Identification of Duplication Genotypes of the Feathering Rate Gene in Chicken by a Multiplex PCR Following Electrophoresis and/or Sanger Sequencing.

Sex-linked phenotypes of late feathering (LF) and early feathering (EF) are controlled by a pair of alleles K and k+. Autosexing based on the feathering rate is widely used in poultry production. It is reported that a tandem duplication of 176,324 base pairs linked to the K locus is responsible for LF expression and could be used as a molecular marker to detect LF chicken. So far, there is no genotyping method that can accurately and stably identify the LF homozygote and heterozygote in all chicken breeds. In the present study, a multiplex PCR test was developed to identify EF, LF homozygote, and heterozygote according to electrophoretic bands and the relative height of the peaks by Sanger sequencing. We tested 413 chickens of six native Chinese breeds with this method. The identification was consistent with the sex and phenotype records of the chickens. Band density analysis was performed, and the results supported our genotyping using the new assay. In order to further verify the accuracy of this test in distinguishing homozygote and heterozygote males, 152 LF males were mated with EF females, and the results of the offspring's phenotypes were consistent with our expectations. Our results support tandem duplication as molecular markers of LF, and this new test is applicable to all LF chickens associated with tandem duplication.

Genome-Wide Association Study Identifies Candidate Genes for Stripe Pattern Feather Color of Rhode Island Red Chicks.

Feather colors of chickens are not only characteristics of breeds but also as phenotypic markers in chicken breeding. Pure-bred Rhode Island Red (RIR) chicks have a stripe pattern and a non-stripe pattern on the back. The stripe pattern of RIR is generally shown as four longitudinal black stripes on the back and is more likely to appear in females. In this study, we performed a genome-wide association study (GWAS) to identify candidate genes controlling the stripe pattern of RIR chicks, and then, based on physical location and biological functions, quantitative RT-PCR analysis was used to validate the differential expression of candidate genes between stripe pattern and non-stripe pattern back skin tissue. The GWAS showed that a major signal contains 768 significant single nucleotide polymorphisms (SNPs) and 87 significant small insertions-deletions (INDELs) spanning 41.78 to 43.05 Mb (~1.27 Mb) on GGA1, corresponding to 16 genes associated with stripe pattern phenotype. Among these 16 genes, KITLG and TMTC3 could be considered candidate genes as they showed different expressions between back skin tissues of stripe pattern and non-stripe pattern chicks in value (p = 0.062) and the significant level (p < 0.05), respectively. This study provided novel insight into the mechanisms underlying feather pigmentation and stripe formation in RIR chicks.

Comparison of seven SNP calling pipelines for the next-generation sequencing data of chickens.

Single nucleotide polymorphisms (SNPs) are widely used in genome-wide association studies and population genetics analyses. Next-generation sequencing (NGS) has become convenient, and many SNP-calling pipelines have been developed for human NGS data. We took advantage of a gap knowledge in selecting the appropriated SNP calling pipeline to handle with high-throughput NGS data. To fill this gap, we studied and compared seven SNP calling pipelines, which include 16GT, genome analysis toolkit (GATK), Bcftools-single (Bcftools single sample mode), Bcftools-multiple (Bcftools multiple sample mode), VarScan2-single (VarScan2 single sample mode), VarScan2-multiple (VarScan2 multiple sample mode) and Freebayes pipelines, using 96 NGS data with the different depth gradients of approximately 5X, 10X, 20X, 30X, 40X, and 50X coverage from 16 Rhode Island Red chickens. The sixteen chickens were also genotyped with a 50K SNP array, and the sensitivity and specificity of each pipeline were assessed by comparison to the results of SNP arrays. For each pipeline, except Freebayes, the number of detected SNPs increased as the input read depth increased. In comparison with other pipelines, 16GT, followed by Bcftools-multiple, obtained the most SNPs when the input coverage exceeded 10X, and Bcftools-multiple obtained the most when the input was 5X and 10X. The sensitivity and specificity of each pipeline increased with increasing input. Bcftools-multiple had the highest sensitivity numerically when the input ranged from 5X to 30X, and 16GT showed the highest sensitivity when the input was 40X and 50X. Bcftools-multiple also had the highest specificity, followed by GATK, at almost all input levels. For most calling pipelines, there were no obvious changes in SNP numbers, sensitivities or specificities beyond 20X. In conclusion, (1) if only SNPs were detected, the sequencing depth did not need to exceed 20X; (2) the Bcftools-multiple may be the best choice for detecting SNPs from chicken NGS data, but for a single sample or sequencing depth greater than 20X, 16GT was recommended. Our findings provide a reference for researchers to select suitable pipelines to obtain SNPs from the NGS data of chickens or nonhuman animals.

Comparison of Rumen Microbiota and Serum Biochemical Indices in White Cashmere Goats Fed Ensiled or Sun-Dried Mulberry Leaves.

Mulberry leaves, which have high nutritional value, have not been fully utilized. Few research systems have indicated whether mulberry leaves can replace traditional feed ingredients in goats. In this study, we investigated the effects of feeding white cashmere goats ensiled (Group E) or sun-dried mulberry leaves (Group S) on changes in ruminal microbial communities, rumen fermentation parameters and serum biochemical indices. The control group (Group C) received a typical total mixed ration (TMR). 16S rRNA gene sequencing revealed 209 genera belonging to 19 bacterial phyla dominated by Firmicutes and Bacteroidetes. Only the relative abundances of Erysipelotrichaceae_UCG-009 were significantly different among the three groups (p < 0.05). Physiological and biochemical findings revealed that only the serum leptin concentrations were significantly decreased when mulberry leaves were added to the diets (p < 0.05). Correlation analysis revealed that Ruminococcus_2 were significantly positively correlated with the butyrate concentration. These findings suggested that supplementation with mulberry leaves only induced minor changes in the abovementioned indicators, implying that the rumen fermentation status was still stable after adding mulberry leaves to the diets.