Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs.
Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs.
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基于染色体距离和基因边界的杜洛克猪低覆盖率测序单倍型基因组预测表型值
DOI：
10.1186/s12711-021-00661-y
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发表时间：
2021-10-07
期刊：
影响因子：
--
通讯作者：
Hu X
中科院分区：
文献类型：
--
作者：
Bian C;Prakapenka D;Tan C;Yang R;Zhu D;Guo X;Liu D;Cai G;Li Y;Liang Z;Wu Z;Da Y;Hu X
利用单核苷酸多态性（SNP）标记进行基因组选择已被广泛用于家畜的遗传改良，但目前大多数基因组选择方法都是基于SNP模型。在这项研究中，我们研究了基于固定染色体距离和基因边界的单倍型模型的预测精度相比，SNP模型的基因组预测的表型值。我们还研究了单倍型基因组预测成功和失败的原因。对3195头杜洛克公猪的体尺、乳头数、年龄、100 kg体重时的腰肌面积、腰肌深度和背膘厚、30 ~ 100 kg体重时的平均日增重和饲料转化率等8个性状进行了分析。基于来自低覆盖测序的488，124个常染色体SNP，使用十倍验证来评估每个SNP模型和每个多等位基因单倍型模型的预测准确性。使用固定的染色体距离或基因边界定义单倍型块。与最佳SNP模型相比，使用单倍型模型预测表型值的准确性分别为BJS 7.4%、AGW 7.1%、ADG 6.6%、FCR 4.9%、LMA 2.7%、LMD 1.9%、BF 1.4%和TN 0.3%。基于基因的单倍型块的使用导致LMA、LMD和TN的最佳预测准确性。与SNP加性遗传力的估计值相比，单倍型上位性遗传力的估计值与单倍型模型预测准确性的增加密切相关。BJS、AGW、ADG和FCR的预测准确度增加最大，它们也具有最大的单倍型上位性遗传力估计值，BJS为24.4%，AGW为14.3%，ADG为14.5%，FCR为17.7%。整个基因组的SNP和单倍型遗传力谱鉴定了对表型具有较大遗传贡献的几个基因：NUDT 3用于LMA、LMD和BF，VRTN用于TN，COL 5A 2用于BJS，BSND用于ADG，CARTPT用于FCR。单倍型预测模型提高了杜洛克猪表型基因组预测的准确性。对于某些性状，使用基因区域定义的单倍型获得了最佳的预测精度，这提供了证据，表明功能基因组信息可以提高某些性状单倍型基因组预测的准确性。在线版本包含补充材料，可通过10.1186/s12711-021-00661-y获得。
Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction. We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries. Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR. Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits. The online version contains supplementary material available at 10.1186/s12711-021-00661-y.
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影响因子：
4.4
作者：
Duijvesteijn N;Veltmaat JM;Knol EF;Harlizius B
通讯作者：
Harlizius B
DOI：
10.1186/1297-9686-45-17
发表时间：
2013-06-13
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Morota G;Koyama M;Rosa GJ;Weigel KA;Gianola D
通讯作者：
Gianola D
影响因子：
4.1
作者：
Ask, Birgitte;Christensen, Ole F.;Nielsen, Hanne M.
通讯作者：
Nielsen, Hanne M.
DOI：
10.1186/s12711-017-0311-8
发表时间：
2017-03-29
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Tan C;Wu Z;Ren J;Huang Z;Liu D;He X;Prakapenka D;Zhang R;Li N;Da Y;Hu X
通讯作者：
Hu X
影响因子：
9.8
作者：
Browning, Brian L.;Zhou, Ying;Browning, Sharon R.
通讯作者：
Browning, Sharon R.