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Activity Number: 527 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #304974
Title: A Novel Statistical Framework for Trio-Based Transcriptome-Wide Association Study
Author(s): Kunling Huang*
Companies: University of Wisconsin-Madison, Statistics Department
Keywords: autism spectrum disorder; conditional logistic regression; common single nucleotide polymorphism; transcriptome-wide association study; neurodevelopmental disease

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with complex genetic architecture. Although rare genetic variants have been implicated in ASD – previous studies have identified numerous genes enriched for de novo mutations and copy number variants, little is known about the role of common variants in ASD etiology. Here, we introduce a novel statistical framework to conduct transcriptome-wide association study in proband-parent trios. Based on parental genotypes, we generate pseudo-sibling controls for each proband through genotype phasing and meiosis simulation. Conditional logistic regression is used to identify associations between autism and brain gene expression. We applied this method to 7,805 ASD trios from three independent studies and identified a total of 4 significant associations after Bonferroni correction (p< 5e-7). Applied to 3245 control trios, our method did not identify significant associations, showing well-controlled type-I error. The newly-identified genes did not overlap with known ASD genes enriched for damaging de novo mutations, hinting at distinct biological processes underlying common and rare genetic variations in ASD.

Authors who are presenting talks have a * after their name.

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