- Animal Breeding and Genetics (5)
- WIAS (5)
- Animal Breeding and Genomics (2)
- LR - Animal Breeding & Genomics (2)
- Resource Ecology (2)
- J. Herrero-Medrano (3)
- W.F. Hooft van (1)
- L. Iacolina (1)
- I. Kavakiotis (1)
- E.F. Knol (2)
- G. Larson (1)
- W. Lutz (1)
- P.K. Mathur (2)
- H.J.W.C. Megens (3)
- H.A. Mulder (2)
- J. Napel ten (2)
- D. Ntelidou (1)
- H.H.T. Prins (1)
- H. Rashidi (2)
- M. Scandura (1)
- A. Triantafyllidis (1)
- G. Tsoumakas (1)
- I. Vlahavas (1)
- S.E. Wieren van (1)
- R.C. Ydenberg (1)
TRES: Identification of Discriminatory and Informative SNPs from Population Genomic Data
Kavakiotis, I. ; Triantafyllidis, A. ; Ntelidou, D. ; Alexandri, P. ; Megens, H.J.W.C. ; Crooijmans, R.P.M.A. ; Groenen, M.A.M. ; Tsoumakas, G. ; Vlahavas, I. - \ 2015
Journal of Heredity 106 (2015)5. - ISSN 0022-1503 - p. 672 - 676.
The advent of high-throughput genomic technologies is enabling analyses on thousands or even millions of single-nucleotide polymorphisms (SNPs). At the same time, the selection of a minimum number of SNPs with the maximum information content is becoming increasingly problematic. Available locus ranking programs have been accused of providing upwardly biased results (concerning the predicted accuracy of the chosen set of markers for population assignment), cannot handle high-dimensional datasets, and some of them are computationally intensive. The toolbox for ranking and evaluation of SNPs (TRES) is a collection of algorithms built in a user-friendly and computationally efficient software that can manipulate and analyze datasets even in the order of millions of genotypes in a matter of seconds. It offers a variety of established methods for evaluating and ranking SNPs on user defined groups of populations and produces a set of predefined number of top ranked loci. Moreover, dataset manipulation algorithms enable users to convert datasets in different file formats, split the initial datasets into train and test sets, and finally create datasets containing only selected SNPs occurring from the SNP selection analysis for later on evaluation in dedicated software such as GENECLASS. This application can aid biologists to select loci with maximum power for optimization of cost-effective panels with applications related to e.g. species identification, wildlife management, and forensic problems.
|Genetic evaluation for disease resistance and tolerance in pigs using reproduction records
Mathur, P.K. ; Herrero-Medrano, J. ; Alexandri, P. ; Knol, E.F. ; Rashidi, H. ; Mulder, H.A. ; Napel, J. ten - \ 2014
Estimating challenge load due to disease outbreaks and other challenges using reproduction records of sows
Mathur, P.K. ; Herrero-Medrano, J. ; Alexandri, P. ; Knol, E.F. ; Napel, J. ten; Rashidi, H. ; Mulder, H.A. - \ 2014
Journal of Animal Science 92 (2014)12. - ISSN 0021-8812 - p. 5374 - 5381.
environment interaction - breeding programs - respiratory syndrome - genotype - pigs - cattle - models
A method was developed and tested to estimate challenge load due to disease outbreaks and other challenges in sows using reproduction records. The method was based on reproduction records from a farm with known disease outbreaks. It was assumed that the reduction in weekly reproductive output within a farm is proportional to the magnitude of the challenge. As the challenge increases beyond certain threshold, it is manifested as an outbreak. The reproduction records were divided into 3 datasets. The first dataset called the Training dataset consisted of 57,135 reproduction records from 10,901 sows from 1 farm in Canada with several outbreaks of porcine reproductive and respiratory syndrome (PRRS). The known disease status of sows was regressed on the traits number born alive, number of losses as a combination of still birth and mummified piglets, and number of weaned piglets. The regression coefficients from this analysis were then used as weighting factors for derivation of an index measure called challenge load indicator. These weighting factors were derived with i) a two-step approach using residuals or year-week solutions estimated from a previous step, and ii) a single-step approach using the trait values directly. Two types of models were used for each approach: a logistic regression model and a general additive model. The estimates of challenge load indicator were then compared based on their ability to detect PRRS outbreaks in a Test dataset consisting of records from 65,826 sows from 15 farms in the Netherlands. These farms differed from the Canadian farm with respect to PRRS virus strains, severity and frequency of outbreaks. The single-step approach using a general additive model was best and detected 14 out of the 15 outbreaks. This approach was then further validated using the third dataset consisting of reproduction records of 831,855 sows in 431 farms located in different countries in Europe and America. A total of 41 out of 48 outbreaks detected using data analysis were confirmed based on diagnostic information received from the farms. Among these, 30 outbreaks were due to PRRS while 11 were due to other diseases and challenging conditions. The results suggest that proposed method could be useful for estimation of challenge load and detection of challenge phases such as disease outbreaks.
|Effects of Isolation and human-mediated introgression in shaping the genomic distinctiveness of an insular large mammal
Iacolina, L. ; Scandura, M. ; Goedbloed, D.J. ; Alexandri, P. ; Crooijmans, R.P.M.A. ; Larson, G. ; Groenen, M. ; Megens, H.J.W.C. - \ 2014
The evolution of island populations in natural systems is driven by local adaptation and genetic drift. However humans can affect evolutionary pathways in several ways, especially in managed species. The wild boar (Sus scrofa) is an iconic game species and is highly managed throughout its distribution range, including islands where it was introduced in prehistoric times. We examined the current genomic diversity of the Sardinian wild boar population analysing the variation at 49,803 SNPs in 99 wild boars collected throughout the island and comparing them with 196 mainland wild specimens and 105 domestic pigs belonging to 11 breeds. The analysis of SNP data by Bayesian clustering approaches and Allele Frequency Spectrum Assessment revealed that the Sardinian wild boar population is highly differentiated from the other European populations (FST = 0.126 - 0.138), and from domestic pigs, including local free-ranging stocks (FST = 0.169). Signatures of introgression were investigated by different methods and detected in 6% of the Sardinian sample. The removal of these introgressed individuals changed only slightly the distinctiveness of the Sardinian population and its overall levels of genomic variation, part of which showed possible signs of local adaptation. The patterns of diversity emerging from our analyses suggest a long history of isolation and demographic stability of the population, followed by more recent admixture. This study confirms the usefulness of genome-wide genotyping in recognizing native versus exotic sources of genetic variation in wild populations and opens new perspectives towards the understanding of microevolutionary processes in managed species
Genome-wide SNP analysis reveals recent genetic introgression from domestic pigs into Northwest European wild boar populations
Goedbloed, D.J. ; Megens, H.J.W.C. ; Hooft, W.F. van; Herrero-Medrano, J. ; Lutz, W. ; Alexandri, P. ; Crooijmans, R.P.M.A. ; Groenen, M.A.M. ; Wieren, S.E. van; Ydenberg, R.C. ; Prins, H.H.T. - \ 2013
Molecular Ecology 22 (2013)3. - ISSN 0962-1083 - p. 856 - 866.
sus-scrofa - snp discovery - dna - association - diversity - program - ecology - density - markers - set
Present-day genetic introgression from domestic pigs into European wild boar has been suggested in various studies. However, no hybrids have been identified beyond doubt mainly because available methods were unable to quantify the extent of introgression and rule out natural processes. Genetic introgression from domestic pigs may have far-reaching ecological consequences by altering traits like the reproduction rate or immunology of wild boar. In this study, we demonstrate a novel approach to investigate genetic introgression in a Northwest (NW) European wild boar data set using a genome-wide single nucleotide polymorphism (SNP) assay developed for domestic pigs. We quantified the extent of introgression using allele frequency spectrum analysis, in silico hybridization simulations and genome distribution patterns of introgressed SNPs. Levels of recent introgression in the study area were expected to be low, as pig farming practices are prevailingly intensive and indoors. However, evidence was found for geographically widespread presence of domestic pig SNPs in 10% of analysed wild boar. This was supported by the identification of two different pig mitochondrial DNA haplotypes in three of the identified hybrid wild boar, suggesting that introgression had occurred from multiple sources (pig breeds). In silico hybridization simulations showed that the level of introgression in the identified hybrid wild boar is equivalent to first-generation hybrids until fifth-generation backcrosses with wild boar. The distribution pattern of introgressed SNPs supported these assignments in four of nine hybrids. The other five hybrids are considered advanced-generation hybrids, resulting from interbreeding among hybrid individuals. Three of nine hybrids were genetically associated with a different wild boar population than the one in which they were sampled. This discrepancy suggests that genetic introgression has occurred through the escape or release of an already hybridized farmed wild boar stock. We conclude that genetic introgression from domestic pigs into NW European wild boar populations is more recent and more common than expected and that genome-wide SNP analysis is a promising tool to quantify recent hybridization in free-living populations.