The Annals of Applied Statistics

A spectral graph approach to discovering genetic ancestry

Ann B. Lee, Diana Luca, and Kathryn Roeder

Full-text: Open access

Abstract

Mapping human genetic variation is fundamentally interesting in fields such as anthropology and forensic inference. At the same time, patterns of genetic diversity confound efforts to determine the genetic basis of complex disease. Due to technological advances, it is now possible to measure hundreds of thousands of genetic variants per individual across the genome. Principal component analysis (PCA) is routinely used to summarize the genetic similarity between subjects. The eigenvectors are interpreted as dimensions of ancestry. We build on this idea using a spectral graph approach. In the process we draw on connections between multidimensional scaling and spectral kernel methods. Our approach, based on a spectral embedding derived from the normalized Laplacian of a graph, can produce more meaningful delineation of ancestry than by using PCA. The method is stable to outliers and can more easily incorporate different similarity measures of genetic data than PCA. We illustrate a new algorithm for genetic clustering and association analysis on a large, genetically heterogeneous sample.

Article information

Source
Ann. Appl. Stat., Volume 4, Number 1 (2010), 179-202.

Dates
First available in Project Euclid: 11 May 2010

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1273584452

Digital Object Identifier
doi:10.1214/09-AOAS281

Mathematical Reviews number (MathSciNet)
MR2758169

Zentralblatt MATH identifier
1189.62170

Keywords
Human genetics dimension reduction multidimensional scaling population structure spectral embedding

Citation

Lee, Ann B.; Luca, Diana; Roeder, Kathryn. A spectral graph approach to discovering genetic ancestry. Ann. Appl. Stat. 4 (2010), no. 1, 179--202. doi:10.1214/09-AOAS281. https://projecteuclid.org/euclid.aoas/1273584452


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