Indirect predictions with a large number of genotyped animals using the algorithm for proven and young
Indirect predictions with a large number of genotyped animals using the algorithm for proven and young
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DOI：
10.1093/jas/skaa154
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发表时间：
2020-06-01
影响因子：
3.3
通讯作者：
Lourenco, Daniela
中科院分区：
文献类型：
--
作者：
Garcia, Andre L. S.;Masuda, Yutaka;Lourenco, Daniela
需要从基因组最佳线性无偏预测BLUP （GBLUP）和单步GBLUP （ssGBLUP）中可靠的单核苷酸多态性（SNP）效应来计算幼龄基因型动物和未包括在官方评估中的动物的间接预测（IP）。获得可靠的SNP效应和IP需要最少数量的动物，当大量基因分型动物可用时，可能需要经过验证和年轻（APY）的算法。因此，本研究的目的是用越来越多的基因型动物来评估IP，并确定计算可靠的SNP效应和IP所需的最小动物数量。出生体重、断奶体重和断奶后增重的基因型和表型由美国安格斯协会提供。具有表型的动物数量超过380万只。基因型动物被分配到三个累积年份：出生到2013年（N = 114,937），出生到2014年（N = 183,847），出生到2015年（N = 280,506）。使用APY算法拟合了19,021只核心动物的三性状模型，分为两种情景：1)核心2013（2013年之前出生的动物随机样本）用于所有年份，2)核心2014（2014年之前出生的动物随机样本）用于2014年年份，核心2015（2015年之前出生的动物随机样本）用于2015年年份。GBLUP仅使用基因分型动物的表型，而ssGBLUP使用所有可用的表型。使用所有基因型动物或仅核心动物的基因组估计育种值（GEBV）预测SNP效应。在2013年出生的动物中，来自GBLUP的GEBV与使用2013年核心SNP效应获得的IP之间的相关性为>= 0.99，而在2014年和2015年出生的动物中，相关性低至0.07。相反，在所有年份出生的动物中，来自ssGBLUP的GEBV与IP的相关性为=0.99。利用ssGBLUP的GEBV和仅基于核心动物的SNP预测计算出的IP预测能力与基于所有基因型动物的预测能力一样高。当使用2k、5k和15k核心动物计算SNP效应时，来自ssGBLUP的GEBV和IP的相关性分别为>= 0.76、>= 0.90和>= 0.98。当SNP预测基于适当数量的核心动物时，基于GBLUP的GEBV可以获得合适的IP，但IP准确性在随后的年份可能会出现相当大的下降。相反，基于非基因分型动物的大量表型的ssGBLUP IP随着时间的推移具有持久的准确性。
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.