Genomic prediction of growth in a commercially, recreationally, and culturally important marine resource, the Australian snapper (Chrysophrys auratus)
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Abstract
Growth is one of the most important traits of an organism. For exploited species, this trait has ecological and evolutionary consequences as well as economical and conservation significance. Rapid changes in growth rate associated with anthropogenic stressors have been reported for several marine fishes, but little is known about the genetic basis of growth traits in teleosts. We used reduced genome representation data and genome-wide association approaches to identify growth-related genetic variation in the commercially, recreationally, and culturally important Australian snapper ( Chrysophrys auratus , Sparidae). Based on 17,490 high-quality SNPs and 363 individuals representing extreme growth phenotypes from 15,000 fish of the same age and reared under identical conditions in a sea pen, we identified 100 unique candidates that were annotated to 51 proteins. We documented a complex polygenic nature of growth in the species that included several loci with small effects and a few loci with larger effects. Overall heritability was high (75.7%), reflected in the high accuracy of the genomic prediction for the phenotype (small vs large). Although the SNPs were distributed across the genome, most candidates (60%) clustered on chromosome 16, which also explains the largest proportion of heritability (16.4%). This study demonstrates that reduced genome representation SNPs and the right bioinformatic tools provide a cost-efficient approach to identify growth-related loci and to describe genomic architectures of complex quantitative traits. Our results help to inform captive aquaculture breeding programmes and are of relevance to monitor growth-related evolutionary shifts in wild populations in response to anthropogenic pressures.
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- Genomic prediction in the wild: A case study in Soay sheep via crossref
- Using genomic prediction to detect microevolutionary change of a quantitative trait via crossref
- doi:10.1534/g3.118.200905 via crossref
- doi:10.1093/bioinformatics/btm108 via crossref
- doi:10.1126/science.aao6868 via crossref
- doi:10.1016/j.aquaculture.2016.04.030 via crossref
- doi:10.1093/bioinformatics/btu170 via crossref
- doi:10.1002/ece3.3239 via crossref
- doi:10.1111/eva.12987 via crossref
- doi:10.1186/1471-2105-10-421 via crossref
- doi:10.1371/journal.pgen.1000907 via crossref
- doi:10.1111/mec.15051 via crossref
- doi:10.1111/mec.12354 via crossref
- doi:10.1038/s41598-018-36524-8 via crossref
- doi:10.1186/s13742-015-0047-8 via crossref
- doi:10.1093/bioinformatics/btr330 via crossref
- doi:10.1093/gigascience/giab008 via crossref
- doi:10.1016/j.aquaculture.2007.08.036 via crossref
- doi:10.1007/s00439-014-1461-1 via crossref
- doi:10.1111/gcb.15298 via crossref
- doi:10.1111/ele.13772 via crossref
- doi:10.1038/35014600 via crossref
- doi:10.1111/j.1752-4571.2009.00077.x via crossref
- doi:10.1007/s00360-020-01268-3 via crossref
- doi:10.1016/s0065-2881(06)52003-6 via crossref
- doi:10.1371/journal.pone.0224347 via crossref
- doi:10.1038/nrg2575 via crossref
- doi:10.1111/eva.13218 via crossref
- doi:10.1086/648604 via crossref
- doi:10.1371/journal.pcbi.1007663 via crossref
- doi:10.1371/journal.pone.0196092 via crossref
- doi:10.1098/rstb.2016.0028 via crossref
- doi:10.1016/j.ecolind.2020.106976 via crossref
- doi:10.1111/1365-2664.13807 via crossref
- doi:10.1111/jfb.14810 via crossref
- doi:10.1093/nar/gkz1031 via crossref
- doi:10.1016/j.ydbio.2011.06.034 via crossref
- doi:10.1111/ele.12273 via crossref
- doi:10.1242/jeb.038620 via crossref
- doi:10.1371/journal.pone.0090568 via crossref
- doi:10.1101/gr.6086307 via crossref
- doi:10.1126/sciadv.abg5285 via crossref
- doi:10.1002/ecm.1427 via crossref
- doi:10.1073/pnas.1509022112 via crossref
- doi:10.2960/j.v41.m628 via crossref
- doi:10.1038/nmeth.1923 via crossref
- doi:10.1126/science.aba0690 via crossref
- doi:10.1002/ece3.6783 via crossref
- doi:10.1093/bioinformatics/btu356 via crossref
- doi:10.1002/humu.22712 via crossref
- doi:10.1016/j.fishres.2016.01.006 via crossref
- doi:10.1186/s12711-019-0522-2 via crossref
- doi:10.1111/1365-2435.13516 via crossref
- doi:10.1002/mpr.1608 via crossref
- doi:10.1098/rspb.2021.0693 via crossref
- doi:10.1073/pnas.2009451118 via crossref
- doi:10.1111/gcb.15490 via crossref
- doi:10.1371/journal.pgen.1004969 via crossref
- doi:10.1007/s11160-017-9474-1 via crossref
- doi:10.1111/cga.12012 via crossref
- doi:10.1038/nclimate1084 via crossref
- doi:10.1074/jbc.m115.706069 via crossref
- doi:10.3389/fmars.2020.00097 via crossref
- doi:10.1139/cjfas-2020-0012 via crossref
- doi:10.1038/s41467-020-18276-0 via crossref
- doi:10.1080/00288330.2014.892013 via crossref
- doi:10.1016/j.marenvres.2020.105089 via crossref
- doi:10.1371/journal.pone.0037135 via crossref
- doi:10.1073/pnas.2025453118 via crossref
- doi:10.3390/ijms17020243 via crossref
- doi:10.1111/gcb.12617 via crossref
- doi:10.1111 /mec. 14526 via crossref
- doi:10.1242/dev.129.3.605 via crossref
- doi:10.1111/j.1752-4571.2009.00080.x via crossref
- doi:10.1038/s41558-020-0878-x via crossref
- doi:10.1186/s12711-019-0522-2 via crossref
- doi:10.1126/science.aah5238 via crossref
- doi:10.1093/nar/gkaa1074 via crossref
- doi:10.1002/dvdy.22537 via crossref
- doi:10.1016/j.matbio.2020.03.004 via crossref
- doi:10.1016/j.ydbio.2020.01.010 via crossref
- doi:10.1111/eva.12268 via crossref
- doi:10.1111/eva.13281 via crossref
- doi:10.1007/s10695-015-0170-6 via crossref
- doi:10.1016/j.tig.2015.12.004 via crossref
- doi:10.1534/g3.118.200647 via crossref
- doi:10.1073/pnas.2100300118 via crossref
- doi:10.1007/s10126-019-09916-8 via crossref
- doi:10.1007/s10528-009-9274-y via crossref
- doi:10.1186/s12864-020-6490-7 via crossref
- doi:10.1016/j.aaf.2017.06.001 via crossref
- doi:10.1093/hmg/ddy271 via crossref
- doi:10.1111/eva.13240 via crossref
- doi:10.1139/cjfas-2015-0230 via crossref
- doi:10.1007/s10126-019-09910-0 via crossref
- doi:10.1016/j.aquaculture.2019.02.034 via crossref
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