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Screening of Streptococcus Suis serotype 2 resistance genes with GWAS and transcriptomic microarray analysis (SNP)

ABSTRACT: Swine streptococcosis has caused great economic loss in the swine industry, and the major pathogen responsible for this disease is Streptococcus Suis serotype 2 (SS2). Disease resistance breeding is a fundamental way of resolving this problem. With the development of GWAS and transcriptomic microarray technology, we now have powerful research tools to identify SS2 resistance genes. In this research, we generated an F2 generation of SS2 resistant C57BL/6 and SS2 susceptive A/J mice. With the F2 generation of these two mice strains and GWAS analysis, we identified 286 significant mouse genome SNPs sites associated with the SS2 resistance trait. Gene expression profiles for C57BL/6 and A/J were analyzed under SS2 infection pressure by microarray. In total, 251 differentially expressed genes were identified between these two mouse strains during SS2 infection. After combining the GWAS and gene expression profile data, we located two genes that were significantly associated with SS2 resistance, which were the UBA domain containing 1 gene (Ubac1) and Epsin 1 gene (Epn 1). GO classification and over-representation analysis revealed nine up-regulated related to immune function, which could potentially be involved in the C57BL/6 SS2 resistance trait. This is the first study to use both SNP chip and gene express profile chip for SS2 resistance gene identification in mouse, and these results will contribute to Swine SS2 resistance breeding.

ORGANISM(S): Mus musculus

PROVIDER: GSE122963 | GEO | 2018/11/27

REPOSITORIES: GEO

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Project description:Introduction: Pre-harvest Sprouting (PHS) seriously affects wheat quality and yield. However, to date there have been limited reports. It is of great urgency to breed resistance varieties via quantitative trait nucleotides (QTNs) or genes for PHS resistance in white-grained wheat. Methods: 629 Chinese wheat varieties, including 373 local wheat varieties from 70 years ago and 256 improved wheat varieties were phenotyped for spike sprouting (SS) in two environments and genotyped by wheat 660K microarray. These phenotypes were used to associate with 314,548 SNP markers for identifying QTNs for PHS resistance using several multi-locus genome-wide association study (GWAS) methods. Their candidate genes were verified by RNA-seq, and the validated candidate genes were further exploited in wheat breeding. Results: As a result, variation coefficients of 50% and 47% for PHS in 629 wheat varieties, respectively, in 2020-2021 and 2021-2022 indicated large phenotypic variation, in particular, 38 white grain varieties appeared at least medium resistance, such as Baipimai, Fengchan 3, and Jimai 20. In GWAS, 22 significant QTNs, with the sizes of 0.06% ~ 38.11%, for PHS resistance were stably identified by multiple multi-locus methods in two environments, e.g., AX-95124645 (chr3D:571.35Mb), with the sizes of 36.390% and 45.850% in 2020-2021 and 2021-2022, respectively, was detected by several multi-locus methods in two environments. As compared with previous studies, the AX-95124645 was used to develop Kompetitive Allele-Specific PCR marker QSS.TAF9-3D (chr3D:569.17Mb~573.55Mb) for the first time, especially, it is available in white-grain wheat varieties. Around this locus, nine genes were significantly differentially expressed, and two of them (TraesCS3D01G466100 and TraesCS3D01G468500) were found by GO annotation to be related to PHS resistance and determined as candidate genes. Discussion: The QTN and two new candidate genes related to PHS resistance were identified in this study. The QTN can be used to effectively identify the PHS resistance materials, especially, all the white-grained varieties with QSS.TAF9-3D-TT haplotype are resistant to spike sprouting. Thus, this study provides candidate genes, materials, and methodological basis for breeding wheat PHS resistance in the future.

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Screening of Streptococcus Suis serotype 2 resistance genes with GWAS and transcriptomic microarray analysis (SNP)

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