Search

Commercial single nucleotide polymorphism (SNP) arrays have been recently developed for several species and can be used to identify informative markers to differentiate breeds or populations for several downstream applications. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this work, we compared several methods of SNPs preselection (Delta, Fst and principal component analyses (PCA)) in addition to Random Forest classifications to analyse SNP data from six dairy cattle breeds, including cosmopolitan (Holstein, Brown and Simmental) and autochthonous Italian breeds raised in two different regions and subjected to limited or no breeding programmes (Cinisara, Modicana, raised only in Sicily and Reggiana, raised only in Emilia Romagna). From these classifications, two panels of 96 and 48 SNPs that contain the most discriminant SNPs were created for each preselection method. These panels were evaluated in terms of the ability to discriminate as a whole and breed-by-breed, as well as linkage disequilibrium within each panel. The obtained results showed that for the 48-SNP panel, the error rate increased mainly for autochthonous breeds, probably as a consequence of their admixed origin lower selection pressure and by ascertaining bias in the construction of the SNP chip. The 96-SNP panels were generally more able to discriminate all breeds. The panel derived by PCA-chrom (obtained by a preselection chromosome by chromosome) could identify informative SNPs that were particularly useful for the assignment of minor breeds that reached the lowest value of Out Of Bag error even in the Cinisara, whose value was quite high in all other panels. Moreover, this panel contained also the lowest number of SNPs in linkage disequilibrium. Several selected SNPs are located nearby genes affecting breed-specific phenotypic traits (coat colour and stature) or associated with production traits. In general, our results demonstrated the usefulness of Random Forest in combination to other reduction techniques to identify population informative SNPs. [ABSTRACT FROM AUTHOR]

Genomic and genetic variation among six Italian chicken native breeds (Livornese, Mericanel della Brianza, Milanino, Bionda Piemontese, Bianca di Saluzzo and Siciliana) were studied using single nucleotide polymorphism (SNP) and copy number variants (CNV) as markers. A total of 94 DNA samples genotyped with Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix) were used in the analyses. The results showed the genetic and genomic variability occurring among the six Italian chicken breeds. The genetic relationship among animals was established with a principal component analysis. The genetic diversity within breeds was calculated using heterozygosity values (expected and observed) and with Wright’s F-statistics. The individual-based CNV calling, based on log R ratio and B-allele frequency values, was done by the Hidden–Markov Model (HMM) of PennCNV software on autosomes. A hierarchical agglomerative clustering was applied in each population according to the absence or presence of definite CNV regions (CNV were grouped by overlapping of at least 1 bp). The CNV map was built on a total of 1003 CNV found in individual samples, after grouping by overlaps, resulting in 564 unique CNV regions (344 gains, 213 losses and 7 complex), for a total of 9.43 Mb of sequence and 1.03% of the chicken assembly autosome. All the approaches using SNP data showed that the Siciliana breed clearly differentiate from other populations, the Livornese breed separates into two distinct groups according to the feather colour (i.e. white and black) and the Bionda Piemontese and Bianca di Saluzzo breeds are closely related. The genetic variability found using SNP is comparable with that found by other authors in the same breeds using microsatellite markers. The CNV markers analysis clearly confirmed the SNP results. [ABSTRACT FROM PUBLISHER]

A selective DNA pooling approach was applied to identify QTL for conjugated linoleic acid ( CLA), vaccenic acid ( VA) and Δ9-desaturase (D9D) milk content in Italian Brown Swiss dairy cattle. Milk samples from 60 animals with higher values (after correction for environmental factors) and 60 animals with lower values for each of these traits from each of five half-sib families were pooled separately. The pools were genotyped using the Illumina Bovine SNP50 BeadChip. Sire allele frequencies were compared between high and low tails at the sire and marker level for SNPs for which the sires were heterozygous. An r procedure was implemented to perform data analysis in a selective DNA pooling design. A correction for multiple tests was applied using the proportion of false positives among all test results. BTA 19 showed the largest number of markers in association with CLA. Associations between SNPs and the VA and Δ9-desaturase traits were found on several chromosomes. A bioinformatics survey identified genes with an important role in pathways for milk fat and fatty acids metabolism within 1 Mb of SNP markers associated with fatty acids contents. [ABSTRACT FROM AUTHOR]

Copy Number Variations (CNVs) have become promising markers, representing a major source of genomic variation. CNV involvement in phenotypic expression and in different diseases onset have been widely demonstrated in humans as well as in many domestic animals. However, this genomic investigation is still missing in Felis catus. This work is the first CNV mapping from a large data set of Whole Genome Sequencing (WGS) data in the domestic cat. A total of 42 cats of 14 different breeds were sequenced on the Illumina XTen (Washington University-St. Louis) which generated approximately 30-fold genome coverage from 150 pairedend reads (99 Lives Initiative). Maverix Biomics mapped the reads on the v6.2 reference assembly. CNV detection was performed using cn.mops and CNVnator, two Read Depth method software. One cat was excluded as outlier while, on the 41 remaining individuals, 1640 CNVs were detected by both the software and used to obtain 2891 CNVRs with BedTools. CNVRs covered the 0.4% of the total cat genome, with 136 loss, 127 gain and 26 complex detected (Figure 1). A total of 164 singletons were identified and 9 CNVRs mapped in at least the 50% of the individuals. The number of CNVs in each cat ranged from 12 to 83. The clustering analysis of the detected CNVs was performed with R package "pvclust" and shows that same breed individuals cluster together. This study has led to the genetic characterization of 14 main cat breeds. Further analyses including other breeds and considering the genes located within these regions, could lead to better evaluate the relationship between the presence of a specific CNV and a specific breed trait. This study can be considered a starting point for genomic CNV identification in the domestic cat, which could be further developed using the new released Felis catus vs9.0 reference aassembly. [ABSTRACT FROM AUTHOR]

This work aimed to confirm previously reported quantitative trait loci (QTL) affecting the somatic cell score (SCS) in dairy cattle on Bos taurus autosomes (BTA) 4 and 26. A granddaughter design with selective genotyping was implemented that included half-sib families from 12 male lines of Italian Holstein cattle. The animals were genotyped for 5 microsatellite markers each on regions of BTA 4 (average marker spacing 9.42 cM) and BTA 26 (average marker spacing 5.26 cM), previously reported by other authors as carrying QTL for somatic cell count. Quantitative trait loci analyses were performed using interval mapping by regressing sire breeding values for SCS onto genotype probabilities at 1-cM intervals along the 2 chromosome regions. Breeding values for SCS were estimated for the whole population using a test-day repeatability animal model. Results were not significant on a chromosome basis, but a possible QTL was found at BM4505 on BTA 26, confirming this region for further studies of QTL affecting SCS in the Italian Holstein population. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWASs) are used to identify quantitative trait loci for phenotypic traits of interest. The use of multilocus mixed models allows to correct for population stratification and account for long-range linkage disequilibrium. In this study, GWASs were conducted to identify the genetic bases of milk production (milk yield, protein and fat composition, and yield) in two autochthonous dual-purpose cattle breeds from the Aosta Valley. Using either the breeding values or the deregressed proofs, common significative single nucleotide polymorphisms have been identified for milk yield, protein percentage, and fat percentage. Two major quantitative trait loci regions have been identified on the chromosomes 5 and 14 for the fat percentage, harbouring the MGST1, CYHR1, VPS28, and CPSF1 genes. For the protein percentage, a candidate region has been identified on BTA 6; in this region, the CSN1S1, CSN2, HSTN, CSN3, and RUFY3 genes are annotated. Most of the identified genes have already been associated with milk composition in other studies on cosmopolitan and local cattle. These results show that the genes involved in milk composition quantitative traits in the Aosta cattle are common also in other cattle breeds and they can be further investigated with the use of whole genome sequencing data., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Mastitis, the most common and expensive disease in dairy cows, implies significant losses in the dairy industry worldwide. Many efforts have been made to improve genetic mastitis resistance in dairy populations, but low heritability of this trait made this process not as effective as desired. The purpose of this study was to identify genomic regions explaining genetic variation of somatic cell count using copy number variations (CNVs) as markers in the Holstein population, genotyped with the Illumina BovineHD BeadChip. We found 24 and 47 copy number variation regions significantly associated with estimated breeding values for somatic cell score (SCS_EBVs) using SVS 8.3.1 and PennCNV-CNVRuler software, respectively. The association analysis performed with these two software allowed the identification of 18 candidate genes (TERT, NOTCH1, SLC6A3, CLPTM1L, PPARα, BCL-2, ABO, VAV2, CACNA1S, TRAF2, RELA, ELF3, DBH, CDK5, NF2, FASN, EWSR1 and MAP3K11) that result classified in the same functional cluster. These genes are also part of two gene networks, whose genes share the 'stress', 'cell death', 'inflammation' and 'immune response' GO terms. Combining CNV detection/association analysis based on two different algorithms helps towards a more complete identification of genes linked to phenotypic variation of the somatic cell count., (© 2016 Blackwell Verlag GmbH.)