Quantitative trait loci mapping for fatty acid composition traits in perirenal and back fat using a Japanese wild boar x Large White intercross

Here, we analysed quantitative trait loci (QTL) for fatty acid composition, one of the factors affecting fat quality, in a Japanese wild boar x Large White cross. We found 25 significant effects for 17 traits at 13 positions at the 5% genome-wise level, of which 16 effects for 12 traits at 10 positions were significant at the 1% level. QTL for saturated fatty acids (SFA) in back fat were mapped to swine (Sus scrofa) chromosomes (SSC) 1p, 9 and 15. QTL for unsaturated fatty acids in back fat were mapped to SSC1p, 1q, 4, 5, 9, 15 and 17. Using a regression model that fits back fat thickness as a covariate, two of the QTL for linoleic acid content on SSC4 and SSC17 were not significant, but one QTL for total SFA composition was detected on SSC5 with correction for back fat thickness. Wild boar alleles at six of seven QTL tended to increase SFAs and to decrease unsaturated fatty acids. QTL for fatty acid composition in perirenal fat were mapped on SSC2, 3, 4, 5, 6, 14, 16 and X. QTL for melting point (in back fat samples) were mapped on SSC1, 2 and 15. Wild boar alleles in QTL on SSC1 and SSC15 were associated with elevated melting points whereas those on SSC2 were associated with lower melting point measurements.     

Detection of quantitative trait loci for meat quality traits in cattle

A whole-genome scan was carried out to detect quantitative trait loci (QTL) affecting sensory, organoleptic, physical and chemical properties of meat. The study used phenotypic data from 235 second-generation cross-bred bull calves of a Charolais × Holstein experimental population. Loin muscle samples were evaluated for yield force, intramuscular fat and nitrogen contents, myofibrillar fragmentation index, haem pigment concentration, moisture content and pH at 24 h postmortem. A sensory assessment was performed on grilled loin and roasted silverside joints by trained panellists. A linear regression analysis based on 165 markers revealed 35 QTL at the 5% chromosome-wide significance level (20 for sensory traits and 15 for physical and chemical traits), five of which were highly significant ( F -value: ≥9). The most significant QTL was located on chromosome 6 (with the best likely position at 39 cM) and affected haem pigment concentration. The Holstein allele for this QTL was associated with an increase of 0.53 SD in the haem scores. A QTL for pH_24h was identified on chromosome 14 (at 40 cM) and a QTL for moisture content was identified on chromosome 22 (at 21 cM). Two highly significant QTL were identified for sensory panel-assessed traits: beef odour intensity (grilled sample) on chromosome 10 (at 119 cM), and juiciness (roast sample) on chromosome 16 (at 70 cM). The proportion of phenotypic variance explained by the significant QTL ranged from 3.6% (for nitrogen content on chromosome 10) to 9.5% (for juiciness, roast sample on chromosome 16).     

Detection of quantitative trait loci for growth and fatness in pigs

A quantitative trait locus (QTL) analysis of growth and fatness data from a three-generation experimental cross between Meishan (MS) and Large White (LW) pig breeds is presented. Six boars and 23 F1 sows, the progeny of six LW boars and six MS sows, produced 530 F2 males and 573 F2 females. Nine growth traits, i.e. body weight at birth and at 3, 10, 13, 17 and 22 weeks of age, average daily gain from birth to 3 weeks, from 3 to 10 weeks and from 10 to 22 weeks of age, as well as backfat thickness at 13, 17 and 22 weeks of age and at 40 and 60 kg live weight were analysed. Animals were typed for a total of 137 markers covering the entire porcine genome. Analyses were performed using two interval mapping methods: 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. Both methods revealed highly significant gene effects for growth on chromosomes 1, 4 and 7 and for backfat thickness on chromosomes 1, 4, 5, 7 and X, and significant gene effects on chromosome 6 for growth and backfat thickness. Suggestive QTLs were also revealed by both methods on chromosomes 2 and 3 for growth and 2 for backfat thickness. Significant gene effects were detected for growth on chromosomes 11, 13, 14, 16 and 18 and for backfat thickness on chromosome 8, 10, 13 and 14. LW alleles were associated with high growth rate and low backfat thickness, except for those of chromosome 7 and to a lesser extent early-growth alleles on chromosomes 1 and 2 and backfat thickness alleles on chromosome 6.     

Quantitative trait loci for porcine white blood cells and platelet-related traits in a White Duroc × Erhualian F2 resource population

White blood cell count and platelets are implicated as risk factors for common complex diseases. Genetic factors substantially affect these traits in humans and mice. However, little is known about the genetic architecture of these traits in pigs. To identify quantitative trait loci (QTL) for leucocyte- and platelet-related traits in pigs, the total leucocyte number and differential leucocyte counts including the fraction of basophils, eosinophils, lymphocytes, monocytes, neutrophils, and a series of platelet parameters including platelet count, mean platelet volume, platelet distribution width and plateletcrit were measured in 1033 F₂ animals on 240 days from a White Duroc × Erhualian intercross resource population. A total of 183 informative microsatellites distributed across 19 pig chromosomes (SSC) were genotyped across the entire resource population. Thirty-three QTL were identified for the examined traits, including eight genome-wide significant QTL for white blood cells and differential leucocyte counts on SSC2, 7, 8, 12 and 15 and six significant QTL for platelet-related traits on SSC2, 8, 13 and X. Erhualian or White Duroc alleles were not systematically associated with increased phenotypic values. These results not only confirmed many QTL identified previously in the mouse and swine, but also revealed a number of novel QTL for the traits recorded. Moreover, it is the first time that QTL for platelet-related traits in pigs have been reported.     

Expression quantitative trait loci: present and future

The last few years have seen the development of large efforts for the analysis of genome function, especially in the context of genome variation. One of the most prominent directions has been the extensive set of studies on expression quantitative trait loci (eQTLs), namely, the discovery of genetic variants that explain variation in gene expression levels. Such studies have offered promise not just for the characterization of functional sequence variation but also for the understanding of basic processes of gene regulation and interpretation of genome-wide association studies. In this review, we discuss some of the key directions of eQTL research and its implications.     

Healing quantitative trait loci in a combined cross analysis using related mouse strain crosses

Inbred mouse strains MRL and LG share the ability to fully heal ear hole punches with the full range of appropriate tissues without scarring. They also share a common ancestry, MRL being formed from a multi-strain cross with two final backcrosses to LG before being inbred by brother-sister mating. Many gene-mapping studies for healing ability have been performed using these two strains, resulting in the location of about 20 quantitative trait loci (QTLs). Here, we combine two of these crosses (N = 638), MRL/lpr × C57BL/6NTac and LG/J × SM/J, in a single combined cross analysis to increase the mapping power, decrease QTL support intervals, separate multiple QTLs and establish allelic states at individual QTL. The combined cross analysis located 11 QTLs, 6 affecting only one cross (5 LG × SM and 1 MRL × B6) and 5 affecting both crosses, approximately the number of common QTLs expected given strain SNP similarity. Amongst the five QTLs mapped in both crosses, three had significantly different genetic effects, additive in one cross and over or underdominant in the other. It is possible that allelic states at these three loci are different in SM and B6 because they lead to differences in dominance interactions with the LG and MRL alleles. QTL support intervals are 40% smaller in the combined cross analysis than in either of the single crosses. Combined cross analysis was successful in enhancing the interpretation of earlier QTL results for these strains.     

Detection of quantitative trait loci for body weights and conformation traits in Beijing ducks

Quantitative trait loci (QTL) for body weights and conformation traits were detected in Beijing ducks. Traits included body weights (BW) at hatching and at 1-7 weeks of age; lengths of the body (BL), keel bone (KBL), shank (SL) and neck (NL) at 7 weeks of age; width of breast (BTW) at 7 weeks; and girths of shank (SG) and breast (BG) at 7 weeks. Using a half-sib analysis with a multiple-QTL model, linkage between the phenotypic traits and 95 microsatellite markers was studied. Six genome-wide suggestive QTL for three body weights and two conformation traits were identified in CAU1, CAU2, CAU6 and CAU12. Chromosome-wide significant QTL influencing one body weight trait and one conformation trait were located in CAU4 and CAU10 respectively. Twelve chromosome-wide suggestive QTL for six body weight traits and four conformation traits were found in seven linkage groups (CAU1, CAU2, CAU3, CAU4, CAU6, CAU10 and CAU12). In addition, the QTL in CAU6 at 21 and 73 cM jointly affected SG and explained 10.6% of the phenotypic variation. This study provides the first evidence for QTL involved in body weights and conformation traits in ducks, and will stimulate further investigations into the genetic architecture of these traits in this species.     

An integrative approach for the identification of quantitative trait loci

The genetic dissection of complex traits is one of the most difficult and most important challenges facing science today. We discuss here an integrative approach to quantitative trait loci (QTL) mapping in mice. This approach makes use of the wealth of genetic tools available in mice, as well as the recent advances in genome sequence data already available for a number of inbred mouse strains. We have developed mapping strategies that allow a stepwise narrowing of a QTL mapping interval, prioritizing candidate genes for further analysis with the potential of identifying the most probable candidate gene for the given trait. This approach integrates traditional mapping tools, fine mapping tools, sequence-based analysis, bioinformatics and gene expression.     

A search for quantitative trait loci for ovulation rate in cattle

Seventy-seven polymorphic microsatellites were analysed in offspring of three elite sires that were part of the foundation of an experimental population selected for twinning rate at the US Meat Animal Research Center, Clay Center, Nebraska. All females were assessed for ovulation rate by rectal palpation of corpora lutea over 8–10 consecutive oestrous cycles from approximately 12 to 18 months of age, and associations between ovulation rate and sire allele were examined in each of the three sire groups. A preliminary analysis was performed using selectively genotyped daughters of each sire. Markers found significant or approaching significance were also genotyped in all daughters, sons and granddaughters of these sires. A test of marker associations limited to the granddaughter data provided an independent confirmation of marker effect and significance relative to the initial test with daughter data. Putative ovulation rate quantitative trait loci were detected on chromosomes 7 and 23. Marker UWCA20 on chromosome 7 was associated with an effect in excess of one phenotypic standard deviation and accounted for approximately 10% of phenotypic variation ovulation rate. Marker CYP21 (steroid 21-hydroxylase) on chromosome 23 was associated with an effect of slightly less than half a phenotypic standard deviation and accounted for approximately 4% of phenotypic variation.     

Detection of quantitative trait loci for reproduction and production traits in Large White and French Landrace pig populations (Open Access publication)

A genome-wide scan was performed in Large White and French Landrace pig populations in order to identify QTL affecting reproduction and production traits. The experiment was based on a granddaughter design, including five Large White and three French Landrace half-sib families identified in the French porcine national database. A total of 239 animals (166 sons and 73 daughters of the eight male founders) distributed in eight families were genotyped for 144 microsatellite markers. The design included 51 262 animals recorded for production traits, and 53 205 litter size records were considered. Three production and three reproduction traits were analysed: average backfat thickness (US_M) and live weight (LWGT) at the end of the on-farm test, age of candidates adjusted at 100 kg live weight, total number of piglets born per litter, and numbers of stillborn (STILLp) and born alive (LIVp) piglets per litter. Ten QTL with medium to large effects were detected at a chromosome-wide significance level of 5% affecting traits US_M (on SSC2, SSC3 and SSC17), LWGT (on SSC4), STILLp (on SSC6, SSC11 and SSC14) and LIVp (on SSC7, SSC16 and SSC18). The number of heterozygous male founders varied from 1 to 3 depending on the QTL.     

Mapping of quantitative trait loci on porcine chromosome 4

A F₂ population derived from a cross between European Large White and Chinese Meishan pigs was established in order to study the genetic basis of breed differences for growth and fat traits. Chromosome 4 was chosen for initial study as previous work had revealed quantitative trait loci (QTLs) on this chromosome affected growth and fat traits in a Wild Boar × Large White cross. Individuals in the F₂ population were typed for nine markers spanning a region of approximately 124 c m . We found evidence for QTLs affecting growth between weaning and the end of test (additive effect: 43·4 g/day) and fat depth measured in the mid-back position (additive effect: 1·82 mm). There was no evidence of interactions between the QTLs and sex, grandparents or F₁ sires, suggesting that the detected QTLs were fixed for alternative alleles in the Meishan and Large White breeds. Comparison of locations suggests that these QTLs could be the same as those found in the Wild Boar × Large White cross.     

Mapping quantitative trait loci for egg production traits in an F2 intercross of Oh-Shamo and White Leghorn chickens

We performed quantitative trait locus (QTL) analyses for egg production traits, including age at first egg (AFE) and egg production rates (EPR) measured every 4 weeks from 22 to 62 weeks of hen age, in a population of 421 F₂ hens derived from an intercross between the Oh‐Shamo (Japanese Large Game) and White Leghorn breeds of chickens. Simple interval mapping revealed a main‐effect QTL for AFE on chromosome 1 and four main‐effect QTL for EPR on chromosomes 1 and 11 (three on chromosome 1 and one on chromosome 11) at the genome‐wide 5% levels. Among the three EPR QTL on chromosome 1, two were identified at the early stage of egg laying (26–34 weeks of hen age) and the remaining one was discovered at the late stage (54–58 weeks). The alleles at the two EPR QTL derived from the Oh‐Shamo breed unexpectedly increased the trait values, irrespective of the Oh‐Shamo being inferior to the White Leghorn in the trait. This suggests that the Oh‐Shamo, one of the indigenous Japanese breeds, is an untapped resource that is important for further improvement of current elite commercial laying chickens. In addition, six epistatic QTL were identified on chromosomes 2, 4, 7, 8, 17 and 19, where none of the above main‐effect QTL were located. This is the first example of detection of epistatic QTL affecting egg production traits. The main and epistatic QTL identified accounted for 4–8% of the phenotypic variance. The total contribution of all QTL detected for each trait to the phenotypic and genetic variances ranged from 4.1% to 16.9% and from 11.5% to 58.5%, respectively.     

Identification of quantitative trait loci for growth and carcass composition in cattle

A genomic screening to detect quantitative trait loci (QTL) affecting growth, carcass composition and meat quality traits was pursued. Two hundred nineteen microsatellite markers were genotyped on 176 of 620 (28%) progeny from a Brahman x Angus sire mated to mostly MARC III dams. Selective genotyping, based on retail product yield (%) and fat yield (%), was used to select individuals to be genotyped. Traits included in the study were birth weight (kg), hot carcass weight (kg), retail product yield, fat yield, marbling score (400 = slight00 and 500 = small00), USDA yield grade, and estimated kidney, heart and pelvic fat (%). The QTL were classified as significant when the expected number of false positives (ENFP) was less than 0.05 (F-statistic greater than 17.3), and suggestive when the ENFP was <1 (F-statistic between 10.2 and 17.3). A significant QTL (F = 19; ENFP = 0.02) was detected for marbling score at centimorgan (cM) 54 on chromosome 2. Suggestive QTL were detected for fat yield at 50 cM, for retail product yield at 53 cM, and for USDA yield grade at 63 cM on chromosome 1, for marbling score at 56 cM, for retail product yield at 70 cM, and for estimated kidney, heart and pelvic fat at 79 cM on chromosome 3, for marbling score at 44 cM, for hot carcass weight at 49 cM, and for estimated kidney, heart and pelvic fat at 62 cM on chromosome 16, and for fat yield at 35 cM on chromosome 17. Two suggestive QTL for birth weight were identified, one at 12 cM on chromosome 20 and the other at 56 cM on chromosome 21. An additional suggestive QTL was detected for retail product yield, for fat yield, and for USDA yield grade at 26 cM on chromosome 26. Results presented here represent the initial search for quantitative trait loci in this family. Validation of detected QTL in other populations will be necessary.     

Detection and parameter estimation for quantitative trait loci using regression models and multiple markers

A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL) that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consider only three cases: 1) the chromosome has only one QTL, 2) one side of the target QTL has one or more QTLs, and 3) either side of the target QTL has one or more QTLs. Analytical formula was derived to estimate marker-QTL recombination frequency for each of the three cases. The formula involves two flanking markers for case 1), two flanking markers plus a conditional marker for case 2), and two flanking markers plus two conditional markers for case 3). Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae. Simulation data show that the formulae for estimating marker-QTL recombination frequency could be a useful statistical tool for fine QTL mapping. With 1 000 observations, a QTL could be mapped to a narrow chromosome region of 1.5 cM if no linked QTL is present, and to a 2.8 cM chromosome region if either side of the target QTL has at least one linked QTL.     

Quantitative trait loci for performance traits in a broiler × layer cross

An F₂ resource population, derived from a broiler × layer cross, was used to map quantitative trait loci (QTL) for body weights at days 1, 35 and 41, weight gain, feed intake, feed efficiency from 35 to 41 days and intestinal length. Up to 577 F₂ chickens were genotyped with 103 genetic markers covering 21 linkage groups. A preliminary QTL mapping report using this same population focused exclusively on GGA1. Regression methods were applied to line-cross and half-sib models for QTL interval mapping. Under the line-cross model, eight QTL were detected for body weight at 35 days (GGA2, 3 and 4), body weight at 41 days (GGA2, 3, 4 and 10) and intestine length (GGA4). Under the half-sib model, using sire as common parent, five QTL were detected for body weight at day 1 (GGA3 and 18), body weight at 35 days (GGA2 and 3) and body weight at 41 days (GGA3). When dam was used as common parent, seven QTL were mapped for body weight at day 1 (GGA2), body weight at day 35 (GGA2, 3 and 4) and body weight at day 41 (GGA2, 3 and 4). Growth differences in chicken lines appear to be controlled by a chronological change in a limited number of chromosomal regions.     

Joint tests for quantitative trait loci in experimental crosses

Selective genotyping is common because it can increase the expected correlation between QTL genotype and phenotype and thus increase the statistical power of linkage tests (i.e., regression-based tests). Linkage can also be tested by assessing whether the marginal genotypic distribution conforms to its expectation, a marginal-based test. We developed a class of joint tests that, by constraining intercepts in regression-based analyses, capitalize on the information available in both regression-based and marginal-based tests. We simulated data corresponding to the null hypothesis of no QTL effect and the alternative of some QTL effect at the locus for a backcross and an F2 intercross between inbred strains. Regression-based and marginal-based tests were compared to corresponding joint tests. We studied the effects of random sampling, selective sampling from a single tail of the phenotypic distribution, and selective sampling from both tails of the phenotypic distribution. Joint tests were nearly as powerful as all competing alternatives for random sampling and two-tailed selection under both backcross and F2 intercross situations. Joint tests were generally more powerful for one-tailed selection under both backcross and F2 intercross situations. However, joint tests cannot be recommended for one-tailed selective genotyping if segregation distortion is suspected.     

Sib mating designs for mapping quantitative trait loci

The power to separate the variance of a quantitative trait locus (QTL) from the polygenic variance is determined by the variability of genes identical by descent (IBD) at the QTL. This variability may increase with inbreeding. Selfing, the most extreme form of inbreeding, increases the variability of the IBD value shared by siblings, and thus has a higher efficiency for QTL mapping than random mating. In self-incompatible organisms, sib mating is the closest form of inbreeding. Similar to selfing, sib mating may also increase the power of QTL detection relative to random mating. In this study, we develop an IBD-based method under sib mating designs for QTL mapping. The efficiency of sib mating is then compared with random mating. Monte Carlo simulations show that sib mating designs notably increase the power for QTL detection. When power is intermediate, the power to detect a QTL using full-sib mating is, on average, 7% higher than under random mating. In addition, the IBD-based method proposed in this paper can be used to combine data from multiple families. As a result, the estimated QTL parameters can be applied to a wide statistical inference space relating to the entire reference population. This revised version was published online in July 2006 with corrections to the Cover Date.