BackgroundExcess fat content in chickens has a negative impact on poultry production. The discovery of QTL associated with fat deposition in the carcass allows the identification of positional candidate genes (PCGs) that might regulate fat deposition and be useful for selection against excess fat content in chicken’s carcass. This study aimed to estimate genomic heritability coefficients and to identify QTLs and PCGs for abdominal fat (ABF) and skin (SKIN) traits in a broiler chicken population, originated from the White Plymouth Rock and White Cornish breeds.ResultsABF and SKIN are moderately heritable traits in our broiler population with estimates ranging from 0.23 to 0.33. Using a high density SNP panel (355,027 informative SNPs), we detected nine unique QTLs that were associated with these fat traits. Among these, four QTL were novel, while five have been previously reported in the literature. Thirteen PCGs were identified that might regulate fat deposition in these QTL regions: JDP2, PLCG1, HNF4A, FITM2, ADIPOR1, PTPN11, MVK, APOA1, APOA4, APOA5, ENSGALG00000000477, ENSGALG00000000483, and ENSGALG00000005043. We used sequence information from founder animals to detect 4843 SNPs in the 13 PCGs. Among those, two were classified as potentially deleterious and two as high impact SNPs.ConclusionsThis study generated novel results that can contribute to a better understanding of fat deposition in chickens. The use of high density array of SNPs increases genome coverage and improves QTL resolution than would have been achieved with low density. The identified PCGs were involved in many biological processes that regulate lipid storage. The SNPs identified in the PCGs, especially those predicted as potentially deleterious and high impact, may affect fat deposition. Validation should be undertaken before using these SNPs for selection against carcass fat accumulation and to improve feed efficiency in broiler chicken production.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4779-6) contains supplementary material, which is available to authorized users.

Chicken is an important source of protein for human nutrition and a model system for growth and developmental biology. Although the genetic architecture of quantitative traits in meat-type chickens has been the subject of ongoing investigation, the identification of mutations associated with carcass traits of economic interest remains challenging. Therefore, our aim was to identify predicted deleterious mutation, which potentially affects protein function, and test if they were associated with carcass traits in chickens. For that, we performed a genome-wide association analysis (GWAS) for breast, thigh and drumstick traits in meat-type chickens and detected 19 unique quantitative trait loci (QTL). We then used: (1) the identified windows; (2) QTL for abdominal fat detected in a previous study with the same population and (3) previously obtained whole genome sequence data, to identify 18 predicted deleterious single nucleotide polymorphisms (SNPs) in those QTL for further association with breast, thigh, drumstick and abdominal fat traits. Using the additive model, a predicted deleterious SNP c.482C > T (SIFT score of 0.4) was associated (p-value < 0.05) with abdominal fat weight and percentage. This SNP is in the second exon of the MYBPH gene, and its allele frequency deviates from Hardy–Weinberg equilibrium. In conclusion, our study provides evidence that the c.482C > T SNP in the MYBPH gene is a putative causal mutation for fat deposition in meat-type chickens.

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Association of predicted deleterious single nucleotide polymorphisms with carcass traits in meat-type chickens Breeding has been the mainly responsible for the increase of poultry efficiency in the last decades. The breeding programs are geared towards higher meat yield and feed efficiency. Among the used genomic approaches, genome wide association studies (GWAS) identified quantitative trait loci (QTLs) associated with carcass traits in a meat-type population (TT Reference Population). GWAS analysis identifies variants in linkage disequilibrium with the possible causal mutation and with the aim of refining these results, association study with missense single nucleotide polymorphisms can be useful. A missense SNP can be predicted as deleterious via Sorting Intolerant From Tolerant (SIFT) score when the amino acid change has the potential to impact the protein function and consequently may affects the phenotype. Therefore, in this study, predicted deleterious SNPs within QTLs regions were identified and associated with thigh, drumstick, abdominal fat and breast weight and their yields. Mixed model was used with sex, incubation and SNPs genotypes as fixed effects and family as random effect. From the 20 SNPs analyzed, six were significantly associated (p <0.05) with weight and yield of thigh, breast and drumstick. Three of them s736010549, rs739508259 and rs313532967 are located in the genes WDR77, VWA8 and BARL, respectively. These genes are involved in biological process as steroid hormone signaling pathway, estrogen binding, and regulation of cell proliferation. We determined these genes as candidates for muscle growth. Our strategy allowed the identification of potential causal mutations associated with muscle growth and development.

Association of predicted deleterious single nucleotide polymorphisms with carcass traits 44 in meat-type chickens 45 46 Phone number: +55 19 3429 4434 54 55 ABSTRACT 56 57In previous studies, we used genome wide association (GWAS) to identify 58 quantitative trait loci (QTL) associated with weight and yield of abdominal fat, 59 drumstick, thigh and breast traits in chickens. However, this methodology assumes that 60 the studied variants are in linkage disequilibrium with the causal mutation and 61 consequently do not identify it. In an attempt to identify causal mutations in candidate 62 genes for carcass traits in broilers, we selected 20 predicted deleterious SNPs within 63QTLs for association analysis. Additive, dominance and allele substitution effects were 64 tested. From the 20 SNPs analyzed, we identified six SNPs with significant association 65 (p-value <0.05) with carcass traits, and three are highlighted here. The SNP 66 rs736010549 was associated with drumstick weight and yield with significant additive 67 and dominance effects. The SNP rs739508259 was associated with thigh weight and 68 yield, and with significant additive and allele substitution effects. The SNP 69 rs313532967 was associated with breast weight and yield. The three SNPs that were 70 associated with carcass traits (rs736010549, rs739508259 and rs313532967) are 71 respectively located in the coding regions of the WDR77, VWA8 and BARL genes. These 72 genes are involved in biological processes such as steroid hormone signaling pathway, 73 estrogen binding, and regulation of cell proliferation. Our strategy allowed the 74 identification of putative casual mutations associated with muscle growth. 75 76 BACKGROUND 77 Chicken is an important source of protein for human nutrition and a model system in 78 growth and developmental biology (Ellegren 2005). The complete genome sequence of 79 a Red Jungle Fowl female (Gallus gallus gallus), that is considered the ancestor of 80 domestic chicken (G. g. domesticus) (Abplanalp 1992; Cassoli 2007; Dodgson et al. 81 2011), was completed in 2004 (Hillier et al. 2004) and opened the opportunity to 82 explore the molecular control of complex phenotypes such as growth and muscle 83 deposition among other traits. 84 High throughput sequencing of several chicken lines allowed the identification 85 of millions of single nucleotide polymorphisms (SNPs) in the chicken genome (Rubin et 86 al. 2010; Boschiero et al. 2018) and develop high density SNP panels (Kranis et al. 87 2013). SNPs are the most common and frequent DNA variant, with approximately 5 88 SNPs per kilobase (kb) in chicken (Rubin et al. 2010). When located in coding and 89 regulatory regions of genes, they may affect traits of economic interest in animal models 90 and livestock species (Roux et al. 2014).91High-density SNP panels were used in genome wide association studies 92 (GWAS) to identify genomic regions associated with quantitative traits such as body 93

Somatic cell count and total bacterial count…………………………………...…20 2.2.3. DNA extraction, library generation, and sequencing……………………………21 2.2.4. Sequencing data analysis, database dereplication, and taxonomy assignment…. 21 2.2.5. Core microbiome and diversity indices………………………………………… 21 2.2.6. Microbiome profile and correlation with SCS and SPS…………………………22 2.3. Results .

A produtividade do leite é influenciada por diversos fatores, dentre eles pela ação de microrganismos deteriorantes e patogênicos. A forma padrão para identificação desses microrganismos é por meio de cultura; porém, atualmente tem-se utilizado tecnologias moleculares, como sequenciamento, que podem fornecer resultados mais rápidos e precisos, identificando muitos microrganismos de uma só vez e de maneira independente do cultivo. O objetivo deste trabalho portanto, é comparar o perfil microbiano do leite, utilizando uma nova região (V2V3) do gene 16S e verificar qual das regiões (V2V3 ou V4) tem melhor capacidade de diferenciação taxonômica a nível de gênero e espécie dos principais microrganismos do leite bovino. Para esta finalidade foram realizadas reações de PCR em 96 amostras de leite, para amplificar a região V2V3 e, em seguida, foi feito o sequenciamento e análise bioinformática. Os resultados obtidos possibilitaram a comparação a nível de gênero e espécie de ambas as regiões, concluindo que a escolha da melhor região dependerá exclusivamente do objetivo da pesquisa com base no grupo de microrganismos de interesse para identificação, pois cada região é capaz de melhor classificar taxonomicamente um grupo em detrimento a outro.

The COVID-19 has severely affected economies and health systems around the world. Mass testing could work as a powerful alternative to restrain disease dissemination, but the shortage of reagents is a limiting factor. A solution to optimize test usage relies on ‘grouping’ or ‘pooling’ strategies, which combine a set of individuals in a single reaction. To compare different group testing configurations, we developed the poolingr package, which performs an innovative hybrid in silico/in vitro approach to search for optimal testing configurations. We used 6759 viral load values, observed in 2389 positive individuals, to simulate a wide range of scenarios. We found that larger groups (>100) framed into multi-stage setups (up to six stages) could largely boost the power to detect spreaders. Although the boost was dependent on the disease prevalence, our method could point to cheaper grouping schemes to better mitigate COVID-19 dissemination through identification and quarantine recommendation for positive individuals.