A 14-bp insertion in endothelin receptor B-like (EDNRB2) is associated with white plumage in Chinese geese

BACKGROUND

Gang goose is a native species with gray plumage in Sichuan, China. As a result of overhunting, the number of gray Gang geese has decreased dramatically. To keep the species from extinction, conservation work for Gang geese was undertaken. In the process of pure breeding of gray Gang geese, approximately 2% of the offspring of each generation were white. This study aims to explain the genetic mechanism of this phenomenon and provide reliable molecular markers for goose-related plumage color breeding.

RESULTS

We used the method of pooled whole genome sequencing and Fst (fixation statistics) to identify the differentiation degree of alleles between gray Gang geese and white Gang geese from their offspring. In this way, EDNRB2, a key gene that affects the migration of melanoblasts, was identified. Then, the transcriptome was sequenced for the two geese plumage color populations, and the DEGs (differentially expressed genes) were analyzed. The results indicated that EDNRB2, as a possible candidate gene, had a significantly differential mRNA expression. In addition, a 14-bp insertion (NW_013185915.1: g. 750,748-750,735 insertion. CACAGGTGAGCTCT) in exon 3 of EDNRB2 was analyzed and found to have a significant association between gray geese and Chinese white breeds (P = 0.00), while this mutation was not found in European geese. Meanwhile, the insertion was homozygous in all the white geese we detected and heterozygous in gray geese, indicating that this mutation is recessive. Furthermore, this 14-bp insertion leads to a frameshift mutation in the EDNRB2 coding region and nonsense-mediated mRNA decay.

CONCLUSION

Our study strongly suggests that the 14-bp insertion in exon 3 of the EDNRB2 gene is associated with the white plumage phenotype in Chinese geese. This study is the first to investigate the relationship between EDNRB2 and white plumage in geese.

Identification of QTL with large effect on seed weight in a selective population of soybean with genome-wide association and fixation index analyses

BACKGROUND

Soybean seed weight is not only a yield component, but also a critical trait for various soybean food products such as sprouts, edamame, soy nuts, natto and miso. Linkage analysis and genome-wide association study (GWAS) are two complementary and powerful tools to connect phenotypic differences to the underlying contributing loci. Linkage analysis is based on progeny derived from two parents, given sufficient sample size and biological replication, it usually has high statistical power to map alleles with relatively small effect on phenotype, however, linkage analysis of the bi-parental population can't detect quantitative trait loci (QTL) that are fixed in the two parents. Because of the small seed weight difference between the two parents in most families of previous studies, these populations are not suitable to detect QTL that have considerable effects on seed weight. GWAS is based on unrelated individuals to detect alleles associated with the trait under investigation. The ability of GWAS to capture major seed weight QTL depends on the frequency of the accessions with small and large seed weight in the population being investigated. Our objective was to identify QTL that had a pronounced effect on seed weight using a selective population of soybean germplasm accessions and the approach of GWAS and fixation index analysis.

RESULTS

We selected 166 accessions from the USDA Soybean Germplasm Collection with either large or small seed weight and could typically grow in the same location. The accessions were evaluated for seed weight in the field for two years and genotyped with the SoySNP50K BeadChip containing >42,000 SNPs. Of the 17 SNPs on six chromosomes that were significantly associated with seed weight in two years based on a GWAS of the selective population, eight on chromosome 4 or chromosome 17 had significant Fst values between the large and small seed weight sub-populations. The seed weight difference of the two alleles of these eight significant SNPs varied from 8.1 g to 11.7 g/100 seeds in two years. We also identified haplotypes in three haplotype blocks with significant effects on seed weight. These findings were validated in a panel with 3753 accessions from the USDA Soybean Germplasm Collection.

CONCLUSION

This study highlighted the usefulness of selective genotyping populations coupled with GWAS and fixation index analysis for the identification of QTL with substantial effects on seed weight in soybean. This approach may help geneticists and breeders to more efficiently identify major QTL controlling other traits. The major regions and haplotypes we have identified that control seed weight differences in soybean will facilitate the identification of genes regulating this important trait.