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      SNP‐ and haplotype‐based single‐step genomic predictions for body weight, wool, and reproductive traits in North American Rambouillet sheep

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          Abstract

          Rambouillet sheep are commonly raised in extensive grazing systems in the US, mainly for wool and meat production. Genomic evaluations in US sheep breeds, including Rambouillet, are still incipient. Therefore, we aimed to evaluate the feasibility of performing genomic prediction of breeding values for various traits in Rambouillet sheep based on single nucleotide polymorphisms (SNP) or haplotypes (fitted as pseudo‐SNP) under a single‐step GBLUP approach. A total of 28,834 records for birth weight (BWT), 23,306 for postweaning weight (PWT), 5,832 for yearling weight (YWT), 9,880 for yearling fibre diameter (YFD), 11,872 for yearling greasy fleece weight (YGFW), and 15,984 for number of lambs born (NLB) were used in this study. Seven hundred forty‐one individuals were genotyped using a moderate (50 K; n = 677) or high (600 K; n = 64) density SNP panel, in which 32 K SNP in common between the two SNP panels (after genotypic quality control) were used for further analyses. Single‐step genomic predictions using SNP (H‐BLUP) or haplotypes (HAP‐BLUP) from blocks with different linkage disequilibrium (LD) thresholds (0.15, 0.35, 0.50, 0.65, and 0.80) were evaluated. We also considered different blending parameters when constructing the genomic relationship matrix used to predict the genomic‐enhanced estimated breeding values (GEBV), with alpha equal to 0.95 or 0.50. The GEBV were compared to the estimated breeding values (EBV) obtained from traditional pedigree‐based evaluations (A‐BLUP). The mean theoretical accuracy ranged from 0.499 (A‐BLUP for PWT) to 0.795 (HAP‐BLUP using haplotypes from blocks with LD threshold of 0.35 and alpha equal to 0.95 for YFD). The prediction accuracies ranged from 0.143 (A‐BLUP for PWT) to 0.330 (A‐BLUP for YGFW) while the prediction bias ranged from −0.104 (H‐BLUP for PWT) to 0.087 (HAP‐BLUP using haplotypes from blocks with LD threshold of 0.15 and alpha equal to 0.95 for YGFW). The GEBV dispersion ranged from 0.428 (A‐BLUP for PWT) to 1.035 (A‐BLUP for YGFW). Similar results were observed for H‐BLUP or HAP‐BLUP, independently of the LD threshold to create the haplotypes, alpha value, or trait analysed. Using genomic information (fitting individual SNP or haplotypes) provided similar or higher prediction and theoretical accuracies and reduced the dispersion of the GEBV for body weight, wool, and reproductive traits in Rambouillet sheep. However, there were no clear improvements in the prediction bias when compared to pedigree‐based predictions. The next step will be to enlarge the training populations for this breed to increase the benefits of genomic predictions.

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            Efficient methods to compute genomic predictions.

            P VanRaden (2008)
            Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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              Introduction to Quantitative Genetics

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                Author and article information

                Contributors
                britol@purdue.edu
                Journal
                J Anim Breed Genet
                J Anim Breed Genet
                10.1111/(ISSN)1439-0388
                JBG
                Journal of Animal Breeding and Genetics
                John Wiley and Sons Inc. (Hoboken )
                0931-2668
                1439-0388
                21 November 2022
                March 2023
                : 140
                : 2 ( doiID: 10.1111/jbg.v140.2 )
                : 216-234
                Affiliations
                [ 1 ] Graduate Program in Animal Sciences State University of Southwestern Bahia Itapetinga Bahia Brazil
                [ 2 ] Department of Animal Sciences Purdue University West Lafayette Indiana USA
                [ 3 ] Department of Biology State University of Southwestern Bahia Jequié Bahia Brazil
                [ 4 ] Department of Animal Sciences University of Nebraska‐Lincoln Lincoln Nebraska USA
                Author notes
                [*] [* ] Correspondence

                Luiz F. Brito, Department of Animal Sciences, Purdue University, West Lafayette, 47907, IN, USA.

                Email: britol@ 123456purdue.edu

                Author information
                https://orcid.org/0000-0002-8057-472X
                https://orcid.org/0000-0002-5819-0922
                Article
                JBG12748 JABG-22-0084.R1
                10.1111/jbg.12748
                10099590
                36408677
                731f5527-5a60-4c0f-9f21-1cea9190f978
                © 2022 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 May 2022
                : 23 October 2022
                Page count
                Figures: 5, Tables: 2, Pages: 19, Words: 12170
                Funding
                Funded by: American Rambouillet Sheep Breeders Association
                Funded by: American Sheep Industry Association Let's Grow program
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil) , doi 10.13039/501100002322;
                Funded by: National Sheep Industry Improvement Center (NSIIC‐USDA)
                Funded by: The State University of Southwest Bahia
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                March 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:13.04.2023

                best linear unbiased predictions,genomic‐enhanced estimated breeding values,haplotype prediction,linkage disequilibrium,small ruminants,single‐step genomic blup

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