Dataset Information

Factors affecting accuracy of estimated effective number of chromosome segments for numerically small breeds.

ABSTRACT: For numerically small breeds, obtaining a sufficiently large breed-specific reference population for genomic prediction is challenging or simply not possible, but may be overcome by adding individuals from another breed. To prioritize among available breeds, the effective number of chromosome segments (M_e ) can be used as an indicator of relatedness between individuals from different breeds. The M_e is also an important parameter in determining the accuracy of genomic prediction. The M_e can be estimated both within a population and between two populations or breeds, as the reciprocal of the variance of genomic relationships. However, the threshold for number of individuals needed to accurately estimate within or between populations M_e is currently unknown. It is also unknown if a discrepancy in number of genotyped individuals in two breeds affects the estimates of M_e between populations. In this study, we conducted a simulation that mimics current domestic cattle populations in order to investigate how estimated M_e is affected by number of genotyped individuals, single-nucleotide polymorphism (SNP) density and pedigree availability. Our results show that a small sample of 10 genotyped individuals may result in substantial over or underestimation of M_e . While estimates of within population M_e were hardly affected by SNP density, between population M_e values were highly dependent on the number of available SNPs, with higher SNP densities being able to detect more independent chromosome segments. When subtracting pedigree from genomic relationships before computing M_e , estimates of within population M_e were three to four times higher than estimates with genotypes only; however, between M_e estimates remained the same. For accurate estimation of within and between population M_e , at least 50 individuals should be genotyped per population. Estimates of within M_e were highly affected by whether pedigree was used or not. For within M_e , even the smallest SNP density (~11k) resulted in accurate representation of family relationships in the population; however, for between M_e , many more markers are needed to capture all independent segments.

SUBMITTER: Marjanovic J

PROVIDER: S-EPMC7891385 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Factors affecting accuracy of estimated effective number of chromosome segments for numerically small breeds.

Marjanovic Jovana J Calus Mario P L MPL

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie 20201010 2

For numerically small breeds, obtaining a sufficiently large breed-specific reference population for genomic prediction is challenging or simply not possible, but may be overcome by adding individuals from another breed. To prioritize among available breeds, the effective number of chromosome segments (M<sub>e</sub> ) can be used as an indicator of relatedness between individuals from different breeds. The M<sub>e</sub> is also an important parameter in determining the accuracy of genomic predic ...[more]

PMID: 33040409

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Project description:Commensal microorganisms are essential to the normal development and function of many aspects of animal biology. However, the dynamic shift patterns of the microbiota of different gut segments in sheep and the correlation between fat type large-tailed phenotype and microbiota remain poorly unknown. This study therefore sought to assess the composition and distribution of the intestinal microbiome, and compared the difference of gut microbiota from different gastrointestinal segments within breeds and same intestinal sections between breeds. For these analyses, 16S rRNA V4 regions from 4 gut sections prepared from each of six individuals (3 from each breed) were sequenced to detect the microbiome composition in these samples. These analyses revealed the presence of 51,173 operational taxonomic units distributed across 24 phyla and 420 genera in these samples, with Firmicutes and Bacteroidetes being the most prevalent phyla of microbes present in these samples. Moreover, the bacterial composition showed distinct microbial communities in different gastrointestinal segments within breed, but showed similar and relative fixed bacterial abundance in the same intestinal segments from individuals of different breeds. We also found that only a few bacterial species (Lachnospiraceae, Akkermansia) were needed to distinguish between Small-tailed Han sheep (STH) and Large-tailed Han sheep (LTH) and their metabolic process maybe influence the fat type large-tailed phenotype formation in sheep. The functional profile analysis revealed that the environment information processing, genetic information processing, and metabolic pathways were enriched in all samples. The main functional roles of the gut microbiota were amino acid metabolism, replication and repair, carbohydrate metabolism, and membrane transport. Finally, our findings suggested that distinguished gut species between STH and LTH have relative fixed and the potential correlation is existing between the intestinal microorganisms and the large-tailed phenotype trait formation of sheep, which may offer clues for further investigation to detect the roles of intestinal microbiota in the metabolism and fat deposition in the tail of sheep.

Project description:BackgroundGenotyping with the medium-density Bovine SNP50 BeadChip® (50K) is now standard in cattle. The high-density BovineHD BeadChip®, which contains 777,609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip.MethodsFive thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software.ResultsMean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed.ConclusionIn all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.

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Factors affecting accuracy of estimated effective number of chromosome segments for numerically small breeds.

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Factors affecting accuracy of estimated effective number of chromosome segments for numerically small breeds.

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