Combined use of phenotypic and genotypic information in sampling animals for genotyping in detection of quantitative trait loci.

Conventional selective genotyping which is using the extreme phenotypes (EP) was compared with alternative criteria to find the most informative animals for genotyping with respects to mapping quantitative trait loci (QTL). Alternative sampling strategies were based on minimizing the sampling error of the estimated QTL effect (MinERR) and maximizing likelihood ratio test (MaxLRT) using both phenotypic and genotypic information. In comparison, animals were randomly genotyped either within or across families. One hundred data sets were simulated each with 30 half-sib families and 120 daughters per family. The strategies were compared in these datasets with respect to estimated effect and position of a QTL within a previously defined genomic region at genotyping 10, 20 or 30% of the animals. Combined linkage disequilibrium linkage analysis (LDLA) was applied in a variance component approach. Power to detect QTL was significantly higher for both MinERR and MaxLRT compared with EP and random genotyping methods (either across or within family), for all the proportions of genotyped animals. Power to detect significant QTL (alpha = 0.01) with 20% genotyping for MinERR and MaxLRT was 80 and 75% of that obtained with complete genotyping compared with 70 and 38% genotyping for EP within and across families respectively. With 30% genotyping, the powers were 78, 83, 78 and 58% respectively. The estimated variance components were unbiased in EP strategies (within and across family), only when at least 30% was genotyped. To decrease the number of genotyped individuals either MinERR or MaxLRT could be considered. With 20% genotyping in MinERR, the estimated QTL variance components were not significant compared with complete genotype information but all studied strategies at 20% genotyping overestimated the QTL effect. Results showed that combining the phenotypic and genotypic information in selective genotyping (e.g. MinERR and MaxLRT) is better than using only the EPs and the combined methods can be considered as alternative approaches to decrease genotyping costs, with unbiased QTL effects, decreased sampling variance of the QTL variance component and also increased the power of QTL detection.     

Fine mapping and detection of the causative mutation underlying Quantitative Trait Loci

The effect on power and precision of including the causative SNP amongst the investigated markers in Quantitative Trait Loci (QTL) mapping experiments was investigated. Three fine mapping methods were tested to see which was most efficient in finding the causative mutation: combined linkage and linkage disequilibrium mapping (LLD); association mapping (MARK); a combination of LLD and association mapping (LLDMARK). Two simulated data sets were analysed: in one set, the causative SNP was included amongst the markers, while in the other set the causative SNP was masked between markers. Including the causative SNP amongst the markers increased both precision and power in the analyses. For the LLD method the number of correctly positioned QTL increased from 17 for the analysis without the causative SNP to 77 for the analysis including the causative SNP. The likelihood of the data analysis increased from 3.4 to 13.3 likelihood units for the MARK method when the causative SNP was included. When the causative SNP was masked between the analysed markers, the LLD method was most efficient in detecting the correct QTL position, while the MARK method was most efficient when the causative SNP was included as a marker in the analysis. The LLDMARK method, combining association mapping and LLD, assumes a QTL as the null hypothesis (using LLD method) and tests whether the ‘putative causative SNP’ explains significantly more variance than a QTL in the region. Thus, if the putative causative SNP does not only give an Identical‐By‐Descent (IBD) signal, but also an Alike‐In‐State (AIS) signal, LLDMARK gives a positive likelihood ratio. LLDMARK detected less than half as many causative SNPs as the other methods, and also had a relatively high false discovery rate when the QTL effect was large. LLDMARK may however be more robust against spurious associations, because the regional IBD is largely corrected for by fitting a QTL effect in the null hypothesis model.     

Non-iterative variance component estimation in QTL analysis

In variance component quantitative trait loci (QTL) analysis, a mixed model is used to detect the most likely chromosome position of a QTL. The putative QTL is included as a random effect and a method is needed to estimate the QTL variance. The standard estimation method used is an iterative method based on the restricted maximum likelihood (REML). In this paper, we present a novel non-iterative variance component estimation method. This method is based on Henderson's method 3, but relaxes the condition of unbiasedness. Two similar estimators were compared, which were developed from two different partitions of the sum of squares in Henderson's method 3. The approach was compared with REML on data from a European wild boar × domestic pig intercross. A meat quality trait was studied on chromosome 6 where a functional gene was known to be located. Both partitions resulted in estimated QTL variances close to the REML estimates. From the non-iterative estimates, we could also compute good approximations of the likelihood ratio curve on the studied chromosome.     

Detection and characterization of quantitative trait loci for meat quality traits in pigs.

In an experimental cross between Meishan and Dutch Large White and Landrace lines, 785 F2 animals with carcass information and their parents were typed for molecular markers covering the entire porcine genome. Linkage was studied between these markers and eight meat quality traits. Quantitative trait locus analyses were performed using interval mapping by regression under two genetic models: 1) the line-cross approach, where the founder lines were assumed to be fixed for different QTL alleles and 2) a half-sib model where a unique allele substitution effect was fitted within each of the 38 half-sib families. The line-cross approach included tests for genomic imprinting and sex-specific QTL effects. In total, three genome-wide significant and 26 suggestive QTL were detected. The significant QTL on chromosomes 3, 4, and 13, affecting meat color, were only detected under the half-sib model. Failure of the line-cross approach to detect the meat color QTL suggests that the founder lines have similar allele frequencies for these QTL. This study provides information on new QTL affecting meat quality traits. It also shows the benefit of analyzing experimental data under different genetic and statistical models.     

Quantitative trait locus detection in commercial broiler lines using candidate regions

A QTL that explained a large proportion of the phenotypic difference between broiler and layer chickens in an experimental cross was evaluated in a commercial broiler line. A three-generation design, consisting of 15 grandsires, 608 half-sib hens, and more than 50,000 third-generation offspring, was implemented within the existing breeding scheme of a broiler breeding company. Four markers from a candidate region on chicken chromosome 4 were selected for their informativeness in the grandsires and used to genotype the first two generations. Using half-sib analyses, linkage was studied between these markers and 13 growth and carcass traits. The QTL analyses confirmed the presence of significant QTL for body weight (P < 0.01) and residual feed intake (P < 0.05) on chicken chromosome 4. Furthermore, evidence was found for QTL affecting the relative weight of bone and muscle in the thigh. Four more markers were added to increase resolution of the QTL positions. This increased the significance of the QTL for body weight (P < 0.001) and residual feed intake (P < 0.01) and showed evidence (P < 0.05) for additional QTL affecting carcass weight and conformation score. This study showed for the first time that a QTL that explains differences between broilers and layers was segregating in lines that have been selected for body weight over 50 generations. A possible explanation could be a pleiotropic or closely linked effect on fitness-related traits that are not part of the present study. The results demonstrate the feasibility of QTL detection and the potential for marker-assisted selection within a commercial broiler line without altering the existing breeding scheme.     

Heterozygosity for genes influencing a quantitative trait

Choosing families to sample for a quantitative trait locus mapping experiment is a critical component of experimental design because only heterozygous families contribute information to the analysis. Additive genetic variance of a paternal half-sib family can be partitioned into two parts: a variance component of maternal source that is constant across different families and a variance component of paternal source that is defined as an index of heterozygosity of a sire. This index is shown to be an upper limit of variance among marker genotypes of a half-sib family and can be used to identify highly heterozygous sires, thus improving the power of detecting QTL in detection studies. Simulated progeny phenotypic data were used to estimate sire's heterozygosity index via an ANOVA method, and accuracy of the estimation was evaluated with the correlation coefficient between the true and estimated index summarized both as the correlation and by the correct ranking of results as measured by the ratio of the true average heterozygosity index of experimentally selected parents to average heterozygosity of all sires. Positive but small correlation can be achieved in the estimation of a sire's heterozygosity when based on the daughters' phenotypic data, and accuracy was improved when progeny-tested sons were used to estimate their grandsire's heterozygosity index, depending on the genetic model of a trait and the size and structure of families.     

A genome scan for quantitative trait loci and imprinted regions affecting reproduction in pigs

Quantitative trait loci for reproductive traits in a three-generation resource population of a cross between low-indexing pigs from a control line and high-indexing pigs from a line selected 10 generations for increased index of ovulation rate and embryonic survival are reported. Phenotypic data were collected in F2 females for birth weight (BWT, n = 428), weaning weight (WWT, n = 405), age at puberty (AP, n = 295), ovulation rate (OR, n = 423), number of fully formed pigs (FF, n = 370), number of pigs born alive (NBA, n = 370), number of mummified pigs (MUM, n = 370), and number of stillborn pigs (NSB, n = 370). Grandparent, F1, and F2 animals were genotyped for 151 microsatellite markers. Sixteen putative QTL (P < 0.10) for reproductive traits were identified in previous analyses of these data with single QTL line-cross models. Data were reanalyzed with multiple QTL models, including imprinting effects. Data also were analyzed with half-sib models. Permutation was used to establish genome-wide significance levels ( = 0.01, 0.05, and 0.10). Thirty-one putative QTL for reproductive traits and two QTL for birth weight were identified (P < 0.10). One Mendelian QTL for FF (P < 0.05), one for NBA (P < 0.05), three for NSB (P < 0.05), three for NN (P < 0.05), seven for AP (P < 0.10), five for MUM (P < 0.10), and one for BWT (P < 0.10) were found. Partial imprinting of QTL affecting OR (P < 0.01), BWT (P < 0.05), and MUM (P < 0.05) was detected. There were four paternally expressed QTL for NN (P < 0.10) and one each for AP (P < 0.05) and MUM (P < 0.10). Maternally expressed QTL affecting NSB (P < 0.10), NN (P < 0.10), and MUM (P < 0.10) were detected. No QTL were detected with half-sib analyses. Multiple QTL models with imprinting effects are more appropriate for analyzing F2 data than single Mendelian QTL line-cross models.     

Multiple QTL on chromosome six in dairy cattle affecting yield and content traits

A granddaughter design containing five half-sib families from German Holstein–Friesian cattle was subjected to QTL analysis starting from the hypothesis of the existence of more than one QTL on chromosome BTA 6 affecting milk yield, fat yield, protein yield and content of fat and protein. The marker map consisted of 16 microsatellite markers with marker heterozygosity varying from 0.44 to 0.94. Two statistical methods were used: least squares (LS) and residual maximum likelihood (REML) allowing for two QTL simultaneously. The test statistics were calculated in steps of one cM along the chromosome. Significant QTL at the chromosome-wise 5% level according to the permutation test critical value were detected mainly in single families. The results were in conformance with the findings of several previous studies with approximate positions of putative QTL at 49 cM for milk yield, at 70 cM for fat and protein yield, and at 46 cM for protein content. Further QTL positions were suggested mostly for yield traits and protein content in the area of the casein gene cluster at 90…95 cM. The results of the two-QTL model analyses when using LS led to family specific inferences of a second QTL for fat yield and content of protein and fat, partly supported by the epistasis model.     

Detection of quantitative trait loci affecting non-return rate in French dairy cattle

The purpose of this study was to map quantitative trait loci (QTL) influencing female fertility estimated by non-return rate (NRR) in the French dairy cattle breeds Prim'Holstein, Normande and Montbeliarde. The first step was a QTL detection study on NRR at 281 days after artificial insemination on 78 half-sib families including 4993 progeny tested bulls. In Prim'Holstein, three QTL were identified on Bos taurus chromosomes BTA01, BTA02 and BTA03 (p < 0.01), whereas one QTL was identified in Normande on BTA01 (p < 0.05). The second step aimed at confirming these three QTL and refining their location by selecting and genotyping additional microsatellite markers on a sub-sample of 41 families from the three breeds using NRR within 56, 90 and 281 days after AI. Only the three QTL initially detected in Prim'Holstein were confirmed. Moreover, the analysis of NRR within 56, 90 and 281 days after AI allowed us to distinguish two FF QTL on BTA02 in Prim'Holstein, one for NRR56 and one for NRR90. Estimated QTL variance was 18%, 14%, 11.5% and 14% of the total genetic variance, respectively, for QTL mapping to BTA01, BTA02 (NRR90 and NRR56) and BTA03.     

Refined mapping of quantitative trait loci for somatic cell score on BTA02 in the German Holstein

The aim of this study was to more precisely map a previously reported quantitative trait locus (QTL) affecting somatic cell score on Bos taurus autosome 2 by increasing the number of markers fourfold, analysing more families and exploiting within‐population linkage disequilibrium (LD). A granddaughter design of 10 German Holstein grandsire families with 1121 progeny tested sons was used. Twenty‐six markers with an average marker spacing of 3.14 cM were genotyped along 81.6 cM. Linkage analysis (LA) was performed using variance‐component methodology. The incorporation of LD was first done using variance‐component methods followed by regression on marker alleles. LA revealed genome‐wide significance (LOD > 3) at 15 contiguous marker‐intervals, with the maximum test‐statistic between DIK2862 and BMS778 and a 1‐lod drop‐off interval of 38 cM. While the variance‐component methods could not detect any LD, two individual markers with a significant effect (ILSTS098, p < 0.05; BMS778, p < 0.01) were found by regression analysis. Compared with previous results QTL‐localisation was substantially narrowed; further fine‐mapping should focus on the close vicinity of BMS778.     

Variance component analysis of quantitative trait loci for pork carcass composition and meat quality on SSC4 and SSC11

In a previous study, QTL for carcass composition and meat quality were identified in a commercial finisher cross. The main objective of the current study was to confirm and fine map the QTL on SSC4 and SSC11 by genotyping an increased number of individuals and markers and to analyze the data using a combined linkage and linkage disequilibrium analysis method. A modified version of the method excludes linkage disequilibrium information from the analysis, enabling the comparison of results based on linkage information only or results based on combined linkage and linkage disequilibrium information. Nine additional paternal half-sib families were genotyped for 18 markers, resulting in a total of 1,855 animals genotyped for 15 and 13 markers on SSC4 and SSC11, respectively. The QTL affecting meat color on SSC4 was confirmed, whereas the QTL affecting LM weight could not be confirmed. The combined linkage and linkage disequilibrium analysis resulted in the identification of new significant effects for 14 traits on the 2 chromosomes. Heritabilities of the QTL effects ranged from 1.8 to 13.2%. The analysis contributed to a more accurate positioning of QTL and further characterized their phenotypic effect. However, results showed that even greater marker densities are required to take full advantage of linkage disequilibrium information and to identify haplotypes associated with favorable QTL alleles.     

Genome-wide scans for QTL affecting carcass traits in Hereford x composite double backcross populations

A genome-wide scan for chromosomal regions influencing carcass traits was conducted spanning 2.413 morgans on 29 bovine autosomes using 229 microsatellite markers. Two paternal half-sib families of backcross progenies were produced by mating Hereford x composite gene combination (CGC) bulls to both Hereford and CGC dams. Progeny of the first sire (n = 146) were born in 1996 and progeny of the second sire (n = 112) were born in 1997. Each year cattle were fed out and slaughtered serially when they were between 614 and 741 d of age. Phenotypes measured at harvest were: live weight; carcass weight; fat depth; marbling; percentage kidney, pelvic, and heart fat (KPH); and rib eye area. Dressing percentage and USDA Yield Grade were calculated from these data. The phenotypes were adjusted to age-, live weight-, and fat depth-constant endpoints using analysis of covariance. The resulting residuals were analyzed by interval mapping to detect QTL. Within family, nominal significance was established by permutation analysis. Approximate genomewide significance levels were established by applying the Bonferroni correction to the nominal probability levels. Regression and error sums of squares and degrees of freedom were pooled across families when suggestive linkage identified in one family was confirmed in the other. The results indicate promising locations for QTL affecting live weight on BTA 17 and marbling on BTA 2 that segregate in Bos taurus. Also, previously identified linkage between central markers on BTA 5 and USDA Yield Grade was confirmed in one family. Greater marker saturation in these regions coupled with refined methods for data analysis will lead to more precise determination of QTL positions.     

Mapping of quantitative trait loci for growth and carcass traits in commercial sheep populations

Quantitative trait loci analyses were applied to data from Suffolk and Texel commercial sheep flocks in the United Kingdom. The populations comprised 489 Suffolk animals in three half-sib families and 903 Texel animals in nine half-sib families. Phenotypic data comprised measurements of live weight at 8 and 20 wk of age and ultrasonically measured fat and muscle depth at 20 wk. Lambs and their sires were genotyped across candidate regions on chromosomes 1, 2, 3, 4, 5, 6, 11, 18, and 20. Data were analyzed at the breed level, at the family level, and across extended families when families were genetically related. The breed-level analyses revealed a suggestive QTL on chromosome 1 in the Suffolk breed, between markers BM8246 and McM130, affecting muscle depth, although the effect was only significant in one of the three Suffolk families. A two-QTL analysis suggested that this effect may be due to two adjacent QTL acting in coupling. In total, 24 suggestive QTL were identified from individual family analyses. The most significant QTL affected fat depth and was segregating in a Texel family on chromosome 2, with an effect of 0.62 mm. The QTL was located around marker ILSTS030, 26 cM distal to myostatin. Two of the Suffolk and two of the Texel sires were related, and a three-generation analysis was applied across these two extended families. Seven suggestive QTL were identified in this analysis, including one that had not been detected in the individual family analysis. The most significant QTL, which affected muscle depth, was located on chromosome 18 near the callipyge and Carwell loci. Based on the phenotypic effect and location of the QTL, the data suggest that a locus similar to the Carwell locus may be segregating in the United Kingdom Texel population.     

Mapping genes for fatness and growth on pig chromosome 13: a search in the region close to the pig PIT1 gene

Previous research has shown that PIT1 polymorphisms in several resource populations and the chromosomal region near PIT1 in some populations have been significantly associated with fatness and growth QTLs on pig chromosome 13. To confirm these previous results and to clarify the role of the PIT1 gene in the putative QTL region, this research project was enlarged to include two microsatellite markers flanking each side of the PIT1 gene ( Swr1008 , S0068 , Sw398 and Sw1056 ). The ISU Chinese × US resource families were used and the traits analysed were birth weight, 21 day weight, 42 day weight, longissimus muscle area, back-fat thickness at several locations, meat colour, marbling and firmness on the carcass, and growth rate for selected time periods. The total number of F₂ pigs used ranged from 241 to 330. The data were analysed using interval mapping for each breed-cross separately as well as with the pooled data, and single marker least squares analyses for the pooled data. Significant evidence of a QTL for first rib back-fat thickness was detected approximately 20 cM from the PIT1 gene by using both single marker (p < 0.01) and interval mapping analyses in the pooled data (p < 0.0001) as well as in one family (p < 0.01). Evidence of a QTL for birth weight was detected at the estimated PIT1 position in the interval mapping analysis by using the pooled data (p < 0.014) and verified by the single marker analyses. These results confirmed the previously published QTL work on pig chromosome 13 for the birth weight QTL but suggest that other genes in the region may be partly responsible for the earlier results on the back-fat thickness QTL in our resource families.     

Fine mapping of a QTL for intramuscular fat on porcine chromosome 6 using combined linkage and linkage disequilibrium mapping

In this study data from a commercial Norwegian slaughter pig cross was analysed to confirm a previous reported quantitative trait locus (QTL) affecting intramuscular fat (IMF) on porcine chromosome 6. The data consisted of an old experiment, in which the QTL was previously detected, and new experimental data from the Norwegian slaughter pig cross. The old and new experimental data were analysed separately and together. A previously described method combining linkage and linkage disequilibrium analysis (LDLA) was used for the analysis, but this method assumes that all animals are descendants from a common base population, which is not realistic in a cross between different breeds. An adjusted version of the method, able to distinguish between different breeds in the cross, is presented here. Using the LDLA method, we were not able to confirm the QTL in the old experimental data, because the genetic variance could be explained by the polygenic effect. Analysis from the new experimental data did however detect the QTL, and analysing the data from both experiments together gave highly significant results for a QTL (p < 0.001) between markers SW1355 and SW1823. The main conclusion is therefore that the previously reported QTL for IMF on porcine chromosome 6 was confirmed within a 8.7‐cM confidence interval.     

Comparison of estimated breeding values, daughter yield deviations and de-regressed proofs within a whole genome scan for QTL

An important issue in quantitative trait loci (QTL) detection is the use of phenotypic measurement as a dependent variable. Daughter yield deviations (DYDs) as the unit of choice are not available for all traits of interest. The use of de-regressed proofs (DRPFs) of estimated breeding values (EBVs) is an alternative to using daughter yield deviations. The objective of this study was to examine possible differences between DYDs and DRPFs within the use of QTL detection. The pedigree used was part of the granddaughter design of the German QTL effort. Consisting marker maps for livestock species were derived from all available data of 16 German Holstein paternal half-sib families with a total of 872 sires. The number of progeny ranged from 19 to 127. A whole genome scan was performed using weighted and unweighted multimarker regression with DYDs, DRPFs and EBVs as dependent variables for the traits milk, fat and protein yields. Results were compared with respect to the number of QTL detected. A similar number of QTL was detected with DRPFs and DYDs. Also, when dependent variables were weighted according to the variance of the trait, a higher number of QTL was detected at the desired level of significance as compared to using unweighted variables.     

Generalized marker regression and interval QTL mapping methods for binary traits in half-sib family designs

A Generalized Marker Regression Mapping (GMR) approach was developed for mapping Quantitative Trait Loci (QTL) affecting binary polygenic traits in a single-family half-sib design. The GMR is based on threshold-liability model theory and regression of offspring phenotype on expected marker genotypes at flanking marker loci. Using simulation, statistical power and bias of QTL mapping for binary traits by GMR was compared with full QTL interval mapping based on a threshold model (GIM) and with a linear marker regression mapping method (LMR). Empirical significance threshold values, power and estimates of QTL location and effect were identical for GIM and GMR when QTL mapping was restricted to within the marker interval. These results show that the theory of the marker regression method for QTL mapping is also applicable to binary traits and possibly for traits with other non-normal distributions. The linear and threshold models based on marker regression (LMR and GMR) also resulted in similar estimates and power for large progeny group sizes, indicating that LMR can be used for binary data for balanced designs with large families, as this method is computationally simpler than GMR. GMR may have a greater potential than LMR for QTL mapping for binary traits in complex situations such as QTL mapping with complex pedigrees, random models and models with interactions.     

Quantitative trait loci analysis for growth and carcass traits in a half-sib family of purebred Japanese Black (Wagyu) cattle

We used a half-sib family of purebred Japanese Black (Wagyu) cattle to locate economically important quantitative trait loci. The family was composed of 348 fattened steers, 236 of which were genotyped for 342 microsatellite markers spanning 2,664 cM of 29 bovine autosomes. The genome scan revealed evidence of 15 significant QTL (<5% chromosome-wise level) affecting growth and carcass traits. Of the 15 QTL, six QTL were significant at the 5% experiment-wise level and were located in bovine chromosomes (BTA) 4, 5, and 14. We analyzed these three chromosomes in more detail in the 348 steers, with an average marker interval of 1.2 cM. The second scan revealed that the same haplotype of the BTA 4 region (52 to 67 cM) positively affected LM area and marbling. We confirmed the QTL for carcass yield estimate on BTA 5 in the region of 45 to 54 cM. Five growth-related QTL located on BTA 14, including slaughter and carcass weights, were positively affected by the same region of the haplotype of BTA 14 (29-51 cM). These data should provide a useful reference for further marker-assisted selection in the family and positional cloning research. The research indicates that progeny design with moderate genotyping efforts is a powerful method for detecting QTL in a purebred half-sib family.     

Quantitative trait locus mapping based on selective DNA pooling

Concepts of a simple method to map quantitative trait loci (QTL) based on selective DNA pooling in half-sib family, backcross, and F₂ designs were developed. It is shown that the position of a QTL can be estimated from differences in allele frequencies for two flanking markers between individuals with high and low phenotypes and does not depend on the phenotypic means of the selected groups. An estimate of the QTL effect was obtained by relating group differences in phenotypic means to differences in QTL frequencies, which can be estimated from the QTL position and marker allele frequencies. Simulation of a half-sib family and a F₂ family of 2000 individuals showed that the method gives close to unbiased results when power is high. Biases increased when measurement errors on marker allele frequencies increased and when the effect of the QTL was small. Similarities of QTL mapping based on selective DNA pooling data and on individual genotyping data are discussed, as are opportunities to extend the selective DNA pooling method to the use of multiple markers and multiple half-sib family designs. This study shows that the use of selective DNA pooling can be extended from the detection of marker associations to the mapping of QTL. Selective DNA pooling can greatly reduce the number of genotypings required.