Dataset Information

Genetic and genomic analysis for cocoon yield traits in silkworm.

ABSTRACT: Domestic species provides a powerful model for examining genetic mechanisms in the evolution of yield traits. The domestic silkworm (Bombyx mori) is an important livestock species in sericulture. While the mechanisms controlling cocoon yield are largely unknown. Here, using B. mori and its wild relative B. mandarina as intercross parents, 100 BC1 individuals were sequenced by restriction site-associated DNA sequencing (RAD-Seq). The linkage map contained 9,632 markers was constructed. We performed high-resolution quantitative trait locus (QTL) mapping for four cocoon yield traits. A total of 11 QTLs were identified, including one yield-enhancing QTL from wild silkworm. By integrating population genomics and transcriptomic analysis with QTLs, some favourable genes were revealed, including 14 domestication-related genes and 71 differentially expressed genes (DEGs) in the fifth-instar larval silk gland transcriptome between B. mori and B. mandarina. The relationships between the expression of two important candidate genes (KWMTBOMO04917 and KWMTBOMO12906) and cocoon yield were supported by quantitative real-time PCR (qPCR). Our results provide some new insights into the molecular mechanisms of complex yield traits in silkworm. The combined method might be an efficient approach for identifying putative causal genes in domestic livestock and wild relatives.

SUBMITTER: Fang SM

PROVIDER: S-EPMC7105477 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Genetic and genomic analysis for cocoon yield traits in silkworm.

Fang Shou-Min SM Zhou Qiu-Zhong QZ Yu Quan-You QY Zhang Ze Z

Scientific reports 20200330 1

Domestic species provides a powerful model for examining genetic mechanisms in the evolution of yield traits. The domestic silkworm (Bombyx mori) is an important livestock species in sericulture. While the mechanisms controlling cocoon yield are largely unknown. Here, using B. mori and its wild relative B. mandarina as intercross parents, 100 BC<sub>1</sub> individuals were sequenced by restriction site-associated DNA sequencing (RAD-Seq). The linkage map contained 9,632 markers was constructed. ...[more]

PMID: 32231221

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Project description:Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions' BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 -RR-BLUP to 0.4756-RKHS) and dry root yield (0.4689 -G-BLUP to 0.4818-RKHS) in comparison with dry matter content (0.5655 -BLASSO to 0.5670 -RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99-1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.

Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.

Dataset Information

Genetic and genomic analysis for cocoon yield traits in silkworm.

Publications

Genetic and genomic analysis for cocoon yield traits in silkworm.

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