Mapping quantitative trait loci affecting susceptibility to Marek's disease virus in a backcross population of layer chickens

Marek's disease (MD), caused by the oncogenic MD avian herpes virus (MDV), is a major source of economic losses to the poultry industry. A reciprocal backcross (BC) population (total 2052 individuals) was generated by crossing two partially inbred commercial Leghorn layer lines known to differ in MDV resistance, measured as survival time after challenge with a (vv+) MDV. QTL affecting resistance were identified by selective DNA pooling using a panel of 198 microsatellite markers covering two-thirds of the chicken genome. Data for each BC were analyzed separately, and as a combined data set. Markers showing significant association with resistance generally appeared in blocks of two or three, separated by blocks of nonsignificant markers. Defined this way, 15 chromosomal regions (QTLR) affecting MDV resistance, distributed among 10 chromosomes (GGA 1, 2, 3, 4, 5, 7, 8, 9, 15, and Z), were identified. The identified QTLR include one gene and three QTL associated with resistance in previous studies of other lines, and three additional QTL associated with resistance in previous studies of the present lines. These QTL could be used in marker-assisted selection (MAS) programs for MDV resistance and as a platform for high-resolution mapping and positional cloning of the resistance genes.     

Genetic mapping of quantitative trait loci affecting body weight, egg character and egg production in F2 intercross chickens

Phenotypic measurements of chicken egg character and production traits are restricted to mature females only. Marker assisted selection of immature chickens using quantitative trait loci (QTL) has the potential to accelerate the genetic improvement of these traits in the chicken population. The QTL for 12 traits (i.e. body weight (BW), six for egg character, three for egg shell colour and two for egg production) of chickens were identified. An F2 population comprising 265 female chickens obtained by crossing White Leghorn and Rhode Island Red breeds and genotyped for 123 microsatellite markers was used for detecting QTL. Ninety-six markers were mapped on 25 autosomal linkage groups, and 13 markers were mapped on one Z chromosomal linkage group. Eight previous unmapped markers were assigned to their respective chromosomes in this study. Significant QTL were detected for BW on chromosomes 4 and 27, egg weight on chromosome 4, the short length of egg on chromosome 4, and redness of egg shell colour (using the L*a*b* colour system) on chromosome 11. A significant QTL on the Z chromosome was linked with age at first egg. Significant QTL could account for 6-19% of the phenotypic variance in the F2 population.     

High resolution mapping and identification of new quantitative trait loci (QTL) affecting susceptibility to Marek's disease

Marek's disease (MD) is a lymphoproliferative disease of chickens that costs the poultry industry approximately $1 billion annually. Genetic resistance to MD is gaining increased attention to augment vaccinal control as disease outbreaks occur more frequently. Previously, analysis of a 272 F2 White Leghorn resource population measured for many MD traits and genotyped for 78 microsatellite markers revealed two and four quantitative trait loci (QTL) with significant and suggestive association, respectively, to one or more MD associated traits. Additional genetic markers have since been scored on the MD resource population to increase QTL resolution and genome coverage. Saturation of four of the QTL regions with 17 markers revealed five new QTL while 32 markers extended the genome coverage by 400 + CM and uncovered three more QTL. QTL analysis by single-point and interval mapping algorithms agreed well when marker saturation was approximately 20 CM or less. Currently 127 genetic markers cover approximately 68% of the genome that contain up to 14 MD QTL associated to one or more MD trait; seven at the significant level and seven at the suggestive level. Individually each QTL accounts for 2-10% of the variation and, in general, resistance was dominant although the resistant allele may come from either parental line. This study suggests that a limited number of genomic regions play a major role in the genetic control of MD resistance. Markers linked to these loci may be useful for selection of MD resistant stock by the poultry industry following verification of the association within their breeding populations.     

Genetic mapping of quantitative trait loci affecting susceptibility in chicken to develop pulmonary hypertension syndrome

Pulmonary hypertension syndrome (PHS), also referred to as ascites syndrome, is a growth-related disorder of chickens frequently observed in fast-growing broilers with insufficient pulmonary vascular capacity at low temperature and/or at high altitude. A cross between two genetically different broiler dam lines that originated from the White Plymouth Rock breed was used to produce a three-generation population. This population was used for the detection and localization of quantitative trait loci (QTL) affecting PHS-related traits. Ten full-sib families consisting of 456 G2 birds were typed with 420 microsatellite markers covering 24 autosomal chromosomes. Phenotypic observations were collected on 4202 G3 birds and a full-sib across family regression interval mapping approach was used to identify QTL. There was statistical evidence for QTL on chicken chromosome 2 (GGA2), GGA4 and GGA6. Suggestive QTL were found on chromosomes 5, 8, 10, 27 and 28. The most significant QTL were located on GGA2 for right and total ventricular weight as percentage of body weight (%RV and %TV respectively). A related trait, the ratio of right ventricular weight as percentage to total ventricular weight (RATIO), reached the suggestive threshold on this chromosome. All three QTL effects identified on GGA2 had their maximum test statistic in the region flanked by markers MCW0185 and MCW0245 (335-421 cM).     

Confirmation of quantitative trait loci affecting fatness in chickens

In this report we describe the analysis of an advanced intercross line (AIL) to confirm the quantitative trait locus (QTL) regions found for fatness traits in a previous study. QTL analysis was performed on chromosomes 1, 3, 4, 15, 18, and 27. The AIL was created by random intercrossing in each generation from generation 2 (G₂) onwards until generation 9 (G₉) was reached. QTL for abdominal fat weight (AFW) and/or percentage abdominal fat (AF%) on chromosomes 1, 3 and 27 were confirmed in the G₉ population. In addition, evidence for QTL for body weight at the age of 5 (BW5) and 7 (BW7) weeks and for the percentage of intramuscular fat (IF%) were found on chromosomes 1, 3, 15, and 27. Significant evidence for QTL was detected on chromosome 1 for BW5 and BW7. Suggestive evidence was found on chromosome 1 for AFW, AF% and IF%, on chromosome 15 for BW5, and on chromosome 27 for AF% and IF%. Furthermore, evidence on the chromosome-wise level was found on chromosome 3 for AFW, AF%, and BW7 and on chromosome 27 for BW5. For chromosomes 4 and 18, test statistics did not exceed the significance threshold.     

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.     

Joint mapping of quantitative trait loci using F2 populations

In this paper, the theory of joint mapping of quantitative trait loci is extended to F₂ populations. Two independent regression equations, related to the additive and dominance effects respectively, are derived. Therefore, there are three alternative strategies for mapping QTLs, called additive-based mapping (ABM), dominance-based mapping (DBM) and additive-dominance-based mapping (ADBM). Simulation results have shown that ADBM is the most appropriate in most situations.     

Mapping quantitative trait loci for binary traits using a heterogeneous residual variance model: an application to Marek's disease susceptibility in chickens

A typical problem in mapping quantitative trait loci (QTLs) comes from missing QTL genotype. A routine method for parameter estimation involving missing data is the mixture model maximum likelihood method. We developed an alternative QTL mapping method that describes a mixture of several distributions by a single model with a heterogeneous residual variance. The two methods produce similar results, but the heterogeneous residual variance method is computationally much faster than the mixture model approach. In addition, the new method can automatically generate sampling variances of the estimated parameters. We derive the new method in the context of QTL mapping for binary traits in a F2 population. Using the heterogeneous residual variance model, we identified a QTL on chromosome IV that controls Marek's disease susceptibility in chickens. The QTL alone explains 7.2% of the total disease variation. This revised version was published online in July 2006 with corrections to the Cover Date.     

Fine mapping of collagen-induced arthritis quantitative trait loci in an advanced intercross line

The generation of advanced intercross lines (AIL) is a powerful approach for high-resolution fine mapping of quantitative trait loci (QTLs), because they accumulate much more recombination events compared with conventional F2 intercross and N2 backcross. However, the application of this approach is severely hampered by the requirements of excessive resources to maintain such crosses, i.e., in terms of animal care, space, and time. Therefore, in this study, we produced an AIL to fine map collagen-induced arthritis (CIA) QTLs using comparatively limited resources. We used only 308 (DBA/1 x FVB/N)F11/12 AIL mice to refine QTLs controlling the severity and onset of arthritis as well as the Ab response and T cell subset in CIA, namely Cia2, Cia27, and Trmq3. These QTLs were originally identified in (DBA/1 x FVB/N)F2 progeny. The confidence intervals of the three QTLs were refined from 40, 43, and 48 Mb to 12, 4.1, and 12 Mb, respectively. The data were complemented by the use of another QTL fine-mapping approach, haplotype analysis, to further refine Cia2 into a 2-Mb genomic region. To aid in the search for candidate genes for the QTLs, genome-wide expression profiling was performed to identify strain-specific differentially expressed genes within the confidence intervals. Of the 1396 strain-specific differentially expressed genes, 3, 3, and 12 genes were within the support intervals of the Cia2, Cia27, and Trmq3, respectively. In addition, this study revealed that Cia27 and Trmq3 controlling anti-CII IgG2a Ab and CD4:CD8 T cell ratio, respectively, also regulated CIA clinical phenotypes.     

Genetic analysis of an F2 intercross between two strains of Japanese quail provided evidence for quantitative trait loci affecting carcass composition and internal organs

The purpose of this study was to identify genomic regions, quantitative trait loci (QTL), affecting carcass traits on chromosome 1 in an F2 population of Japanese quail. For this purpose, two white and wild strains of Japanese quail (16 birds) were crossed reciprocally and F1 generation (34 birds) was created. The F2 generation was produced by intercrossing of the F1 birds. Phenotypic data including carcass weight, internal organs and carcass parts were collected on F2 animals (422 birds). The total mapping population (472 birds) was genotyped for 8 microsatellite markers on chromosome 1. QTL analysis was performed with interval mapping method applying the line-cross model. Significant QTL were identified for breast weight at 0 (P < 0.01), 172 (P < 0.05) and 206 (P < 0.01), carcass weight at 91 (P < 0.05), carcass fatness at 0 (P < 0.01), pre-stomach weight at 206 (P < 0.01) and uropygial weight gland at 197 (P < 0.01) cM on chromosome 1. There was also evidence for imprinted QTL affecting breast weight (P < 0.01) on chromosome 1. The proportion of the F2 phenotypic variation explained by the significant additive, dominance and imprinted QTL effects ranged from 1.0 to 7.3 %, 1.2 to 3.3 % and 1.4 to 2.2 %, respectively.     

Genetic mapping and analysis of quantitative trait loci affecting fiber and lignin content in maize

Plant cell walls of forage provide a major source of energy for ruminant animals. Digestion of cell walls is limited by the presence of lignin, therefore the improving the digestibility of forages by reducing lignin content is a major goal in forage crop breeding programs. A recombinant inbred line maize population was used to map quantitative trait loci (QTL) for neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) of leaf-sheath and stalk tissues. All traits were positively genetically correlated. The larger genetic correlations were between NDF and ADF in sheaths (r = 0.84), NDF and ADF (r = 0.96), ADF and ADL (r = 0.83), and NDF and ADL (r = 0.76) in stalks. Twelve QTL were detected for NDF and 11 QTL for ADF in leaf-sheaths. Eight QTL detected for both traits were defined by the same or linked marker loci. Eight QTL were associated with leaf-sheath ADL. Eleven QTL were detected for NDF and ADF, and 12 QTL for ADL in stalks. Nine of eleven QTL detected for both NDF and ADF in stalks coincided in their genomic position. A high proportion of QTL detected for these traits had the same parental effects and genomic locations, suggesting that it is only necessary to select on one fiber component (NDF or ADF) to improve digestibility. Favorable correlated responses of unselected fiber components are expected due to coincident genomic locations of QTL and the high genetic correlation between fiber components. Several QTL detected in this study coincided in their positions with putative cellulose synthase genes from maize.     

Deficiency mapping of quantitative trait loci affecting longevity in Drosophila melanogaster

In a previous study, sex-specific quantitative trait loci (QTL) affecting adult longevity were mapped by linkage to polymorphic roo transposable element markers, in a population of recombinant inbred lines derived from the Oregon and 2b strains of Drosophila melanogaster. Two life span QTL were each located on chromosomes 2 and 3, within sections 33E-46C and 65D-85F on the cytological map, respectively. We used quantitative deficiency complementation mapping to further resolve the locations of life span QTL within these regions. The Oregon and 2b strains were each crossed to 47 deficiencies spanning cytological regions 32F-44E and 64C-76B, and quantitative failure of the QTL alleles to complement the deficiencies was assessed. We initially detected a minimum of five and four QTL in the chromosome 2 and 3 regions, respectively, illustrating that multiple linked factors contribute to each QTL detected by recombination mapping. The QTL locations inferred from deficiency mapping did not generally correspond to those of candidate genes affecting oxidative and thermal stress or glucose metabolism. The chromosome 2 QTL in the 35B-E region was further resolved to a minimum of three tightly linked QTL, containing six genetically defined loci, 24 genes, and predicted genes that are positional candidates corresponding to life span QTL. This region was also associated with quantitative variation in life span in a sample of 10 genotypes collected from nature. Quantitative deficiency complementation is an efficient method for fine-scale QTL mapping in Drosophila and can be further improved by controlling the background genotype of the strains to be tested.     

Model for mapping imprinted quantitative trait loci in an inbred F2 design

The role of imprinting in shaping development has been ubiquitously observed in plants, animals, and humans. However, a statistical method that can detect and estimate the effects of imprinted quantitative trait loci (iQTL) over the genome has not been extensively developed. In this article, we propose a maximum likelihood approach for testing and estimating the imprinted effects of iQTL that contribute to variation in a quantitative trait. This approach, implemented with the EM algorithm, allows for a genome-wide scan for the existence of iQTL. This approach was used to reanalyze published data in an F(2) family derived from the LG/S and SM/S mouse strains. Several iQTL that regulate the growth of body weight by expressing paternally inherited alleles were identified. Our approach provides a standard procedure for testing the statistical significance of iQTL involved in the genetic control of complex traits.     

An improved method for quantitative trait loci detection and identification of within-line segregation in F2 intercross designs

We present a new flexible, simple, and powerful genome-scan method (flexible intercross analysis, FIA) for detecting quantitative trait loci (QTL) in experimental line crosses. The method is based on a pure random-effects model that simultaneously models between- and within-line QTL variation for single as well as epistatic QTL. It utilizes the score statistic and thereby facilitates computationally efficient significance testing based on empirical significance thresholds obtained by means of permutations. The properties of the method are explored using simulations and analyses of experimental data. The simulations showed that the power of FIA was as good as, or better than, Haley-Knott regression and that FIA was rather insensitive to the level of allelic fixation in the founders, especially for pedigrees with few founders. A chromosome scan was conducted for a meat quality trait in an F(2) intercross in pigs where a mutation in the halothane (Ryanodine receptor, RYR1) gene with a large effect on meat quality was known to segregate in one founder line. FIA obtained significant support for the halothane-associated QTL and identified the base generation allele with the mutated allele. A genome scan was also performed in a previously analyzed chicken F(2) intercross. In the chicken intercross analysis, four previously detected QTL were confirmed at a 5% genomewide significance level, and FIA gave strong evidence (P < 0.01) for two of these QTL to be segregating within the founder lines. FIA was also extended to account for epistasis and using simulations we show that the method provides good estimates of epistatic QTL variance even for segregating QTL. Extensions of FIA and its applications on other intercross populations including backcrosses, advanced intercross lines, and heterogeneous stocks are also discussed.     

Mapping of quantitative trait loci affecting resistance/susceptibility to Sarcocystis miescheriana in swine

The outcome of infectious diseases in vertebrates is under genetic control at least to some extent. In swine, e.g., marked differences in resistance/susceptibility to Sarcocystis miescheriana have been shown between Chinese Meishan and European Pietrain pigs, and these differences are associated with high heritabilities. A first step toward the identification of genes and polymorphisms causal for these differences may be the mapping of quantitative trait loci (QTLs). Considering clinical, immunological, and parasitological traits in the above model system, this survey represents the first QTL study on parasite resistance in pigs. QTL mapping was performed in 139 F(2) pigs of a Meishan/Pietrain family infected with S. miescheriana. Fourteen genome-wide significant QTLs were mapped to several chromosomal areas. Among others, major QTLs were identified for bradyzoite numbers in skeletal muscles (F = 17.4; p < 0.001) and for S. miescheriana-specific plasma IgG(2) levels determined 42 days p.i. (F = 20.9; p < 0.001). The QTLs were mapped to different regions of chromosome 7, i.e., to the region of the major histocompatibility complex (bradyzoites) and to an immunoglobulin heavy chain cluster, respectively. These results provide evidence for a direct and causal role for gene variants within these gene clusters (cis-acting) in differences in resistance to S. miescheriana.     

Genetic mapping of quantitative trait loci for resistance to Haemonchus contortus in sheep

This paper presents results from a mapping experiment to detect quantitative trait loci (QTL) for resistance to Haemonchus contortus infestation in merino sheep. The primary trait analysed was faecal worm egg count in response to artificial challenge at 6 months of age. In the first stage of the experiment, whole genome linkage analysis was used for broad-scale mapping. The animal resource used was a designed flock comprising 571 individuals from four half-sib families. The average marker spacing was about 20 cM. For the primary trait, 11 QTL (as chromosomal/family combinations) were significant at the 5% chromosome-wide level, with allelic substitution effects of between 0.19 and 0.38 phenotypic standard deviation units. In general, these QTL did not have a significant effect on faecal worm egg count recorded at 13 months of age. In the second stage of the experiment, three promising regions (located on chromosomes 1, 3 and 4) were fine-mapped. This involved typing more closely spaced markers on individuals from the designed flock as well as an additional 495 individuals selected from a related population with a deeper pedigree. Analysis was performed using a linkage disequilibrium–linkage approach, under additive, dominant and multiple QTL models. Of these, the multiple QTL model resulted in the most refined QTL positions, with resolutions of <10 cM achieved for two regions. Because of the moderate size of effect of the QTL, and the apparent age and/or immune status specificity of the QTL, it is suggested that a panel of QTL will be required for significant genetic gains to be achieved within industry via marker-assisted selection.     

Genetic mapping of quantitative trait loci for aseasonal reproduction in sheep

The productivity and economic prosperity of sheep farming could benefit greatly from more effective methods of selection for year-round lambing. Identification of QTL for aseasonal reproduction in sheep could lead to more accurate selection and faster genetic improvement. One hundred and twenty microsatellite markers were genotyped on 159 backcross ewes from a Dorset × East Friesian crossbred pedigree. Interval mapping was undertaken to map the QTL underlying several traits describing aseasonal reproduction including the number of oestrous cycles, maximum level of progesterone prior to breeding, pregnancy status determined by progesterone level, pregnancy status determined by ultrasound, lambing status and number of lambs born. Seven chromosomes (1, 3, 12, 17, 19, 20 and 24) were identified to harbour putative QTL for one or more component traits used to describe aseasonal reproduction. Ovine chromosomes 12, 17, 19 and 24 harbour QTL significant at the 5% chromosome-wide level, chromosomes 3 and 20 harbour QTL that exceeded the threshold at the 1% chromosome-wide level, while the QTL identified on chromosome 1 exceeded the 1% experiment-wide significance level. These results are a first step towards understanding the genetic mechanism of this complex trait and show that variation in aseasonal reproduction is associated with multiple chromosomal regions.     

Genome‐wide association study identifies quantitative trait loci affecting hematological traits in an F2 intercross between Landrace and Korean native pigs

Changes affecting the status of health and robustness can bring about physiological alterations including hematological parameters in swine. To identify quantitative trait loci (QTL) associated with eight hematological traits (one leukocyte trait, six erythrocyte traits and one platelet trait), we conducted a genome‐wide association study using the PorcineSNP60K BeadChip in a resource population derived from an intercross between Landrace and Korean native pigs. A total of 36 740 SNPs from 816 F₂ progeny were analyzed for each blood‐related trait after filtering for quality control. Data were analyzed by the genome‐wide rapid association using mixed model and regression (GRAMMAR) approach. A total of 257 significant SNPs (P < 1.36 × 10^?6) on SSC3, 6, 8, 13 and 17 were identified for blood‐related traits in this study. Interestingly, the genomic region between 17.9 and 130 Mb on SSC8 was found to be significantly associated with red blood cell, mean corpuscular volume and mean corpuscular hemoglobin. Our results include the identification of five significant SNPs within five candidate genes (KIT, IL15, TXK, ARAP2 and ERG) for hematopoiesis. Further validation of these identified SNPs could give valuable information for understanding the variation of hematological traits in pigs.     

Genetic complexity and quantitative trait loci mapping of yeast morphological traits

Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes.     

High-resolution mapping of quantitative trait loci affecting increased life span in Drosophila melanogaster

Limited life span and senescence are near-universal characteristics of eukaryotic organisms, controlled by many interacting quantitative trait loci (QTL) with individually small effects, whose expression is sensitive to the environment. Analyses of mutations in model organisms have shown that genes affecting stress resistance and metabolism affect life span across diverse taxa. However, there is considerable segregating variation for life span in nature, and relatively little is known about the genetic basis of this variation. Replicated lines of Drosophila that have evolved increased longevity as a correlated response to selection for postponed senescence are valuable resources for identifying QTL affecting naturally occurring variation in life span. Here, we used deficiency complementation mapping to identify at least 11 QTL on chromosome 3 that affect variation in life span between five old (O) lines selected for postponed senescence and their five base (B) population control lines. Most QTL were sex specific, and all but one affected multiple O lines. The latter observation is consistent with alleles at intermediate frequency in the base population contributing to the response to selection for postponed senescence. The QTL were mapped with high resolution and contained from 12 to 170 positional candidate genes.