Ming Li, PhD, Department of Epidemiology and Biostatistics, Indiana University Bloomington

Title TBA

Emerging studies using next-generation sequencing technology hold great promise for the identification and fine mapping of novel genetic variants, especially rare variants, contributing to complex human diseases. However, detecting these disease-susceptibility rare variants remains a great challenge because of the heterogeneous nature and low frequency of rare variants. Multiple rare variants within the same gene can independently influence the disease (i.e., allelic heterogeneity), and rare variants in different genes can also be involved in related pathways underlying diseases (i.e., locus heterogeneity). Advanced analytical methods are in great need to account for the genetic heterogeneity of complex human diseases. In this talk, we will introduce a family-based genetic random filed method for association analyses of sequencing data in family-based association studies. By utilizing information from family members, the proposed method is robust to population stratification and gains improved performance in presence of genetic heterogeneity. The proposed method is compared to other existing methods through simulation studies and real data applications for investigating the genetic etiology of complex diseases/traits.

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