Seminar – Qiongshi Lu, PhD – “Transcriptome-Wide Transmission Disequilibrium Analysis Identifies Novel Risk Genes for Autism Spectrum Disorder”

Qiongshi Lu, PhD
University of Wisconsin–Madison

About the Talk: Large-scale genetic studies have identified numerous point mutations and copy number variants implicating risk of autism spectrum disorder (ASD). However, these genetic variations are only found in a small proportion of ASD probands. Recent advances in genome-wide association studies (GWAS) have highlighted the critical involvement of common genetic variants in the etiology of ASD, but our understanding of their mechanistic roles is incomplete. Here, we introduce a novel statistical framework to quantify the transmission disequilibrium of genetically regulated gene expression from parents to offspring. We generate pseudo-sibling controls for each proband based on phased parental genotypes, apply genetic prediction models to impute gene expression levels in multiple tissues, and use conditional logistic regression to identify over- or under-transmission of imputed expression activities. We applied our method to conduct a transcriptome-wide association study using 7,805 ASD proband-parents trios, and replicated our findings using independent case-control samples from the iPSYCH project (N=35,740). We identified significant transmission disequilibrium of 18 genes including POU3F2 (P=2.1e-7), a key transcription factor involved in neurogenesis. Although the identified genes showed minimal overlap with loss-of-function intolerant genes or known ASD risk genes enriched for pathogenic mutations, we demonstrate that the downstream genes regulated by POU3F2 are significantly enriched for ASD risk genes. These results provide new insights into the interplay between common regulatory variants and rare pathogenic mutations in ASD.

About the Speaker: Qiongshi “Q” Lu received his B.S. in mathematics from Tsinghua University in 2012 and Ph.D. in biostatistics from Yale University in 2017. Lu’s research focuses on developing statistical and computational methods to functionally annotate the human genome and dissect the genetic architecture of complex human diseases. In particular, he is interested in leveraging functional annotation information in genetic association studies to improve functional variant fine-mapping, genetic risk prediction, and genetic correlation estimation. In 2017 he was appointed as an Assistant Professor of Biostatistics in the Department of Biostatistics and Medical Informatics at University of Wisconsin-Madison.

For Further Information: Contact Teresa Palumbo at 608.263.5837 or palumbo@waisman.wisc.edu

The seminar series is funded by the John D. Wiley Conference Center Fund, the Friends of the Waisman Center and NIH grant U54 HD090256.