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Article: Deciphering the genomic architecture of the stickleback brain with a novel multilocus gene-mapping approach

TitleDeciphering the genomic architecture of the stickleback brain with a novel multilocus gene-mapping approach
Authors
KeywordsSNP
evolution
GWAS
de-biased LASSO
brain size
quantitative trait loci
multilocus mapping
Issue Date2017
Citation
Molecular Ecology, 2017, v. 26, n. 6, p. 1557-1575 How to Cite?
Abstract© 2017 John Wiley & Sons Ltd Quantitative traits important to organismal function and fitness, such as brain size, are presumably controlled by many small-effect loci. Deciphering the genetic architecture of such traits with traditional quantitative trait locus (QTL) mapping methods is challenging. Here, we investigated the genetic architecture of brain size (and the size of five different brain parts) in nine-spined sticklebacks (Pungitius pungitius) with the aid of novel multilocus QTL-mapping approaches based on a de-biased LASSO method. Apart from having more statistical power to detect QTL and reduced rate of false positives than conventional QTL-mapping approaches, the developed methods can handle large marker panels and provide estimates of genomic heritability. Single-locus analyses of an F2 interpopulation cross with 239 individuals and 15 198, fully informative single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with variation in stickleback brain size traits. Many of these loci were in strong linkage disequilibrium (LD) with each other, and consequently, a multilocus mapping of individual SNPs, accounting for LD structure in the data, recovered only four significant QTL. However, a multilocus mapping of SNPs grouped by linkage group (LG) identified 14 LGs (1–6 depending on the trait) that influence variation in brain traits. For instance, 17.6% of the variation in relative brain size was explainable by cumulative effects of SNPs distributed over six LGs, whereas 42% of the variation was accounted for by all 21 LGs. Hence, the results suggest that variation in stickleback brain traits is influenced by many small-effect loci. Apart from suggesting moderately heritable (h2 ≈ 0.15–0.42) multifactorial genetic architecture of brain traits, the results highlight the challenges in identifying the loci contributing to variation in quantitative traits. Nevertheless, the results demonstrate that the novel QTL-mapping approach developed here has distinctive advantages over the traditional QTL-mapping methods in analyses of dense marker panels.
Persistent Identifierhttp://hdl.handle.net/10722/292995
ISSN
2023 Impact Factor: 4.5
2023 SCImago Journal Rankings: 1.705
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Zitong-
dc.contributor.authorGuo, Baocheng-
dc.contributor.authorYang, Jing-
dc.contributor.authorHerczeg, Gábor-
dc.contributor.authorGonda, Abigél-
dc.contributor.authorBalázs, Gergely-
dc.contributor.authorShikano, Takahito-
dc.contributor.authorCalboli, Federico C.F.-
dc.contributor.authorMerilä, Juha-
dc.date.accessioned2020-11-17T14:57:39Z-
dc.date.available2020-11-17T14:57:39Z-
dc.date.issued2017-
dc.identifier.citationMolecular Ecology, 2017, v. 26, n. 6, p. 1557-1575-
dc.identifier.issn0962-1083-
dc.identifier.urihttp://hdl.handle.net/10722/292995-
dc.description.abstract© 2017 John Wiley & Sons Ltd Quantitative traits important to organismal function and fitness, such as brain size, are presumably controlled by many small-effect loci. Deciphering the genetic architecture of such traits with traditional quantitative trait locus (QTL) mapping methods is challenging. Here, we investigated the genetic architecture of brain size (and the size of five different brain parts) in nine-spined sticklebacks (Pungitius pungitius) with the aid of novel multilocus QTL-mapping approaches based on a de-biased LASSO method. Apart from having more statistical power to detect QTL and reduced rate of false positives than conventional QTL-mapping approaches, the developed methods can handle large marker panels and provide estimates of genomic heritability. Single-locus analyses of an F2 interpopulation cross with 239 individuals and 15 198, fully informative single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with variation in stickleback brain size traits. Many of these loci were in strong linkage disequilibrium (LD) with each other, and consequently, a multilocus mapping of individual SNPs, accounting for LD structure in the data, recovered only four significant QTL. However, a multilocus mapping of SNPs grouped by linkage group (LG) identified 14 LGs (1–6 depending on the trait) that influence variation in brain traits. For instance, 17.6% of the variation in relative brain size was explainable by cumulative effects of SNPs distributed over six LGs, whereas 42% of the variation was accounted for by all 21 LGs. Hence, the results suggest that variation in stickleback brain traits is influenced by many small-effect loci. Apart from suggesting moderately heritable (h2 ≈ 0.15–0.42) multifactorial genetic architecture of brain traits, the results highlight the challenges in identifying the loci contributing to variation in quantitative traits. Nevertheless, the results demonstrate that the novel QTL-mapping approach developed here has distinctive advantages over the traditional QTL-mapping methods in analyses of dense marker panels.-
dc.languageeng-
dc.relation.ispartofMolecular Ecology-
dc.subjectSNP-
dc.subjectevolution-
dc.subjectGWAS-
dc.subjectde-biased LASSO-
dc.subjectbrain size-
dc.subjectquantitative trait loci-
dc.subjectmultilocus mapping-
dc.titleDeciphering the genomic architecture of the stickleback brain with a novel multilocus gene-mapping approach-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/mec.14005-
dc.identifier.pmid28052431-
dc.identifier.scopuseid_2-s2.0-85011343115-
dc.identifier.volume26-
dc.identifier.issue6-
dc.identifier.spage1557-
dc.identifier.epage1575-
dc.identifier.eissn1365-294X-
dc.identifier.isiWOS:000397494300009-
dc.identifier.issnl0962-1083-

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