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PubMed2002

QTL analysis for grain protein content using SSR markers and validation studies using NILs in bread wheat.

QTL interval mapping for grain protein content (GPC) in bread wheat was conducted for the first time, using a framework map based on a mapping population, which was available in the form of 100 recombinant inbred lines (RILs). The data on GPC for QTL mapping was recorded by growing the RILs in five different environments representing three wheat growing locations from Northern India; one of these locations was repeated for 3 years. Distribution of GPC values followed normal distributions in all the environments, which could be explained by significant g x e interactions observed through analyses of variances, which also gave significant effects due to genotypes and environments. Thirteen (13) QTLs were identified in individual environments following three methods (single-marker analysis or SMA, simple interval mapping or SIM and composite interval mapping or CIM) and using LOD scores that ranged from 2.5 to 6.5. Threshold LOD scores (ranging from 3.05 to 3.57), worked out and used in each case, however, detected only seven of the above 13 QTLs. Only four (QGpc.ccsu-2B.1; QGpc.ccsu-2D.1; QGpc.ccsu-3D.1 and QGpc.ccsu-7A.1) of these QTLs were identified either in more than one location or following one more method other than CIM; another QTL (QGpc.ccsu-3D.2), which was identified using means for all the environments, was also considered to be important. These five QTLs have been recommended for marker-assisted selection (MAS). The QTLs identified as above were also validated using ten NILs derived from three crosses. Five of the ten NILs possessed 38 introgressed segments from 16 chromosomes and carried 42 of the 173 markers that were mapped. All the seven QTLs were associated with one or more of the markers carried by the above introgressed segments, thus validating the corresponding markers. More markers associated with many more QTLs to be identified should become available in the future by effective MAS for GPC improvement.

Analysis of Variance↗
PubMed2003

Mapping of chromosomal regions influencing immunological responses to gastrointestinal nematode infections in mice.

This paper reports the results of a genome-wide search for quantitative trait loci (QTL) influencing immunological responses to infection with the gastro-intestinal nematode parasite Heligmosomoides polygyrus in an F2 population created by crossing the resistant SWR and the susceptible CBA inbred mouse strains. Following infections, intestinal granuloma score at post mortem, mucosal mast cell protease 1, and IgE and IgG1 titres were recorded. The susceptible CBA mice had significantly higher IgG1, but significantly lower IgE, mucosal mast cell protease 1 and granuloma scores than SWR mice. Significant QTL were mapped to chromosomes 4, 11, 13 and 17 for granuloma score; chromosomes 12 and 17 for IgE; chromosome 10, 17 and 18 for IgG1 and chromosomes 1, 9, 10, 11, 17 and 18 for mucosal mast cell protease 1. Chromosomes 10, 11, 17 and 18 had QTL affecting more than one trait, and these are most likely to represent single QTL with multiple effects rather than multiple QTL. Some of these QTL map to regions known to harbour genes responsible for the induction of immunological responses to intestinal worms.

Animals↗
PubMed1999

Multiple interval mapping for quantitative trait loci.

A new statistical method for mapping quantitative trait loci (QTL), called multiple interval mapping (MIM), is presented. It uses multiple marker intervals simultaneously to fit multiple putative QTL directly in the model for mapping QTL. The MIM model is based on Cockerham's model for interpreting genetic parameters and the method of maximum likelihood for estimating genetic parameters. With the MIM approach, the precision and power of QTL mapping could be improved. Also, epistasis between QTL, genotypic values of individuals, and heritabilities of quantitative traits can be readily estimated and analyzed. Using the MIM model, a stepwise selection procedure with likelihood ratio test statistic as a criterion is proposed to identify QTL. This MIM method was applied to a mapping data set of radiata pine on three traits: brown cone number, tree diameter, and branch quality scores. Based on the MIM result, seven, six, and five QTL were detected for the three traits, respectively. The detected QTL individually contributed from approximately 1 to 27% of the total genetic variation. Significant epistasis between four pairs of QTL in two traits was detected, and the four pairs of QTL contributed approximately 10.38 and 14.14% of the total genetic variation. The asymptotic variances of QTL positions and effects were also provided to construct the confidence intervals. The estimated heritabilities were 0.5606, 0.5226, and 0. 3630 for the three traits, respectively. With the estimated QTL effects and positions, the best strategy of marker-assisted selection for trait improvement for a specific purpose and requirement can be explored. The MIM FORTRAN program is available on the worldwide web (http://www.stat.sinica.edu.tw/chkao/).

Chromosome Mapping↗
PubMedDate not supplied

The efficiency of designs for fine-mapping of quantitative trait loci using combined linkage disequilibrium and linkage.

In a simulation study, different designs were compared for efficiency of fine-mapping of QTL. The variance component method for fine-mapping of QTL was used to estimate QTL position and variance components. The design of many families with small size gave a higher mapping resolution than a design with few families of large size. However, the difference is small in half sib designs. The proportion of replicates with the QTL positioned within 3 cM of the true position is 0.71 in the best design, and 0.68 in the worst design applied to 128 animals with a phenotypic record and a QTL explaining 25% of the phenotypic variance. The design of two half sib families each of size 64 was further investigated for a hypothetical population with effective size of 1000 simulated for 6000 generations with a marker density of 0.25 cM and with marker mutation rate 4 x10(-4) per generation. In mapping using bi-allelic markers, 42 approximately 55 of replicated simulations could position QTL within 0.75 cM of the true position whereas this was higher for multi allelic markers (48 approximately 76 ). The accuracy was lowest (48%) when mutation age was 100 generations and increased to 68% and 76% for mutation ages of 200 and 500 generations, respectively, after which it was about 70% for mutation ages of 1000 generations and older. When effective size was linearly decreasing in the last 50 generations, the accuracy was decreased (56 to 70%). We show that half sib designs that have often been used for linkage mapping can have sufficient information for fine-mapping of QTL. It is suggested that the same design with the same animals for linkage mapping should be used for fine-mapping so gene mapping can be cost effective in livestock populations.

Alleles↗
PubMed2004

Microarray gene expression analysis of the Fob3b obesity QTL identifies positional candidate gene Sqle and perturbed cholesterol and glycolysis pathways.

Obesity-related diseases are poised to become the primary cause of death in developed nations. While a number of monogenic causes of obesity have recently been identified, these are responsible for only a small proportion of human cases of obesity. Quantitative trait locus (QTL) studies using animal models have revealed hundreds of potential loci that affect obesity; however, few have been further analyzed beyond the original QTL scan. We previously mapped four QTL in an F(2) between divergently selected Fat (F) and Lean (L) lines. A QTL of large effect on chromosome 15 (Fob3) was subsequently mapped to a higher resolution into two smaller-effect QTL (Fob3a and Fob3b) using crosses between the F-line and a congenic line containing L-line alleles at the Fob3 QTL region. Here we report the gene expression characterization of Fob3b. Microarray expression analysis using the NIA-NIH 15K cDNA array set containing 14,938 mouse ESTs was employed to identify candidate genes and pathways that are differentially expressed between the F-line and a congenic line containing only the Fob3b QTL (Fob3b-line). Our study suggests squalene epoxidase (Sqle), a cholesterol biosynthesis enzyme, as a strong positional candidate gene for Fob3b. Several other cholesterol biosynthesis pathway genes unlinked to Fob3b were found to be differentially expressed, suggesting that a perturbation of this pathway could be in part responsible for the phenotypic difference between the F-line and Fob3b-line mice.

Animals↗
PubMed2001

A major quantitative trait locus for resistance to Potato leafroll virus is located in a resistance hotspot on potato chromosome XI and is tightly linked to N-gene-like markers.

Potato leafroll virus (PLRV) causes one of the most widespread and important virus diseases in potato. Resistance to PLRV is controlled by genetic factors that limit plant infection by viruliferous aphids or virus multiplication and accumulation. Quantitative trait locus (QTL) analysis of resistance to virus accumulation revealed one major and two minor QTL. The major QTL, PLRV.1, mapped to potato chromosome XI in a resistance hotspot containing several genes for qualitative and quantitative resistance to viruses and other potato pathogens. This QTL explained between 50 and 60% of the phenotypic variance. The two minor QTL mapped to chromosomes V and VI. Genes with sequence similarity to the tobacco N gene for resistance to Tobacco mosaic virus were tightly linked to PLRV.1. The cDNA sequence of an N-like gene was used to develop the sequence characterized amplified region (SCAR) marker N127(1164) that can assist in the selection of potatoes with resistance to PLRV.

Chromosome Mapping↗
PubMed2006

Multiple-interval mapping for ordinal traits.

Many statistical methods have been developed to map multiple quantitative trait loci (QTL) in experimental cross populations. Among these methods, multiple-interval mapping (MIM) can map QTL with epistasis simultaneously. However, the previous implementation of MIM is for continuously distributed traits. In this study we extend MIM to ordinal traits on the basis of a threshold model. The method inherits the properties and advantages of MIM and can fit a model of multiple QTL effects and epistasis on the underlying liability score. We study a number of statistical issues associated with the method, such as the efficiency and stability of maximization and model selection. We also use computer simulation to study the performance of the method and compare it to other alternative approaches. The method has been implemented in QTL Cartographer to facilitate its general usage for QTL mapping data analysis on binary and ordinal traits.

Algorithms↗
PubMed2005

Genetic consequences of selection during the evolution of cultivated sunflower.

We mapped quantitative trait loci (QTL) controlling differences in seed oil content and composition between cultivated and wild sunflower and used the results, along with those of a previous study of domestication-related QTL, to guide a genome-wide analysis of genetic variation for evidence of past selection. The effects of the seed oil QTL were almost exclusively in the expected direction with respect to the parental phenotypes. A major, oil-related QTL cluster mapped near a cluster of domestication-related QTL on linkage group six (LG06), the majority of which have previously been shown to have effects that are inconsistent with the parental phenotypes. To test the hypothesis that this region was the target of a past selective sweep, perhaps resulting in the fixation of the antagonistic domestication-related QTL, we analyzed simple sequence repeat (SSR) diversity from 102 markers dispersed throughout the sunflower genome. Our results indicate that LG06 was most likely the target of multiple selective sweeps during the postdomestication era. Strong directional selection in concert with genetic hitchhiking therefore offers a possible explanation for the occurrence of numerous domestication-related QTL with apparently maladaptive phenotypic effects.

Biological Evolution↗
PubMed2000

Mapping of quantitative trait loci for hypnotic sensitivity to ethanol in crosses derived from the C57BL/6 and DBA/2 mouse strains.

BACKGROUND: Acute ethanol sensitivity is thought to be a predisposing factor toward the development of alcoholism. Accumulated evidence suggests that this characteristic may be at least partly heritable. A widely accepted approach for identifying genes thought to contribute to alcoholism is to map quantitative trait loci (QTLs) for various ethanol-related behaviors in rodent models. METHODS: Ethanol sensitivity QTLs were interval-mapped in a C57BL/6 (B6) X DBA/2 (D2) F2 intercross that contained 391 mice. Sensitivity was measured as the duration of loss of righting reflex (LORR) after 4.1 g/kg ip. LORR also was evaluated in a chromosome 1 marker-assisted congenic strain that had an approximately 30 centiMorgan (cM) portion of D2 DNA from the distal end of chromosome 1 introgressed onto a B6 background. RESULTS: A suggestive QTL was mapped on chromosome 1 (LOD = 3.3; approximately 80 cM) and a provisional QTL on chromosome 5 (LOD = 2.3; approximately 26 cM). The provisional chromosome 5 QTL was found to be sex-specific (LOD = 2.5 for males; LOD < 1 for females) with the D2 allele increasing LORR. The chromosome 1 D2 allele decreased LORR. Consistent with the F2 QTL mapping, congenic mice heterozygous for the chromosome 1 interval (B6/D2) had a significantly different mean (+/- SEM) LORR of 74.0 +/- 4.9 min (n = 36) compared with 90.8 +/- 6.2 min (n = 33) for their homozygous (B6/B6) littermates (p = 0.02). Blood ethanol concentration at regain of righting reflex was 377 +/- 10 mg% for the B6/D2 and 368 +/- 10 mg% (p = NS) for the B6/B6. CONCLUSIONS: LORR results in the chromosome 1 congenic mice were consistent with and very similar to what was predicted from the QTL analysis in the B6 X D2 F2 population. These results support a suggestive LORR QTL on the distal end of mouse chromosome 1. The results also indicate that there is a provisional sex-specific LORR QTL on chromosome 5.

Alcoholism↗
PubMed1996

Quantitative trait locus gene mapping: a new method for locating alcohol response genes.

Alcoholism is a multigenic trait with important non-genetic determinants. Studies with genetic animal models of susceptibility to several of alcohol's effects suggest that several genes contributing modest effects on susceptibility (Quantitative Trait Loci, or QTLs) are important. A new technique of QTL gene mapping has allowed the identification of the location in mouse genome of several such QTLs. The method is described, and the locations of QTLs affecting the acute alcohol withdrawal reaction are described as an example of the method. Verification of these QTLs in ancillary studies is described and the strengths, limitations, and future directions to be pursued are discussed. QTL mapping is a promising method for identifying genes in rodents with the hope of directly extrapolating the results to the human genome. This review is based on a paper presented at the First International Congress of the Latin American Society for Biomedical Research on Alcoholism, Santiago, Chile, November 1994.

Journal Article↗
PubMed2004

Mapping of bovine ovulation rate QTL; an analytical approach for three generation pedigrees.

Interval mapping was conducted for ovulation rate quantitative trait loci (QTL) using data from two related families from the United States Department of Agriculture, US Meat Animal Research Center twinning cattle herd. Both families are extended, three generation pedigrees from which records of sons, daughters and granddaughters were analysed. Both a method of analysis and results from that analysis are reported herein. Results from one of the two families (839802) were previously reported, but reanalysis here including the second, related family (839803) and a revised statistical model lessens support for the previously reported QTL. Results from interval mapping provided evidence for QTL in regions corresponding to those previously suggested for chromosomes 7 (chromosome-wise P < 0.05) and 19 (chromosome-wise P < 0.01) in the 839802 family, although statistical significance was reduced. In contrast to the previous report, evidence for a chromosome 5 QTL in the same family was greatly reduced while support for a QTL on chromosome 10 increased (chromosome-wise P < 0.01). Analysis of data from the related 839803 family failed to replicate evidence of QTL observed on either chromosome 7 or chromosome 19 in the 839802 family.

Agriculture↗
PubMed2005

Interval mapping methods for detecting QTL affecting survival and time-to-event phenotypes.

Quantitative trait loci (QTL) are usually searched for using classical interval mapping methods which assume that the trait of interest follows a normal distribution. However, these methods cannot take into account features of most survival data such as a non-normal distribution and the presence of censored data. We propose two new QTL detection approaches which allow the consideration of censored data. One interval mapping method uses a Weibull model (W), which is popular in parametrical modelling of survival traits, and the other uses a Cox model (C), which avoids making any assumption on the trait distribution. Data were simulated following the structure of a published experiment. Using simulated data, we compare W, C and a classical interval mapping method using a Gaussian model on uncensored data (G) or on all data (G'=censored data analysed as though records were uncensored). An adequate mathematical transformation was used for all parametric methods (G, G' and W). When data were not censored, the four methods gave similar results. However, when some data were censored, the power of QTL detection and accuracy of QTL location and of estimation of QTL effects for G decreased considerably with censoring, particularly when censoring was at a fixed date. This decrease with censoring was observed also with G', but it was less severe. Censoring had a negligible effect on results obtained with the W and C methods.

Animals↗
PubMed2000

Fine-mapping of quantitative trait loci in half-sib families using current recombinations.

Two groups of methods are being developed to fine-map quantitative trait loci (QTLs): identity-by-descent methods or methods using historical recombinations, and genetic chromosome dissection methods or methods utilizing current recombinations. Here we propose two methods that fall into the second group: contrast mapping and substitution mapping. A QTL has previously been detected via linkage mapping in a half-sib design (granddaughter or daughter design), and sires (grandsires) likely to be heterozygous at the QTL have been identified. A sire (grandsire) and its recombinant offspring are then genotyped for a series of ordered markers spanning the initial marker interval. Offspring are grouped by paternal multi-marker haplotype with haplotypes differing in the location of the recombination event. In the contrast method, contrasts between the phenotypic averages of haplotypes or offspring groups are calculated which correspond to marker intervals within the original interval. The expected value of the contrast for the true QTL interval is always maximum, hence the interval with maximum observed contrast is assumed to contain the QTL. Alternative statistics for determining the interval most likely to contain a QTL are presented for contrast mapping, as well as a bootstrap estimation of the probability of having identified the correct interval. For an initial marker bracket of 20 cM and 10 additional equidistant markers, the probability of assigning the QTL to the correct 2 cM marker interval or to a combined 4 cM interval was calculated. For substitution effects of 0.093, 0.232, 0.464, 0.696 and 0.928 (in additive genetic SD), power values near 0.14, 0.26, 0.48, 0.67 and 0.80 (0.25, 0.53, 0.86, 0.97 and 0.99) are achieved for a family of 200 (1000) sons, respectively. In substitution mapping, QTL segregation status of recombinant sons must be determined using daughter genotyping. Combinations of two haplotypes with their segregation status are required to assign the QTL to an interval. Probabilities of correct QTL assignment were calculated assuming absence of the mutant QTL allele in dams of sons. For a 2 cM interval and a QTL at the midpoint of an interval, power near 0.95 (0.90) is reached when the number of recombinant sons is 70 (60), or total number of sons is 424 (363). For QTL positions away from the midpoint, power decreases but can be improved by combining marker intervals. For a QTL located halfway to the midpoint, and 182 sons in a family resulting in 30 recombinant sons, probability is 0.94 for assignment to either a 2 cM or a combined 4 cM interval. Effect of type I and type II errors in segregation status determination on power of QTL assignment was found to be small. Errors in segregation status due to QTL segregation in dams have an impact if the frequency of the mutant QTL allele is intermediate to high.

Alleles↗
PubMed2000

Mapping quantitative trait loci in complex pedigrees: a two-step variance component approach.

There is a growing need for the development of statistical techniques capable of mapping quantitative trait loci (QTL) in general outbred animal populations. Presently used variance component methods, which correctly account for the complex relationships that may exist between individuals, are challenged by the difficulties incurred through unknown marker genotypes, inbred individuals, partially or unknown marker phases, and multigenerational data. In this article, a two-step variance component approach that enables practitioners to routinely map QTL in populations with the aforementioned difficulties is explored. The performance of the QTL mapping methodology is assessed via its application to simulated data. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.

Animals↗
PubMed2002

Use of randomization testing to detect multiple epistatic QTLs.

Here, we describe a randomization testing strategy for mapping interacting quantitative trait loci (QTLs). In a forward selection strategy, non-interacting QTLs and simultaneously mapped interacting QTL pairs are added to a total genetic model. Simultaneous mapping of epistatic QTLs increases the power of the mapping strategy by allowing detection of interacting QTL pairs where none of the QTL can be detected by their marginal additive and dominance effects. Randomization testing is used to derive empirical significance thresholds for every model selection step in the procedure. A simulation study was used to evaluate the statistical properties of the proposed randomization tests and for which types of epistasis simultaneous mapping of epistatic QTLs adds power. Least squares regression was used for QTL parameter estimation but any other QTL mapping method can be used. A genetic algorithm was used to search for interacting QTL pairs, which makes the proposed strategy feasible for single processor computers. We believe that this method will facilitate the evaluation of the importance at epistatic interaction among QTLs controlling multifactorial traits and disorders.

Algorithms↗
PubMed2002

A genetic linkage map of water yam ( Dioscorea alata L.) based on AFLP markers and QTL analysis for anthracnose resistance.

A genetic linkage map of the tetraploid water yam ( Dioscorea alata L.) genome was constructed based on 469 co-dominantly scored amplified fragment length polymorphism (AFLP) markers segregating in an intraspecific F(1) cross. The F(1) was obtained by crossing two improved breeding lines, TDa 95/00328 as female parent and TDa 87/01091 as male parent. Since the mapping population was an F(1) cross between presumed heterozygous parents, marker segregation data from both parents were initially split into maternal and paternal data sets, and separate genetic linkage maps were constructed. Later, data analysis showed that this was not necessary and thus the combined markers from both parents were used to construct a genetic linkage map. The 469 markers were mapped on 20 linkage groups with a total map length of 1,233 cM and a mean marker spacing of 2.62 cM. The markers segregated like a diploid cross-pollinator population suggesting that the water yam genome is allo-tetraploid (2n = 4 x = 40). QTL mapping revealed one AFLP marker E-14/M52-307 located on linkage group 2 that was associated with anthracnose resistance, explaining 10% of the total phenotypic variance. This map covers 65% of the yam genome and is the first linkage map reported for D. alata. The map provides a tool for further genetic analysis of traits of agronomic importance and for using marker-assisted selection in D. alata breeding programmes. QTL mapping opens new avenues for accumulating anthracnose resistance genes in preferred D. alata cultivars.

Journal Article↗
PubMed2005

Exclusion of the swine leukocyte antigens as candidate region and reduction of the position interval for the Sus scrofa chromosome 7 QTL affecting growth and fatness.

Pig chromosome 7 (SSC 7) has been shown to be rich in QTL affecting performance and quality traits. Most studies mapped the QTL close to the swine leukocyte antigens (SLA), which has a large effect on adaptability and natural selection. Previous comparative mapping studies suggested that the 15-cM region limited by markers LRA1 (mapped at 55 cM) and S0102 (mapped at 70 cM) contains hundreds of genes. To decrease the number of candidate genes, we improved the mapping resolution with a genetic chromosome dissection through a backcross recombinant progeny test program between Meishan (MS) and European (EU; i.e., Large White or Landrace) breeds. Three first-generation backcross--(EU x MS) x EU--and two second-generation backcross--([EU x MS] x EU) x EU--sires carrying a recombination in the QTL mapping interval were progeny-tested (i.e., measured for a total of 44 growth, fatness, carcass and meat quality traits). Progeny family size varied from 29 to 119 pigs. Animals were genotyped for markers covering the region of interest. Progeny-test results allowed the QTL interval to be decreased from 15 to 20 cM down to 10 cM, and even less than 6 cM if we assumed that the EU pigs used in this study share only one QTL allele. Except for a putative QTL affecting some carcass composition traits, the SLA is excluded as a candidate region, suggesting that it might be possible to apply a marker-assisted selection strategy for this QTL, while controlling SLA allele diversity. The strong QTL effects remaining in animals with only 12.5% (issued from first-generation backcross boars) and 6.25% (issued from second-generation back-cross boars) Meishan genetic background shows that epistatic interactions are likely to be limited. Finally, the QTL does not have strong effects on meat quality traits.

Adipose Tissue↗