Comparison of analyses of the QTLMAS XIII common dataset. II: QTL analysis

Background - Five participants of the QTL-MAS 2009 workshop applied QTL analyses to the workshop common data set which contained a time-related trait: cumulative yield. Underlying the trait were 18 QTLs for three parameters of a logistic growth curve that was used for simulating the trait. Methods - Different statistical models and methods were employed to detect QTLs and estimate position and effect sizes of QTLs. Here we compare the results with respect to the numbers of QTLs detected, estimated positions and percentage explained variance. Furthermore, limiting factors in the QTL detection are evaluated. Results - All QTLs for the asymptote and the scaling factor of the logistic curve were detected by at least one of the participants. Only one out of six of the QTLs for the inflection point was detected. None of the QTLs were detected by all participants. Dominant, epistatic and imprinted QTLs were reported while only additive QTLs were simulated. The power to map QTLs for the inflection point increased when more time points were added. Conclusions - For the detection of QTLs related to the asymptote and the scaling factor, there were no strong differences between the methods used here. Also, it did not matter much whether the time course data were analyzed per single time point or whether parameters of a growth curve were first estimated and then analyzed. In contrast, the power for detection of QTLs for the inflection point was very low and the frequency of time points appeared to be a limiting factor. This can be explained by a low accuracy in estimating the inflection point from a limited time range and a limited number of time points, and by the low correlation between the simulated values for this parameter and the phenotypic data available for the individual time point

Using SNP Markers to Estimate Additive, Dominance and Imprinting Genetic Variance

The contributions of additive, dominance and imprinting effects to the variance of number of teats (NT) were evaluated in two purebred pig populations using SNP markers. Three different random regression models were evaluated, accounting for the mean and: 1) additive effects (MA), 2) additive and dominance effects (MAD) and 3) additive, dominance and imprinting effects (MADI). Additive heritability estimates were 0.30, 0.28 and 0.27-0.28 in both lines using MA, MAD and MADI, respectively. Dominance heritability ranged from 0.06 to 0.08 using MAD and MADI. Imprinting heritability ranged from 0.01 to 0.02. Dominance effects make an important contribution to the genetic variation of NT in the two lines evaluated. Imprinting effects appeared less important for NT than additive and dominance effects. The SNP random regression model presented and evaluated in this study is a feasible approach to estimate additive, dominance and imprinting variance

Whole genome QTL mapping for growth, meat quality and breast meat yield traits in turkey

Background The turkey (Meleagris gallopavo) is an important agricultural species and is the second largest contributor to the world's poultry meat production. Demand of turkey meat is increasing very rapidly. Genetic markers linked to genes affecting quantitative traits can increase the selection response of animal breeding programs. The use of these molecular markers for the identification of quantitative trait loci, and subsequently fine-mapping of quantitative trait loci regions, allows for pinpointing of genes that underlie such economically important traits. Results The quantitative trait loci analyses of the growth curve, body weight, breast yield and the meat quality traits showed putative quantitative trait loci on 21 of the 27 turkey chromosomes covered by the linkage map. Forty-five quantitative trait loci were detected across all traits and these were found in 29 different regions on 21 chromosomes. Out of the 45 quantitative trait loci, twelve showed significant (p <0.01) evidence of linkage while the remaining 33 showed suggestive evidence (p <0.05) of linkage with different growth, growth curve, meat quality and breast yield traits. Conclusion A large number of quantitative trait loci were detected across the turkey genome, which affected growth, breast yield and meat quality traits. Pleiotropic effects or close linkages between quantitative trait loci were suggested for several of the chromosomal regions. The comparative analysis regarding the location of quantitative trait loci on different turkey, and on the syntenic chicken chromosomes, along with their phenotypic associations, revealed signs of functional conservation between these specie

A Genome-Wide Association Study Reveals Dominance Effects on Number of Teats in Pigs

Dominance has been suggested as one of the genetic mechanisms explaining heterosis. However, using traditional quantitative genetic methods it is difficult to obtain accurate estimates of dominance effects. With the availability of dense SNP (Single Nucleotide Polymorphism) panels, we now have new opportunities for the detection and use of dominance at individual loci. Thus, the aim of this study was to detect additive and dominance effects on number of teats (NT), specifically to investigate the importance of dominance in a Landrace-based population of pigs. In total, 1,550 animals, genotyped for 32,911 SNPs, were used in single SNP analysis. SNPs with a significant genetic effect were tested for their mode of gene action being additive, dominant or a combination. In total, 21 SNPs were associated with NT, located in three regions with additive (SSC6, 7 and 12) and one region with dominant effects (SSC4). Estimates of additive effects ranged from 0.24 to 0.29 teats. The dominance effect of the QTL located on SSC4 was negative (-0.26 teats). The additive variance of the four QTLs together explained 7.37% of the total phenotypic variance. The dominance variance of the four QTLs together explained 1.82% of the total phenotypic variance, which corresponds to one-fourth of the variance explained by additive effects. The results suggest that dominance effects play a relevant role in the genetic architecture of NT. The QTL region on SSC7 contains the most promising candidate gene: VRTN. This gene has been suggested to be related to the number of vertebrae, a trait correlated with NT

High Imputation Accuracy in Layer Chicken from Sequence Data on a Few Key Ancestors

We assessed a scenario designed to mimic the imputation of full genome sequence data in White layer chickens, genotyped at medium (60K) density. Factors affecting accuracy were the size of the reference population, the level of the relationship between the reference and test populations and minor allele frequency of the SNP being imputed. Genotype imputation based on 22 or 62 carefully selected reference animals resulted in accuracies between 0.78 and 0.87. So, a very small reference population already provided satisfactory results. These results suggest that full genome SNP imputation is possible in layer chicken when a suitable pool of key ancestors is sequenced. SNPs with low MAF were more difficult to impute. Accuracies did not reduce when test populations were 1, 2, or 3 generations away from the reference animal

Fine mapping and single nucleotide polymorphism effects estimation on pig chromosomes 1, 4, 7, 8, 17 and X

Fine mapping of quantitative trait loci (QTL) from previous linkage studies was performed on pig chromosomes 1, 4, 7, 8, 17, and X which were known to harbor QTL. Traits were divided into: growth performance, carcass, internal organs, cut yields, and meat quality. Fifty families were used of a F2 population produced by crossing local Brazilian Piau boars with commercial sows. The linkage map consisted of 237 SNP and 37 microsatellite markers covering 866 centimorgans. QTL were identified by regression interval mapping using GridQTL. Individual marker effects were estimated by Bayesian LASSO regression using R. In total, 32 QTL affecting the evaluated traits were detected along the chromosomes studied. Seven of the QTL were known from previous studies using our F2 population, and 25 novel QTL resulted from the increased marker coverage. Six of the seven QTL that were significant at the 5% genome-wide level had SNPs within their confidence interval whose effects were among the 5% largest effects. The combined use of microsatellites along with SNP markers increased the saturation of the genome map and led to smaller confidence intervals of the QTL. The results showed that the tested models yield similar improvements in QTL mapping accuracy

Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures

Background: Genomic selection has become an important tool in the genetic improvement of animals and plants. The objective of this study was to investigate the impacts of breeding value estimation method, reference population structure, and trait genetic architecture, on long-term response to genomic selection without updating marker effects. Methods: Three methods were used to estimate genomic breeding values: a BLUP method with relationships estimated from genome-wide markers (GBLUP), a Bayesian method, and a partial least squares regression method (PLSR). A shallow (individuals from one generation) or deep reference population (individuals from five generations) was used with each method. The effects of the different selection approaches were compared under four different genetic architectures for the trait under selection. Selection was based on one of the three genomic breeding values, on pedigree BLUP breeding values, or performed at random. Selection continued for ten generations. Results: Differences in long-term selection response were small. For a genetic architecture with a very small number of three to four quantitative trait loci (QTL), the Bayesian method achieved a response that was 0.05 to 0.1 genetic standard deviation higher than other methods in generation 10. For genetic architectures with approximately 30 to 300 QTL, PLSR (shallow reference) or GBLUP (deep reference) had an average advantage of 0.2 genetic standard deviation over the Bayesian method in generation 10. GBLUP resulted in 0.6% and 0.9% less inbreeding than PLSR and BM and on average a one third smaller reduction of genetic variance. Responses in early generations were greater with the shallow reference population while long-term response was not affected by reference population structure. Conclusions: The ranking of estimation methods was different with than without selection. Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was achieved. The reference population structure had a limited effect on long-term accuracy and response. Use of a shallow reference population, most closely related to the selection candidates, gave early benefits while in later generations, when marker effects were not updated, the estimation of marker effects based on a deeper reference population did not pay off

Genetic parameters of fillet fatty acids and fat deposition in gilthead seabream (Sparus aurata) using the novel 30 k Medfish SNP array

Lipid-related traits are important candidates for a breeding goal for gilthead seabream, because they affect both fish and human health, as well as production efficiency. However, to date there have been very few estimates of genetic parameters for these traits, and the genetic relationship between fatty acids and other important traits have never been reported for gilthead seabream. Therefore, the aim of this study was to estimate genomic heritability and genetic relationships of fat deposition traits and individual muscle fatty acids in a commercial population of gilthead seabream using the novel ~30 k MedFish SNP array. In total 967 gilthead seabream fed with a commercial feed were genotyped with the MedFish SNP chip which included ~30 K informative markers for this species. On average, the fish weighed 372 g. The mean content of eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) was 822 mg per 100 g fillet. The heritability of muscle fat, viscera weight and percentage viscera were in the range of 0.34–0.46. The genetic correlation of body weight with muscle fat was 0.12, indicating that genetic variation in muscle fat is largely independent of the weight of the fish. The heritability of the product of endogenous fatty acid synthesis (n = 240), palmitoleic acid (16:1n-7), was high (0.43). The estimated heritability of EPA (%) and DHA (%) was 0.39 and 0.33, respectively. Both EPA and DHA had low, non-significant genetic correlations with body weight, and DHA had a negative genetic correlation with muscle fat (−0.53). It is possible to increase EPA and DHA content in gilthead seabream fillets by selective breeding. The high heritability of 16:1n-7, a marker of de novo lipogenesis, suggests that there is a strong genetic component to this metabolic pathway in gilthead seabream. Muscle fat deposition and body weight seem to be independent traits, and selective breeding for faster growth is not likely to influence the proportional content of EPA and DHA