Integration of common and rare genetic variation across complex traits and neuropsychiatric disorders

Research into the genetic basis of human disease has progressed rapidly over the past 20 years. Genome-wide association studies have identified thousands of links between common variants and phenotypic outcomes. More recently, whole exome and genome sequencing has catalogued patterns of ultra-rare variation and associations with complex traits and diseases. However, there has been relatively little progress integrating the insights from studies of common and rare genetic variation. In this thesis, we present methods and applications aimed at bridging this divide. Chapter 1 describes background and motivation. In Chapter 2, we present the Abstract Mediation Model (AMM), a novel statistical approach for estimating gene- mediated common-variant heritability without measured molecular phenotypes. In Chapter 3, we introduce Burden Heritability Regression for estimating the genetic architecture of rare variation, and we apply the method to exome sequencing results from nearly 400,000 UK Biobank exomes. This analysis leverages AMM in presenting a systematic comparison of common and rare variation across complex traits and common diseases. Chapters 4 and 5 analyze the joint influence of common and rare variation in autism spectrum disorder. Chapter 4 presents evidence for statistical and functional convergence of common and rare variant influences on autism arising from the p-arm of chromosome 16, a region harboring the cryptic recurrent copy number variant at 16p11.2. In Chapter 5, we demonstrate that common variant influences on autism vary by both rare variant burden and whether the diagnosed individual is male or female – reflecting the “female protective effect”. Chapter 6 presents a discussion and future directions. In summary, this thesis offers unifying frameworks for defining the joint influences of common and rare genetic variation across complex traits and common diseases.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Grantor

Harvard University Graduate School of Arts and Sciences

Year dc:date.issued

2022

Author dc:creator

• Weiner, Daniel

Advisor dc:contributor.advisor

• Robinson, Elise

Committee members dc:contributor.committeemember

• Talkowski, Michael
• Walsh, Christopher
• Patel, Chirag
• State, Matt

• Bioinformatics
• Biology
• Psychology

Language dc:language.iso

en

Dc Identifier Other

29394077

OAI identifier oai:identifier

oai:dash.harvard.edu:1/37373729