Size Of Reference Population Research Articles

Practical Considerations When Using Mendelian Sampling Variances for Selection Decisions in Genomic Selection Programs

ABSTRACTThe purpose of this work was to test the application of selection criteria that consider the genetic variances of future generations. This has not been done previously in numerically large livestock breeding programs based on estimated rather than assumed known marker effects. A generic pure‐line pig breeding program was simulated in which 40 males and 400 females were selected every generation. Daily gain was used as an example trait. Three variance‐considering criteria as well as different reference population sizes were compared to investigate the effect of differences in genomic prediction accuracy on selection decisions. All variance‐considering criteria retained more genetic variance than if selection was based on estimated breeding values (max. 20%). This effect was more pronounced for higher prediction accuracies and criteria assessing the variance more generations ahead. After 20 generations, the criterion with the longest planning horizon combined with the largest reference population resulted in a 2% higher genetic level of boars selected to produce finisher pigs. While the advantage of accounting for future generations diminished with lower accuracy or shorter planning horizons, the variance‐considering criteria never performed worse than selection based on genomic estimated breeding values (GEBV) with respect to commercial genetic gain. We are reporting various accuracy metrics to help judge the effectiveness of using one of our tested criteria in real breeding programs. While we did not find large benefits for genetic gain when considering future variances in selection decisions, we also did not find negative side effects, while considerably more genetic variance was maintained. This means that using variance‐considering criteria results in either equally good or better performance than truncation selection based on estimated breeding values. Our criteria can be applied to any genomic breeding program as long as phased genotypes, estimated marker effects and a genetic map are available.

Genomic Diversity of U.S. Katahdin Hair Sheep.

In the late 1950s, Katahdin hair sheep were developed as a composite breed of medium size and moderate prolificacy, with potential to express resistance to gastrointestinal nematodes. With increasing popularity and the recent adoption of genomic prediction in their genetic evaluation, there is a risk of decreasing variation with selection based on genomically enhanced estimated breeding values. While Katahdin pedigrees are readily available for monitoring diversity, they may not capture the entirety of genetic relationships. We aimed to characterise the genomic population structure and diversity present in the breed, and how these impact the size of a reference population necessary to achieve accurate genomic predictions. Genotypes of Katahdin sheep from 81 member flocks in the National Sheep Improvement Program (NSIP) were used. After quality control, there were 9704 animals and 31,984 autosomal single nucleotide polymorphisms analysed. Population structure was minimal as a single ancestral population explained 99.9% of the genetic variation among animals. The current Ne was estimated to be 150, and despite differences in trait heritabilities, the effect of Ne on the accuracy of genomic predictions suggested the breed should aim for a reference population size of 15,000 individuals. The average degree of inbreeding estimated from runs of homozygosity (ROH) was 16.6% ± 4.7. Four genomic regions of interest, previously associated with production traits, contained ROH shared among > 50% of the breed. Based on four additional methods, average genomic inbreeding coefficients ranged from 0.011 to 0.012. The current population structure and diversity of the breed reflects genetic connectedness across flocks due to the sharing of animals. Shared regions of ROH should be further explored for incorporation of functional effects into genomic predictions to increase selection gains. Negative impacts on genetic diversity due to genomic selection are not of immediate concern for Katahdin sheep engaged in NSIP.

Accuracy of Genomic Predictions Cross Populations with Different Linkage Disequilibrium Patterns.

There is a considerable global population of beef cattle, with numerous small-scale groups. Establishing separate reference groups for each breed in breeding practices is challenging, severely limiting the genome selection (GS) application. Combining data from multiple populations becomes particularly attractive and practical for small-scale populations, offering increased reference population size, operational ease, and data sharing. To evaluate potential for Chinese indigenous cattle, we evaluated the influence of combining multiple populations on genomic prediction reliability for 10 breeds using simulated data. Within-breed evaluations consistently yielded the highest accuracies across various simulated genetic architectures. Genomic selection accuracy was lower in Group B populations referencing a Group A population (n = 400), but significantly higher in Group A populations with the addition of a small Group B (n = 200). However, accuracy remained low when using the Group A reference group (n = 400) to predict Group B. Incorporating a few Group B individuals (n = 200) into the reference group resulted in relatively high accuracy (~60% of Group A predictions). Accuracy increased with the growing number of individuals from Group B joining the reference group. Our results suggested that multi-breed genomic selection was feasible for Chinese indigenous cattle populations with genetic relationships. This study's results also offer valuable insights into genome selection of multipopulations.

Integrating large-scale meta-analysis of genome-wide association studies improve the genomic prediction accuracy for combined pig populations.

The strategy of combining reference populations has been widely recognized as an effective way to enhance the accuracy of genomic prediction (GP). This study investigated the efficiency of genomic prediction using prior information and combined reference population. In total, prior information considering trait-associated single nucleotide polymorphisms (SNPs) obtained from meta-analysis of genome-wide association studies (GWAS meta-analysis) was incorporated into three models to assess the performance of GP using combined reference populations. Two different Yorkshire populations with imputed whole genome sequence (WGS) data (9,741,620 SNPs), named as P1 (1259 individuals) and P2 (1018 individuals), were used to predict genomic estimated breeding values for three live carcass traits, including backfat thickness, loin muscle area, and loin muscle depth. A 10 × 5 fold cross-validation was used to evaluate the prediction accuracy of 203 randomly selected candidate pigs from the P2 population and the reference population consisted of the remaining pigs from P2 and the stepwise added pigs from P1. By integrating SNPs with different p-value thresholds from GWAS meta-analysis downloaded from PigGTEx Project, the prediction accuracy of GBLUP, genomic feature BLUP (GFBLUP) and GBLUP given genetic architecture (BLUP|GA) were compared. Moreover, we explored effects of reference population size and heritability enrichment of genomic features on the prediction accuracy improvement of GFBLUP and BLUP|GA relative to GBLUP. The prediction accuracy of GBLUP using all WGS markers showed average improvement of 4.380% using the P1 + P2 reference population compared with the P2 reference population. Using the combined reference population, GFBLUP and BLUP|GA yielded 6.179% and 5.525% higher accuracies than GBLUP using all SNPs based on the single reference population, respectively. Positive regression coefficients were estimated in relation to the improvement in prediction accuracy (between GFBLUP/BLUP|GA and GBLUP) and the size of the reference as well as the heritability enrichment of genomic features. Compared to the classic GBLUP model, GFBLUP and BLUP|GA models integrating GWAS meta-analysis information increase the prediction accuracy and using combined populations with enlarged reference population size further enhances prediction accuracy of the two approaches. The heritability enrichment of genomic features can be used as an indicator to reflect weather prior information is accurately presented.

The predicted benefits of genomic selection on pig breeding objectives.

The premise was tested that the additional genetic gain was achieved in the overall breeding objective in a pig breeding program using genomic selection (GS) compared to a conventional breeding program, however, some traits achieved larger gain than other traits. GS scenarios based on different reference population sizes were evaluated. The scenarios were compared using a deterministic simulation model to predict genetic gain in scenarios with and without using genomic information as an additional information source. All scenarios were compared based on selection accuracy and predicted genetic gain per round of selection for objective traits in both sire and dam lines. The results showed that GS scenarios increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively. The difference in response resulted from differences in the size of the reference population. Although all traits achieved higher selection accuracy in GS, traits with limited phenotypic information at the time of selection or with low heritability, such as sow longevity, number of piglets born alive, pre- and post-weaning survival, as well as meat and carcass quality traits achieved the largest additional response. This additional response came at the expense of smaller responses for traits that are easy to measure, such as back fat and average daily gain in GS compared to the conventional breeding program. Sow longevity and drip loss percentage did not change in a favourable direction in GS with a reference population of 500 pigs. With a reference population of 1000 pigs or onwards, sow longevity and drip loss percentage began to change in a favourable direction. Despite the smaller responses for average daily gain and back fat thickness in GS, the overall breeding objective achieved additional gain in GS.

225 Genomic diversity of Katahdin sheep and impacts on genomic prediction

Abstract In the late 1950s, Katahdin hair sheep were developed as a composite breed of medium size and moderate prolificacy, with potential to express resistance to gastrointestinal nematodes. With its increasing popularity and the recent adoption of genomic prediction in the Katahdin breeding program, evaluating the genetic diversity in this breed is necessary. Prediction accuracies rely on the strength of genetic relationships. That strength often increases with selection using genomically-enhanced estimated breeding values; however, it carries the risk of decreasing diversity. We aim to characterize the genetic diversity present in the breed and the subsequent impact on the accuracies of genomic predictions. Genotypes on Katahdin sheep from member flocks in the National Sheep Improvement Program were used. Data from two medium density 50k bead chips and one high density 600k bead chip were merged and limited to shared autosomal single nucleotide polymorphisms (SNP). After quality control, there were 9,705 animals and 31,984 SNP remaining. Only for analyses of inbreeding and heterozygosity, linkage disequilibrium (LD) pruning was performed; variants with an LD score exceeding 0.8 were removed with 29,904 SNP remaining. To determine the extent of population structure, principal component (PC) analysis was conducted with PC one and PC two explaining 4.9% and 3.8% of the variation in the population, respectively. When examining subpopulations, a single ancestral population explained 99.9% of the genetic variation among animals. When clustered by identity-by-state, all animals were placed in a single cluster. Inbreeding coefficients were estimated with four methods: method of moments, variance-standardized relationship minus one, excess homozygosity, and correlation between uniting gametes. Across methods, average inbreeding coefficients ranged from 1.14% to 1.20% with Pearson and Spearman correlations ranging from -0.51 to 0.91 (Table 1). Historical effective population sizes (Ne) were estimated for earlier generations, and a linear regression fit to predict more recent Ne. At the founding of the breed, historical Ne was estimated to be 310, and the current Ne was estimated to be 150. Effects of Ne on the accuracy of genomic predictions were examined for reference population sizes ranging from 1k to 25k for traits of low (0.1), moderate (0.3), and high (0.5) heritabilities. The corresponding number of effective chromosomal segments were 2NeL, where L was the length of the genome in morgans (26M). Accuracies of genomic predictions increased as the reference population size grew, with only marginal gains once the population exceeded 15k. Overall, there were no detectable genetic subpopulations in the Katahdin breed. Current levels of inbreeding were low. With a small Ne the breed can achieve high accuracy for genomic predictions by aiming for a reference population of 15k animals. Negative impacts on genetic diversity due to genomic selection are not of immediate concern.

Effects of reference population size and structure on genomic prediction of maternal traits in two pig lines using whole-genome sequence-, high-density- and combined annotation-dependent depletion genotypes.

The aim of this study was to investigate the reference population size required to obtain substantial prediction accuracy within- and across-lines and the effect of using a multi-line reference population for genomic predictions of maternal traits in pigs. The data consisted of two nucleus pig populations, one pure-bred Landrace (L) and one Synthetic (S) Yorkshire/Large White line. All animals were genotyped with up to 30 K animals in each line, and all had records on maternal traits. Prediction accuracy was tested with three different marker data sets: High-density SNP (HD), whole genome sequence (WGS), and markers derived from WGS based on pig combined annotation dependent depletion-score (pCADD). Also, two different genomic prediction methods (GBLUP and Bayes GC) were compared for four maternal traits; total number piglets born (TNB), total number of stillborn piglets (STB), Shoulder Lesion Score and Body Condition Score. The main results from this study showed that a reference population of 3 K-6 K animals for within-line prediction generally was sufficient to achieve high prediction accuracy. However, when the number of animals in the reference population was increased to 30 K, the prediction accuracy significantly increased for the traits TNB and STB. For multi-line prediction accuracy, the accuracy was most dependent on the number of within-line animals in the reference data. The S-line provided a generally higher prediction accuracy compared to the L-line. Using pCADD scores to reduce the number of markers from WGS data in combination with the GBLUP method generally reduced prediction accuracies relative to GBLUP using HD genotypes. The BayesGC method benefited from a large reference population and was less dependent on the different genotype marker datasets to achieve a high prediction accuracy.

Impacts of reference population size and methods on the accuracy of genomic prediction for fleece traits in Inner Mongolia Cashmere Goats.

Inner Mongolia Cashmere Goats (IMCGs) are famous for its cashmere quality and it's a unique genetic resource in China. Therefore, it is necessary to use genomic selection to improve the accuracy of selection for fleece traits in Inner Mongolia cashmere goats. The aim of this study was to determine the effect of methods (GBLUP, BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) and the reference population size on accuracy of genomic selection in IMCGs. This study fully utilizes the pedigree and phenotype records of fleece traits in 2255 individuals, genotype of 50794 SNPs after quality control, and environmental data to perform genomic selection of fleece traits. Then GBLUP and Bayes series methods (BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) were used to perform estimates of genetic parameter and genomic breeding value. And the accuracy of genomic estimated breeding value (GEBV) is evaluated using the five-fold cross validation method. And the analysis of variance and multiple comparison methods were used to determine the best method for genomic selection in fleece traits of IMCGs. Further the different reference population sizes (500, 1000, 1500, and 2000) was set. Then the best method was applied to estimate genome breeding values, and evaluate the impact of reference population sizes on the accuracy of genome selection for fleece traits in IMCGs. It was found that the genomic prediction accuracy for each fleece trait in IMCGs by GBLUP method is highest, and it is significantly higher than that obtained by Bayesian method. The accuracy of breeding value estimation is 58.52% -68.49%. Also, it was found that the size of the reference population has a significant impact on the accuracy of genome prediction of fleece traits. When the reference population size is 2000, the accuracy of genomic prediction for each fleece trait is significantly higher than other levels, with accuracy of 55.47% -67.87%. This provides a theoretical basis for design a reasonable genome selection plan for Inner Mongolia cashmere goats in the later stag.

100 Pedigree Effective Population Size Affects Genomic Prediction Accuracy in Katahdin Sheep

Abstract Katahdin sheep are a composite hair breed of medium-size, relatively prolific, with potential to exhibit resistance to gastrointestinal parasites. Recently, genomic predictions of breeding values were introduced in Katahdins. Prediction accuracy depends on the genetic variation present, and selection using genomically-enhanced breeding values may reduce diversity. We aimed to characterize the current genetic variability in Katahdins utilizing their extensive pedigree. The National Sheep Improvement Program provided the pedigree of 92,030 sheep born from 1984-2019. Two reference populations were defined for animals born between 2017-2019: all Katahdins (n = 23,494), and Katahdins with at least three generations of known Katahdin ancestry (n = 9,327). Using ENDOG and PopReport, effective population sizes (Ne) were estimated. We then considered the effect of Ne on the size of the genomic reference population needed to achieve certain levels of prediction accuracy. Weaning weight, a lowly heritable trait for this breed (h2 = 0.13), was evaluated. Based on individual increase in inbreeding, inbreeding coefficient of animal adjusted for equivalent complete generations, the estimated Ne for the full pedigree, reference 1, and reference 2 were 92, 104, and 87, respectively. When inbreeding coefficients were regressed or log-regressed on birth date and adjusted for generation interval, the Ne estimates were 53 and 49 for reference 1, and 76 and 67 for reference 2, respectively. For our average Ne = 75 with 3,960 effective chromosomal segments, the accuracy of prediction for weaning weight was estimated for genomic reference population sizes (N) of 10k, 15k, 20k, and 25k. Starting at N = 10k, the prediction accuracy was 0.58, and as N increased by 5k increments the accuracy increased to 0.65, 0.70, and 0.74. Conversely, with our smallest Ne = 49 with 2,548 effective chromosomal segments, the prediction accuracy was 0.64, 0.71, 0.75, and 0.78 for N = 10k, 15k, 20k, and 25k, respectively. Estimated Ne varied depending on the method utilized, and tended to be larger for reference 1 than 2. However, this was not the case for Ne based on regression. Inbreeding coefficients were greater in reference 2 than 1, and the generation interval was shorter in reference 2 (2.84 years) than in 1 (2.97 years). These combined differences led to larger Ne estimates for reference 2 than 1, reflecting the impact of different criteria used to obtain this statistic. The generally small, estimated Ne benefitted genomic prediction due to a greater shared number of effective chromosomal segments. Investing in larger genomic reference populations will only marginally increase prediction accuracy even for lowly heritable traits. Selection using genomically-enhanced breeding values will accelerate genetic gains, potentially reducing the Ne of Katahdins and thereby increasing prediction accuracy. Yet, long-term gains may be at risk due to the loss of genetic diversity.

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Genomic selection (GS) has been widely used in livestock, which greatly accelerated the genetic progress of complex traits. The population size was one of the significant factors affecting the prediction accuracy, while it was limited by the purebred population. Compared to directly combining two uncorrelated purebred populations to extend the reference population size, it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction. In this study, we simulated purebred offspring (PAS and PBS) and crossbred offspring (CAB) base on real genotype data of two base purebred populations (PA and PB), to evaluate the performance of genomic selection on purebred while incorporating crossbred information. The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship (REL) was better than the other methods (individuals closet or farthest to the purebred population, CP/FP) in term of the prediction accuracy. Furthermore, the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA, which was similar to combine PA and PAS. Moreover, the rank correlation between the multiple of the increased relationship (MIR) and reliability improvement was 0.60–0.70. But for individuals with low correlation (Cor(Pi, PA or B), the reliability improvement was significantly lower than other individuals. Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only. The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Effect of subdivision of the Lacaune dairy sheep breed on the accuracy of genomic prediction

Genomic selection was deployed in Lacaune dairy breed in 2015. Lacaune population split in 1972 into 2 breeding companies with associated flocks, and there have been very few exchanges of animals between the subpopulations, leading to divergence of the 2 subpopulations. In spite of that, there is a joint genomic prediction. The objective of this study is to understand how this structuring affects prediction accuracy. We analyzed all the data available from Lacaune breeding program for milk yield: around 6 million phenotypes, 2 million animals in the pedigree and more than 29,000 genotyped animals, including 3,434 and 2,868 AI rams for each company. To consider missing pedigree, we set up genetic groups using the theory of metafounders. First, we studied the pedigree and genomic structures of the 2 subpopulations calculating Fst, evolution of average pedigree relationships across time and principal components analysis of genomic relationships. In a second part, we compared the reliability between different scenarios: an evaluation with a single reference population (Alone), an evaluation with a joint reference population (Together) and an evaluation of one subpopulation based on the reference population of the other group (Indirect). The low Fst value (0.02) reveals that the 2 subpopulations are still genetically close. Nevertheless, a low and constant average relationship between the animals of the 2 subpopulations confirms the absence of recent connections between them. We can see with principal component analysis results that even if they are close, they diverge over time. Finally, we observe small gains in accuracy of Together versus Alone, in spite of whereas doubling the reference population size in Together. These gains vary across years and subpopulations: less than 0.08 (0.46 to 0.54; ratio of accuracy for the partial and whole evaluations-corresponding to the greatest change in this ratio for breeding company 1, observed for the cohort 2016) for one subpopulation and between 0.03 (0.55 to 0.58) and 0.17 (0.48 to 0.65) for the other. To conclude, the 2 subpopulations remain close enough genetically so that their combined evaluation is advantageous, even if only slightly.

Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data

BackgroundBreed identification is useful in a variety of biological contexts. Breed identification usually involves two stages, i.e., detection of breed-informative SNPs and breed assignment. For both stages, there are several methods proposed. However, what is the optimal combination of these methods remain unclear. In this study, using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project, we compared the combinations of three methods (Delta, FST, and In) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated the accuracy of breed identification using SNP chip data of different densities.ResultsWe found that all combinations performed quite well with identification accuracies over 95% in all scenarios. However, there was no combination which performed the best and robust across all scenarios. We proposed to integrate the three breed-informative detection methods, named DFI, and integrate the three machine learning methods, KNN, SVM, and RF, named KSR. We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99% in most cases and was very robust in all scenarios. The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.ConclusionsThe current study showed that the combination of DFI and KSR was the optimal strategy. Using sequence data resulted in higher accuracies than using chip data in most cases. However, the differences were generally small. In view of the cost of genotyping, using chip data is also a good option for breed identification.

Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system

BackgroundGenomic selection is widely applied for genetic improvement in livestock crossbreeding systems to select excellent nucleus purebred (PB) animals and to improve the performance of commercial crossbred (CB) animals. Most current predictions are based solely on PB performance. Our objective was to explore the potential application of genomic selection of PB animals using genotypes of CB animals with extreme phenotypes in a three-way crossbreeding system as the reference population. Using real genotyped PB as ancestors, we simulated the production of 100,000 pigs for a Duroc x (Landrace x Yorkshire) DLY crossbreeding system. The predictive performance of breeding values of PB animals for CB performance using genotypes and phenotypes of (1) PB animals, (2) DLY animals with extreme phenotypes, and (3) random DLY animals for traits of different heritabilities ({h}^{2} = 0.1, 0.3, and 0.5) was compared across different reference population sizes (500 to 6500) and prediction models (genomic best linear unbiased prediction (GBLUP) and Bayesian sparse linear mixed model (BSLMM)).ResultsUsing a reference population consisting of CB animals with extreme phenotypes showed a definite predictive advantage for medium- and low-heritability traits and, in combination with the BSLMM model, significantly improved selection response for CB performance. For high-heritability traits, the predictive performance of a reference population of extreme CB phenotypes was comparable to that of PB phenotypes when the effect of the genetic correlation between PB and CB performance ({r}_{pc}) on the accuracy obtained with a PB reference population was considered, and the former could exceed the latter if the reference size was large enough. For the selection of the first and terminal sires in a three-way crossbreeding system, prediction using extreme CB phenotypes outperformed the use of PB phenotypes, while the optimal design of the reference group for the first dam depended on the percentage of individuals from the corresponding breed that the PB reference data comprised and on the heritability of the target trait.ConclusionsA commercial crossbred population is promising for the design of the reference population for genomic prediction, and selective genotyping of CB animals with extreme phenotypes has the potential for maximizing genetic improvement for CB performance in the pig industry.

Genomic Prediction Using LD-Based Haplotypes in Combined Pig Populations.

The size of reference population is an important factor affecting genomic prediction. Thus, combining different populations in genomic prediction is an attractive way to improve prediction ability. However, combining multireference population roughly cannot increase the prediction accuracy as well as expected in pig. This may be due to different linkage disequilibrium (LD) pattern differences between population. In this study, we used the imputed whole-genome sequencing (WGS) data to construct LD-based haplotypes for genomic prediction in combined population to explore the impact of different single-nucleotide polymorphism (SNP) densities, variant representation (SNPs or haplotype alleles), and reference population size on the prediction accuracy for reproduction traits. Our results showed that genomic best linear unbiased prediction (GBLUP) using the WGS data can improve prediction accuracy in multi-population but not within-population. Not only the genomic prediction accuracy of the haplotype method using 80 K chip data in multi-population but also GBLUP for the multi-population (3.4–5.9%) was higher than that within-population (1.2–4.3%). More importantly, we have found that using the haplotype method based on the WGS data in multi-population has better genomic prediction performance, and our results showed that building haploblock in this scenario based on low LD threshold (r 2 = 0.2–0.3) produced an optimal set of variables for reproduction traits in Yorkshire pig population. Our results suggested that whether the use of the haplotype method based on the chip data or GBLUP (individual SNP method) based on the WGS data were beneficial for genomic prediction in multi-population, while simultaneously combining the haplotype method and WGS data was a better strategy for multi-population genomic evaluation.

Risk of SARS-CoV-2 infection among front-line healthcare workers in Northeast Brazil: a respondent-driven sampling approach

ObjectivesWe assessed the prevalence of SARS-CoV-2 infection, personal protective equipment (PPE) shortages and occurrence of biological accidents among front-line healthcare workers (HCW).Design, setting and participantsUsing respondent-driven sampling, the study recruited...

Accuracy of Genomic Selection for Important Economic Traits of Cashmere and Meat Goats Assessed by Simulation Study.

Genomic selection in plants and animals has become a standard tool for breeding because of the advantages of high accuracy and short generation intervals. Implementation of this technology is hindered by the high cost of genotyping and other factors. The aim of this study was to determine an optional marker density panel and reference population size for using genomic selection of goats, with speculation on the number of QTLs that affect the important economic traits of goats. In addition, the effect of buck population size in the reference population on the accuracy of genomic estimated breeding value (GEBV) was discussed. Based on the previous genetic evaluation results of Inner Mongolia White Cashmere Goats, live body weight (LBW, h2 = 0.11) and fiber diameter (FD, h2 = 0.34) were chosen to perform genomic selection in this study. Reasonable genome parameters and generation transmission processes were set, and phenotypic and genotype data of the two traits were simulated. Then, different sizes of the reference population and validation population were selected from progeny. The GEBVs were obtained by six methods, including GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP (Single Step Genomic Best Linear Unbiased Prediction), BayesA, BayesB, Bayesian ridge regression, and Bayesian LASSO. The correlation coefficient between the predicted and realized phenotypes from simulation was calculated and used as a measure of the accuracy of GEBV in each trait. The results showed that the medium marker density Panel (45 K) could be used for genomic selection in goats, which can ensure the accuracy of the GEBV. The reference population size of 1,500 can achieve greater genetic progress in genomic selection for fiber diameter and live body weight in goats by comparing with the population size below this level. The accuracy of the GEBV for live body weight and fiber diameter was better when the number of QTLs was 100 and 50, respectively. Additionally, the accuracy of GEBV was discovered to be good when the buck population size was up to 200. Meanwhile, the accuracy of the GEBV for medium heritability traits (FDs) was found to be higher than the accuracy of the GEBV for low heritability traits (LBWs). These findings will provide theoretical guidance for genomic selection in goats by using real data.

Accuracy of genotype imputation to whole genome sequencing level using different populations of Nile tilapia

A cost-effective strategy to obtain ultra-dense genomic information is to sequence part of population and perform imputation from lower density genotypes to sequence level for the remaining animals. The aims of this study were to evaluate the feasibility of genotype imputation from medium density to sequence level in Nile tilapia and to investigate the impacts of size and origin of reference population in the accuracy of imputation. Genomic DNA was extracted from fin-clip samples of 326 animals from 3 different populations (PA, PB and PC). After sequencing, alignment, variant calling and quality control of genotypes, approximately 4.6 million of single-nucleotide polymorphisms (SNPs) in common to all populations were retained and used for further imputation analyses. Four scenarios were evaluated to assess imputation accuracy on each population, including: two reference sizes (10 or 90% of animals of each reference population) and two reference origins (two different populations only or all three populations used as reference). The animals in the validation set had part of their genotypes masked keeping only 49,216 SNPs available and the accuracy of imputation was assessed using the correlation between the imputed and observed genotypes (R2). Imputation was carried out using FImpute3 software. At individual level, the R2 showed intermediate values ranging from 0.37 ± 0.04 to 0.56 ± 0.07 for PA, 0.43 ± 0.05 to 0.58 ± 0.08 for PB and 0.43 ± 0.05 to 0.58 ± 0.07 for PC. An increase in the R2 was observed when 90% of animals from the same population were used as reference in comparison to only 10% (0.37 ± 0.04 to 0.54 ± 0.07 for PA, 0.43 ± 0.05 to 0.57 ± 0.07 for PB and 0.43 ± 0.05 to 0.58 ± 0.07 for PC). At SNP level, the use of all three populations as reference yielded the best results in terms of number of SNPs imputed with accuracy greater than 0.8. On average, 676,233 ± 142,291, 666,559 ± 52,648 and 592,187 ± 89,663 SNPs were imputed with accuracy >0.8 for PA, PB and PC, respectively. Considering only these highly accurate imputed SNPs, the average imputation accuracy of samples was equal to 0.95 ± 0.06 for PA and 0.92 ± 0.07 for PB and PC, for scenarios that included more animals as reference (90% of same population as reference, two and three populations). There were no significant differences for R2 between scenarios that used 90% of animals from the same population and used animals from the three population as reference showing that the strategy of using information from other population to increase the reference population had minor effect on accuracy of imputation. In conclusion, it was feasible to impute from 50 K to approximately 700 K with high accuracy using tilapia sequence data. We also expect that the use of more animals from these populations or animals from ascending lines as reference could help in the imputation process to obtain millions of imputed SNPs with high accuracy.

Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data.

Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.

Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies.

Growing genomic and phenotypic datasets require different groups around the world to collaborate and integrate these valuable resources to maximize their benefit and increase reference population sizes for genomic prediction and genome-wide association studies (GWAS). However, different studies use different genotyping techniques which requires a synchronizing step for the genotyped variants called "imputation" before combining them. Optimally, different GWAS datasets can be analysed within a meta-analysis, which recruits summary statistics instead of actual data. This chapter describes the general principles for genotypic imputation and meta-GWAS analysis with a description of study designs and command lines required for such analyses.

Evaluation of INDUSCHIP-1 and selected low-density SNP panel for imputation to higher density in Gir dairy cattle of Gujarat

Application of Genomic Selection among other selection methods mainly depends upon the cost of genotyping, as cheaper the cost more animals can be genotyped to increase reference population size. Imputation approaches have been useful in reducing the cost. Imputation strategies and GS have been comprehensively studied in several taurine dairy cattle populations but very limited information is available on Bos Indicus populations. Factors that affect the efficiency of imputation are population structure, linkage disequilibrium between markers, and marker density in target and reference SPN panels. For present study, INDUSCHIP-1, a customized Illumina bovine microarray chip for indigenous cattle breeds, designed by NDDB, Anand was used for genotyping Gir cattle in India. The objective of the study is to evaluate the performance of INDUSCHIP-1 for imputation to Illumina BovineHD and also to select an LD SNP panel useful to impute at INDUSCHIP-1 level in Gir cattle. A fivefold cross validation by masking genotypes of animals to keep either INDUSCHIP-1 SNPs or LD SNPs and imputing to either HD level or at INDUSCHIP-1 level respectively, was performed. Population-based imputation algorithm BEAGLE was used without including pedigree information. Imputation accuracies evaluated as concordance level (allele correct rate expressed in percentage). All imputation accuracy, represented as % concordance between actual genotypes and imputed genotypes were showed as an average of five replicates for each chromosome. Mean concordance of 92.33% was observed when INDUSCHIP-1 genotypes were imputed at Illumina HD 777K level. The use of INDUSCHIP-1 as a HD Panel resulted in the accuracy of 88.42%. SNP selection criteria used for LD panel in the present study was very simple and seems effective. Distribution of polymorphic SNPs along the length of chromosomes and across all the chromosomes showed that this custom selected panel is a promising option for developing LD genotyping chip for Gir cattle.