Genome‐wide association study and genomic evaluation of feed efficiency traits in Japanese Black cattle using single‐step genomic best linear unbiased prediction method

2019 ◽  
Vol 91 (1) ◽  
Author(s):  
Masayuki Takeda ◽  
Yoshinobu Uemoto ◽  
Keiichi Inoue ◽  
Atushi Ogino ◽  
Takayoshi Nozaki ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Genome Wide Association Study ◽  
Prediction Method ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Japanese Black Cattle ◽  
Genomic Evaluation ◽  
Linear Unbiased Prediction ◽  
Genome Wide ◽  
Best Linear Unbiased

scholarly journals Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 266
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deuk Hwan Lee ◽  
Chungil Cho ◽  
Taejeong Choi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Beef Cattle ◽  
Genomic Prediction ◽  
Quantitative Trait ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Best Linear Unbiased

The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

scholarly journals Indirect predictions with a large number of genotyped animals using the algorithm for proven and young

2020 ◽  
Vol 98 (6) ◽  
Author(s):  
Andre L S Garcia ◽  
Yutaka Masuda ◽  
Shogo Tsuruta ◽  
Stephen Miller ◽  
Ignacy Misztal ◽  
...  
Keyword(s):  
Random Sample ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Nucleotide Polymorphisms ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Large Numbers ◽  
Minimum Number ◽  
Best Linear Unbiased ◽  
Estimated Breeding Values

Abstract Reliable single-nucleotide polymorphisms (SNP) effects from genomic best linear unbiased prediction BLUP (GBLUP) and single-step GBLUP (ssGBLUP) are needed to calculate indirect predictions (IP) for young genotyped animals and animals not included in official evaluations. Obtaining reliable SNP effects and IP requires a minimum number of animals and when a large number of genotyped animals are available, the algorithm for proven and young (APY) may be needed. Thus, the objectives of this study were to evaluate IP with an increasingly larger number of genotyped animals and to determine the minimum number of animals needed to compute reliable SNP effects and IP. Genotypes and phenotypes for birth weight, weaning weight, and postweaning gain were provided by the American Angus Association. The number of animals with phenotypes was more than 3.8 million. Genotyped animals were assigned to three cumulative year-classes: born until 2013 (N = 114,937), born until 2014 (N = 183,847), and born until 2015 (N = 280,506). A three-trait model was fitted using the APY algorithm with 19,021 core animals under two scenarios: 1) core 2013 (random sample of animals born until 2013) used for all year-classes and 2) core 2014 (random sample of animals born until 2014) used for year-class 2014 and core 2015 (random sample of animals born until 2015) used for year-class 2015. GBLUP used phenotypes from genotyped animals only, whereas ssGBLUP used all available phenotypes. SNP effects were predicted using genomic estimated breeding values (GEBV) from either all genotyped animals or only core animals. The correlations between GEBV from GBLUP and IP obtained using SNP effects from core 2013 were ≥0.99 for animals born in 2013 but as low as 0.07 for animals born in 2014 and 2015. Conversely, the correlations between GEBV from ssGBLUP and IP were ≥0.99 for animals born in all years. IP predictive abilities computed with GEBV from ssGBLUP and SNP predictions based on only core animals were as high as those based on all genotyped animals. The correlations between GEBV and IP from ssGBLUP were ≥0.76, ≥0.90, and ≥0.98 when SNP effects were computed using 2k, 5k, and 15k core animals. Suitable IP based on GEBV from GBLUP can be obtained when SNP predictions are based on an appropriate number of core animals, but a considerable decline in IP accuracy can occur in subsequent years. Conversely, IP from ssGBLUP based on large numbers of phenotypes from non-genotyped animals have persistent accuracy over time.

scholarly journals Genome-wide Hierarchical Mixed Model Association Analysis

2021 ◽  
Author(s):  
Runqing Yang ◽  
Di Liu ◽  
Zhiyu Hao ◽  
Yuxin Song ◽  
Runqing Yang ◽  
...  
Keyword(s):  
Association Analysis ◽  
Statistical Power ◽  
Mixed Model ◽  
False Negative ◽  
Best Linear Unbiased Prediction ◽  
Least Square ◽  
Linear Unbiased Prediction ◽  
Genomic Breeding ◽  
Genome Wide ◽  
Best Linear Unbiased

Abstract We partitioned the genomic mixed model into two hierarchies to firstly estimate genomic breeding values (GBVs) using the genomic best linear unbiased prediction and then statistically infer the association of GBVs with each SNP using the generalized least square. The genome-wide hierarchical mixed model association study (named Hi-LMM) can correct effectively confounders with polygenic effects as residuals in association tests, preventing potential false negative errors produced with GRAMMAR or EMMAX. The Hi-LMM performs the same statistical power as the exact FaST-LMM with the same computing efficiency as EMMAX. When the GBVs have been estimated precisely, Hi-LMM outperforms existing methods in statistical power, especially through joint association analysis.

scholarly journals The impact of truncating data on the predictive ability for single-step genomic best linear unbiased prediction

2018 ◽  
Vol 135 (4) ◽  
pp. 251-262 ◽  
Author(s):  
Jeremy T. Howard ◽  
Tom A. Rathje ◽  
Caitlyn E. Bruns ◽  
Danielle F. Wilson-Wells ◽  
Stephen D. Kachman ◽  
...  
Keyword(s):  
Predictive Ability ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
The Impact ◽  
Unbiased Prediction
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