Identification of Laying-Related SNP Markers in Geese Using RAD Sequencing.

Laying performance is an important economical trait of goose production. As laying performance is of low heritability, it is of significance to develop a marker-assisted selection (MAS) strategy for this trait. Definition of sequence variation related to the target trait is a prerequisite of quantitating MAS, but little is presently known about the goose genome, which greatly hinders the identification of genetic markers for the laying traits of geese. Recently developed restriction site-associated DNA (RAD) sequencing is a possible approach for discerning large-scale single nucleotide polymorphism (SNP) and reducing the complexity of a genome without having reference genomic information available. In the present study, we developed a pooled RAD sequencing strategy for detecting geese laying-related SNP. Two DNA pools were constructed, each consisting of equal amounts of genomic DNA from 10 individuals with either high estimated breeding value (HEBV) or low estimated breeding value (LEBV). A total of 139,013 SNP were obtained from 42,291,356 sequences, of which 18,771,943 were for LEBV and 23,519,413 were for HEBV cohorts. Fifty-five SNP which had different allelic frequencies in the two DNA pools were further validated by individual-based AS-PCR genotyping in the LEBV and HEBV cohorts. Ten out of 55 SNP exhibited distinct allele distributions in these two cohorts. These 10 SNP were further genotyped in a goose population of 492 geese to verify the association with egg numbers. The result showed that 8 of 10 SNP were associated with egg numbers. Additionally, liner regression analysis revealed that SNP Record-111407, 106975 and 112359 were involved in a multiplegene network affecting laying performance. We used IPCR to extend the unknown regions flanking the candidate RAD tags. The obtained sequences were subjected to BLAST to retrieve the orthologous genes in either ducks or chickens. Five novel genes were cloned for geese which harbored the candidate laying-related SNP, including membrane associated guanylate kinase (MAGI-1), KIAA1462, Rho GTPase activating protein 21 (ARHGAP21), acyl-CoA synthetase family member 2 (ACSF2), astrotactin 2 (ASTN2). Collectively, our data suggests that 8 SNP and 5 genes might be promising candidate markers or targets for marker-assisted selection of egg numbers in geese.

Identification and differential expression of microRNAs in ovaries of laying and Broody geese (Anser cygnoides) by Solexa sequencing.

BACKGROUND: Recent functional studies have demonstrated that the microRNAs (miRNAs) play critical roles in ovarian gonadal development, steroidogenesis, apoptosis, and ovulation in mammals. However, little is known about the involvement of miRNAs in the ovarian function of fowl. The goose (Anas cygnoides) is a commercially important food that is cultivated widely in China but the goose industry has been hampered by high broodiness and poor egg laying performance, which are influenced by ovarian function. METHODOLOGY/PRINCIPAL FINDINGS: In this study, the miRNA transcriptomes of ovaries from laying and broody geese were profiled using Solexa deep sequencing and bioinformatics was used to determine differential expression of the miRNAs. As a result, 11,350,396 and 9,890,887 clean reads were obtained in laying and broodiness goose, respectively, and 1,328 conserved known miRNAs and 22 novel potential miRNA candidates were identified. A total of 353 conserved microRNAs were significantly differentially expressed between laying and broody ovaries. Compared with miRNA expression in the laying ovary, 127 miRNAs were up-regulated and 126 miRNAs were down-regulated in the ovary of broody birds. A subset of the differentially expressed miRNAs (G-miR-320, G-miR-202, G-miR-146, and G-miR-143*) were validated using real-time quantitative PCR. In addition, 130,458 annotated mRNA transcripts were identified as putative target genes. Gene ontology annotation and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis suggested that the differentially expressed miRNAs are involved in ovarian function, including hormone secretion, reproduction processes and so on. CONCLUSIONS: The present study provides the first global miRNA transcriptome data in A. cygnoides and identifies novel and known miRNAs that are differentially expressed between the ovaries of laying and broody geese. These findings contribute to our understanding of the functional involvement of miRNAs in the broody period of goose.