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PubMed2006

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Chromosome Mapping↗
PubMed2003

Limits of fine-mapping a quantitative trait.

Once a significant linkage is found, an important goal is reducing the error in the estimated location of the linked locus. A common approach to reducing location error, called fine-mapping, is the genotyping of additional markers in the linked region to increase the genetic information. The utility of fine-mapping for quantitative trait linkage analysis is largely unknown. To explore this issue, we performed a fine-mapping simulation in which the region containing a significant linkage at a 10-centiMorgan (cM) resolution was fine-mapped at 2, 1, and 0.5 cM. We simulated six quantitative trait models in which the proportion of variation due to the quantitative trait locus (QTL) ranged from 0.20-0.90. We used four sampling designs that were all combinations of 100 and 200 families of sizes 5 and 7. Variance components linkage analysis (Genehunter) was performed until 1,000 replicates were found with a maximum lodscore greater than 3.0. For each of these 1,000 replications, we repeated the linkage analysis three times: once for each of the fine-map resolutions. For the most realistic model, reduction in the average location error ranged from 3-15% for 2-cM fine-mapping and from 3-18% for 1-cM fine-mapping, depending on the number of families and family size. Fine-mapping at 0.5 cM did not differ from the 1-cM results. Thus, if the QTL accounts for a small proportion of the variation, as is the case for realistic traits, fine-mapping has little value.

Chromosome Mapping↗
PubMed1994

Selective DNA pooling for determination of linkage between a molecular marker and a quantitative trait locus.

Selective genotyping is a method to reduce costs in marker-quantitative trait locus (QTL) linkage determination by genotyping only those individuals with extreme, and hence most informative, quantitative trait values. The DNA pooling strategy (termed: "selective DNA pooling") takes this one step further by pooling DNA from the selected individuals at each of the two phenotypic extremes, and basing the test for linkage on marker allele frequencies as estimated from the pooled samples only. This can reduce genotyping costs of marker-QTL linkage determination by up to two orders of magnitude. Theoretical analysis of selective DNA pooling shows that for experiments involving backcross, F2 and half-sib designs, the power of selective DNA pooling for detecting genes with large effect, can be the same as that obtained by individual selective genotyping. Power for detecting genes with small effect, however, was found to decrease strongly with increase in the technical error of estimating allele frequencies in the pooled samples. The effect of technical error, however, can be markedly reduced by replication of technical procedures. It is also shown that a proportion selected of 0.1 at each tail will be appropriate for a wide range of experimental conditions.

Chromosome Mapping↗
PubMed2003

Molecular mapping of capsaicinoid biosynthesis genes and quantitative trait loci analysis for capsaicinoid content in Capsicum.

Quantitative variation in the accumulation of two major capsaicinoids responsible for pungency in the fruit of chile peppers, capsaicin and dihydrocapsaicin, was analyzed in a cross between the non-pungent Capsicum annuum parent cv. Maor and a pungent Capsicum frutescens parent, accession BG 2816. In order to identify quantitative trait loci (QTLs) for capsaicinoid content, we employed the bulked segregant analysis method and screened bulked DNA from F2 individuals at the extremes of the distribution of capsaicinoid content with RAPD primers. Screening with 400 primers allowed the identification of three loci that were polymorphic between the bulks. These RAPD markers were converted to SCARs and subsequently mapped with additional RFLP markers to chromosome 7 of pepper. QTL interval analysis for individual and total capsaicinoid content identified a major QTL, termed cap, which explained 34-38% of the phenotypic variation for this trait in two growing environments. For all measurements, the allele of the pungent parent BG 2816 at cap contributed to the increased level of pungency. To determine whether known structural genes in the pathway could define a candidate for this QTL, 12 clones obtained from differentially expressed transcripts from placental tissue in pungent peppers were also mapped. None of them had a significant effect on this trait, nor did the allelic state at the locus C, the on/off switch for pungency in pepper, located on chromosome 2. The identity of cap and its effect on capsaicin content in other backgrounds will be addressed in future studies.

Alleles↗
PubMed2003

Mapping quantitative trait loci for bean traits and ovule number in Theobroma cacao L.

Quantitative trait loci (QTL) mapping for bean traits and the number of ovules per ovary was carried out in cocoa (Theobroma cacao L.) using three test-cross progenies derived from crosses between a lower Amazon Forastero male parent (Catongo) and three female parents: one upper Amazon Forastero (IMC78) and two Trinitario (DR1 and S52). RFLP (restriction fragment length polymorphism), microsatellite, and AFLP (amplified fragment lengthpolymorphism) markers were used for mapping. Between one and six QTL for bean traits (length, weight, and shape index) and one and four QTL for the number of ovules per ovary were detected using composite interval mapping (CIM). Individual QTL explained between 5 and 24% of the phenotypic variation. QTL clusters were identified on several chromosomes, but particularly on chromosome 4. QTL related to bean traits were detected in the same region in both Trinitario parents and in a close region in the upper Amazon Forastero parent. In reference to a previous diversity study where alleles specific to Criollo and Forastero genotypes were identified, it was possible to speculate on the putative origin (Criollo or Forastero) of favorable QTL alleles segregating in both Trinitario studied.

Cacao↗
PubMed1999

Testing the correspondence between map positions of quantitative trait loci.

There are several instances in which quantitative trait locus (QTL) mapping experiments have been independently carried out for similar traits in different laboratories. We develop a permutation test of the correspondence between the test statistics obtained from genome-wide QTL scans in two such experiments to test whether the same QTLs are segregating in the experimental pair. In simulations, we show that the permutation test has the desired properties if chromosomes are of equal length, but bias can occur if chromosomes are of unequal length, a problem connected with autocorrelation of test statistic values. We apply the test to data from three recent mouse body weight QTL mapping experiments. The results from the test are non-significant, and imply a lack of overall concordance between the QTLs that were segregating in these experiments.

Animals↗
PubMed2000

A unified sampling approach for multipoint analysis of qualitative and quantitative traits in sib pairs.

Recent advances in molecular biology have enhanced the opportunity to conduct multipoint mapping for complex diseases. Concurrently, one sees a growing interest in the use of quantitative traits in linkage studies. Here, we present a multipoint sib-pair approach to locate the map position (tau) of a trait locus that controls the observed phenotype (qualitative or quantitative), along with a measure of statistical uncertainty. This method builds on a parametric representation for the expected identical-by-descent statistic at an arbitrary locus, conditional on an event reflecting the sampling scheme, such as affected sib pairs, for qualitative traits, or extreme discordant (ED) sib pairs, for quantitative traits. Our results suggest that the variance about tau&d4;, the estimator of tau, can be reduced by as much as 60%-70% by reducing the length of intervals between markers by one half. For quantitative traits, we examine the precision gain (measured by the variance reduction in tau&d4;) by genotyping extremely concordant (EC) sib pairs and including them along with ED sib pairs in the statistical analysis. The precision gain depends heavily on the residual correlation of the quantitative trait for sib pairs but considerably less on the allele frequency and exact genetic mechanism. Since complex traits involve multiple loci and, hence, the residual correlation cannot be ignored, our finding strongly suggests that one should incorporate EC sib pairs along with ED sib pairs, in both design and analysis. Finally, we empirically establish a simple linear relationship between the magnitude of precision gain and the ratio of the number of ED pairs to the number of EC pairs. This relationship allows investigators to address issues of cost effectiveness that are due to the need for phenotyping and genotyping subjects.

Alleles↗
PubMed1999

Short communication: quantitative trait loci analysis on 17 nonproduction traits in the New Zealand dairy population.

Seven New Zealand Holstein-Friesian families and two Jersey families, a total of 274 sires, were analyzed in a granddaughter design for marker-quantitative trait loci associations. For 17 nonproduction traits (management, size, and conformation traits), an across-family analysis was undertaken using multimarker regression procedures. Threshold levels were set empirically by permuting the data. A quantitative trait locus for stature was identified on chromosome 14, which was significant at the 15% experimentwise level (suggestive linkage). The quantitative trait locus was identified to be segregating in three of the nine families.

Animals↗
PubMed1998

Further investigation on the regression method of mapping quantitative trait loci.

The simple regression method of mapping quantitative trait loci (QTL) is further investigated in comparison with the mixture model maximum likelihood method under high heritabilities, dominant and missing markers. No significant difference between the two methods is detected in terms of errors of parameter estimation and statistical powers, with the exception that the estimation of residual variance provided by the regression method is confounded with part of the QTL variance. The test statistic profiles show some difference between the two methods, but the difference is only detectable at the micro level. An alternative method, referred to as iteratively reweighted least squares, is proposed, which can correct the deficiency of parameter confounding in the regression method yet retains the properties of simplicity and rapidity of the ordinary regression method. Like the existing regression method, the weighted least squares method can be useful in QTL mapping in conjunction with the permutation tests and construction of confidence intervals by bootstrapping.

Chromosome Mapping↗
PubMed2004

Modifying the Schwarz Bayesian information criterion to locate multiple interacting quantitative trait loci.

The problem of locating multiple interacting quantitative trait loci (QTL) can be addressed as a multiple regression problem, with marker genotypes being the regressor variables. An important and difficult part in fitting such a regression model is the estimation of the QTL number and respective interactions. Among the many model selection criteria that can be used to estimate the number of regressor variables, none are used to estimate the number of interactions. Our simulations demonstrate that epistatic terms appearing in a model without the related main effects cause the standard model selection criteria to have a strong tendency to overestimate the number of interactions, and so the QTL number. With this as our motivation we investigate the behavior of the Schwarz Bayesian information criterion (BIC) by explaining the phenomenon of the overestimation and proposing a novel modification of BIC that allows the detection of main effects and pairwise interactions in a backcross population. Results of an extensive simulation study demonstrate that our modified version of BIC performs very well in practice. Our methodology can be extended to general populations and higher-order interactions.

Bayes Theorem↗
PubMed1998

Congenic strain confirms putative quantitative trait locus for body weight in the rat.

Quantitative trait loci (QTLs) affecting body weight were investigated in the backcross population derived from non-diabetic BB/OK and spontaneously hypertensive rat (SHR) strains. The F1 hybrids were backcrossed onto SHR rats, and QTL analysis was performed separately with the resulting backcross populations for each sex on Chromosomes (Chrs) 1, 3, 4, 10, 13, and 18. The body weight was determined at the age of 14 weeks, and the statistical analysis was performed with MAPMAKER/QTL 1.1b computer program. According to the stringent threshold for a lod score of 3.0, markers on Chr 1 were found to be linked with body weight. The QTL with a peak lod score (5.1) on Chr 1 for a male population was located within markers Igf2 and D1Mgh12. In contrast, in the female population the body weight affecting QTL (lod = 5.7) on Chr 1 was located between the D1Mit3 and Lsn markers. The existence of QTLs on Chr 1 affecting body weight in the male population was confirmed by congenic BB.Sa rats, carrying chromosomal region of SHR (Sa-Igf2) on the genetic background of BB rat.

Animals↗
PubMed2002

Novel linkage mapping approach using DNA pooling in human and animal genetics. II. Detection of quantitative traits loci in dairy cattle.

Selective DNA pooling is an advanced methodology for linkage mapping of quantitative trait loci (QTL) in farm animals. The principle is based on densitometric estimates of marker allele frequency in pooled DNA samples of phenotypically extreme individuals from half-sib, backcross and F(2) experimental designs in farm animals. This methodology provides a rapid and efficient analysis of a large number of individuals with short tandem repeat markers that are essential to detect QTL through the genome - wide searching approach. Several strategies involving whole genome scanning with a high statistical power have been developed for systematic search to detect the quantitative traits loci and linked loci of complex traits. In recent studies, greater success has been achieved in mapping several QTLs in Israel-Holstein cattle using selective DNA pooling. This paper outlines the currently emerged novel strategies of linkage mapping to identify QTL based on selective DNA pooling with more emphasis on its theoretical pre-requisite to detect linked QTLs, applications, a general theory for experimental half-sib designs, the power of statistics and its feasibility to identify genetic markers linked QTL in dairy cattle. The study reveals that the application of selective DNA pooling in dairy cattle can be best exploited in the genome-wide detection of linked loci with small and large QTL effects and applied to a moderately sized half-sib family of about 500 animals.

Animals↗
PubMed2004

Comparing linkage disequilibrium-based methods for fine mapping quantitative trait loci.

Recently, a method for fine mapping quantitative trait loci (QTL) using linkage disequilibrium was proposed to map QTL by modeling covariance between individuals, due to identical-by-descent (IBD) QTL alleles, on the basis of the similarity of their marker haplotypes under an assumed population history. In the work presented here, the advantage of using marker haplotype information for fine mapping QTL was studied by comparing the IBD-based method with 10 markers to regression on a single marker, a pair of markers, or a two-locus haplotype under alternative population histories. When 10 markers were genotyped, the IBD-based method estimated the position of the QTL more accurately than did single-marker regression in all populations. When 20 markers were genotyped for regression, as single-marker methods do not require knowledge of haplotypes, the mapping accuracy of regression in all populations was similar to or greater than that of the IBD-based method using 10 markers. Thus for populations similar to those simulated here, the IBD-based method is comparable to single-marker regression analysis for fine mapping QTL.

Alleles↗
PubMed2005

Software for quantitative trait analysis.

This paper provides a brief overview of software currently available for the genetic analysis of quantitative traits in humans. Programs that implement variance components, Markov Chain Monte Carlo (MCMC), Haseman-Elston (H-E) and penetrance model-based linkage analyses are discussed, as are programs for measured genotype association analyses and quantitative trait transmission disequilibrium tests. The software compared includes LINKAGE, FASTLINK, PAP, SOLAR, SEGPATH, ACT, Mx, MERLIN, GENEHUNTER, Loki, Mendel, SAGE, QTDT and FBAT. Where possible, the paper provides URLs for acquiring these programs through the internet, details of the platforms for which the software is available and the types of analyses performed.

Chromosome Mapping↗
PubMed2003

[Localization of genes, determining quantitative traits in wheat: amendment to the "catalog of chromosomal mapping of genes in domestic cultivars of wheat"].

An amendment to the catalog of chromosome location of genes in Russian wheat cultivars was constructed with the published data of the recent decade. The results of chromosomal localization were summarized and analyzed by methods of multivariate statistics. Chromosomes critical for 40 quantitative traits under study proved to cluster according to their homeology, i.e., by homeological groups. The hypotheses providing an explanation for this finding are considered. It is suggested that quantitative traits are similarly controlled by genes located on homeological chromosomes in common wheat, making it possible to isolate a limited number of major genes for each particular quantitative trait.

Chromosome Mapping↗
PubMedDate not supplied

Use of quantitative trait loci to map murine lung tumor susceptibility genes.

During the last decade new methods for mapping quantitative trait loci (QTLs) have helped geneticists uncover disease-associated genes. Genetic dissection of complex multigenic diseases such as cancer is being accomplished in part by mapping QTLs in experimental crosses of mice [1]. With the recent construction of dense genetic linkage maps for the mouse, mapping of quantitative trait loci has become practical [2]. Over 6000 polymorphic simple sequence length repeat markers (microsatellite markers) have been mapped in the mouse genome [3], and new analytical approaches to linkage analysis have made QTL mapping a powerful technique for identifying cancer genes [4-7]. In this overview we discuss the design of QTL mapping studies and some of the findings from studies on the mapping of murine lung tumor susceptibility loci.

Animals↗
PubMed2026

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.

Humans↗
PubMed2001

Quantitative trait loci mapping of serum IgE in an isolated Hutterite population.

Serum immunoglobulin E (IgE) is a quantitative trait associated with asthma. A variance components approach to the analysis of data from a subset of a founder population of Hutterites indicated that over half the variability in IgE levels may be due to genes. Two variance components methods suggest that the genetic model underlying IgE does not consist of a single major gene, but of multiple loci with more modest effects. Both approaches identified putative quantitative trait loci on chromosomes 1 and 21.

Adult↗