Events Calendar

21 Sep
Cathedral
Event Type

Defenses

Topic

Research

Target Audience

Faculty, Graduate Students, Postdocs

Website

https://calendar.pitt.edu/department/...

University Unit
Department of Human Genetics
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Dongjing Liu: Integrated Genome-Wide Analysis of Human Facial Morphology

Doctoral Candidate Dongjing Liu of the Department of Human Genetics will defend her dissertation entitled "Integrated Genome-Wide Analysis of Human Facial Morphology."

Advisor: John R. Shaffer, PhD

Committee Members: Daniel E. Weeks, Seth M. Weinberg, PhD, Jiebiao Wang, PhD

Abstract:

The human face is a highly multipartite structure resulting from the intrinsic complexity of facial morphogenesis and the intricate coordination of multiple factors that impact facial morphology over the lifespan. The strong genetic basis and high heritability of human facial morphology have been long appreciated, yet knowledge about the contributions of specific genes is limited. Knowledge of the genetic architecture of facial morphology is important for understanding craniofacial morphogenesis, and how these processes contribute to orofacial birth defects and craniofacial syndromes.

Genome-wide data on well-characterized human cohorts has great potential in generating novel insights in the post-GWAS era. Moving beyond the conventional single variant-single trait association analysis in GWAS, this study analyzed existing genome-wide data using three different approaches to glean insights into facial morphology, by leveraging state-of-the-art advances in 3D facial phenotype modeling and multivariate statistical approaches. This study presents critical steps to make full use of the genome-wide data in the post-GWAS era. The application of various statistical genetic approaches also provided opportunities to evaluate their usefulness for polygenic, multivariate, and morphological traits, which will help inform future study of other complex phenotypes.

Dial-In Information

Advance Registration Required for Event: https://pitt.co1.qualtrics.com/jfe/form/SV_eFkHmqx3V8Y6s97

Monday, September 21 at 1:00 p.m. to 3:00 p.m.

Virtual Event

Dongjing Liu: Integrated Genome-Wide Analysis of Human Facial Morphology

Doctoral Candidate Dongjing Liu of the Department of Human Genetics will defend her dissertation entitled "Integrated Genome-Wide Analysis of Human Facial Morphology."

Advisor: John R. Shaffer, PhD

Committee Members: Daniel E. Weeks, Seth M. Weinberg, PhD, Jiebiao Wang, PhD

Abstract:

The human face is a highly multipartite structure resulting from the intrinsic complexity of facial morphogenesis and the intricate coordination of multiple factors that impact facial morphology over the lifespan. The strong genetic basis and high heritability of human facial morphology have been long appreciated, yet knowledge about the contributions of specific genes is limited. Knowledge of the genetic architecture of facial morphology is important for understanding craniofacial morphogenesis, and how these processes contribute to orofacial birth defects and craniofacial syndromes.

Genome-wide data on well-characterized human cohorts has great potential in generating novel insights in the post-GWAS era. Moving beyond the conventional single variant-single trait association analysis in GWAS, this study analyzed existing genome-wide data using three different approaches to glean insights into facial morphology, by leveraging state-of-the-art advances in 3D facial phenotype modeling and multivariate statistical approaches. This study presents critical steps to make full use of the genome-wide data in the post-GWAS era. The application of various statistical genetic approaches also provided opportunities to evaluate their usefulness for polygenic, multivariate, and morphological traits, which will help inform future study of other complex phenotypes.

Dial-In Information

Advance Registration Required for Event: https://pitt.co1.qualtrics.com/jfe/form/SV_eFkHmqx3V8Y6s97

Monday, September 21 at 1:00 p.m. to 3:00 p.m.

Virtual Event

Event Type

Defenses

Topic

Research

Target Audience

Faculty, Graduate Students, Postdocs