Flexible Mixture Model Approaches That Accommodate Footprint Size Variability for Robust Detection of Balancing Selection.
Flexible Mixture Model Approaches That Accommodate Footprint Size Variability for Robust Detection of Balancing Selection.
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DOI:
10.1093/molbev/msaa134
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发表时间:
2020-11-01
影响因子:
10.7
通讯作者:
DeGiorgio M
中科院分区:
文献类型:
--
作者:
Cheng X;DeGiorgio M
Long-term balancing selection typically leaves narrow footprints of increased genetic diversity, and therefore most detection approaches only achieve optimal performances when sufficiently small genomic regions (i.e., windows) are examined. Such methods are sensitive to window sizes and suffer substantial losses in power when windows are large. Here, we employ mixture models to construct a set of five composite likelihood ratio test statistics, which we collectively term B statistics. These statistics are agnostic to window sizes and can operate on diverse forms of input data. Through simulations, we show that they exhibit comparable power to the best-performing current methods, and retain substantially high power regardless of window sizes. They also display considerable robustness to high mutation rates and uneven recombination landscapes, as well as an array of other common confounding scenarios. Moreover, we applied a specific version of the B statistics, termed B2, to a human population-genomic data set and recovered many top candidates from prior studies, including the then-uncharacterized STPG2 and CCDC169–SOHLH2, both of which are related to gamete functions. We further applied B2 on a bonobo population-genomic data set. In addition to the MHC-DQ genes, we uncovered several novel candidate genes, such as KLRD1, involved in viral defense, and SCN9A, associated with pain perception. Finally, we show that our methods can be extended to account for multiallelic balancing selection and integrated the set of statistics into open-source software named BalLeRMix for future applications by the scientific community.
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影响因子:
64.8
作者:
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者:
Montgomery SB
影响因子:
3.2
作者:
de Groot, Natasja G.;Heijmans, Corrine M. C.;Bontrop, Ronald E.
通讯作者:
Bontrop, Ronald E.
DOI:
10.1111/j.2517-6161.1995.tb02031.x
发表时间:
1995-01-01
影响因子:
5.8
作者:
BENJAMINI, Y;HOCHBERG, Y
通讯作者:
HOCHBERG, Y
影响因子:
3.3
作者:
Bitarello BD;de Filippo C;Teixeira JC;Schmidt JM;Kleinert P;Meyer D;Andrés AM
通讯作者:
Andrés AM
DOI:
10.1126/science.aag2602
发表时间:
2016-10-28
期刊:
Science (New York, N.Y.)
影响因子:
--
作者:
de Manuel M;Kuhlwilm M;Frandsen P;Sousa VC;Desai T;Prado-Martinez J;Hernandez-Rodriguez J;Dupanloup I;Lao O;Hallast P;Schmidt JM;Heredia-Genestar JM;Benazzo A;Barbujani G;Peter BM;Kuderna LF;Casals F;Angedakin S;Arandjelovic M;Boesch C;Kühl H;Vigilant L;Langergraber K;Novembre J;Gut M;Gut I;Navarro A;Carlsen F;Andrés AM;Siegismund HR;Scally A;Excoffier L;Tyler-Smith C;Castellano S;Xue Y;Hvilsom C;Marques-Bonet T
通讯作者:
Marques-Bonet T