Refining Genetically Inferred Relationships Using Treelet Covariance Smoothing

• Activity Number: 383
• Type: Topic Contributed
• Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Ann Lee*+
• Companies: Carnegie Mellon University
• Keywords: covariance estimation ; cryptic relatedness ; genome-wide association ; heritability ; kinship

Abstract

Recent technological advances coupled with large sample sets have uncovered many factors underlying the genetic basis of traits and the predisposition to complex disease, but much is left to discover. A common thread to most genetic investigations is familial relationships. Close relatives can be identified from family records, and more distant relatives can be inferred from large panels of genetic markers. Unfortunately these empirical estimates can be noisy, especially regarding distant relatives. We propose a new method for denoising genetically inferred relationship matrices by exploiting the underlying structure due to hierarchical groupings of correlated individuals. The approach, which we call Treelet Covariance Smoothing, employs a multi-scale decomposition of covariance matrices to improve estimates of pairwise relationships. On both simulated and real data, we show that smoothing leads to better estimates of the relatedness amongst distantly related individuals. We also show that by using smoothed relationship matrices we can estimate heritability of body mass index quite accurately from a large genome-wide association study.