Genome-wide mapping of Quantitative Trait Loci for fatness,fat cell characteristics and fat metabolism in three porcine F2 crosses

Background

QTL affecting fat deposition related performance traits have been considered in several studies and mapped on numerous porcine chromosomes. However, activity of specific enzymes, protein content and cell structure in fat tissue probably depend on a smaller number of genes than traits related to fat content in carcass. Thus, in this work traits related to metabolic and cytological features of back fat tissue and fat related performance traits were investigated in a genome-wide QTL analysis. QTL similarities and differences were examined between three F₂ crosses, and between male and female animals.

Methods

A total of 966 F₂ animals originating from crosses between Meishan (M), Pietrain (P) and European wild boar (W) were analysed for traits related to fat performance (11), enzymatic activity (9) and number and volume of fat cells (20). Per cross, 216 (M × P), 169 (W × P) and 195 (W × M) genome-wide distributed marker loci were genotyped. QTL mapping was performed separately for each cross in steps of 1 cM and steps were reduced when the distance between loci was shorter. The additive and dominant components of QTL positions were detected stepwise by using a multiple position model.

Results

A total of 147 genome-wide significant QTL (76 at P < 0.05 and 71 at P < 0.01) were detected for the three crosses. Most of the QTL were identified on SSC1 (between 76-78 and 87-90 cM), SSC7 (predominantly in the MHC region) and SSCX (in the vicinity of the gene CAPN6). Additional genome-wide significant QTL were found on SSC8, 12, 13, 14, 16, and 18. In many cases, the QTL are mainly additive and differ between F₂ crosses. Many of the QTL profiles possess multiple peaks especially in regions with a high marker density. Sex specific analyses, performed for example on SSC6, SSC7 and SSCX, show that for some traits the positions differ between male and female animals. For the selected traits, the additive and dominant components that were analysed for QTL positions on different chromosomes, explain in combination up to 23% of the total trait variance.

Conclusions

Our results reveal specific and partly new QTL positions across genetically diverse pig crosses. For some of the traits associated with specific enzymes, protein content and cell structure in fat tissue, it is the first time that they are included in a QTL analysis. They provide large-scale information to analyse causative genes and useful data for the pig industry.     

Epistatic QTL pairs associated with meat quality and carcass composition traits in a porcine Duroc × Pietrain population

Background

Quantitative trait loci (QTL) analyses in pig have revealed numerous individual QTL affecting growth, carcass composition, reproduction and meat quality, indicating a complex genetic architecture. In general, statistical QTL models consider only additive and dominance effects and identification of epistatic effects in livestock is not yet widespread. The aim of this study was to identify and characterize epistatic effects between common and novel QTL regions for carcass composition and meat quality traits in pig.

Methods

Five hundred and eighty five F₂ pigs from a Duroc × Pietrain resource population were genotyped using 131 genetic markers (microsatellites and SNP) spread over the 18 pig autosomes. Phenotypic information for 26 carcass composition and meat quality traits was available for all F₂ animals. Linkage analysis was performed in a two-step procedure using a maximum likelihood approach implemented in the QxPak program.

Results

A number of interacting QTL was observed for different traits, leading to the identification of a variety of networks among chromosomal regions throughout the porcine genome. We distinguished 17 epistatic QTL pairs for carcass composition and 39 for meat quality traits. These interacting QTL pairs explained up to 8% of the phenotypic variance.

Conclusions

Our findings demonstrate the significance of epistasis in pigs. We have revealed evidence for epistatic relationships between different chromosomal regions, confirmed known QTL loci and connected regions reported in other studies. Considering interactions between loci allowed us to identify several novel QTL and trait-specific relationships of loci within and across chromosomes.     

Genome-wide QTL analysis of meat quality-related traits in a large F2 intercross between Landrace and Korean native pigs

Background

We conducted a genome-wide linkage analysis to identify quantitative trait loci (QTL) that influence meat quality-related traits in a large F₂ intercross between Landrace and Korean native pigs. Thirteen meat quality-related traits of the m. longissimus lumborum et thoracis were measured in more than 830 F₂ progeny. All these animals were genotyped with 173 microsatellite markers located throughout the pig genome, and the GridQTL program based on the least squares regression model was used to perform the QTL analysis.

Results

We identified 23 genome-wide significant QTL in eight chromosome regions (SSC1, 2, 6, 7, 9, 12, 13, and 16) (SSC for Sus Scrofa) and detected 51 suggestive QTL in the 17 chromosome regions. QTL that affect 10 meat quality traits were detected on SSC12 and were highly significant at the genome-wide level. In particular, the QTL with the largest effect affected crude fat percentage and explained 22.5% of the phenotypic variance (F-ratio = 278.0 under the additive model, nominal P = 5.5 × 10⁻⁵⁵). Interestingly, the QTL on SSC12 that influenced meat quality traits showed an obvious trend for co-localization.

Conclusions

Our results confirm several previously reported QTL. In addition, we identified novel QTL for meat quality traits, which together with the associated positional candidate genes improve the knowledge on the genetic structure that underlies genetic variation for meat quality traits in pigs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0080-6) contains supplementary material, which is available to authorized users.     

Impact of QTL properties on the accuracy of multi-breed genomic prediction

Background

Although simulation studies show that combining multiple breeds in one reference population increases accuracy of genomic prediction, this is not always confirmed in empirical studies. This discrepancy might be due to the assumptions on quantitative trait loci (QTL) properties applied in simulation studies, including number of QTL, spectrum of QTL allele frequencies across breeds, and distribution of allele substitution effects. We investigated the effects of QTL properties and of including a random across- and within-breed animal effect in a genomic best linear unbiased prediction (GBLUP) model on accuracy of multi-breed genomic prediction using genotypes of Holstein-Friesian and Jersey cows.

Methods

Genotypes of three classes of variants obtained from whole-genome sequence data, with moderately low, very low or extremely low average minor allele frequencies (MAF), were imputed in 3000 Holstein-Friesian and 3000 Jersey cows that had real high-density genotypes. Phenotypes of traits controlled by QTL with different properties were simulated by sampling 100 or 1000 QTL from one class of variants and their allele substitution effects either randomly from a gamma distribution, or computed such that each QTL explained the same variance, i.e. rare alleles had a large effect. Genomic breeding values for 1000 selection candidates per breed were estimated using GBLUP modelsincluding a random across- and a within-breed animal effect.

Results

For all three classes of QTL allele frequency spectra, accuracies of genomic prediction were not affected by the addition of 2000 individuals of the other breed to a reference population of the same breed as the selection candidates. Accuracies of both single- and multi-breed genomic prediction decreased as MAF of QTL decreased, especially when rare alleles had a large effect. Accuracies of genomic prediction were similar for the models with and without a random within-breed animal effect, probably because of insufficient power to separate across- and within-breed animal effects.

Conclusions

Accuracy of both single- and multi-breed genomic prediction depends on the properties of the QTL that underlie the trait. As QTL MAF decreased, accuracy decreased, especially when rare alleles had a large effect. This demonstrates that QTL properties are key parameters that determine the accuracy of genomic prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0124-6) contains supplementary material, which is available to authorized users.     

Genome-wide association analyses for meat quality traits in Chinese Erhualian pigs and a Western Duroc?×?(Landrace × Yorkshire) commercial population

Background

Understanding the genetic mechanisms that underlie meat quality traits is essential to improve pork quality. To date, most quantitative trait loci (QTL) analyses have been performed on F₂ crosses between outbred pig strains and have led to the identification of numerous QTL. However, because linkage disequilibrium is high in such crosses, QTL mapping precision is unsatisfactory and only a few QTL have been found to segregate within outbred strains, which limits their use to improve animal performance. To detect QTL in outbred pig populations of Chinese and Western origins, we performed genome-wide association studies (GWAS) for meat quality traits in Chinese purebred Erhualian pigs and a Western Duroc × (Landrace × Yorkshire) (DLY) commercial population.

Methods

Three hundred and thirty six Chinese Erhualian and 610 DLY pigs were genotyped using the Illumina PorcineSNP60K Beadchip and evaluated for 20 meat quality traits. After quality control, 35 985 and 56 216 single nucleotide polymorphisms (SNPs) were available for the Chinese Erhualian and DLY datasets, respectively, and were used to perform two separate GWAS. We also performed a meta-analysis that combined P-values and effects of 29 516 SNPs that were common to Erhualian, DLY, F₂ and Sutai pig populations.

Results

We detected 28 and nine suggestive SNPs that surpassed the significance level for meat quality in Erhualian and DLY pigs, respectively. Among these SNPs, ss131261254 on pig chromosome 4 (SSC4) was the most significant (P = 7.97E-09) and was associated with drip loss in Erhualian pigs. Our results suggested that at least two QTL on SSC12 and on SSC15 may have pleiotropic effects on several related traits. All the QTL that were detected by GWAS were population-specific, including 12 novel regions. However, the meta-analysis revealed seven novel QTL for meat characteristics, which suggests the existence of common underlying variants that may differ in frequency across populations. These QTL regions contain several relevant candidate genes.

Conclusions

These findings provide valuable insights into the molecular basis of convergent evolution of meat quality traits in Chinese and Western breeds that show divergent phenotypes. They may contribute to genetic improvement of purebreds for crossbred performance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0120-x) contains supplementary material, which is available to authorized users.     

Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle

Background

The major obstacles for the implementation of genomic selection in Australian beef cattle are the variety of breeds and in general, small numbers of genotyped and phenotyped individuals per breed. The Australian Beef Cooperative Research Center (Beef CRC) investigated these issues by deriving genomic prediction equations (PE) from a training set of animals that covers a range of breeds and crosses including Angus, Murray Grey, Shorthorn, Hereford, Brahman, Belmont Red, Santa Gertrudis and Tropical Composite. This paper presents accuracies of genomically estimated breeding values (GEBV) that were calculated from these PE in the commercial pure-breed beef cattle seed stock sector.

Methods

PE derived by the Beef CRC from multi-breed and pure-breed training populations were applied to genotyped Angus, Limousin and Brahman sires and young animals, but with no pure-breed Limousin in the training population. The accuracy of the resulting GEBV was assessed by their genetic correlation to their phenotypic target trait in a bi-variate REML approach that models GEBV as trait observations.

Results

Accuracies of most GEBV for Angus and Brahman were between 0.1 and 0.4, with accuracies for abattoir carcass traits generally greater than for live animal body composition traits and reproduction traits. Estimated accuracies greater than 0.5 were only observed for Brahman abattoir carcass traits and for Angus carcass rib fat. Averaged across traits within breeds, accuracies of GEBV were highest when PE from the pooled across-breed training population were used. However, for the Angus and Brahman breeds the difference in accuracy from using pure-breed PE was small. For the Limousin breed no reasonable results could be achieved for any trait.

Conclusion

Although accuracies were generally low compared to published accuracies estimated within breeds, they are in line with those derived in other multi-breed populations. Thus PE developed by the Beef CRC can contribute to the implementation of genomic selection in Australian beef cattle breeding.     

Overlapping mouse subcongenic strains successfully separate two linked body fat QTL on distal MMU 2

Background

Mouse chromosome 2 is linked to growth and body fat phenotypes in many mouse crosses. With the goal to identify the underlying genes regulating growth and body fat on mouse chromosome 2, we developed five overlapping subcongenic strains that contained CAST/EiJ donor regions in a C57BL/6J^hg/hg background (hg is a spontaneous deletion of 500 Kb on mouse chromosome 10). To fine map QTL on distal mouse chromosome 2 a total of 1,712 F2 mice from the five subcongenic strains, plus 278 F2 mice from the HG2D founder congenic strain were phenotyped and analyzed. Interval mapping (IM) and composite IM (CIM) were performed on body weight and body fat traits on a combination of SNP and microsatellite markers, which generated a high-density genotyping panel.

Results

Phenotypic analysis and interval mapping of total fat mass identified two QTL on distal mouse chromosome 2. One QTL between 150 and 161 Mb, Fatq2a, and the second between 173.3 and 175.6 Mb, Fatq2b. The two QTL reside in different congenic strains with significant total fat differences between homozygous cast/cast and b6/b6 littermates. Both of these QTL were previously identified only as a single QTL affecting body fat, Fatq2. Furthermore, through a novel approach referred here as replicated CIM, Fatq2b was mapped to the Gnas imprinted locus.

Conclusions

The integration of subcongenic strains, high-density genotyping, and CIM succesfully partitioned two previously linked QTL 20 Mb apart, and the strongest QTL, Fatq2b, was fine mapped to a ~2.3 Mb region interval encompassing the Gnas imprinted locus.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-014-1191-8) contains supplementary material, which is available to authorized users.     

Fine mapping and replication of QTL in outbred chicken advanced intercross lines

Background

Linkage mapping is used to identify genomic regions affecting the expression of complex traits. However, when experimental crosses such as F₂ populations or backcrosses are used to map regions containing a Quantitative Trait Locus (QTL), the size of the regions identified remains quite large, i.e. 10 or more Mb. Thus, other experimental strategies are needed to refine the QTL locations. Advanced Intercross Lines (AIL) are produced by repeated intercrossing of F₂ animals and successive generations, which decrease linkage disequilibrium in a controlled manner. Although this approach is seen as promising, both to replicate QTL analyses and fine-map QTL, only a few AIL datasets, all originating from inbred founders, have been reported in the literature.

Methods

We have produced a nine-generation AIL pedigree (n = 1529) from two outbred chicken lines divergently selected for body weight at eight weeks of age. All animals were weighed at eight weeks of age and genotyped for SNP located in nine genomic regions where significant or suggestive QTL had previously been detected in the F₂ population. In parallel, we have developed a novel strategy to analyse the data that uses both genotype and pedigree information of all AIL individuals to replicate the detection of and fine-map QTL affecting juvenile body weight.

Results

Five of the nine QTL detected with the original F₂ population were confirmed and fine-mapped with the AIL, while for the remaining four, only suggestive evidence of their existence was obtained. All original QTL were confirmed as a single locus, except for one, which split into two linked QTL.

Conclusions

Our results indicate that many of the QTL, which are genome-wide significant or suggestive in the analyses of large intercross populations, are true effects that can be replicated and fine-mapped using AIL. Key factors for success are the use of large populations and powerful statistical tools. Moreover, we believe that the statistical methods we have developed to efficiently study outbred AIL populations will increase the number of organisms for which in-depth complex traits can be analyzed.     

Association between polymorphism in the FTO gene and growth and carcass traits in pig crosses

Background

Independent studies have shown that several single nucleotide polymorphisms (SNP) in the human FTO (fat mass and obesity associated) gene are associated with obesity. SNP have also been identified in the pig FTO gene, among which some are associated with selected fat-deposition traits in F₂ crosses and commercial populations. In this study, using both commercial pig populations and an experimental Meishan × Pietrain F₂ population, we have investigated the association between one FTO SNP and several growth and carcass traits. Association analyses were performed with the FTO polymorphism either alone or in combination with polymorphisms in flanking loci.

Methods

SNP ({"type":"entrez-nucleotide","attrs":{"text":"FM244720","term_id":"209570741","term_text":"FM244720"}}FM244720:g.400C>G) in exon 3 of porcine FTO was genotyped by PCR-RFLP and tested for associations with some growth, carcass and fat-related traits. Proportions of genetic variance of four pig chromosome 6 genes (FTO, RYR1, LIPE and TGFB1) on selected traits were evaluated using single- and multi-locus models.

Results

Linkage analysis placed FTO on the p arm of pig chromosome 6, approximately 22 cM from RYR1. In the commercial populations, allele C of the FTO SNP was significantly associated with back fat depth and allele G with muscling traits. In the Meishan × Pietrain F₂ pigs, heterozygotes with allele C from the Pietrain sows and allele G from the Meishan boar were more significantly associated with fat-related traits compared to homozygotes with allele G from the Pietrain and allele G from the Meishan breed. In single- and multi-locus models, genes RYR1, TGFB1 and FTO showed high associations. The contribution in genetic variance from the polymorphism in the FTO gene was highest for back fat depth, meat area on the musculus longissimus lumborum et thoracis tissues and metabolite glucose-6-phosphate dehydrogenase.

Conclusions

Our results show that in pig, FTO influences back fat depth in the commercial populations, while in the Meishan × Pietrain F₂ pigs with a CG genotype, heterosis occurs for several fat-related traits.     

Genetic architecture of hybrid male sterility in Drosophila: analysis of intraspecies variation for interspecies isolation

Background

The genetic basis of postzygotic isolation is a central puzzle in evolutionary biology. Evolutionary forces causing hybrid sterility or inviability act on the responsible genes while they still are polymorphic, thus we have to study these traits as they arise, before isolation is complete.

Methodology/Principal Findings

Isofemale strains of D. mojavensis vary significantly in their production of sterile F₁ sons when females are crossed to D. arizonae males. We took advantage of the intraspecific polymorphism, in a novel design, to perform quantitative trait locus (QTL) mapping analyses directly on F₁ hybrid male sterility itself. We found that the genetic architecture of the polymorphism for hybrid male sterility (HMS) in the F₁ is complex, involving multiple QTL, epistasis, and cytoplasmic effects.

Conclusions/Significance

The role of extensive intraspecific polymorphism, multiple QTL, and epistatic interactions in HMS in this young species pair shows that HMS is arising as a complex trait in this system. Directional selection alone would be unlikely to maintain polymorphism at multiple loci, thus we hypothesize that directional selection is unlikely to be the only evolutionary force influencing postzygotic isolation.     

Mapping of quantitative trait loci for flesh colour and growth traits in Atlantic salmon (Salmo salar)

Background

Flesh colour and growth related traits in salmonids are both commercially important and of great interest from a physiological and evolutionary perspective. The aim of this study was to identify quantitative trait loci (QTL) affecting flesh colour and growth related traits in an F2 population derived from an isolated, landlocked wild population in Norway (Byglands Bleke) and a commercial production population.

Methods

One hundred and twenty-eight informative microsatellite loci distributed across all 29 linkage groups in Atlantic salmon were genotyped in individuals from four F2 families that were selected from the ends of the flesh colour distribution. Genotyping of 23 additional loci and two additional families was performed on a number of linkage groups harbouring putative QTL. QTL analysis was performed using a line-cross model assuming fixation of alternate QTL alleles and a half-sib model with no assumptions about the number and frequency of QTL alleles in the founder populations.

Results

A moderate to strong phenotypic correlation was found between colour, length and weight traits. In total, 13 genome-wide significant QTL were detected for all traits using the line-cross model, including three genome-wide significant QTL for flesh colour (Chr 6, Chr 26 and Chr 4). In addition, 32 suggestive QTL were detected (chromosome-wide P < 0.05). Using the half-sib model, six genome-wide significant QTL were detected for all traits, including two for flesh colour (Chr 26 and Chr 4) and 41 suggestive QTL were detected (chromosome-wide P < 0.05). Based on the half-sib analysis, these two genome-wide significant QTL for flesh colour explained 24% of the phenotypic variance for this trait.

Conclusions

A large number of significant and suggestive QTL for flesh colour and growth traits were found in an F2 population of Atlantic salmon. Chr 26 and Chr 4 presented the strongest evidence for significant QTL affecting flesh colour, while Chr 10, Chr 5, and Chr 4 presented the strongest evidence for significant QTL affecting growth traits (length and weight). These QTL could be strong candidates for use in marker-assisted selection and provide a starting point for further characterisation of the genetic components underlying flesh colour and growth.     

Fine mapping of a QTL for ear size on porcine chromosome 5 and identification of high mobility group AT-hook 2 (HMGA2) as a positional candidate gene

Background

Ear size and shape are distinct conformation characteristics of pig breeds. Previously, we identified a significant quantitative trait locus (QTL) influencing ear surface on pig chromosome 5 in a White Duroc × Erhualian F₂ resource population. This QTL explained more than 17% of the phenotypic variance.

Methods

Four new markers on pig chromosome 5 were genotyped across this F₂ population. RT-PCR was performed to obtain expression profiles of different candidate genes in ear tissue. Standard association test, marker-assisted association test and F-drop test were applied to determine the effects of single nucleotide polymorphisms (SNP) on ear size. Three synthetic commercial lines were also used for the association test.

Results

We refined the QTL to an 8.7-cM interval and identified three positional candidate genes i.e. HMGA2, SOX5 and PTHLH that are expressed in ear tissue. Seven SNP within these three candidate genes were selected and genotyped in the F₂ population. Of the seven SNP, HMGA2 SNP ({"type":"entrez-nucleotide","attrs":{"text":"JF748727","term_id":"347311053","term_text":"JF748727"}}JF748727: g.2836 A > G) showed the strongest association with ear size in the standard association test and marker-assisted association test. With the F-drop test, F value decreased by more than 97% only when the genotypes of HMGA2 g.2836 A > G were included as a fixed effect. Furthermore, the significant association between g.2836 A > G and ear size was also demonstrated in the synthetic commercial Sutai pig line. The haplotype-based association test showed that the phenotypic variance explained by HMGA2 was similar to that explained by the QTL and at a much higher level than by SOX5. More interestingly, HMGA2 is also located within the dog orthologous chromosome region, which has been shown to be associated with ear type and size.

Conclusions

HMGA2 was the closest gene with a potential functional effect to the QTL or marker for ear size on chromosome 5. This study will contribute to identify the causative gene and mutation underlying this QTL.     

A gene-derived SNP-based high resolution linkage map of carrot including the location of QTL conditioning root and leaf anthocyanin pigmentation

Background

Purple carrots accumulate large quantities of anthocyanins in their roots and leaves. These flavonoid pigments possess antioxidant activity and are implicated in providing health benefits. Informative, saturated linkage maps associated with well characterized populations segregating for anthocyanin pigmentation have not been developed. To investigate the genetic architecture conditioning anthocyanin pigmentation we scored root color visually, quantified root anthocyanin pigments by high performance liquid chromatography in segregating F₂, F₃ and F₄ generations of a mapping population, mapped quantitative trait loci (QTL) onto a dense gene-derived single nucleotide polymorphism (SNP)-based linkage map, and performed comparative trait mapping with two unrelated populations.

Results

Root pigmentation, scored visually as presence or absence of purple coloration, segregated in a pattern consistent with a two gene model in an F₂, and progeny testing of F₃-F₄ families confirmed the proposed genetic model. Purple petiole pigmentation was conditioned by a single dominant gene that co-segregates with one of the genes conditioning root pigmentation. Root total pigment estimate (RTPE) was scored as the percentage of the root with purple color.All five anthocyanin glycosides previously reported in carrot, as well as RTPE, varied quantitatively in the F₂ population. For the purpose of QTL analysis, a high resolution gene-derived SNP-based linkage map of carrot was constructed with 894 markers covering 635.1 cM with a 1.3 cM map resolution. A total of 15 significant QTL for all anthocyanin pigments and for RTPE mapped to six chromosomes. Eight QTL with the largest phenotypic effects mapped to two regions of chromosome 3 with co-localized QTL for several anthocyanin glycosides and for RTPE. A single dominant gene conditioning anthocyanin acylation was identified and mapped.Comparative mapping with two other carrot populations segregating for purple color indicated that carrot anthocyanin pigmentation is controlled by at least three genes, in contrast to monogenic control reported previously.

Conclusions

This study generated the first high resolution gene-derived SNP-based linkage map in the Apiaceae. Two regions of chromosome 3 with co-localized QTL for all anthocyanin pigments and for RTPE, largely condition anthocyanin accumulation in carrot roots and leaves. Loci controlling root and petiole anthocyanin pigmentation differ across diverse carrot genetic backgrounds.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1118) contains supplementary material, which is available to authorized users.     

History and structure of the closed pedigreed population of Icelandic Sheepdogs

Background

Dog breeds lose genetic diversity because of high selection pressure. Breeding policies aim to minimize kinship and therefore maintain genetic diversity. However, policies like mean kinship and optimal contributions, might be impractical. Cluster analysis of kinship can elucidate the population structure, since this method divides the population in clusters of related individuals. Kinship-based analyses have been carried out on the entire Icelandic Sheepdog population, a sheep-herding breed.

Results

Analyses showed that despite increasing population size and deliberately transferring dogs, considerable genetic diversity has been lost. When cluster analysis was based on kinships calculated seven generation backwards, as performed in previous studies, results differ markedly from those based on calculations going back to the founder-population, and thus invalidate recommendations based on previous research. When calculated back to the founder-population, kinship-based clustering reveals the distribution of genetic diversity, similarly to strategies using mean kinship.

Conclusion

Although the base population consisted of 36 Icelandic Sheepdog founders, the current diversity is equivalent to that of only 2.2 equally contributing founders with no loss of founder alleles in descendants. The maximum attainable diversity is 4.7, unlikely achievable in a non-supervised breeding population like the Icelandic Sheepdog. Cluster analysis of kinship coefficients can provide a supporting tool to assess the distribution of available genetic diversity for captive population management.     

Quantitative trait loci for resistance to trichostrongylid infection in Spanish Churra sheep

Background

For ruminants reared on grazing systems, gastrointestinal nematode (GIN) parasite infections represent the class of diseases with the greatest impact on animal health and productivity. Among the many possible strategies for controlling GIN infection, the enhancement of host resistance through the selection of resistant animals has been suggested by many authors. Because of the difficulty of routinely collecting phenotypic indicators of parasite resistance, information derived from molecular markers may be used to improve the efficiency of classical genetic breeding.

Methods

A total of 181 microsatellite markers evenly distributed along the 26 sheep autosomes were used in a genome scan analysis performed in a commercial population of Spanish Churra sheep to detect chromosomal regions associated with parasite resistance. Following a daughter design, we analysed 322 ewes distributed in eight half-sib families. The phenotypes studied included two faecal egg counts (LFEC0 and LFEC1), anti-Teladorsagia circumcincta LIV IgA levels (IgA) and serum pepsinogen levels (Peps).

Results

The regression analysis revealed one QTL at the 5% genome-wise significance level on chromosome 6 for LFEC1 within the marker interval BM4621-CSN3. This QTL was found to be segregating in three out of the eight families analysed. Four other QTL were identified at the 5% chromosome-wise level on chromosomes 1, 10 and 14. Three of these QTL influenced faecal egg count, and the other one had an effect on IgA levels.

Conclusion

This study has successfully identified segregating QTL for parasite resistance traits in a commercial population. For some of the QTL detected, we have identified interesting coincidences with QTL previously reported in sheep, although most of those studies have been focused on young animals. Some of these coincidences might indicate that some common underlying loci affect parasite resistance traits in different sheep breeds. The identification of new QTL may suggest the existence of complex host-parasite relationships that have unique features depending on the host-parasite combination, perhaps due to the different mechanisms underlying resistance in adult sheep (hypersensitivity reactions) and lambs (immunity). The most significant QTL identified on chromosome 6 for LFEC₁ may be the target for future fine-mapping research efforts.     

QTL mapping of egg albumen quality in egg layers

Background

A fresh, good quality egg has a firm and gelatinous albumen that anchors the yolk and restricts growth of microbiological pathogens. As the egg ages, the gel-like structure collapses, resulting in thin and runny albumen. Occasionally thin albumen is found in a fresh egg, giving the impression of a low quality product. A mapping population consisting of 1599 F₂ hens from a cross between White Rock and Rhode Island Red lines was set up, to identify loci controlling albumen quality. The phenotype for albumen quality was evaluated by albumen height and in Haugh units (HU) measured on three consecutive eggs from each F₂ hen at the age of 40 weeks. For the fine-mapping analysis, albumen height and HU were used simultaneously to eliminate contribution of the egg size to the phenotype.

Results

Linkage analysis in a small population of seven half-sib families (668 F₂) with 162 microsatellite markers spread across 27 chromosomes revealed two genome-wide significant regions with additive effects for HU on chromosomes 7 and Z. In addition, two putative genome-wide quantitative trait loci (QTL) regions were identified on chromosomes 4 and 26. The QTL effects ranged from 2 to 4% of the phenotypic variance. The genome-wide significant QTL regions on chromosomes 7 and Z were selected for fine-mapping in the full set composed of 16 half-sib families. In addition, their existence was confirmed by an association analysis in an independent commercial Hy-Line pure line.

Conclusions

We identified four chicken genomic regions that affect albumen quality. Our results also suggest that genes that affect albumen quality act both directly and indirectly through several different mechanisms. For instance, the QTL regions on both fine-mapped chromosomes 7 and Z overlapped with a previously reported QTL for eggshell quality, indicating that eggshell membranes may play a role in albumen quality.     

Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle

Background

Four traits related to carcass performance have been identified as economically important in beef production: carcass weight, carcass fat, carcass conformation of progeny and cull cow carcass weight. Although Holstein-Friesian cattle are primarily utilized for milk production, they are also an important source of meat for beef production and export. Because of this, there is great interest in understanding the underlying genomic structure influencing these traits. Several genome-wide association studies have identified regions of the bovine genome associated with growth or carcass traits, however, little is known about the mechanisms or underlying biological pathways involved. This study aims to detect regions of the bovine genome associated with carcass performance traits (employing a panel of 54,001 SNPs) using measures of genetic merit (as predicted transmitting abilities) for 5,705 Irish Holstein-Friesian animals. Candidate genes and biological pathways were then identified for each trait under investigation.

Results

Following adjustment for false discovery (q-value < 0.05), 479 quantitative trait loci (QTL) were associated with at least one of the four carcass traits using a single SNP regression approach. Using a Bayesian approach, 46 QTL were associated (posterior probability > 0.5) with at least one of the four traits. In total, 557 unique bovine genes, which mapped to 426 human orthologs, were within 500kbs of QTL found associated with a trait using the Bayesian approach. Using this information, 24 significantly over-represented pathways were identified across all traits. The most significantly over-represented biological pathway was the peroxisome proliferator-activated receptor (PPAR) signaling pathway.

Conclusions

A large number of genomic regions putatively associated with bovine carcass traits were detected using two different statistical approaches. Notably, several significant associations were detected in close proximity to genes with a known role in animal growth such as glucagon and leptin. Several biological pathways, including PPAR signaling, were shown to be involved in various aspects of bovine carcass performance. These core genes and biological processes may form the foundation for further investigation to identify causative mutations involved in each trait. Results reported here support previous findings suggesting conservation of key biological processes involved in growth and metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-837) contains supplementary material, which is available to authorized users.     

High-density marker imputation accuracy in sixteen French cattle breeds

Background

Genotyping with the medium-density Bovine SNP50 BeadChip^® (50K) is now standard in cattle. The high-density BovineHD BeadChip^®, which contains 777 609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip.

Methods

Five thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software.

Results

Mean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed.

Conclusion

In all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.