BackgroundPigs were domesticated independently in Eastern and Western Eurasia early during the agricultural revolution, and have since been transported and traded across the globe. Here, we present a worldwide survey on 60K genome-wide single nucleotide polymorphism (SNP) data for 2093 pigs, including 1839 domestic pigs representing 122 local and commercial breeds, 215 wild boars, and 39 out-group suids, from Asia, Europe, America, Oceania and Africa. The aim of this study was to infer global patterns in pig domestication and diversity related to demography, migration, and selection.ResultsA deep phylogeographic division reflects the dichotomy between early domestication centers. In the core Eastern and Western domestication regions, Chinese pigs show differentiation between breeds due to geographic isolation, whereas this is less pronounced in European pigs. The inferred European origin of pigs in the Americas, Africa, and Australia reflects European expansion during the sixteenth to nineteenth centuries. Human-mediated introgression, which is due, in particular, to importing Chinese pigs into the UK during the eighteenth and nineteenth centuries, played an important role in the formation of modern pig breeds. Inbreeding levels vary markedly between populations, from almost no runs of homozygosity (ROH) in a number of Asian wild boar populations, to up to 20% of the genome covered by ROH in a number of Southern European breeds. Commercial populations show moderate ROH statistics. For domesticated pigs and wild boars in Asia and Europe, we identified highly differentiated loci that include candidate genes related to muscle and body development, central nervous system, reproduction, and energy balance, which are putatively under artificial selection.ConclusionsKey events related to domestication, dispersal, and mixing of pigs from different regions are reflected in the 60K SNP data, including the globalization that has recently become full circle since Chinese pig breeders in the past decades started selecting Western breeds to improve local Chinese pigs. Furthermore, signatures of ongoing and past selection, acting at different times and on different genetic backgrounds, enhance our insight in the mechanism of domestication and selection. The global diversity statistics presented here highlight concerns for maintaining agrodiversity, but also provide a necessary framework for directing genetic conservation. Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0345-y) contains supplementary material, which is available to authorized users.

The extent of linkage disequilibrium (LD) and effective population size in Finnish Landrace and Finnish Yorkshire pig populations were studied using a whole genome SNP panel (Illumina PorcineSNP60 BeadChip) and pedigree data. Genotypic data included 86 Finnish Landrace and 32 Finnish Yorkshire boars. Pedigree data included 608,138 Finnish Landrace 554,237 and Finnish Yorkshire pigs, and on average 15 ancestral generations were known for the reference animals, born in 2005 to 2009. The breeding animals of the 2 populations have been kept separate in the breeding programs. Based on the pedigree data, the current effective population size for Finnish Landrace is 91 and for Finnish Yorkshire 61. Linkage disequilibrium measures (D' and r(2)) were estimated for over 1.5 million pairs of SNP. Average r(2) for SNP 30 kb apart was 0.47 and 0.49 and for SNP 5 Mb apart 0.09 and 0.12 for Finnish Landrace and Finnish Yorkshire, respectively. Average LD (r(2)) between adjacent SNP in the Illumina PorcineSNP60 BeadChip was 0.43 (57% of the adjacent SNP pairs had r(2) > 0.2) for Finnish Landrace and 0.46 (60% of the adjacent SNP pairs had r(2) > 0.2) for Finnish Yorkshire, and average r(2) > 0.2 extended to 1.0 and 1.5 Mb for Finnish Landrace and Finnish Yorkshire, respectively. Effective population size estimates based on the decay of r(2) with distance were similar to those based on the pedigree data: 80 and 55 for Finnish Landrace and Finnish Yorkshire, respectively. Thus, the results indicate that the effective population size of Finnish Yorkshire is smaller than of Finnish Landrace and has a clear effect on the extent of LD. The current effective population size of both breeds is above the recommended minimum of 50 but may get smaller than that in the near future, if no action is taken to balance the inbreeding rate and selection response. Because a moderate level of LD extends over a long distance, selection based on whole genome SNP markers (genomic selection) is expected to be efficient for both breeds.

Type 2 diabetes (T2D) is a common, polygenic chronic disease with high heritability. The purpose of this whole-genome association study was to discover novel T2D-associated genes. We genotyped 500 familial cases and 497 controls with >300,000 HapMap-derived tagging single-nucleotide-polymorphism (SNP) markers. When a stringent statistical correction for multiple testing was used, the only significant SNP was at TCF7L2, which has already been discovered and confirmed as a T2D-susceptibility gene. For a replication study, we selected 10 SNPs in six chromosomal regions with the strongest association (singly or as part of a haplotype) for retesting in an independent case-control set including 2,573 T2D cases and 2,776 controls. The most significant replicated result was found at the AHI1-LOC441171 gene region.

QTL mapping experiments in plant breeding may involve multiple populations or pedigrees that are related through their ancestors. These known relationships have often been ignored for the sake of statistical analysis, despite their potential increase in power of mapping. We describe here a Bayesian method for QTL mapping in complex plant populations and reported the results from its application to a (previously analysed) potato data set. This Bayesian method was originally developed for human genetics data, and we have proved that it is useful for complex plant populations as well, based on a sensitivity analysis that was performed here. The method accommodates robustness to complex structures in pedigree data, full flexibility in the estimation of the number of QTL across multiple chromosomes, thereby accounting for uncertainties in the transmission of QTL and marker alleles due to incomplete marker information, and the simultaneous inclusion of non-genetic factors affecting the quantitative trait.

Single nucleotide polymorphism (SNP) data enable the estimation of inbreeding at the genome level. In this study, we estimated inbreeding levels for 19,075 Finnish Ayrshire cows genotyped with a low-density SNP panel (8K). The genotypes were imputed to 50K density, and after quality control, 39,144 SNPs remained for the analysis. Inbreeding coefficients were estimated for each animal based on the percentage of homozygous SNPs (F ), runs of homozygosity (F ) and pedigree (F ). Phenotypic records were available for 13,712 animals including non-return rate (NRR), number of inseminations (AIS) and interval from first to last insemination (IFL) for heifers and up to three parities for cows, as well as interval from calving to first insemination (ICF) for cows. Average F was 0.02, F 0.06 and F 0.63. A correlation of 0.71 was found between F and F , 0.66 between F and F and 0.94 between F and F . Pedigree-based inbreeding coefficients did not show inbreeding depression in any of the traits. However, when F or F was used as a covariate, significant inbreeding depression was observed; a 10% increase in F was associated with 5 days longer IFL0 and IFL1, 2 weeks longer IFL3 and 3 days longer ICF2 compared to non-inbred cows.

BackgroundGood genetic progress for pig reproduction traits has been achieved using a quantitative genetics-based multi-trait BLUP evaluation system. At present, whole-genome single nucleotide polymorphisms (SNP) panels provide a new tool for pig selection. The purpose of this study was to identify SNP associated with reproduction traits in the Finnish Landrace pig breed using the Illumina PorcineSNP60 BeadChip.MethodsAssociation of each SNP with different traits was tested with a weighted linear model, using SNP genotype as a covariate and animal as a random variable. Deregressed estimated breeding values of the progeny tested boars were used as the dependent variable and weights were based on their reliabilities. Statistical significance of the associations was based on Bonferroni-corrected P-values.ResultsDeregressed estimated breeding values were available for 328 genotyped boars. Of the 62 163 SNP in the chip, 57 868 SNP had a call rate > 0.9 and 7 632 SNP were monomorphic. Statistically significant results (P-value < 2.0E-06) were obtained for total number of piglets born in first and later parities and piglet mortality between birth and weaning in later parity, and suggestive associations (P-value < 4.0E-06) for piglet mortality between birth and weaning in first parity, number of stillborn piglets in later parity, first farrowing interval and second farrowing interval. Two of the statistically significant regions for total number of piglets born in first and later parities are located on chromosome 9 around 95 and 79 Mb. The estimated SNP effect in these regions was approximately one piglet between the two homozygote classes. By combining the two most significant SNP in these regions, favourable double homozygote animals are expected to have 1.3 piglets (P-value = 1.69E-08) more than unfavourable double homozygote animals. A region on chromosome 9 (66 Mb) was statistically significant for piglet mortality between birth and weaning in later parity (0.44 piglets between homozygotes, P-value = 6.94E-08).ConclusionsThree separate regions on chromosome 9 gave significant results for litter size and pig mortality. The frequencies of favourable alleles of the significant SNP are moderate in the Finnish Landrace population and these SNP are thus valuable candidates for possible marker-assisted selection.

A Bayesian method for multipoint oligogenic analysis of quantitative and qualitative traits is presented. This method can be applied to general pedigrees, which do not necessarily have to be "peelable" and can have large numbers of markers. The number of quantitative/qualitative trait loci (QTL), their map positions in the genome, and phenotypic effects (mode of inheritances) are all estimated simultaneously within the same framework. The summaries of the estimated parameters are based on the marginal posterior distributions that are obtained through Markov chain Monte Carlo (MCMC) methods. The method uses founder alleles together with segregation indicators in order to determine the genotypes of the trait loci of all individuals in the pedigree. To improve mixing properties of the sampler, we propose (1) joint sampling of map position and segregation indicators, (2) omitting data augmentation for untyped or uninformative markers (homozygous parent), and (3) updating several markers jointly within a single block. The performance of the method was tested with two replicate GAW10 data sets (considering two levels of available marker information). The results were concordant and similar to those presented earlier with other methods. These analyses clearly illustrate the utility and wide applicability of the method.

A major proportion of the costs of pork production is related to feed. The feed conversion rate (FCR) or residual feed intake (RFI) is thus commonly included in breeding programmes. Feeding behaviour traits do not directly have economic value but, if correlated with production traits, can be used as auxiliary traits. The aim of this study was to estimate the heritability of feeding behaviour traits and their genetic correlations with production traits in the Finnish Yorkshire pig population. The data were available from 3,235 pigs. Feeding behaviour was measured as the number of visits per day (NVD), time spent in feeding per day (TPD), daily feed intake (DFI), time spent feeding per visit (TPV), feed intake per visit (FPV) and feed intake rate (FR). The test station phase was divided into five periods. Estimates of heritabilities of feeding behaviour traits varied from 0.17 to 0.47. Strong genetic correlations were obtained between behaviour traits in all periods. However, only DFI was strongly correlated with the production traits. Interestingly, a moderate positive genetic correlation was obtained between FR and backfat thickness (0.1-0.5) and between FR and average daily gain (0.3-0.4), depending on the period. Based on the results, there is no additional benefit from including feeding-related traits other than those commonly used (FCR and RFI) in the breeding programme. However, if correlated with animal welfare, the feeding behaviour traits could be valuable in the breeding programme. K E Y W O R D Seating rate, feeding behaviour, feeding rate, genetic correlation, heritability, pigs | 493 KAVLAK And UIMARI ORCID Alper T. Kavlak https://orcid.org/0000-0001-6829-4242