Silvia Sorbolini

Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy

Increased inbreeding is an inevitable consequence of selection in livestock populations. The analysis of high-density single nucleotide polymorphisms (SNPs) facilitates the identification of long and uninterrupted runs of homozygosity (ROH) that can be used to identify chromosomal regions that are identical by descent. In this work, the distribution of ROH of different lengths in five Italian cattle breeds is described. A total of 4095 bulls from five cattle breeds (2093 Italian Holstein, 749 Italian Brown, 364 Piedmontese, 410 Marchigiana and 479 Italian Simmental) were genotyped at 54K SNP loci. ROH were identified and used to estimate molecular inbreeding coefficients (FROH ), which were compared with inbreeding coefficients estimated from pedigree information (FPED ) and using the genomic relationship matrix (FGRM ). The average number of ROH per animal ranged from 54 ± 7.2 in Piedmontese to 94.6 ± 11.6 in Italian Brown. The highest number of short ROH (related to ancient consanguinity) was found in Piedmontese, followed by Simmental. The Italian Brown and Holstein had a higher proportion of longer ROH distributed across the whole genome, revealing recent inbreeding. The FPED were moderately correlated with FROH > 1 Mb (0.662, 0.700 and 0.669 in Italian Brown, Italian Holstein and Italian Simmental respectively) but poorly correlated with FGRM (0.134, 0.128 and 0.448 for Italian Brown, Italian Holstein and Italian Simmental respectively). The inclusion of ROH > 8 Mb in the inbreeding calculation improved the correlation of FROH with FPED and FGRM . ROH are a direct measure of autozygosity at the DNA level and can overcome approximations and errors resulting from incomplete pedigree data. In populations with high linkage disequilibrium (LD) and recent inbreeding (e.g. Italian Holstein and Italian Brown), a medium-density marker panel, such as the one used here, may provide a good estimate of inbreeding. However, in populations with low LD and ancient inbreeding, marker density would have to be increased to identify short ROH that are identical by descent more precisely.

Use of the canonical discriminant analysis to select SNP markers for bovine breed assignment and traceability purposes

Several market research studies have shown that consumers are primarily concerned with the provenance of the food they eat. Among the available identification methods, only DNA-based techniques appear able to completely prevent frauds. In this study, a new method to discriminate among different bovine breeds and assign new individuals to groups was developed. Bulls of three cattle breeds farmed in Italy - Holstein, Brown, and Simmental - were genotyped using the 50K SNP Illumina BeadChip. Multivariate canonical discriminant analysis was used to discriminate among breeds, and discriminant analysis (DA) was used to assign new observations. This method was able to completely identify the three groups at chromosome level. Moreover, a genome-wide analysis developed using 340 linearly independent SNPs yielded a significant separation among groups. Using the reduced set of markers, the DA was able to assign 30 independent individuals to the proper breed. Finally, a set of 48 high discriminant SNPs was selected and used to develop a new run of the analysis. Again, the procedure was able to significantly identify the three breeds and to correctly assign new observations. These results suggest that an assay with the selected 48 SNP could be used to routinely track monobreed products.

Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories

BackgroundDomestication and selection are processes that alter the pattern of within- and between-population genetic variability. They can be investigated at the genomic level by tracing the so-called selection signatures. Recently, sequence polymorphisms at the genome-wide level have been investigated in a wide range of animals. A common approach to detect selection signatures is to compare breeds that have been selected for different breeding goals (i.e. dairy and beef cattle). However, genetic variations in different breeds with similar production aptitudes and similar phenotypes can be related to differences in their selection history.MethodsIn this study, we investigated selection signatures between two Italian beef cattle breeds, Piemontese and Marchigiana, using genotyping data that was obtained with the Illumina BovineSNP50 BeadChip. The comparison was based on the fixation index (Fst), combined with a locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach. In addition, analyses of Fst were carried out to confirm candidate genes. In particular, data were processed using the varLD method, which compares the regional variation of linkage disequilibrium between populations.ResultsGenome scans confirmed the presence of selective sweeps in the genomic regions that harbour candidate genes that are known to affect productive traits in cattle such as DGAT1, ABCG2, CAPN3, MSTN and FTO. In addition, several new putative candidate genes (for example ALAS1, ABCB8, ACADS and SOD1) were detected.ConclusionsThis study provided evidence on the different selection histories of two cattle breeds and the usefulness of genomic scans to detect selective sweeps even in cattle breeds that are bred for similar production aptitudes.

Melanocortin 1 receptor (MC1R) gene polymorphisms in three Italian cattle breeds

The Melanocortin 1 receptor (MC1R) is one of the main genes implicated in the determination of the coat colour in mammals. This locus showed a relevant genetic variation between breeds that can be exploited for breed traceability of the animal productions. Modicana, Cinisara and Sardo-Modicana are three Italian endangered cattle breeds. Genetic characterisation by molecular markers is a fundamental prerequisite for managing genetic resources and for developing potential genetic traceability protocols. In order to improve the knowledge on Modicana, Cinisara and Sardo-Modicana breeds and to evaluate the possibility to develop DNA-based protocols for their mono-breeds products traceability, the genetic structure of MC1R gene was analysed. Four main alleles were observed in a representative sample of 162 animals. In the black coated Cinisara breed (n = 42), the ED and E+ alleles segregated with a frequency of 0.93 for ED allele. In the red coated Modicana (n = 60) and Sardo-Modicana (n = 60) breeds the E+ and E1 alleles segregated with frequencies of 0.42, 0.57 and 0.52, 0.47, respectively. The recessive allele e showed a low frequency (0.01) in both breeds. Sequencing a subsample of 34 animals the rare E2 allele was found only in Modicana and Sardo-Modicana at a good frequency (0.50). A new PCR-RFLP test, based on BstOI restriction endonuclease, was devised to assay for this allele. Results of the work indicate that red coat in Modicana and Sardo-Modicana cattle is genetically determined by the E+ and E1 alleles instead of the e allele at homozygote status, as occurs in other red European breeds. In these three Italian breeds of local importance, MC1R polymorphisms can be used to discriminate Cinisara from Modicana and Sardo-Modicana, but it was not able to distinguish between the two red coat populations.

Maximum difference analysis: a new empirical method for genome-wide association studies

Abstract The availability of high-density single nucleotide polymorphism (SNPs) panels for humans and, recently, for several livestock species has given a great impulse to genome-wide association studies towards the identification of genes associated with complex traits and diseases. The frequentist and the Bayesian approach are commonly used to investigate marker associations with traits of interest. Briefly, the former is the most widely used method, being intuitive and easily to apply, whereas the latter requires deeper statistical knowledge, but has the advantage to include prior information to obtain a posterior probability of association. Both methods, however, require parameters or distributions to be set a priori by the researcher. In this work, we suggest a new empirical method for genome-wide studies (GWAS), which verifies marker-trait associations using the bootstrap resampling and Chebyshev’s inequality. This method, called Maximum Difference Analysis (MDA), was tested on a real dataset of 2093 Italian Holstein bulls with the objective of finding associations between SNPs and milk, fat and protein yield and fat and protein percentage. Results of the MDA method were compared with those obtained to a genome-wide association analysis performed using the R package GenABEL. In addition, we assessed the bovine annotated genes related to the traits under study. The MDA method was able to locate known important loci for milk productive traits, such as the DGAT1, PRLR, GHR and SCD. Moreover, some new putative candidate genes were detected. The python script of MDA procedure is available at www.animalbreeding.uniss.it.