Effects of a quantitative trait locus for increased muscularity on carcass traits measured by subjective conformation and fat class scores and video image analysis in crossbred lambs

A quantitative trait locus (QTL) for increased loin muscularity (TM-QTL) has previously been identified in purebred Texel sheep. Crossbred lambs born out of Mule ewes mated to heterozygous Texel sires for the TM-QTL were evaluated for a range of carcass traits. Lambs were genotyped and classified as carriers (n = 62) of a single copy of the TM-QTL and non-carriers (n = 49). In this study, the effects of the TM-QTL on carcass attributes were investigated using subjective classification scores for conformation and fatness, and measurements from a video image analysis (VIA) system. In addition, refined prediction equations to estimate weights of primal joints (leg, chump, loin, breast and shoulder) were obtained by calibrating the VIA system against computer tomography (CT) measurements in the loin region. The new refined prediction models increased the accuracy of prediction of all primal cuts on an average of 16% compared to previously derived standard VIA prediction equations. The coefficient of determination (R2) of the VIA system to predict in vivo CT measurements ranged from 0.39 to 0.72 for measurements of Musculus longissimus lumborum (MLL) area, width and depth, lumbar spine length, loin muscle volume and loin muscularity index. Using VIA estimates of CT-measured loin muscle traits, a significant increase in depth (+2.7%) of the MLL was found to be associated with the TM-QTL. Conformation and fatness scores and the shape of the carcass measured as individual lengths, widths and areas by VIA were not significantly influenced by the TM-QTL. Primal meat yields estimated using both standard and refined VIA prediction equations were not significantly affected by the TM-QTL. However, carcass 'compactness' was found to have significantly increased in carrier lambs. The weight of the dissected MLL estimated using VIA information was greater (+2.6%) for carriers compared to non-carriers. To conclude, neither the current industry carcass evaluation system for conformation and fatness nor the standard VIA system is able to identify the effect of the TM-QTL in the loin region in the moment. However, the calibration of the VIA system against CT measurements resulted in improved VIA prediction equations for primal meat yields and also showed moderate potential to estimate loin muscle traits measured by CT and to detect, partially, the effect of the TM-QTL on these traits.     

Detection of quantitative trait loci for carcass composition traits in pigs

A quantitative trait locus (QTL) analysis of carcass composition data from a three-generation experimental cross between Meishan (MS) and Large White (LW) pig breeds is presented. A total of 488 F2 males issued from six F1 boars and 23 F1 sows, the progeny of six LW boars and six MS sows, were slaughtered at approximately 80 kg live weight and were submitted to a standardised cutting of the carcass. Fifteen traits, i.e. dressing percentage, loin, ham, shoulder, belly, backfat, leaf fat, feet and head weights, two backfat thickness and one muscle depth measurements, ham + loin and back + leaf fat percentages and estimated carcass lean content were analysed. Animals were typed for a total of 137 markers covering the entire porcine genome. Analyses were performed using a line-cross (LC) regression method where founder lines were assumed to be fixed for different QTL alleles and a half/full sib (HFS) maximum likelihood method where allele substitution effects were estimated within each half-/full-sib family. Additional analyses were performed to search for multiple linked QTL and imprinting effects. Significant gene effects were evidenced for both leanness and fatness traits in the telomeric regions of SSC 1q and SSC 2p, on SSC 4, SSC 7 and SSC X. Additional significant QTL were identified for ham weight on SSC 5, for head weight on SSC 1 and SSC 7, for feet weight on SSC 7 and for dressing percentage on SSC X. LW alleles were associated with a higher lean content and a lower fat content of the carcass, except for the fatness trait on SSC 7. Suggestive evidence of linked QTL on SSC 7 and of imprinting effects on SSC 6, SSC 7, SSC 9 and SSC 17 were also obtained.     

Quantitative trait loci associated with AutoFOM grading characteristics, carcass cuts and chemical body composition during growth of Sus scrofa

A three-generation full-sib resource family was constructed by crossing two commercial pig lines. Genotypes for 37 molecular markers covering chromosomes SSC1, SSC6, SSC7 and SSC13 were obtained for 315 F2 animals of 49 families and their parents and grandparents. Phenotypic records of traits including carcass characteristics measured by the AutoFOM grading system, dissected carcass cuts and meat quality characteristics were recorded at 140 kg slaughter weight. Furthermore, phenotypic records on live animals were obtained for chemical composition of the empty body, protein and lipid accretion (determined by the deuterium dilution technique), daily gain and feed intake during the course of growth from 30 to 140 kg body weight. Quantitative trait loci (QTL) detection was conducted using least-squares regression interval mapping. Highest significance at the 0.1% chromosome-wise level was obtained for five QTL: AutoFOM belly weight on SSC1; ham lean-meat weight, percentage of fat of primal cuts and daily feed intake between 60 and 90 kg live weight on SSC6; and loin lean-meat weight on SSC13. QTL affecting daily gain and protein accretion were found on SSC1 in the same region. QTL for protein and lipid content of empty body at 60 kg liveweight were located close to the ryanodine receptor 1 (RYR1) locus on SSC6. On SSC13, significant QTL for protein accretion and feed conversion ratio were detected during growth from 60 to 90 kg. In general, additive genetic effects of alleles originating from the Piétrain line were associated with lower fatness and larger muscularity as well as lower daily gain and lower protein accretion rates. Most of the QTL for carcass characteristics were found on SSC6 and were estimated after adjustment for the RYR1 gene. QTL for carcass traits, fatness and growth on SSC7 reported in the literature, mainly detected in crosses of commercial lines x obese breeds, were not obtained in the present study using crosses of only commercial lines, suggesting that these QTL are not segregating in the analysed commercial lines.     

Quantitative trait loci for live animal and carcass composition traits in Jersey and Limousin back-cross cattle finished on pasture or feedlot

A quantitative trait locus (QTL) study was carried out in two countries, recording live animal and carcass composition traits. Back-cross calves (385 heifers and 398 steers) were generated, with Jersey and Limousin breed backgrounds. The New Zealand cattle were reared on pasture to carcass weights averaging 229 kg, whilst the Australian cattle were reared on grass and finished on grain (for at least 180 days) to carcass weights averaging 335 kg. From 11 live animal traits and 31 carcass composition traits respectively, 5 and 22 QTL were detected in combined-sire analyses, which were significant ( P  <   0.05) on a genome-wise basis. Fourteen significant traits for carcass composition QTL were on chromosome 2 and these were traits associated with muscling and fatness. This chromosome carried a variant myostatin allele (F94L), segregating from the Limousin ancestry. Despite very different cattle management systems between the two countries, the two populations had a large number of QTL in common. Of the 18 traits which were common to both countries, and which had significant QTL at the genome-wise level, eight were significant in both countries.     

Meta - and combined - QTL analysis of different experiments on immune traits in chickens

Meta and/or combined QTL analysis from multiple studies can improve quantitative trait loci (QTL) position estimates compared to the individual experiments. Hereby we present results of a meta-analysis of QTL on chicken chromosome 9, 14 and 18 using data from three separate experiments and joint QTL analysis for chromosome 14 and 18. Meta QTL analysis uses information from multiple QTLs studies. Joint QTL analysis is based on combining raw data from different QTL experimental populations. QTLs under the study were related to specific antibody response to keyhole lymphet hemocyanin (KLH), and natural antibodies to environmental antigens, lipopolisaccharide (LPS) and lipoteichoic acid (LTA). Meta QTL analysis resulted in narrowing down the confidence interval for two QTLs on GGA14. The first one for natural antibodies against LTA and the second one for specific antibody response toward KLH. Also, a confidence interval of a QTL for natural antibodies against LPS located on GGA18 was narrowed down. Combined QTL analysis was successful for two QTLs: for specific antibody response toward KLH on GGA14, and for natural antibodies against LPS on GGA18. The greatest statistical power for QTL detection in joint analysis was achieved when raw data from segregating half–sib families from different populations under the study was used.     

Detection of quantitative trait loci for growth- and fatness-related traits in a large-scale White Duroc × Erhualian intercross pig population

Growth and fatness are economically important traits in pigs. In this study, a genome scan was performed to detect quantitative trait loci (QTL) for 14 growth and fatness traits related to body weight, backfat thickness and fat weight in a large-scale White Duroc × Erhualian F(2) intercross. A total of 76 genome-wide significant QTL were mapped to 16 chromosomes. The most significant QTL was found on pig chromosome (SSC) 7 for fatness with unexpectedly small confidence intervals of ～2 cM, providing an excellent starting point to identify causal variants. Common QTL for both fatness and growth traits were found on SSC4, 5, 7 and 8, and shared QTL for fat deposition were detected on SSC1, 2 and X. Time-series analysis of QTL for body weight at six growth stages revealed the continuously significant effects of the QTL on SSC4 at the fattening period and the temporal-specific expression of the QTL on SSC7 at the foetus and fattening stages. For fatness traits, Chinese Erhualian alleles were associated with increased fat deposition except that at the major QTL on SSC7. For growth traits, most of White Duroc alleles enhanced growth rates except for those at three significant QTL on SSC6, 7 and 9. The results confirmed many previously reported QTL and also detected novel QTL, revealing the complexity of the genetic basis of growth and fatness in pigs.     

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.     

A genome scan reveals QTL for growth, fatness, leanness and meat quality in a Duroc-Pietrain resource population

We performed a genome-wide QTL scan for production traits in a line cross between Duroc and Pietrain breeds of pigs, which included 585 F(2) progeny produced from 31 full-sib families genotyped with 106 informative microsatellites. A linkage map covering all 18 autosomes and spanning 1987 Kosambi cM was constructed. Thirty-five phenotypic traits including body weight, growth, carcass composition and meat quality traits were analysed using least square regression interval mapping. Twenty-four QTL exceeded the genome-wide significance threshold, while 47 QTL reached the suggestive threshold. These QTL were located at 28 genomic regions on 16 autosomal chromosomes and QTL in 11 regions were significant at the genome-wide level. A QTL affecting pH value in loin was detected on SSC1 between marker-interval S0312-S0113 with strong statistical support (P < 3.0 x 10(-14)); this QTL was also associated with meat colour and conductivity. QTL for carcass composition and average daily gain was also found on SSC1, suggesting multiple QTL. Seventeen genomic segments had only a single QTL that reached at least suggestive significance. Forty QTL exhibited additive inheritance whereas 31 QTL showed (over-) dominance effects. Two QTL for trait backfat thickness were detected on SSC2; a significant paternal effect was found for a QTL in the IGF2 region while another QTL in the middle of SSC2 showed Mendelian expression.     

QTL mapping for growth and carcass traits in an Iberian by Landrace pig intercross: additive,dominant and epistatic effects

Results from a QTL experiment on growth and carcass traits in an experimental F2 cross between Iberian and Landrace pigs are reported. Phenotypic data for growth, length of carcass and muscle mass, fat deposition and carcass composition traits from 321 individuals corresponding to 58 families were recorded. Animals were genotyped for 92 markers covering the 18 porcine autosomes (SSC). The results from the genomic scan show genomewide significant QTL in SSC2 (longissimus muscle area and backfat thickness), SSC4 (length of carcass, backfat thickness, loin, shoulder and belly bacon weights) and SSC6 (longissimus muscle area, backfat thickness, loin, shoulder and belly bacon weights). Suggestive QTL were also found on SSC1, SSC5, SSC7, SSC8, SSC9, SSC13, SCC14, SSC16 and SSC17. A bidimensional genomic scan every 10 cM was performed to detect interaction between QTL. The joint action of two suggestive QTL in SSC2 and SSC17 led to a genome-wide significant effect in live weight. The results of the bidimensional genomic scan showed that the genetic architecture was mainly additive or the experimental set-up did not have enough power to detect epistatic interactions.     

Mapping carcass and meat quality QTL on Sus Scrofa chromosome 2 in commercial finishing pigs

Quantitative trait loci (QTL) affecting carcass and meat quality located on SSC2 were identified using variance component methods. A large number of traits involved in meat and carcass quality was detected in a commercial crossbred population: 1855 pigs sired by 17 boars from a synthetic line, which where homozygous (A/A) for IGF2. Using combined linkage and linkage disequilibrium mapping (LDLA), several QTL significantly affecting loin muscle mass, ham weight and ham muscles (outer ham and knuckle ham) and meat quality traits, such as Minolta-L* and -b*, ultimate pH and Japanese colour score were detected. These results agreed well with previous QTL-studies involving SSC2. Since our study is carried out on crossbreds, different QTL may be segregating in the parental lines. To address this question, we compared models with a single QTL-variance component with models allowing for separate sire and dam QTL-variance components. The same QTL were identified using a single QTL variance component model compared to a model allowing for separate variances with minor differences with respect to QTL location. However, the variance component method made it possible to detect QTL segregating in the paternal line (e.g. HAMB), the maternal lines (e.g. Ham) or in both (e.g. pHu). Combining association and linkage information among haplotypes improved slightly the significance of the QTL compared to an analysis using linkage information only.     

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

ABSTRACT: BACKGROUND: Although many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software. RESULTS: Throughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits. CONCLUSIONS: The MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.     

A molecular genome scan analysis to identify chromosomal regions influencing economic traits in the pig. I. Growth and body composition

Genome scans can be employed to identify chromosomal regions and eventually genes (quantitative trait loci or QTL) that control quantitative traits of economic importance. A three-generation resource family was developed by using two Berkshire grand sires and nine Yorkshire grand dams to detect QTL for growth and body composition traits in pigs. A total of 525 F₂ progeny were produced from 65 matings. All F₂ animals were phenotyped for birth weight, 16-day weight, growth rate, carcass weight, carcass length, back fat thickness, and loin eye area. Animals were genotyped for 125 microsatellite markers covering the genome. Least squares regression interval mapping was used for QTL detection. All carcass traits were adjusted for live weight at slaughter. A total of 16 significant QTL, as determined by a permutation test, were detected at the 5% chromosome-wise level for growth traits on Chromosomes (Chrs) 1, 2, 3, 4, 6, 7, 8, 9, 11, 13, 14, and X, of which two were significant at the 5% genome-wise level and two at the 1% genome-wise level (on Chrs 1, 2, and 4). For composition traits, 20 QTL were significant at the 5% chromosome-wise level (on Chrs 1, 4, 5, 6, 7, 12, 13, 14, 18), of which one was significant at the 5% genome-wise level and three were significant at the 1% genome-wise level (on Chrs 1, 5, and 7). For several QTL the favorable allele originated from the breed with the lower trait mean. Received: 29 November 2000 / Accepted: 27 March 2001     

A Multi-Trait,Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature,Fatness and Reproduction in Beef Cattle

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t''V⁻¹t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.     

Quantitative trait loci associated with chemical composition of the chicken carcass

Major objectives of the poultry industry are to increase meat production and to reduce carcass fatness, mainly abdominal fat. Information on growth performance and carcass composition are important for the selection of leaner meat chickens. To enhance our understanding of the genetic architecture underlying the chemical composition of chicken carcasses, an F₂ population developed from a broiler × layer cross was used to map quantitative trait loci (QTL) affecting protein, fat, water and ash contents in chicken carcasses. Two genetic models were applied in the QTL analysis: the line‐cross and the half‐sib models, both using the regression interval mapping method. Six significant and five suggestive QTL were mapped in the line‐cross analysis, and four significant and six suggestive QTL were mapped in the half‐sib analysis. A total of eleven QTL were mapped for fat (ether extract), five for protein, four for ash and one for water contents in the carcass using both analyses. No study to date has reported QTL for carcass chemical composition in chickens. Some QTL mapped here for carcass fat content match, as expected, QTL regions previously associated with abdominal fat in the same or in different populations, and novel QTL for protein, ash and water contents in the carcass are presented here. The results described here also reinforce the need for fine mapping and to perform multi‐trait analyses to better understand the genetic architecture of these traits.     

A QTL affecting daily feed intake maps to Chromosome 2 in pigs

Our understanding of the molecular genetic basis of several key performance traits in pigs has been significantly advanced through the quantitative trait loci (QTL) mapping approach. However, in contrast to growth and fatness traits, the genetic basis of feed intake traits has rarely been investigated through QTL mapping. Since feed intake is an important component of efficient pig production, the identification of QTL affecting feed intake may lead to the identification of genetic markers that can be used in selection programs. In this study a QTL analysis for feed intake, feeding behavior, and growth traits was performed in an F₂ population derived from a cross between Chinese Meishan and European Large White pigs. A QTL with a significant effect on daily feed intake (DFI) was identified on Sus scrofa Chromosome 2 (SSC2). A number of suggestive QTL with effects on daily gain, feed conversion, and feeding behavior traits were also located. The significant QTL lies close to a previously identified mutation in the insulin-like growth factor 2 gene (IGF2) that affects carcass composition traits, although the IGF2 mutation is not segregating in the populations analyzed in the current study. Therefore, a distinct causal variant may exist on the P arm of SSC2 with an effect on feed intake.     

Quantitative trait loci analysis of a Duroc commercial population highlights differences in the genetic determination of meat quality traits at two different muscles

We performed a whole‐genome scan with 110 informative microsatellites in a commercial Duroc population for which growth, fatness, carcass and meat quality phenotypes were available. Importantly, meat quality traits were recorded in two different muscles, that is, gluteus medius (GM) and longissimus thoracis et lumborum (LTL), to find out whether these traits are determined by muscle‐specific genetic factors. At the whole‐population level, three genome‐wide QTL were identified for carcass weight (SSC7, 60 cM), meat redness (SSC13, 84 cM) and yellowness (SSC15, 108 cM). Within‐family analyses allowed us to detect genome‐wide significant QTL for muscle loin depth between the 3rd and 4th ribs (SSC15, 54 cM), backfat thickness (BFT) in vivo (SSC10, 58 cM), ham weight (SSC9, 69 cM), carcass weight (SSC7, 60 cM; SSC9, 68 cM), BFT on the last rib (SSC11, 48 cM) and GM redness (SSC8, 85 cM; SSC13, 84 cM). Interestingly, there was low positional concordance between meat quality QTL maps obtained for GM and LTL. As a matter of fact, the three genome‐wide significant QTL for colour traits (SSC8, SSC13 and SSC15) that we detected in our study were all GM specific. This result suggests that QTL effects might be modulated to a certain extent by genetic and environmental factors linked to muscle function and anatomical location.     

An empirical Bayes method for updating inferences in analysis of quantitative trait loci using information from related genome scans

Individual genome scans for quantitative trait loci (QTL) mapping often suffer from low statistical power and imprecise estimates of QTL location and effect. This lack of precision yields large confidence intervals for QTL location, which are problematic for subsequent fine mapping and positional cloning. In prioritizing areas for follow-up after an initial genome scan and in evaluating the credibility of apparent linkage signals, investigators typically examine the results of other genome scans of the same phenotype and informally update their beliefs about which linkage signals in their scan most merit confidence and follow-up via a subjective-intuitive integration approach. A method that acknowledges the wisdom of this general paradigm but formally borrows information from other scans to increase confidence in objectivity would be a benefit. We developed an empirical Bayes analytic method to integrate information from multiple genome scans. The linkage statistic obtained from a single genome scan study is updated by incorporating statistics from other genome scans as prior information. This technique does not require that all studies have an identical marker map or a common estimated QTL effect. The updated linkage statistic can then be used for the estimation of QTL location and effect. We evaluate the performance of our method by using extensive simulations based on actual marker spacing and allele frequencies from available data. Results indicate that the empirical Bayes method can account for between-study heterogeneity, estimate the QTL location and effect more precisely, and provide narrower confidence intervals than results from any single individual study. We also compared the empirical Bayes method with a method originally developed for meta-analysis (a closely related but distinct purpose). In the face of marked heterogeneity among studies, the empirical Bayes method outperforms the comparator.     

Mapping of porcine genetic markers generated by representational difference analysis

Representational difference analysis (RDA) was performed using pig genomic DNA from a Landrace non-selected control population and a Landrace population selected for increased loin muscle area (LMA) for five generations. Pigs used for the analysis differed phenotypically for various carcass traits and were divergent in genotype at the skeletal muscle ryanodine receptor 1 locus. Two RDA experiments were performed using BamHI and BglII. Fourteen BamHI and 37 BglII difference products were cloned and sequenced. Oligonucleotide primers were designed to amplify RDA difference products and sequence-tagged sites (STS) were developed for 16 RDA fragments (two BamHI and 14 BglII). These 16 STS were mapped using the INRA-Minnesota porcine Radiation Hybrid panel. Polymorphisms identified in nine of the STS were used to place these markers on the PiGMaP genetic linkage map. Sequence-tagged sites were localized to 11 different chromosomes including three markers on chromosome 11 and four markers on chromosome 14. Development of RDA markers increases the resolution of the pig genome maps and markers located within putative quantitative trait locus (QTL) regions can be used to refine QTL positions.     

Meta-analysis of QTL involved in silage quality of maize and comparison with the position of candidate genes

A meta-analysis of quantitative trait loci (QTL) associated with plant digestibility and cell wall composition in maize was carried out using results from 11 different mapping experiments. Statistical methods implemented in “MetaQTL” software were used to build a consensus map, project QTL positions and perform meta-analysis. Fifty-nine QTL for traits associated with digestibility and 150 QTL for traits associated with cell wall composition were included in the analysis. We identified 26 and 42 metaQTL for digestibility and cell wall composition traits, respectively. Fifteen metaQTL with confidence interval (CI) smaller than 10 cM were identified. As expected from trait correlations, 42% of metaQTL for digestibility displayed overlapping CIs with metaQTL for cell wall composition traits. Coincidences were particularly strong on chromosomes 1 and 3. In a second step, 356 genes selected from the MAIZEWALL database as candidates for the cell wall biosynthesis pathway were positioned on our consensus map. Colocalizations between candidate genes and metaQTL positions appeared globally significant based on χ² tests. This study contributed in identifying key chromosomal regions involved in silage quality and potentially associated genes for most of these regions. These genes deserve further investigation, in particular through association mapping.     

A genome scan for loci affecting adipocyte size and number in abdominal fat in a White Duroc × Erhualian F2 resource population

Adipocyte size and number are correlated with fat deposition, which is of major concern to human health and pork producers. To identify quantitative trait loci (QTL) for adipocyte size and number in pigs, a total of 341 F2 animals at 240 days in a White Duroc × Erhualian cross were measured for the area, perimeters, volume and number of adipocyte in abdominal fat. A genome scan was performed on these animals and their parents and grandparents with 183 microsatellite markers spanning the pig genome. Five chromosomal regions showed effects on the traits measured, predominantly on adipocyte size, on pig chromosome (SSC) 1, 4, 7 and 9. Neither of these QTL has been reported before this study. The QTL for adipocyte size detected in this study perfectly correspond to the previously reported QTL for fatness traits on SSC1, 4 and 7. The most significant association was evidenced at 58 cM on SSC7. At the locus, the favorable allele decreasing adipocyte size was unusually originated from the obese Erhualian breed. Only a suggestive QTL was detected for adipocyte number on SSC9. The results shed new lights on the understanding of the genetic basis of fatness traits in pigs.