76 research outputs found
Use of different marker pre-selection methods based on single SNP regression in the estimation of Genomic-EBVs
Two methods of SNPs pre-selection based on single marker regression for the estimation
of genomic breeding values (G-EBVs) were compared using simulated data provided by the
XII QTL-MAS workshop: i) Bonferroni correction of the significance threshold and ii) Permutation test
to obtain the reference distribution of the null hypothesis and identify significant markers at P<0.01
and P<0.001 significance thresholds. From the set of markers significant at P<0.001, random subsets
of 50% and 25% markers were extracted, to evaluate the effect of further reducing the number of
significant SNPs on G-EBV predictions. The Bonferroni correction method allowed the identification
of 595 significant SNPs that gave the best G-EBV accuracies in prediction generations (82.80%). The
permutation methods gave slightly lower G-EBV accuracies even if a larger number of SNPs resulted
significant (2,053 and 1,352 for 0.01 and 0.001 significance thresholds, respectively). Interestingly,
halving or dividing by four the number of SNPs significant at P<0.001 resulted in an only slightly decrease
of G-EBV accuracies. The genetic structure of the simulated population with few QTL carrying
large effects, might have favoured the Bonferroni method
Short communication: Imputing genotypes using PedImpute fast algorithm combining pedigree and population information
Routine genomic evaluations frequently include a preliminary imputation step, requiring high accuracy and reduced computing time. A new algorithm, PedImpute (http://dekoppel.eu/pedimpute/), was developed and compared with findhap (http://aipl.arsusda.gov/software/findhap/) and BEAGLE (http://faculty.washington.edu/browning/beagle/beagle.html), using 19,904 Holstein genotypes from a 4-country international collaboration (United States, Canada, UK, and Italy). Different scenarios were evaluated on a sample subset that included only single nucleotide polymorphism from the Bovine low-density (LD) Illumina BeadChip (Illumina Inc., San Diego, CA). Comparative criteria were computing time, percentage of missing alleles, percentage of wrongly imputed alleles, and the allelic squared correlation. Imputation accuracy on ungenotyped animals was also analyzed. The algorithm PedImpute was slightly more accurate and faster than findhap and BEAGLE when sire, dam, and maternal grandsire were genotyped at high density. On the other hand, BEAGLE performed better than both PedImpute and findhap for animals with at least one close relative not genotyped or genotyped at low density. However, computing time and resources using BEAGLE were incompatible with routine genomic evaluations in Italy. Error rate and allelic squared correlation attained by PedImpute ranged from 0.2 to 1.1% and from 96.6 to 99.3%, respectively. When complete genomic information on sire, dam, and maternal grandsire are available, as expected to be the case in the close future in (at least) dairy cattle, and considering accuracies obtained and computation time required, PedImpute represents a valuable choice in routine evaluations among the algorithms tested
Genome wide scan for somatic cell counts in holstein bulls
Mastitis is the most costly disease for dairy production, and control of the disease is often difficult, due to its multi-factorial nature. Susceptibility to mastitis is under partial genetic control and the industry uses indirect selection for decreased concentrations of somatic cells in milk to reduce mastitis.
Background: Mastitis is the most costly disease for dairy production, and control of the disease is often difficult,
due to its multi-factorial nature. Susceptibility to mastitis is under partial genetic control and the industry uses
indirect selection for decreased concentrations of somatic cells in milk to reduce mastitis.
Methods: A genome-wide scan was performed to identify genomic regions associated with deregressed estimated
breeding values (EBVs) for somatic cell counts (SCC) in Holstein bulls. In total 1183 proven bulls of the Italian of
Holstein population, were genotyped with the BovineSNP50 BeadChip (Illumina, San Diego, CA) and a whole
genome association analysis was performed using the R package GenABEL.
Results: Two chromosomal regions showed association with SCC, a region on chromosome 14 with high
significance (P < 5x10-6) and a region on chromosome 6 with moderate significance (P < 5x10-5).
Conclusions: Two regions with effects on SCC have been identified with good statistical support. A further study
of these candidate regions will be performed to verify the results and identify the causal mutations
SNPchiMp: a database to disentangle the SNPchip jungle in bovine livestock
BACKGROUND: Currently, six commercial whole-genome SNP chips are available for cattle genotyping, produced by two different genotyping platforms. Technical issues need to be addressed to combine data that originates from the different platforms, or different versions of the same array generated by the manufacturer. For example: i) genome coordinates for SNPs may refer to different genome assemblies; ii) reference genome sequences are updated over time changing the positions, or even removing sequences which contain SNPs; iii) not all commercial SNP ID’s are searchable within public databases; iv) SNPs can be coded using different formats and referencing different strands (e.g. A/B or A/C/T/G alleles, referencing forward/reverse, top/bottom or plus/minus strand); v) Due to new information being discovered, higher density chips do not necessarily include all the SNPs present in the lower density chips; and, vi) SNP IDs may not be consistent across chips and platforms. Most researchers and breed associations manage SNP data in real-time and thus require tools to standardise data in a user-friendly manner. DESCRIPTION: Here we present SNPchiMp, a MySQL database linked to an open access web-based interface. Features of this interface include, but are not limited to, the following functions: 1) referencing the SNP mapping information to the latest genome assembly, 2) extraction of information contained in dbSNP for SNPs present in all commercially available bovine chips, and 3) identification of SNPs in common between two or more bovine chips (e.g. for SNP imputation from lower to higher density). In addition, SNPchiMp can retrieve this information on subsets of SNPs, accessing such data either via physical position on a supported assembly, or by a list of SNP IDs, rs or ss identifiers. CONCLUSIONS: This tool combines many different sources of information, that otherwise are time consuming to obtain and difficult to integrate. The SNPchiMp not only provides the information in a user-friendly format, but also enables researchers to perform a large number of operations with a few clicks of the mouse. This significantly reduces the time needed to execute the large number of operations required to manage SNP data
Genome-wide patterns of homozygosity provide clues about the population history and adaptation of goats
Abstract Background Patterns of homozygosity can be influenced by several factors, such as demography, recombination, and selection. Using the goat SNP50 BeadChip, we genotyped 3171 goats belonging to 117 populations with a worldwide distribution. Our objectives were to characterize the number and length of runs of homozygosity (ROH) and to detect ROH hotspots in order to gain new insights into the consequences of neutral and selection processes on the genome-wide homozygosity patterns of goats. Results The proportion of the goat genome covered by ROH is, in general, less than 15% with an inverse relationship between ROH length and frequency i.e. short ROH ( 0.20) F ROH values. For populations from Asia, the average number of ROH is smaller and their coverage is lower in goats from the Near East than in goats from Central Asia, which is consistent with the role of the Fertile Crescent as the primary centre of goat domestication. We also observed that local breeds with small population sizes tend to have a larger fraction of the genome covered by ROH compared to breeds with tens or hundreds of thousands of individuals. Five regions on three goat chromosomes i.e. 11, 12 and 18, contain ROH hotspots that overlap with signatures of selection. Conclusions Patterns of homozygosity (average number of ROH of 77 and genome coverage of 248 Mb; F ROH < 0.15) are similar in goats from different geographic areas. The increased homozygosity in local breeds is the consequence of their small population size and geographic isolation as well as of founder effects and recent inbreeding. The existence of three ROH hotspots that co-localize with signatures of selection demonstrates that selection has also played an important role in increasing the homozygosity of specific regions in the goat genome. Finally, most of the goat breeds analysed in this work display low levels of homozygosity, which is favourable for their genetic management and viability
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