A spatially aware likelihood test to detect sweeps from haplotype distributions.
A spatially aware likelihood test to detect sweeps from haplotype distributions.
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一种空间意识到的可能性测试，可检测单倍型分布的扫描。
DOI：
10.1371/journal.pgen.1010134
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发表时间：
2022-04
期刊：
影响因子：
4.5
通讯作者：
中科院分区：
文献类型：
--
作者：
基因组中正选择的推断是进化基因组学中的一个重要问题。通过识别基因组中含有适应性突变的假定区域，我们能够了解生物体的生物学及其进化历史。在这里，我们介绍了一种复合似然方法，确定最近完成或正在进行的积极选择，通过搜索极端扭曲的单倍型频谱的空间分布沿着基因组相对于全基因组的期望作为中立。此外，该方法同时推断了扫描的两个参数：扫描单倍型的数量和扫描的“宽度”，这与选择的强度和时机有关。我们证明，这种方法优于领先的基于单体型的选择统计，虽然在低重组区域的强信号值得额外的审查。作为阳性对照，我们将其应用于两个经过充分研究的人群，从1000个基因组计划，并检查单倍型频谱模式在LCT和MHC基因座。我们还将其应用于在纽约市采样的棕色大鼠的数据集，并确定与嗅觉感知相关的基因。为了方便使用这种方法，我们已经在用户友好的开源软件中实现了它。识别包含适应性变异的基因组区域是进化生物学的基本兴趣，提供了对生物体历史和生物学的深入了解。当正选择是最近或正在进行的，我们希望找到基因组模式，如高频率的单倍型和低遗传多样性的适应位点附近。在这里，我们开发了一个统计，以确定这些地区的基础上从背景分布的单倍型频谱的失真。我们评估了许多现实的设置下的利益，以pharmacists和证明其上级性能相对于其他单倍型为基础的选择统计量的统计性能。我们也将此统计应用于真实的群体遗传数据。作为阳性对照，我们探索了两个研究充分的基因座，LCT和MHC，在欧洲和非洲人口显示出强有力的证据选择。我们还将这一统计数据应用于城市棕色大鼠种群的基因组，在那里我们发现了嗅觉感知基因适应的证据。我们发布了用户友好的软件来实现这一统计数据。
The inference of positive selection in genomes is a problem of great interest in evolutionary genomics. By identifying putative regions of the genome that contain adaptive mutations, we are able to learn about the biology of organisms and their evolutionary history. Here we introduce a composite likelihood method that identifies recently completed or ongoing positive selection by searching for extreme distortions in the spatial distribution of the haplotype frequency spectrum along the genome relative to the genome-wide expectation taken as neutrality. Furthermore, the method simultaneously infers two parameters of the sweep: the number of sweeping haplotypes and the “width” of the sweep, which is related to the strength and timing of selection. We demonstrate that this method outperforms the leading haplotype-based selection statistics, though strong signals in low-recombination regions merit extra scrutiny. As a positive control, we apply it to two well-studied human populations from the 1000 Genomes Project and examine haplotype frequency spectrum patterns at the LCT and MHC loci. We also apply it to a data set of brown rats sampled in NYC and identify genes related to olfactory perception. To facilitate use of this method, we have implemented it in user-friendly open source software. Identifying regions of the genome that contain adaptive variation is of fundamental interest in evolutionary biology, providing insight into an organism’s history and biology. When positive selection is recent or ongoing, we expect to find genomic patterns such as high frequency haplotypes and low genetic diversity in the vicinity of the adaptive locus. Here we develop a statistic to identify these regions based on distortions of the haplotype frequency spectrum from a background distribution. We evaluate the performance of this statistic under numerous realistic settings of interest to empiricists and demonstrate its superior performance relative to other haplotype-based selection statistics. We also apply this statistic to real population-genetic data. As a positive control, we explore two well-studied loci, LCT and MHC, in a European and an African human population that show strong evidence for selection. We also apply this statistic to the genomes of an urban brown rat population, where we uncover evidence for adaptation in olfactory perception genes. We release user-friendly software implementing this statistic.
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影响因子：
64.8
作者：
Frazer, Kelly A.;Ballinger, Dennis G.;Cox, David R.;Hinds, David A.;Stuve, Laura L.;Gibbs, Richard A.;Belmont, John W.;Boudreau, Andrew;Hardenbol, Paul;Leal, Suzanne M.;Pasternak, Shiran;Wheeler, David A.;Willis, Thomas D.;Yu, Fuli;Yang, Huanming;Zeng, Changqing;Gao, Yang;Hu, Haoran;Hu, Weitao;Li, Chaohua;Lin, Wei;Liu, Siqi;Pan, Hao;Tang, Xiaoli;Wang, Jian;Wang, Wei;Yu, Jun;Zhang, Bo;Zhang, Qingrun;Zhao, Hongbin;Zhao, Hui;Zhou, Jun;Gabriel, Stacey B.;Barry, Rachel;Blumenstiel, Brendan;Camargo, Amy;Defelice, Matthew;Faggart, Maura;Goyette, Mary;Gupta, Supriya;Moore, Jamie;Nguyen, Huy;Onofrio, Robert C.;Parkin, Melissa;Roy, Jessica;Stahl, Erich;Winchester, Ellen;Ziaugra, Liuda;Altshuler, David;Shen, Yan;Yao, Zhijian;Huang, Wei;Chu, Xun;He, Yungang;Jin, Li;Liu, Yangfan;Shen, Yayun;Sun, Weiwei;Wang, Haifeng;Wang, Yi;Wang, Ying;Xiong, Xiaoyan;Xu, Liang;Waye, Mary M. Y.;Tsui, Stephen K. W.;Wong, J. Tze-Fei;Galver, Luana M.;Fan, Jian-Bing;Gunderson, Kevin;Murray, Sarah S.;Oliphant, Arnold R.;Chee, Mark S.;Montpetit, Alexandre;Chagnon, Fanny;Ferretti, Vincent;Leboeuf, Martin;Olivier, Jean-Franccois;Phillips, Michael S.;Roumy, Stephanie;Sallee, Clementine;Verner, Andrei;Hudson, Thomas J.;Kwok, Pui-Yan;Cai, Dongmei;Koboldt, Daniel C.;Miller, Raymond D.;Pawlikowska, Ludmila;Taillon-Miller, Patricia;Xiao, Ming;Tsui, Lap-Chee;Mak, William;Song, You Qiang;Tam, Paul K. H.;Nakamura, Yusuke;Kawaguchi, Takahisa;Kitamoto, Takuya;Morizono, Takashi;Nagashima, Atsushi;Ohnishi, Yozo;Sekine, Akihiro;Tanaka, Toshihiro;Tsunoda, Tatsuhiko;Deloukas, Panos;Bird, Christine P.;Delgado, Marcos;Dermitzakis, Emmanouil T.;Gwilliam, Rhian;Hunt, Sarah;Morrison, Jonathan;Powell, Don;Stranger, Barbara E.;Whittaker, Pamela;Bentley, David R.;Daly, Mark J.;de Bakker, Paul I. W.;Barrett, Jeff;Chretien, Yves R.;Maller, Julian;McCarroll, Steve;Patterson, Nick;Pe'er, Itsik;Price, Alkes;Purcell, Shaun;Richter, Daniel J.;Sabeti, Pardis;Saxena, Richa;Schaffner, Stephen F.;Sham, Pak C.;Varilly, Patrick;Altshuler, David;Stein, Lincoln D.;Krishnan, Lalitha;Smith, Albert Vernon;Tello-Ruiz, Marcela K.;Thorisson, Gudmundur A.;Chakravarti, Aravinda;Chen, Peter E.;Cutler, David J.;Kashuk, Carl S.;Lin, Shin;Abecasis, Goncalo R.;Guan, Weihua;Li, Yun;Munro, Heather M.;Qin, Zhaohui Steve;Thomas, Daryl J.;McVean, Gilean;Auton, Adam;Bottolo, Leonardo;Cardin, Niall;Eyheramendy, Susana;Freeman, Colin;Marchini, Jonathan;Myers, Simon;Spencer, Chris;Stephens, Matthew;Donnelly, Peter;Cardon, Lon R.;Clarke, Geraldine;Evans, David M.;Morris, Andrew P.;Weir, Bruce S.;Tsunoda, Tatsuhiko;Johnson, Todd A.;Mullikin, James C.;Sherry, Stephen T.;Feolo, Michael;Skol, Andrew
通讯作者：
Skol, Andrew
影响因子：
3.7
作者：
Derrien T;Estellé J;Marco Sola S;Knowles DG;Raineri E;Guigó R;Ribeca P
通讯作者：
Ribeca P
影响因子：
7
作者：
Chen, Hua;Patterson, Nick;Reich, David
通讯作者：
Reich, David
影响因子：
9.8
作者：
Bersaglieri, T;Sabeti, PC;Hirschhorn, JN
通讯作者：
Hirschhorn, JN
影响因子：
7.7
作者：
Adrion, Jeffrey R.;Cole, Christopher B.;Kern, Andrew D.
通讯作者：
Kern, Andrew D.