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 affecting chicken body size traits via genome scanning

Body size traits reflect the condition of body development, are always mentioned when a breed is described, and are also targets in breeding programmes. In chicken, there are several reports focused on body size traits, such as shank length, tibia length or bone traits. However, no study was carried out on chest width (CW), chest depth (CD), body slope length (BL) and head width (HW) traits. In this study, genome scans were conducted on an F₂ resource population (238 F₂ individuals from 15 full‐sib families derived from an intercross of the White Plymouth Rock with the Silkies Fowl) to identify quantitative trait loci (QTL) associated with CW, CD, BL and HW from 7 to 12 weeks of age. In total, 21 significant or suggestive QTL were found that affected four body size traits. Four QTL reached 1% genome‐wide significance level: at 297 cM on GGA3 (associated with CW at 9 weeks of age), between 155 and 184 cM on GGA1 (affecting BL traits at 9 and 10 weeks of age), at 22 cM on GGA2 (related with BL traits at 12 weeks of age) and at 36 cM on GGA1 (for HW trait at 8 weeks of age).     

Mapping quantitative trait loci underlying triploid endosperm traits

Endosperm, which is derived from two polar nuclei fusing with one sperm, is a triploid tissue in cereals. Endosperm tissue determines the grain quality of cereals. Improving grain quality is one of the important breeding objectives in cereals. However, current statistical methods for mapping quantitative trait loci (QTL) under diploid genetic control have not been effective for dealing with endosperm traits because of the complexity of their triploid inheritance. In this paper, we derive for the first time the conditional probabilities of F(3) endosperm QTL genotypes given different flanking marker genotypes in F(2) plants. Using these probabilities, we develop a multiple linear regression method implemented via the iteratively reweighted least-squares (IRWLS) algorithm and a maximum likelihood method (ML) implemented via the expectation-maximization (EM) algorithm to map QTL underlying endosperm traits. We use the mean value of endosperm traits of F(3) seeds as the dependent variable and the expectations of genotypic indicators for additive and dominance effect of a putative QTL flanked by a pair of markers as independent variables for IRWLS mapping. However, if an endosperm trait is measured quantitatively using a single endosperm sample, the ML mapping method can be used to separate the two dominance effects. Efficiency of the methods is verified through extensive Monte Carlo simulation studies. Results of simulation show that the proposed methods provide accurate estimates of both the QTL effects and locations with very high statistical power. With these methods, we are now ready to map endosperm traits, as we can for regular quantitative trait under diploid control.     

Mapping quantitative trait loci regulating chicken body composition traits

Genome scans were conducted on an F₂ resource population derived from intercross of the White Plymouth Rock with the Silkies Fowl to detect QTL affecting chicken body composition traits. The population was genotyped with 129 microsatellite markers and phenotyped for 12 body composition traits on 238 F₂ individuals from 15 full-sib families. In total, 21 genome-wide QTL were found to be responsible for 11 traits, including two newly studied traits of proventriculus weight and shank girth. Three QTL were genome-wide significant: at 499 c m on GGA1 (explained 3.6% of phenotypic variance, P  < 0.01) and 51 c m on GGA5 (explained 3.3% of phenotypic variance, P  < 0.05) for the shank & claw weight and 502 c m on GGA1 (explained 1.4% of phenotypic variance, P  < 0.05) for wing weight. The QTL on GGA1 seemed to have pleiotropic effects, also affecting gizzard weight at 490 c m , shank girth at 489 c m and intestine length at 481 c m . It is suggested that further efforts be made to understand the possible pleiotropic effects of the QTL on GGA1 and that on GGA5 for two shank-related traits.     

Mapping quantitative trait loci for traits defined as ratios

Many traits are defined as ratios of two quantitative traits. Methods of QTL mapping for regular quantitative traits are not optimal when applied to ratios due to lack of normality for traits defined as ratios. We develop a new method of QTL mapping for traits defined as ratios. The new method uses a special linear combination of the two component traits, and thus takes advantage of the normal property of the new variable. Simulation study shows that the new method can substantially increase the statistical power of QTL detection relative to the method which treats ratios as regular quantitative traits. The new method also outperforms the method that uses Box-Cox transformed ratio as the phenotype. A real example of QTL mapping for relative growth rate in soybean demonstrates that the new method can detect more QTL than existing methods of QTL mapping for traits defined as ratios.     

Mapping of quantitative trait loci affecting behaviour in swine

Behavioural indices in vertebrates are under genetic control at least to some extent. In spite of significant behavioural problems in farm animals, information on the genetic background of behaviour is sparse. The aim of this study was to map QTL for behavioural indices in swine under healthy conditions and after infection with Sarcocystis miescheriana , as behaviour can be significantly influenced by disease . This well-described parasite model subsequently leads to acute (day 14 p.i.), subclinical (day 28 p.i.) and chronic disease (day 42 p.i.), allowing the study and comparison of the behaviour of pigs under four different states of health or disease. The study was based on a well-described Pietrain/Meishan F₂ family that has recently allowed the detection of QTL for disease resistance. We have mapped six genome-wide significant and 24 chromosome-wide significant QTL for six behavioural indices in swine. Six of these QTL (i.e. 20% of total QTL) showed effects on behavioural traits of the healthy pigs (day 0). Some of them (QTL on SSC11 and 18) lost influence on behavioural activities during disease, while the effects of others (QTL on SSC5, SSC8) partly remained during the whole experiment, although with different effects on the distinct behavioural indices. The disease model has been of high relevance to detect effects of gene loci on behavioural indices. Considering the importance of segregating alleles and environmental conditions that allow the identification of the phenotype, we conclude that there are indeed QTL with interesting effects on behavioural indices in swine.     

Mapping quantitative trait loci for longitudinal traits in line crosses

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.     

Mapping of multiple quantitative trait loci affecting bovine spongiform encephalopathy

A whole-genome scan was conducted to map quantitative trait loci (QTL) for BSE resistance or susceptibility. Cows from four half-sib families were included and 173 microsatellite markers were used to construct a 2835-cM (Kosambi) linkage map covering 29 autosomes and the pseudoautosomal region of the sex chromosome. Interval mapping by linear regression was applied and extended to a multiple-QTL analysis approach that used identified QTL on other chromosomes as cofactors to increase mapping power. In the multiple-QTL analysis, two genome-wide significant QTL (BTA17 and X/Y(ps)) and four genome-wide suggestive QTL (BTA1, 6, 13, and 19) were revealed. The QTL identified here using linkage analysis do not overlap with regions previously identified using TDT analysis. One factor that may explain the disparity between the results is that a more extensive data set was used in the present study. Furthermore, methodological differences between TDT and linkage analyses may affect the power of these approaches.     

Mapping quantitative trait loci affecting some carcass and meat traits in duck (Anas platyrhynchos)

Contrary to chicken and livestock mammals, duck genome has not been explored much. Nowadays a relatively small number of reports on molecular variability and mapping of loci in Peking ducks has been published. Therefore, the objective of this study was to detect single loci affecting body weight, carcass and meat traits in Peking ducks (Anas platyrhynchos). The study was based on an F2 cross between two parental lines A-55 and GL-30. Phenotypes of 387 birds from generation F2 including carcass and meat quality traits were collected. Linkage map, of the linkage group CAU1, consisting of 29 microsatellite markers was constructed. One highly significant (p?p?相似文献   

Age-dependent quantitative trait loci affecting growth traits in Scottish Blackface sheep

To dissect age-dependent quantitative trait loci (QTL) associated with growth and to examine changes in QTL effects over time, the Gompertz growth model was fitted to longitudinal live weight data on 788 Scottish Blackface lambs from nine half-sib families. QTL were mapped for model parameters and weekly live weights and growth rates using microsatellite markers on chromosomes 1, 2, 3, 5, 14, 18, 20 and 21. QTL significance (using α = 0.05 chromosome-wide significance thresholds, unless otherwise stated) varied with age, and those for growth rate occurred earlier than equivalent QTL for live weight. A chromosome 20 QTL for growth rate was significant from 4 to 9 weeks (maximum significance at 6 weeks) and for maximum growth rate. For live weight, this QTL was significant from 8 to 16 weeks (maximum significance at 12 weeks). A nominally significant chromosome 14 QTL was detected for growth rates from birth to week 2 in the same families and location as an 8-week weight QTL. In addition, at the same position on chromosome 14, a QTL was significant for growth rate for 17–28 weeks (maximum significance at 24 weeks). A chromosome 3 QTL was significant for weights at early ages (birth to week 4) and a growth rate QTL on chromosome 18 was significant from 8 to 12 weeks. Fitting growth curves allowed the combination of information from multiple measurements into a few biologically meaningful variables, and the detection of growth QTL that were not observed from analyses of raw weight data. These QTL describe distinct parts of an animal's growth curve trajectory, possibly enabling manipulation of this trajectory.     

Mapping quantitative trait loci affecting circadian photosensitivity in retinally degenerate mice

It is known that retinally degenerate C57BL/6J (rd/rd) mice have unattenuated circadian photosensitivity. However, the authors have previously found that CBA/J (rd/rd) mice that carry the same rd mutation have attenuated circadian photosensitivity compared to normal CBA/N (+/+) mice. In the present study, a quantitative trait locus (QTL) analysis using C57BL/6J (rd/rd) and CBA/J (rd/rd) mice was conducted in order to identify the genes affecting circadian photosensitivity of the rd mice. As a result, several putative QTLs onthree separate chromosomes (8, 12, 17) were detected, which indicates that circadian photosensitivity in rd mice is altered by multiple genes. Identification of these genes may provide new insights into the understanding of regulation of circadian photoentrainment and sleep-wake disorders.     

Fine mapping quantitative trait loci affecting milk production traits on bovine chromosome 6 in a Chinese Holstein population

To fine map the previously detected quantitative trait loci （QTLs） affecting milk production traits on bovine chromosome 6 （BTA6）, 15 microsatellite markers situated within an interval of 14.3 cM spanning from BMS690 to BM4528 were selected and 918 daughters of 8 sires were genotyped. Two mapping approaches, haplotype sharing based LD mapping and single marker regression mapping, were used to analyze the data. Both approaches revealed a quantitative trait locus （QTL） with significant effects on milk yield, fat yield and protein yield located in the segment flanked by markers BMS483 and MNB209, which spans a genetic distance of 0.6 cM and a physical distance of 1.5 Mb. In addition, the single marker regression mapping also revealed a QTL affecting fat percentage and protein percentage at marker DIK2291. Our fine mapping work will facilitate the cloning of candidate genes underlying the QTLs for milk production traits.     

Mapping of quantitative trait loci for seminal vesicle mass and litter size to rat chromosome 8

The spontaneously hypertensive rat (SHR) and the Brown Norway (BN) rat differ significantly in litter size (7.6 versus 4.5 pups). In the HXB and BXH sets of recombinant inbred (RI) strains derived from SHR and BN rats, heritability of litter size and of selected male reproductive parameters such as sperm production, sperm count, sperm morphology and motility, and the mass of the testis, epididymides, and seminal vesicles were estimated and a search was undertaken for quantitative trait loci (QTL) associated with these phenotypes. The mass of seminal vesicles was significantly associated with a QTL near the D8Cebr204S21 marker on chromosome 8 (LOD score = 4.1, P = 0.00001); this QTL was responsible for 46% of the genetic variability of the trait. The same gene marker on chromosome 8 also showed a suggestive association with the litter size. Litter size was significantly correlated with the mass of seminal vesicles (r = 0.58, P = 0.003). These findings indicate that the variability in litter size among RI strains may be due in part to differences in the mass of seminal vesicles and it is possible that both mass of seminal vesicles and litter size are determined by a pleiotropic effect of the same QTL on rat chromosome 8.     

Mapping quantitative trait loci controlling variation in forage quality traits in barley

Barley forage quality has a direct relationship to animal performance, but forage quality traits are often neglected or not accessible to the plant breeders. Doubled haploid lines (145) from the cross Steptoe × Morex were grown in 2 years of trails under irrigated conditions to evaluate the variation in forage quality characteristics, identify quantitative trait loci (QTL) for these traits and determine if variation in forage quality characteristics among barley lines is heritable. Forage quality traits were determined at plant anthesis and at peak forage yield stages. A total of 32 QTL were identified that conditioned forage traits at anthesis stage, and 10 QTLs were identified at peak forage yield. At anthesis, forage traits were highly to moderate heritablely, while at peak forage yield, all traits were weakly heritable, indicating that selection progress for these traits will be effective at early stages of maturity. This research has identified and mapped QTL for barley forage quality and will allow deployment of genes for improved forage quality via marker-assisted selection.     

Mapping of quantitative trait loci for clinical–chemical traits in swine

Clinical–chemical traits are diagnostic parameters essential for characterization of health and disease in veterinary practice. The traits show significant variability and are under genetic control, but little is known about the fundamental genetic architecture of this variability, especially in swine. We have identified QTL for alkaline phosphatase (ALP), lactate (LAC), bilirubin (BIL), creatinine (CRE) and ionized sodium (Na⁺), potassium (K⁺) and calcium (Ca⁺⁺) from the serum of 139 F₂ pigs from a Meishan/Pietrain family before and after challenge with Sarcocystis miescheriana , a protozoan parasite of muscle. After infection, the pigs passed through three stages representing acute disease, subclinical disease and chronic disease. Forty-two QTL influencing clinical–chemical traits during these different stages were identified on 15 chromosomes. Eleven of the QTL were significant on a genome-wide level; 31 QTL were chromosome-wide significant. QTL showed specific health/disease patterns with respect to the baseline values of the traits as well as the values obtained through the different stages of disease. QTL influencing different traits at different times were found primarily on chromosomes 1, 3, 7 and 14. The most prominent QTL for the investigated clinical–chemical traits mapped to SSC3 and 7. Baseline traits of ALP, LAC, BIL, Ca⁺⁺ and K⁺ were influenced by QTL regions on SSC3, 6, 7, 8 and 13. Single QTL explained up to 21.7% of F₂ phenotypic variance. Our analysis confirms that variation of clinical–chemical traits is associated with multiple chromosomal regions.     

Mapping quantitative trait loci for anxiety in chromosome substitution strains of mice

Anxious behavior in the mouse is a complex quantitative phenotype that varies widely among inbred mouse strains. We examined a panel of chromosome substitution strains bearing individual A/J chromosomes in an otherwise C57BL/6J background in open-field and light-dark transition tests. Our results confirmed previous reports of quantitative trait loci (QTL) on chromosomes 1, 4, and 15 and identified novel loci on chromosomes 6 and 17. The studies were replicated in two separate laboratories. Systematic differences in the overall activity level were found between the two facilities, but the presence of the QTL was confirmed in both laboratories. We also identified specific effects on open-field defecation and center avoidance and distinguished them from overall open-field activity.     

Evaluation of quantitative trait loci affecting intramuscular fat and reproductive traits in pigs using marker‐assisted introgression

We investigated the effects of previously identified quantitative trait loci (QTL) in an experimental backcross (BC) between Chinese Meishan pigs and commercial Duroc pigs. We performed marker‐assisted introgression of two QTL for intramuscular fat (IMF) content (IMF population) and three QTL for reproductive traits (reproduction population) from a donor Meishan pig into a recipient Duroc pig. At the fourth BC generation of the IMF population and third BC generation of the reproduction population, carrier animals were selected for the production of animals homozygous for the QTL. Our previous studies have shown that the presence of a Meishan allele on the IMF QTL is associated with low IMF values, and the Meishan allele on the reproductive QTL is associated with large litters. In this study, the presence of a Duroc allele at the IMF QTL on SSC9 resulted in a 0.27% increase in IMF (additive effect = 0.27 ± 0.08), whereas the presence of a Meishan allele at the IMF QTL on SSC7 resulted in a 0.34% increase in IMF (additive effect = ?0.34 ± 0.09). The presence of the Meishan allele at the IMF QTL on SSC7 thus had the opposite effect to our previous studies, that is, increased IMF. In the reproduction population, we observed no differences between the genotypes of the three QTL in regard to number of corpora lutea or litter size. Marker‐assisted introgression at these QTL is thus unlikely to result in an associated increase in litter size. These results show that it is possible to introgress alleles from other breeds into a selection population using molecular markers; any unexpected results might be associated with the genetic background.     

Mapping quantitative trait loci in oligogenic models

We discuss strategies for mapping quantitative trait loci with emphasis on certain issues of study design that have recently received attention: e.g. genotyping only selected pedigrees and the comparative value of large pedigrees versus sib pairs. We use a standard variance components model and a parametrization of the genetic effects in which the 'segregation' parameters are locally orthogonal to the 'linkage' parameters. This permits simple explicit expressions for the expectation of the score statistic, which we use to compare the power of different strategies. We also discuss robustness of the score statistic.     

Mapping quantitative trait loci in tetraploid populations

Knowledge of quantitative trait locus (QTL) mapping in polyploids is almost void, albeit many exquisite strategies of QTL mapping have been proposed and extensive investigations have been carried out in diploid animals and plants. In this paper we develop a simple algorithm which uses an iteratively reweighted least square method to map QTLs in tetraploid populations. The method uses information from all markers in a linkage group to infer the probability distribution of QTL genotype under the assumption of random chromosome segregation. Unlike QTL mapping in diploid species, here we estimate and test the compound 'gametic effect', which consists of the composite 'genic effect' of alleles and higher-order gene interactions. The validity and efficiency of the proposed method are investigated through simulation studies. Results show that the method can successfully locate QTLs and separates different sources (e.g. additive and dominance) of variance components contributed by the QTLs.