Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study
CONCLUSIONS: Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits.PMID:37460964 | PMC:PMC10351171 | DOI:10.1186/s12711-023-00823-0 (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 17, 2023 Category: Genetics & Stem Cells Authors: Sungbong Jang Shogo Tsuruta Natalia Galoro Leite Ignacy Misztal Daniela Lourenco Source Type: research
Impact of kinship matrices on genetic gain and inbreeding with optimum contribution selection in a genomic dairy cattle breeding program
CONCLUSIONS: Using genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals.PMID:37460999 | PMC:PMC10351146 | DOI:10.1186/s12711-023-00826-x (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 17, 2023 Category: Genetics & Stem Cells Authors: Egill Gautason Goutam Sahana Bernt Guldbrandtsen Peer Berg Source Type: research
Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study
CONCLUSIONS: Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits.PMID:37460964 | PMC:PMC10351171 | DOI:10.1186/s12711-023-00823-0 (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 17, 2023 Category: Genetics & Stem Cells Authors: Sungbong Jang Shogo Tsuruta Natalia Galoro Leite Ignacy Misztal Daniela Lourenco Source Type: research
Impact of kinship matrices on genetic gain and inbreeding with optimum contribution selection in a genomic dairy cattle breeding program
CONCLUSIONS: Using genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals.PMID:37460999 | PMC:PMC10351146 | DOI:10.1186/s12711-023-00826-x (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 17, 2023 Category: Genetics & Stem Cells Authors: Egill Gautason Goutam Sahana Bernt Guldbrandtsen Peer Berg Source Type: research
Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study
CONCLUSIONS: Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, on the dimensionality of genomic information. This dimensionality can help identify the most suitable sample size for GWA and could be considered for variant selection, especially when resources are restricted. Even when variants are accurately identified, their inclusion in prediction models has limited benefits.PMID:37460964 | DOI:10.1186/s12711-023-00823-0 (Source: Genet Sel Evol)
Source: Genet Sel Evol - July 17, 2023 Category: Genetics & Stem Cells Authors: Sungbong Jang Shogo Tsuruta Natalia Galoro Leite Ignacy Misztal Daniela Lourenco Source Type: research
