ArticleGenetics, selection, evolution : GSE2018
Required properties for markers used to calculate unbiased estimates of the genetic correlation between populations.
Article in Genetics, selection, evolution : GSE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- GWAS and genomic prediction of milk urea nitrogen in Australian and New Zealand dairy cattle.Genetics, selection, evolution : GSE · 2022Pooled it
- Accounting for population structure in genomic prediction of strawberry sweetness at a global scale.Scientific reports · 2025Article
- Predictive ability of multi-population genomic prediction methods of phenotypes for reproduction traits in Chinese and Austrian pigs.Genetics, selection, evolution : GSE · 2024Article
- Accuracy of genomic prediction using multiple Atlantic salmon populations.Genetics, selection, evolution : GSE · 2024Article
- Predicting the impact of genotype-by-genotype interaction on the purebred-crossbred genetic correlation from phenotype and genotype marker data of parental lines.Genetics, selection, evolution : GSE · 2023Article
- Conserved QTL and chromosomal inversion affect resistance to columnaris disease in 2 rainbow trout (Oncorhyncus mykiss) populations.G3 (Bethesda, Md.) · 2022Article
- The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture.Genetics, selection, evolution : GSE · 2022Article
- Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Predicting the purebred-crossbred genetic correlation from the genetic variance components in the parental lines.Genetics, selection, evolution : GSE · 2021Article
- A deterministic equation to predict the accuracy of multi-population genomic prediction with multiple genomic relationship matrices.Genetics, selection, evolution : GSE · 2020Article
- Development of diagnostic SNP markers for quality assurance and control in sweetpotato [Ipomoea batatas (L.) Lam.] breeding programs.PloS one · 2020Article
- Estimating the purebred-crossbred genetic correlation of body weight in broiler chickens with pedigree or genomic relationships.Genetics, selection, evolution : GSE · 2019Article
- Genomic prediction for numerically small breeds, using models with pre-selected and differentially weighted markers.Genetics, selection, evolution : GSE · 2018Article
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4 authors.
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Abstract
backgroundGenerally, populations differ in terms of environmental and genetic factors, which can create differences in allele substitution effects between populations. Therefore, a single genotype may have different additive genetic values in different populations. The correlation between the two additive genetic values of a single genotype in two populations is known as the additive genetic correlation between populations and thus, can differ from 1. Our objective was to investigate whether differences in linkage disequilibrium (LD) and allele frequencies of markers and causal loci between populations affect the bias of the estimated genetic correlation. We simulated two populations that were separated by 50 generations and differed in LD pattern between markers and causal loci, as measured by the LD-statistic r. We used a high marker density to represent a high consistency of LD between populations, and lower marker densities to represent situations with a lower consistency of LD between populations. Markers and causal loci were selected to have either similar or different allele frequencies in the two populations.
resultsOur results show that genetic correlations were underestimated only slightly when the difference in allele frequencies between the two populations was similar for the markers and the causal loci. A lower marker density, representing a lower consistency of LD between populations, had only a minor effect on the underestimation of the genetic correlation. When the difference in allele frequencies between the two populations was not similar for markers and causal loci, genetic correlations were severely underestimated. This bias occurred because the markers did not predict accurately the relationships at causal loci.
conclusionsFor an unbiased estimation of the genetic correlation between populations, the markers should accurately predict the relationships at the causal loci. To achieve this, it is essential that the difference in allele frequencies between populations is similar for markers and causal loci. Our results show that differences in LD phase between causal loci and markers across populations have little effect on the estimated genetic correlation.
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