Publication Details
Title: A Model-Based Approach for Analysis of Spatial Structure in Genetic Data
Author: W. Yang, J. Novembre, E. Eskin, and E. Halperin
Group: Algorithms
Date: November 2012
PDF: [Not available online]
Overview:
Characterizing genetic diversity within and between populations has broad applications in studies of human disease and evolution. Two key step towards this objective are spatially global ancestry inference, which aims at predicting geographical locations for the ancestries of individual, and spatially local ancestry inference, which aims at predicting the geographical locations for chromosome segments, or ancestry blocks. We propose a new approach, SPALL (SPatial Ancestry analysis LocaL), for solving the two inference problems in a unified probabilistic model. This model takes linkage disequilibrium into account and can be solved efficiently by Expectation Maximization (EM) algorithm in conjunction with forward-backward algorithm. This new method allows us to assign geographical locations for parents, grandparents, and ancestries from more generations ago of an given individual. It also allows us to assign geographical locations for each locus-specific variant. We analyzed a European and a worldwide dataset, and showed that the SPALL can actually predict locations with a high accuracy. The proposed model is build as a generalization of our recently published work called Spatial Ancestry Analysis (SPA), which explicitly models the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space. The method allows us to assign an individual, or an admixed individual to geographical locations instead of predefined categories of population. A software including all the proposed methods is freely available in our website http://genetics.cs.ucla.edu/spa.
Bibliographic Information:
Presented at the Annual Meeting of the American Society of Human Genetics, San Francisco, California
Bibliographic Reference:
W. Yang, J. Novembre, E. Eskin, and E. Halperin. A Model-Based Approach for Analysis of Spatial Structure in Genetic Data. Presented at the Annual Meeting of the American Society of Human Genetics, San Francisco, California, November 2012
Author: W. Yang, J. Novembre, E. Eskin, and E. Halperin
Group: Algorithms
Date: November 2012
PDF: [Not available online]
Overview:
Characterizing genetic diversity within and between populations has broad applications in studies of human disease and evolution. Two key step towards this objective are spatially global ancestry inference, which aims at predicting geographical locations for the ancestries of individual, and spatially local ancestry inference, which aims at predicting the geographical locations for chromosome segments, or ancestry blocks. We propose a new approach, SPALL (SPatial Ancestry analysis LocaL), for solving the two inference problems in a unified probabilistic model. This model takes linkage disequilibrium into account and can be solved efficiently by Expectation Maximization (EM) algorithm in conjunction with forward-backward algorithm. This new method allows us to assign geographical locations for parents, grandparents, and ancestries from more generations ago of an given individual. It also allows us to assign geographical locations for each locus-specific variant. We analyzed a European and a worldwide dataset, and showed that the SPALL can actually predict locations with a high accuracy. The proposed model is build as a generalization of our recently published work called Spatial Ancestry Analysis (SPA), which explicitly models the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space. The method allows us to assign an individual, or an admixed individual to geographical locations instead of predefined categories of population. A software including all the proposed methods is freely available in our website http://genetics.cs.ucla.edu/spa.
Bibliographic Information:
Presented at the Annual Meeting of the American Society of Human Genetics, San Francisco, California
Bibliographic Reference:
W. Yang, J. Novembre, E. Eskin, and E. Halperin. A Model-Based Approach for Analysis of Spatial Structure in Genetic Data. Presented at the Annual Meeting of the American Society of Human Genetics, San Francisco, California, November 2012