherited rare variants are unlikely to play a substantial role within EE genes previously identified through de novo mutation studies.