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Xie & Xu (2000) present a model for mapping quantitative trait loci in an autotetraploid population. However, one aspect of their model, namely gamete formation, does not properly represent the biological process in autotetraploid species. This paper gives a more realistic formulation for this part of the model, and discusses the consequences for multipoint mapping.
Extreme discordant sibling pairs (EDSPs) are theoretically powerful for the mapping of quantitative-trait loci (QTLs) in humans. EDSPs have not been used much in practice, however, because of the need to screen very large populations to find enough pairs that are extreme and discordant. Given appropriate statistical methods, another alternative is to use moderately discordant sibling pairs (MDSPs)--pairs that are discordant but not at the far extremes of the distribution. Such pairs can be powerful yet far easier to collect than extreme discordant pairs. Recent work on statistical methods for QTL mapping in humans has included a number of methods that, though not developed specifically for discordant pairs, may well be powerful for MDSPs and possibly even EDSPs. In the present article, we survey the new statistics and discuss their applicability to discordant pairs. We then use simulation to study the type I error and the power of various statistics for EDSPs and for MDSPs. We conclude that the best statistic(s) for discordant pairs (moderate or extreme) is (are) to be found among the new statistics. We suggest that the new statistics are appropriate for many other designs as well-and that, in fact, they open the way for the exploration of entirely novel designs.
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Genome research in animals used in agriculture has progressed rapidly in recent years, moving from rudimentary genome maps to trait maps to gene discovery. These advances are the result of animal genome projects following closely in the footsteps of the Human Genome Project, which has opened the door to genome research in farm animals. In return, genome research in livestock species is contributing to our understanding of chromosome evolution and to informing the human genome. Enhancement of these contributions plus the much anticipated application of DNA-based tools to animal health and production can be expected as livestock genomics enters its sequencing era.
In order to study duck microsatellites, we constructed a library enriched for (CA)n, (CAG)n, (GCC)n and (TTTC)n. A total of 35 pairs of primers from these microsatellites were developed and used to detect polymorphisms in 31 unrelated Peking ducks. Twenty-eight loci were polymorphic and seven loci were monomorphic. A total of 117 alleles were observed from these polymorphic microsatellite markers, which ranged from 2 to 14 with an average of 4.18 per locus. The frequencies of the 117 alleles ranged from 0.02 to 0.98. The highest heterozygosity (0.97) was observed at the CAUD019 microsatellite locus and the lowest heterozygosity (0.04) at the CAUD008 locus, and 11 loci had heterozygosities greater than 0.50 (46.43%). The polymorphism information content (PIC) of 28 loci ranged from 0.04 to 0.88 with an average of 0.42. All the above markers were used to screen the polymorphism in other bird species. Two markers produced specific monomorphic products with the chicken DNA. Fourteen markers generated specific fragments with the goose DNA: 5 were polymorphic and 9 were monomorphic. But no specific product was detected with the peacock DNA. Based on sequence comparisons of the flanking sequence and repeat, we conclude that 2 chicken loci and 14 goose loci were true homologous loci of the duck loci. The microsatellite markers identified and characterized in the present study will contribute to the genetic map, quantitative traits mapping, and phylogenetic analysis in the duck and goose.
Two commercial broiler lines were used in an experiment to map quantitative trait loci (QTL) affecting disease resistance. Chickens from these lines were genotyped with 27 microsatellite markers to estimate heterozygosity and polymorphism information content (PIC), the probability that one parent is heterozygous at a marker locus and the other has a different genotype. Heterozygosity estimated from allelic frequencies was 0.52 for the two lines; however, heterozygosity calculated from actual counts of heterozygous loci was much lower (0.36) than the estimated heterozygosity. The PIC was 0.45 in these lines, and average allele number per marker locus was about 3.5. Twenty-five males produced from a cross between these two lines were screened with the DNA markers to select birds with high heterozygosity at marker loci. Mating simulation showed that uninformative matings could be reduced by about 5% if 12 pairs of males and females with the highest heterozygosity at marker loci were selected, which was about a 25% reduction in total uninformative matings. This experiment demonstrated that the heterozygosity and PIC in commercial broiler lines were low and selection for the birds with high heterozygosity at marker loci could increase informative content in chickens used in the experiments of QTL mapping, thus increasing detection power for QTL mapping.
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.
Quantitative trait loci mapping was used to identify the chromosomal location of genes which contribute to oral morphine preference (in a two-bottle choice paradigm) of C57BL/6J mice, compared to DBA/2J mice. An F2 intercross of these two strains (606 mice) was phenotyped for morphine preference and those mice demonstrating extreme values for morphine consumption (the highest and lowest 7.7%) were genotyped for 157 murine microsatellite polymorphisms. Maximum likelihood methods revealed three loci on murine chromosomes 1, 6 and 10 which are responsible for nearly 85% of the genetic variance observed between the two parental strains.
Locus heterogeneity is a concern for quantitative trait locus mapping where phenotypes are likely to be influenced by more than one gene. We introduce a model which generalizes the locus heterogeneity model of Smith (1961) from dichotomous traits to quantitative traits and consider some test statistics for this model. The type I error rates and the power of these statistics are assessed through simulation studies. These statistics are applied to a linkage study of asthma genes.
Intercrosses between divergent lines of domestic animals adapted to different environmental conditions and/or production systems provide unique opportunities for mapping phenotypic trait loci. The strategy resembles the use of interspecific crosses developed for general linkage mapping. We have made an intercross between the wild pig and domestic pigs of the Large White breed. A comprehensive linkage map comprising more than 230 genetic markers has been established. The successful use of this intercross for mapping loci for some monogenic traits (coat color and an intestinal receptor for E. coli K88 pili) as well as quantitative trait loci (QTLs) with major effects of growth and fatness is reviewed.
Quantitative trait loci (QTL) can implicate an unbiased sampling of genes underlying a complex, polygenic phenotype. QTL affecting longevity in Caenorhabditis elegans were mapped using a CL2a x Bergerac-BO recombinant-inbred population. Genotypes were compared at 30 transposon-specific markers for two paired sample sets totaling 171 young controls and 172 longevity-selected worms (the last-surviving 1%) from a synchronously aged population. A third sample set, totaling 161 worms from an independent culture, was analyzed for confirmation of loci. At least six highly significant QTL affecting life span were detected both by single-marker (chi(2)) analysis and by two interval-mapping procedures--one intended for nonparametric traits and another developed specifically for mapping of categorical traits. These life-span QTL were located on chromosomes I (near the hP4 locus), III (near stP127), IV (near stP44), V (a cluster of three peaks, near stP192, stP23, and stP6), and X (two distinct peaks, near stP129 and stP2). Epistatic effects on longevity were also analyzed by Fisher's exact test, which indicated a significant life-span interaction between markers on chromosomes V (stP128) and III (stP127). Several further interactions were significant in the initial unselected population; two of these, between distal loci on chromosome V, were completely eliminated in the long-lived subset. Allelic longevity effects for two QTL, on chromosomes IV and V, were confirmed in backcrossed congenic lines and were highly significant in two very different environments-growth on solid agar medium and in liquid suspension culture.
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.
AIM: To present a novel strategy for mapping quantitative trait loci (QTL), using human metapopulations. The strategy is based on the expectation that in geographic clusters of small and distinct human isolates, a combination of founder effect and genetic drift can dramatically increase population frequency of rare QTL variants with large effect. In such cases, the distribution of QT measurements in an (affected) isolate is expected to deviate from that observed in neighboring isolates. METHODS: We tested this hypothesis in 9 villages from a larger Croatian isolate resource, where 7 Mendelian disorders have been previously reported. The values of 10 physiological and biochemical QTs were measured in a random sample of 1001 individuals (100 inhabitants of each of 9 villages and 101 immigrant controls). RESULTS: Significant over- or under- representation of individuals from specific villages in extreme ends of standardized QT measurement distribution was found 10 times more frequently than expected by chance. The large majority of such clusters of individuals with extreme QT values (34/36, 94.4%) originated from the 6 villages with the most pronounced geographic isolation and endogamy. CONCLUSION: Early epidemiological assessment supports the feasibility of the proposed strategy. Clusters of individuals with extreme QT values responsible for over-representation of single villages can usually be linked to a larger pedigree and may be useful for further QTL mapping, using linkage analysis.
Quantitative trait locus (QTL) mapping is a valuable new tool for locating genomic regions that underlie variation in important traits such as insecticide resistance. Because QTL mapping complements a candidate gene strategy for understanding the genetic architecture of important traits, it may also facilitate the identification of genes causing important variation. After mapping the QTL locations, markers closely linked to QTL can be used for genetic analysis of population structure and to measure the spread and increase of resistance-causing QTL alleles. In this study, QTL influencing resistance to the pyrethroid insecticide esfenvalerate were mapped in the Colorado potato beetle Leptinotarsa decemlineata (Say) (CPB). Three QTL contributing to esfenvalerate resistance were identified from a mapping population of 79 individuals analyzed at 90 marker loci. One QTL had a large effect and two QTL had smaller effects. The major QTL occurs on the X chromosome, overlapping the position of a candidate gene (Leptinotarsa decemlineata Voltage sensitive sodium channel [LdVssc1]) previously implicated in pyrethroid resistance. Resistance-increasing alleles at the two minor-effect QTL originated with the susceptible parent, suggesting that alleles of small effect may be segregating in susceptible populations. Comparison of the New York population from which the susceptible parent originated with a more-susceptible population from North Carolina suggests that the minor-effect loci identified here may explain some of the variation in tolerance observed among susceptible populations. DNA sequencing of a portion of LdVssc1 shows that the resistance-conferring allele from the resistant parent does not contain the kdr mutation previously found in CPB and typically observed in other insects that are resistant to pyrethroid insecticides because of changes in this gene.
The identification of genes that affect quantitative traits has been of great interest to geneticists for many decades, and many statistical methods have been developed to map quantitative trait loci (QTL). Most QTL mapping studies in experimental organisms use purely inbred lines, where the two homologous chromosomes in each individual are identical. As a result, many existing QTL mapping methods developed for experimental organisms are applicable only to genetic crosses between inbred lines. However, it may be difficult to obtain inbred lines for certain organisms, e.g., mosquitoes. Although statistical methods for QTL mapping in outbred populations, e.g., humans, can be applied for such crosses, these methods may not fully take advantage of the uniqueness of these crosses. For example, we can generally assume that the two grandparental lines are homozygous at the QTL of interest, but such information is not be utilized through methods developed for outbred populations. In addition, mating types and phases can be relatively easy to establish through the analysis of adjacent markers due to the large number of offspring that can be collected, substantially simplifying the computational need. In this article, motivated by a mosquito intercross experiment involving two selected lines that are not genetically homozygous across the genome, we develop statistical methods for QTL mapping for genetic crosses involving noninbred lines. In our procedure, we first infer parental mating types and use likelihood-based methods to infer phases in each parent on the basis of genotypes of offspring and one parent. A hidden Markov model is then employed to estimate the number of high-risk alleles at marker positions and putative QTL positions between markers in each offspring, and QTL mapping is finally conducted through the inferred QTL configuration across all offspring in all crosses. The performance of the proposed methods is assessed through simulation studies, and the usefulness of this method is demonstrated through its application to a mosquito data set.
Most current statistical methods developed for mapping quantitative trait loci (QTL) based on inbred line designs apply to crosses from two inbred lines. Analysis of QTL in these crosses is restricted by the parental genetic differences between lines. Crosses from multiple inbred lines or multiple families are common in plant and animal breeding programmes, and can be used to increase the efficiency of a QTL mapping study. A general statistical method using mixture model procedures and the EM algorithm is developed for mapping QTL from various cross designs of multiple inbred lines. The general procedure features three cross design matrices, W, that define the contribution of parental lines to a particular cross and a genetic design matrix, D, that specifies the genetic model used in multiple line crosses. By appropriately specifying W matrices, the statistical method can be applied to various cross designs, such as diallel, factorial, cyclic, parallel or arbitrary-pattern cross designs with two or multiple parental lines. Also, with appropriate specification for the D matrix, the method can be used to analyse different kinds of cross populations, such as F2 backcross, four-way cross and mixed crosses (e.g. combining backcross and F2). Simulation studies were conducted to explore the properties of the method, and confirmed its applicability to diverse experimental designs.
The variance-components model is the method of choice for mapping quantitative trait loci in general human pedigrees. This model assumes normally distributed trait values and includes a major gene effect, random polygenic and environmental effects, and covariate effects. Violation of the normality assumption has detrimental effects on the type I error and power. One possible way of achieving normality is to transform trait values. The true transformation is unknown in practice, and different transformations may yield conflicting results. In addition, the commonly used transformations are ineffective in dealing with outlying trait values. We propose a novel extension of the variance-components model that allows the true transformation function to be completely unspecified. We present efficient likelihood-based procedures to estimate variance components and to test for genetic linkage. Simulation studies demonstrated that the new method is as powerful as the existing variance-components methods when the normality assumption holds; when the normality assumption fails, the new method still provides accurate control of type I error and is substantially more powerful than the existing methods. We performed a genomewide scan of monoamine oxidase B for the Collaborative Study on the Genetics of Alcoholism. In that study, the results that are based on the existing variance-components method changed dramatically when three outlying trait values were excluded from the analysis, whereas our method yielded essentially the same answers with or without those three outliers. The computer program that implements the new method is freely available.
Many quantitative traits of economical importance are ordinal in nature. Although methods of mapping quantitative trait loci (QTLs) for continuous quantitative characters are well developed, such methods for ordinal characters are generally lacking. In this paper, we develop a method based on the framework of a generalized linear model using four-way cross populations. The method estimates and tests the average effects of a gene substitution in the parents. All markers in the same linkage group are simultaneously used to infer the allelic transmission of a putative QTL. General results of the method are demonstrated by a few simulation experiments. We discuss extensions of the method to QTL mapping in full-sib families.