Genome-wide association studies using copy number variants in Brown Swiss Dairy cattle.

Detecting Copy Number Variation (CNV) in cattle provides the opportunity to study their association with quantitative traits (Winchester et al., 2009; Zhang et al., 2009; Hou et al., 2011; Clop et al., 2012; de Almeida et al., 2016;). The aim of this study was to map CNVs in 1,410 Brown Swiss males and females using Illumina BovineHD Genotyping BeadChip data and to perform a genome-wide association analysis for production functional and health traits. After quality control, CNVs were called with the GoldenHelix SVS 8.3.1 and PennCNV software and were summarized to CNV regions (CNVRs) at a population level, using BEDTools. Additionally, common CNVRs between the two software were set as consensus. CNV-association studies were executed with the CNVRuler software using a linear regression model. Genes within significant associated CNVRs for each trait were annotated with a GO analysis using the DAVID Bioinformatics Resources 6.7.The quality control filtered out 294 samples. The GoldenHelix SVS 8.3.1 software identified 25,030 CNVs summarized to 398 CNVRs while PennCNV identified 62,341 CNVs summarized to 5,578 CNVRs. A total of 127 CNVRs were identified to be significantly associated with one or more of the evaluated traits. The result of this study is a comprehensive genomic analysis of the Brown Swiss breed, which enriches the bovine CNV map in its genome. Finally, the results of the association studies deliver new information for quantitative traits considered in selection programs of the Brown Swiss breed

A high-resolution CNV map across Brown Swiss cattle populations.

Genomic studies and their use in selection programs are having a strong impact in dairy cattle selection (E. Liu et al., 2010). The first aim was to create a high resolution map of CNV regions (CNVRs) in Brown Swiss cattle and the characterization of identified CNVs as markers for quantitative and population genetic studies. CNVs were called in a set of 164 sires with PennCNV and genoCN. PennCNV identified 2,377 CNVRs comprising 1,162 and 1,131 gain and loss events, respectively, and 84 regions of complex nature. GenoCN detected 41,519 CNVRs comprising 3,475 and 34,485 gain and loss events, respectively, and 3,559 regions of complex ones. Consensus calls between algorithms were summarized to CNVRs at the population level. GenoCN was also used to identify total allelic content in consensus CNVRs. Moreover, population haplotype frequencies were calculated. Linkage disequilibrium (LD) was established between CNVs and SNPs in and around CNVRs. In this study the potential contribution of CNVs as genetic markers for genome wide association studies (GWAS) has been assessed thanks to PIC and LD values. The next aim is to investigate genomic structural variation in cattle using dense SNP information in more than 1000 samples of the Italian and Swiss Brown Swiss breed genotyped on HD Bovine BeadChips. Today there is still no CNV map available across Brown Swiss populations belonging to different countries. This study therefore expands the catalogue of CNVRs in the bovine genome, delivers an international based high-resolution map of CNVRs specific to Brown Swiss dairy cattle and will lastly provide information for GEBV estimation with CNVs

Analysis of BTA6 in Bruna Italiana and Pezzata Rossa cattle assayed with 2,535 SNPs

A high density SNP marker panel (54,000 SNPs) was used to investigate the genome of 775 Bruna Italiana and 493 Pezzata Rossa bulls. Observed and expected heterozygosities were calculated overall and per chromosome. In both breeds, values were not significantly different. Bos taurus Chromosome 6 (BTA6), carrying the casein loci, was analysed in higher detail. Overall, 2,535 markers were assayed on this chromosome. After discarding monomorphic markers, those having more than 10 missing values, and those having minor allele frequency below 2%, 1,814 and 2,061 SNPs were retained in Bruna Italiana and Pezzata Rossa, respectively. To detect signatures of ancient and recent selection, we calculated FIS inbreeding coefficient values of all BTA6 polymorphic markers, within sliding windows of groups of 5 adjacent SNPs and within 122 adjacent regions spanning 1 Mb intervals. These preliminary analyses indicated that genotyping of several thousand SNPs potentially allows the detection of the footprint of selection dodging the confounding effects of the population demographic history (i.e., effective population size, genetic structure, and mating pattern). A wider understanding of how and where selection shaped patterns of genetic variation along the genome may provide important insights into the dynamics of evolutionary change, facilitating both the identification of functionally significant genomic regions and genotype-phenotype correlations. Outlining such regions could allow focusing the fine mapping strategy to identify candidate genes and causative mutations affecting important economic or adaptive traits

Assessment of 29 candidate genes for milk traits in Italian dairy cattle

Several investigations have recently searched for significant association between gene polymorphisms and milk traits in livestock and model species. In several cases, it remains rather difficult to assess if the observed effects are caused by the mutation tested, by a nearby mutation in the same gene or by a mutation in a different gene or DNA region in linkage disequilibrium with the former. As a consequence, only in a few cases (e.g., κ-casein, SCD, DGAT1) the causative mutation seems to have been identified and, even when evidence is rather clear, genetic heterogeneity and genetic background may influence the size of allele substitution effects. Therefore, the significance of gene-trait associations and the estimate of their effect have to be verified in any new population in which this information is planned to be used, to estimate its actual utility in gene assisted breeding. In the SelMol project, we selected 29 candidate genes on the basis of known relationships between physiological or biochemical processes and evidence of significant association with milk traits in cattle, in related (e.g., sheep and goats) and model (e.g., mouse) species. A total of 106 SNPs were selected, using either information available in literature, or in silico, searching the NCBI dbSNP database. SNPs found significantly associated in other investigations were preferentially targeted. Otherwise non-synonymous SNPs and those in putative control regions (e.g., in promoter binding sites) were selected from dbSNP. If within a gene no SNP having one of these characteristics was available in dbSNP, synonymous SNPs, occurring in introns and untranslated non-control regions were chosen. DNA was extracted from semen of elite sires. SNPs polymorphism was confirmed by screening a panel of 32 individuals each of Pezzata Rossa (PR), Bruna Italiana (BI), and Frisona Italiana (FI) dairy cattle breeds. A total of 73 SNPs were confirmed as polymorphic in at least one breed: 63 in PR, 61 in BI, and 68 in FI. Polymorphic SNPs were genotyped on 400 individuals of PR and 600 of BI. Statistical tests were applied to detect selection sweeps, significant association to EBVs and phenotypic traits related to milk production and quality (milk yield, protein and fat yield and percentage), together with a number of functional traits (fertility, SCS as indicator of mastitis resistance, conformational traits, and milkability)

Interfamiliar specific fertility in Italian Brown Swiss cattle

The aim of this study is to evaluate the effects of interaction between sire of cow and service sire on the success/unsuccess of inseminations. Data from insemination events of Italian Brown Swiss cows collected from January 1993 through August 2007 were restricted to repeat breeder cows. A cluster analysis was carried out to group herds with very few observations in clusters with at least 15 observations. The edited data set included 102,710 services of 10,708 cows, daughters of 1,716 sires and mated to 3,108 service sires. The success or unsuccess at each insemination was evaluated by a linear mixed model including the fixed effects of herd-year interaction, month of insemination, age, and the random effects of sire service-sire of cow interaction and residual. The distribution of bull combination estimates was bimodal. When the tails of distribution (best and worst 5% of estimates) were considered, 271 service sires were included in both tails. Results suggest that major gene can affect the survival of embryos and that positive or negative interactions between paternal and maternal genotype can affect this reproductive trait

Identification and validation of copy number variants in Italian Brown Swiss dairy cattle using Illumina Bovine SNP50 Beadchip®

The determination of copy number variation (CNV) is very important for the evaluation of genomic traits in several species because they are a major source for the genetic variation, influencing gene expression, phenotypic variation, adaptation and the development of diseases. The aim of this study was to obtain a CNV genome map using the Illumina Bovine SNP50 BeadChip data of 651 bulls of the Italian Brown Swiss breed. PennCNV and SVS7 (Golden Helix) software were used for the detection of the CNVs and Copy Number Variation Regions (CNVRs). A total of 5,099 and 1,289 CNVs were identified with PennCNV and SVS7 software, respectively. These were grouped at the population level into 1101 (220 losses, 774 gains, 107 complex) and 277 (185 losses, 56 gains and 36 complex) CNVR. Ten of the selected CNVR were experimentally validated with a qPCR experiment. The GO and pathway analyses were conducted and they identified genes (false discovery rate corrected) in the CNVR related to biological processes cellular component, molecular function and metabolic pathways. Among those, we found the FCGR2B, PPARα, KATNAL1, DNAJC15, PTK2, TG, STAT family, NPM1, GATA2, LMF1, ECHS1 genes, already known in literature because of their association with various traits in cattle. Although there is variability in the CNVRs detection across methods and platforms, this study allowed the identification of CNVRs in Italian Brown Swiss, overlapping those already detected in other breeds and finding additional ones, thus producing new knowledge for association studies with traits of interest in cattle

The Use of Kosher Phenotyping for Mapping QTL Affecting Susceptibility to Bovine Respiratory Disease

<div><p>Bovine respiratory disease (BRD) is the leading cause of morbidity and mortality in feedlot cattle, caused by multiple pathogens that become more virulent in response to stress. As clinical signs often go undetected and various preventive strategies failed, identification of genes affecting BRD is essential for selection for resistance. Selective DNA pooling (SDP) was applied in a genome wide association study (GWAS) to map BRD QTLs in Israeli Holstein male calves. Kosher scoring of lung adhesions was used to allocate 122 and 62 animals to High (Glatt Kosher) and Low (Non-Kosher) resistant groups, respectively. Genotyping was performed using the Illumina BovineHD BeadChip according to the Infinium protocol. Moving average of -logP was used to map QTLs and Log drop was used to define their boundaries (QTLRs). The combined procedure was efficient for high resolution mapping. Nineteen QTLRs distributed over 13 autosomes were found, some overlapping previous studies. The QTLRs contain polymorphic functional and expression candidate genes to affect kosher status, with putative immunological and wound healing activities. Kosher phenotyping was shown to be a reliable means to map QTLs affecting BRD morbidity.</p></div

A cluster of significant -LogP values on BTA 29 at about 30 Mb (red arrow; Fig 2 and Table 2).

<p>Blue diamonds, -LogP values of the markers; Avg 100K, moving average -LogP values of windows of 23 markers (≈ 100Kb; see text). Note that for this cluster the peak value of the moving average exceeds the -LogP = 2.0 threshold chosen to declare significance.</p

Number of individuals in the pools.

<p>Number of individuals in the pools.</p

Effects of QTLR’s genes and pathogens.

<p>Effects of QTLR’s genes and pathogens.</p