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Studies of Natural Selection in the Era of Next-generation Sequencing | OMICS International | Abstract
ISSN: 2329-9002

Journal of Phylogenetics & Evolutionary Biology
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Review Article

Studies of Natural Selection in the Era of Next-generation Sequencing

Momiao Xiong1*, Dan Xie1,2, Pengfei Hu3 and Zheng Hou3
1Human Genetics Center, Division of Biostatistics, The University of Texas Health Science Center at Houston, P.O. Box 20186, Houston, Texas 77225, USA
2College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, Hubei, 430065, China
3State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200433, China
Corresponding Author : Dr. Momiao Xiong
Human Genetics Center
The University of Texas Health Science Center at Houston
P.O. Box 20186, Houston, Texas 77225, USA
Tel: 713-500-9894
Fax: 713-500-0900
E-mail: [email protected]
Received April 01, 2013; Accepted April 25, 2013; Published April 29, 2013
Citation: Xiong M, Xie D, Hu P, Hou Z (2013) Studies of Natural Selection in the Era of Next-generation Sequencing. J Phylogen Evolution Biol 1:104. doi:10.4172/2329-9002-1-104
Copyright: © 2013 Xiong M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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A deeper understanding of positive selection is of fundamental importance. Evolutionary pressures acting on the genome as a whole and the specific role of environmental pressures on divergence shape the natural selection. However, despite the intense interest in genome-wide scans, a coherent study of recent human evolutionary history has yet to emerge due to the limitations of data, models, and analytical tools. Fast and cheaper Next Generation Sequencing (NGS) technologies will generate unprecedentedly massive and highly-dimensional genomic variation data. NGS will offer unprecedented opportunities for population genetic and natural selection studies, but also raise great challenges. In the conventional population genetics, most researches have primarily focused on natural selection acting on a single locus. Little attention has been paid to determining how the natural selection acts on multiple interacted genes in response to environmental perturbation. In addition, the current paradigm for analysis of natural selection on gene-expression is to use a single value of summarizing statistic to represent gene expression level and overlook all information on expression difference in exons, genomic position and alleles. To address these critical limitations, we develop a unified framework and statistical methods for genome-wide scans for natural selection and investigation of natural selection on pathways. To explore observed expression variation in exons or in genomic position across the genes, we extend one dimensional diffusion process to multidimensional diffusion process and a single variate stochastic differential equation to multivariate stochastic differential equations. We then use the extended multidimensional diffusion processes to model the evolution of gene-expression acted by natural selection. We hope that the present new development of natural selection analysis for NGS data will open a new avenue for natural selection analysis with NGS data.