Data imputation and machine learning improve association analysis and genomic prediction for resistance to fish photobacteriosis in the gilthead sea bream

被引：19

作者：

Bargelloni, Luca ^{[1
]}

Tassiello, Oronzo ^{[1
]}

Babbucci, Massimiliano ^{[1
]}

Ferraresso, Serena ^{[1
]}

Franch, Rafaella ^{[1
]}

Montanucci, Ludovica ^{[1
]}

Carnier, Paolo ^{[1
]}

机构：

[1] Univ Padua, Sch Agr & Vet Med, Dept Comparat Biomed & Food Sci, I-35020 Legnaro, Italy

来源：

AQUACULTURE REPORTS | 2021年 / 20卷

关键词：

Disease resistance; Genomic prediction; Data imputation; Machine learning; Sea bream; SELECTION;

D O I：

10.1016/j.aqrep.2021.100661

中图分类号：

S9 [水产、渔业];

学科分类号：

0908 ;

摘要：

Disease resistance represents a key trait for breeding programs in aquaculture species. Here we re-analysed 2bRAD sequence data from two experimental challenges of gilthead sea bream with Photobacterium damsealae piscicida. Using a high quality reference genome, we carried out variant calling and data imputation with Beagle to obtain a large set of SNPs (80,744). This allowed the identification of eight novel QTLs for resistance to photobacteriosis across different chromosomes and revealed a highly polygenic genetic architecture. Bayesian regression approaches and machine learning methods (support vector machines and linear bagging) were compared to evaluate relative performance to classify susceptible-resistant individuals. Both data sets showed higher Matthew Correlation Coefficient (MCC) and accuracy values for machine learning methods, particularly linear bagging, with 20-70 % increase in prediction performance. Overall, machine learning methods should be explored in parallel with parametric regression approaches to increase the chances of highly effective genomic prediction.

引用

页数：6

共 27 条

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