Quantitative Trait Linkage Analysis Research Articles

Interacting genetic loci cause airway hyperresponsiveness

Airway hyperresponsiveness (AHR) is a key physiological component of asthma, and the genetic basis of this complex trait has remained elusive. We created recombinant congenic mice with increased naive AHR by serially backcrossing A/J mice (which have elevated naive AHR) with C57BL/6J mice and selecting for mice with an elevated naive AHR phenotype. The seventh backcross-generation hyperresponsive mice retained A/J loci in three regions. Quantitative trait linkage (QTL) analysis of 123 unselected N8 progeny demonstrated that the AHR phenotype was not associated with any single locus but was significantly associated with an interaction of loci on chromosomes 2 and 6. These findings were confirmed in an independent analysis of chromosome substitution strain mice. The identification of genomic regions containing loci causally associated with AHR and the demonstration that this trait requires their interaction have important implications for the dissection of the genetic etiology of asthma in humans.

Mapping of a Major Locus that Determines Telomere Length in Humans

Telomere length is a crucial factor for both normal chromosomal function and senescence. Mean telomere length in humans shows considerable interindividual variation and strong genetic determination. To see if a locus (or loci) affecting telomere length in humans could be mapped, we performed a quantitative-trait linkage analysis of mean leukocyte telomere-restriction-fragment (TRF) lengths, measured by Southern blotting, in 383 adult subjects comprising 258 sib pairs. Heritability of mean (+/-SE) TRF was 81.9%+/-11.8%. There was significant linkage (LOD score 3.20) of mean TRF length to a locus on chromosome 12, which explained 49% of the overall variability in mean TRF length. We present preliminary analysis of a strong candidate gene in the region, the DNA helicase DDX11. In conclusion, we report mapping of the first locus that determines mean telomere length in humans. Identification of the gene involved and elucidation of its mechanism of action could have important implications for our understanding of chromosomal assembly, telomere biology, and susceptibility to age-related diseases.

A Quantitative Trait Locus on 13q14.2 for Trunk Strength

Previous findings show strong evidence for the role of retinoblastoma (Rb) in myoblast proliferation and differentiation. However, it is not known whether variation in the retinoblastoma gene (RB1 ) is responsible for normal variation in human muscle strength. Therefore, a linkage analysis for quantitative traits was performed on 329 young male siblings from 146 families with muscle strength, using a polymorphic marker in RB1 (D13S153 on 13q14.2). Trunk strength, a general strength indicator that requires activation of large muscle groups, was measured on a Cybex TEF isokinetic dynamometer. We found evidence for linkage between locus D13S153 at 13q14.2 and several measurements of trunk flexion with LOD scores between 1.62 and 2.78 (.002< p <.0002). No evidence for linkage was found with trunk extension. This first exploration of the relationship between RB1 and human muscle strength through linkage analysis warrants efforts for further fine mapping of this region.

A Quantitative Trait Locus on 13q14.2 for Trunk Strength

Previous findings show strong evidence for the role of retinoblastoma (Rb) in myoblast proliferation and differentiation. However, it is not known whether variation in the retinoblastoma gene (RB1) is responsible for normal variation in human muscle strength. Therefore, a linkage analysis for quantitative traits was performed on 329 young male siblings from 146 families with muscle strength, using a polymorphic marker in RB1 (D13S153 on 13q14.2). Trunk strength, a general strength indicator that requires activation of large muscle groups, was measured on a Cybex TEF isokinetic dynamometer. We found evidence for linkage between locus D13S153 at 13q14.2 and several measurements of trunk flexion with LOD scores between 1.62 and 2.78 (.002 &lt; p &lt; .0002). No evidence for linkage was found with trunk extension. This first exploration of the relationship between RB1 and human muscle strength through linkage analysis warrants efforts for further fine mapping of this region.

Power of Variance Component Linkage Analysis—II. Discrete Traits

We determine the power of variance component linkage analysis in the case of discrete, dichotomous traits analyzed under a classical liability threshold model. For simplicity we consider randomly ascertained samples and an additive model of variation incorporating a qtl, residual additive genetic factors, and individual-specific random environmental effects. We derive an expression for the power of variance component linkage analysis in arbitrary relative pairs, and compare the power of discrete and quantitative trait linkage analysis in the specific case of sibpairs. The predicted sample sizes required in linkage analysis of sibpairs are confirmed by analysis of simulated data. Unlike the affected-sibpair method, the power of discrete trait variance component analysis increases with trait prevalence. The relative efficiency of a discrete trait for linkage analysis increases with population trait prevalence, but does not exceed about 40% and is typically much less.

Genetic analysis of genome-wide variation in human gene expression.

Natural variation in gene expression is extensive in humans and other organisms, and variation in the baseline expression level of many genes has a heritable component. To localize the genetic determinants of these quantitative traits (expression phenotypes) in humans, we used microarrays to measure gene expression levels and performed genome-wide linkage analysis for expression levels of 3,554 genes in 14 large families. For approximately 1,000 expression phenotypes, there was significant evidence of linkage to specific chromosomal regions. Both cis- and trans-acting loci regulate variation in the expression levels of genes, although most act in trans. Many gene expression phenotypes are influenced by several genetic determinants. Furthermore, we found hotspots of transcriptional regulation where significant evidence of linkage for several expression phenotypes (up to 31) coincides, and expression levels of many genes that share the same regulatory region are significantly correlated. The combination of microarray techniques for phenotyping and linkage analysis for quantitative traits allows the genetic mapping of determinants that contribute to variation in human gene expression.

Bias toward the Null Hypothesis in Model-Free Linkage Analysis Is Highly Dependent on the Test Statistic Used

Recently, it has been suggested that traditional nonparametric multipoint-linkage procedures can show a "bias" toward the null hypothesis of no effect when there is incomplete information about allele sharing at genotyped marker loci (or at positions in between marker loci). Here, I investigate the extent of this bias for a variety of test statistics commonly used in qualitative- ("affecteds only") and quantitative-trait linkage analysis. Through simulation and analytical derivation, I show that many of the test statistics available in standard linkage analysis packages (such as Genehunter, Merlin, and Allegro) are, in fact, not affected by this bias problem. A few test statistics--most notably the nonparametric linkage statistic and, to a lesser extent, the Aspex-MLS and Haseman-Elston statistics--are affected by the bias. Variance-components procedures, although unbiased, can show inflation or deflation of the test statistic attributable to the inclusion of pairs with incomplete identity-by-descent information. Results obtained--for instance, in genome scans--using these methods might therefore be worth revisiting to see if greater power can be obtained by use of an alternative statistic or by eliminating or downweighting uninformative relative pairs.

Effect of winsorization on power and type 1 error of variance components and related methods of QTL detection.

Variance components analysis provides an efficient method for performing linkage analysis for quantitative traits. However, power and type 1 error of variance components-based likelihood ratio testing may be affected when phenotypic data are nonnormally distributed (especially with high values of kurtosis) and there is moderate to high correlation among the siblings. Winsorization can reduce the effect of outliers on statistical analyses. Here, we considered the effect of winsorization on variance components-based tests. We considered the likelihood ratio test (LRT), the Wald test, and some robust variance components tests. We compared these tests with Haseman-Elston least squares-based tests. We found that power to detect linkage is significantly increased after winsorization of the nonnormal phenotypes. Winsorization does not greatly diminish the type 1 error for the variance components-based tests for markedly nonnormal data. A robust version of the LRT that adjusts for sample kurtosis showed the best power for nonnormal data. Finally, phenotype winsorization of nonnormal data reduces the bias in estimation of the major gene variance component.

Exploring positional candidate genes: linkage conditional on measured genotype.

Variance component methods for linkage analysis of quantitative traits are now well established and have been applied with great success. Numerous QTLs influencing both normal variation between individuals and biomedically relevant quantitative risk factors have been localized in recent years. These findings have motivated recent methodological developments focused on the next step in the analytical process, moving beyond QTL localization by linkage to identifying specific genes and the functional polymorphisms in them that influence the phenotype of interest. In this paper, we describe one approach to exploring positional candidate genes in a region of linkage, linkage analysis conditional on measured genotype. We illustrate the properties of this method using simulated data from Genetic Analysis Workshops 10 and 12 and discuss example applications of this method to human quantitative trait data.

The genetic contribution to muscle strength, knee pain, cartilage volume, bone size, and radiographic osteoarthritis: a sibpair study.

To estimate the heritability of muscle strength, knee pain, cartilage volume, bone size, and radiographic osteoarthritis (ROA), and to assess whether heritability of the knee structural components is independent of ROA. A sibpair design was utilized. Sagittal T1-weighted fat-suppressed magnetic resonance imaging (MRI) of the right knee was performed to determine cartilage volume and bone size. Standing semiflexed radiographs of the same knee were obtained to assess the presence of ROA. Knee pain was assessed by questionnaire and muscle strength by dynamometry. Heritability was estimated using the genetic analysis program SOLAR. A total of 128 subjects (61 men, 67 women; mean age 45 years) from 51 families representing 115 sibpairs were studied. Lower limb muscle strength had high heritability (42%; P = 0.02), as did knee pain (44%; P = 0.07). Heritability estimates for cartilage volume were 65% for medial tibial cartilage, 77% for lateral tibial cartilage, and 84% for patellar cartilage, and heritability estimates for bone size were 85% for medial tibial bone area, 57% for lateral tibial bone area, and 70% for patella bone volume (all P < or = 0.004). For ROA, heritability was 61% for presence (with a large standard error) (P = 0.16) and 61% for severity (P = 0.02). The estimates for tibial bone areas were the only ones markedly reduced after adjustment for body size, while all estimates with the exception of knee pain were independent of ROA. Cartilage and, to a lesser extent, bone sites investigated by MRI were largely under independent genetic control, with a lesser shared genetic component. With the exception of prevalent ROA, all knee modalities assessed had high heritability, most likely reflecting a strong genetic component. Cartilage volume, bone size, and muscle strength all have the potential to be studied in quantitative trait linkage analyses, but their exact relevance with regard to OA remains uncertain at this time.

Quantitative trait linkage analysis by generalized estimating equations: unification of variance components and Haseman-Elston regression.

Two of the major approaches for linkage analysis with quantitative traits in humans include variance components and Haseman-Elston regression. Previously, these were viewed as quite separate methods. We describe a general model, fit by use of generalized estimating equations (GEE), for which the variance components and Haseman-Elston methods (including many of the extensions to the original Haseman-Elston method) are special cases, corresponding to different choices for a working covariance matrix. We also show that the regression-based test of Sham et al. ([2002] Am. J. Hum. Genet. 71:238-253) is equivalent to a robust score statistic derived from our GEE approach. These results have several important implications. First, this work provides new insight regarding the connection between these methods. Second, asymptotic approximations for power and sample size allow clear comparisons regarding the relative efficiency of the different methods. Third, our general framework suggests important extensions to the Haseman-Elston approach which make more complete use of the data in extended pedigrees and allow a natural incorporation of environmental and other covariates.

Multipoint linkage analysis of quantitative traits on sex‐chromosomes

Variance component models form a powerful and flexible tool for multipoint linkage analysis of quantitative traits. Estimates of genetic similarity are needed for the variance component model to detect linkage and to locate genes, and two methods are commonly used to calculate multipoint identity-by-descent (IBD) estimates for autosomes. Fulker et al. ([1995] Am. J. Hum. Genet. 56: 1229-1233) and Almasy and Blangero ([1998] Am. J. Hum. Genet. 62: 119-121) used multiple regression to estimate the IBD sharing along a chromosome, while the approach of Kruglyak and Lander ([1995] Am. J. Hum. Genet. 57: 439-454) is based on a hidden Markov model. In this paper, we modify the variance component model to accommodate sex-chromosomes, and we extend both multipoint IBD estimation methods to accommodate sex-linked loci. Simulation studies demonstrate the power and precision of the variance component model to detect QTLs located on the sex-chromosome. The two multipoint IBD estimation methods have the same accuracy to identify QTL position, but the hidden Markov model yields a larger average maximum LOD score to detect linkage than the regression model. The extension of the multipoint IBD estimation methods and the variance component model to the X chromosome shows that the variance component model is a powerful and flexible tool for linkage analysis of quantitative traits on both autosomes and sex-chromosomes.

Pedigree transmission disequilibrium test for QTL mapping of threshold trait

With biological and economic importance, threshold traits are discrete in phenotype but have the same polygenic genetic basis as quantitative traits. The traditional linkage analysis for quantitative traits is invalid for threshold traits due to their special characters. The transmission disequilibrium test (TDT) has received great attention recently in localizing human disease genes due to its simplicity and powerfulness. But TDT only deals with data from independent nuclear families and it will lose information about extended pedigree that incorporates information not only from parents and siblings but also from other relatives. The pedigree disequilibrium test (PDT) proposed by Martin in 2001 can be used to analyze the extended pedigree in human. In this study, PDT was introduced into the QTL mapping of threshold traits for farm animals, and was modified in order to accommodate the pedigree structures of farm animals. The modified PDT was renamed pedigree transmission disequilibrium test (PTDT) and its power and type I error were investigated and compared with that of PDT by Monte Carlo simulation. It was shown that PTDT is a robust and valid approach to mapping QTL of threshold trait. When the parental information is complete, PTDT and PDT are almost the same in terms of power and type I error. However, if the parental information is missing to a certain extent, PTDT is higher in power and lower in type I error than PDT. These results imply that PTDT can be a novel approach to QTL fine mapping of threshold traits based on the existing coarse mapping information.

Power and robustness of linkage tests for quantitative traits in general pedigrees

There are numerous statistical methods for quantitative trait linkage analysis in human studies. An ideal such method would have high power to detect genetic loci contributing to the trait, would be robust to non-normality in the phenotype distribution, would be appropriate for general pedigrees, would allow the incorporation of environmental covariates, and would be appropriate in the presence of selective sampling. We recently described a general framework for quantitative trait linkage analysis, based on generalized estimating equations, for which many current methods are special cases. This procedure is appropriate for general pedigrees and easily accommodates environmental covariates. In this report, we use computer simulations to investigate the power and robustness of a variety of linkage test statistics built upon our general framework. We also propose two novel test statistics that take account of higher moments of the phenotype distribution, in order to accommodate non-normality. These new linkage tests are shown to have high power and to be robust to non-normality. While we have not yet examined the performance of our procedures in the context of selective sampling via computer simulations, the proposed tests satisfy all of the other qualities of an ideal quantitative trait linkage analysis method.

Confirmed locus on chromosome 11p and candidate loci on 6q and 8p for the triglyceride and cholesterol traits of combined hyperlipidemia.

Background- Combined hyperlipidemia is a common disorder characterized by a highly atherogenic lipoprotein profile and increased risk of coronary heart disease. The etiology of the lipid abnormalities (increased serum cholesterol and triglyceride or either lipid alone) is unknown. We assembled 2 large cohorts of families with familial combined hyperlipidemia (FCHL) and performed disease and quantitative trait linkage analyses to evaluate the inheritance of the lipid abnormalities. Chromosomal regions 6q16.1-q16.3, 8p23.3-p22, and 11p14.1-q12.1 produced evidence for linkage to FCHL. Chromosomes 6 and 8 are newly identified candidate loci that may respectively contribute to the triglyceride (logarithm of odds [LOD], 1.43; P=0.005) and cholesterol (LOD, 2.2; P=0.0007) components of this condition. The data for chromosome 11 readily fulfil the guidelines required for a confirmed linkage. The causative alleles may contribute to the inheritance of the cholesterol (LOD, 2.04 at 35.2 cM; P=0.0011) component of FCHL as well as the triglyceride trait (LOD, 2.7 at 48.7 cM; P=0.0002). Genetic analyses identify 2 potentially new loci for FCHL and provide important positional information for cloning the genes within the chromosome 11p14.1-q12.1 interval that contributes to the lipid abnormalities of this highly atherogenic disorder.

ENU large-scale mutagenesis and quantitative trait linkage (QTL) analysis in mice: novel technologies for searching polygenetic determinants of craniofacial abnormalities.

Discrepancies in size and shape of the jaws are the underlying etiology in many orthodontic and orthognathic surgery patients. Genetic factors combined with environmental interactions have been postulated to play a causal or contributory role in these craniofacial abnormalities. Along with the soon-to-be-available complete human and mouse genomic sequence data, mouse mutants have become a valuable tool in the functional mapping of genes involved in the development of human maxillofacial dysmorphologies. We review two powerful methods in such efforts: N-ethyl-N-nitrosourea (ENU) large-scale mutagenesis and quantitative trait linkage (QTL) analysis. The former aims at producing a plethora of novel variants of particular trait(s), and ultimately mapping the point mutations responsible for the appearance of these new traits. In contrast, the latter applies intensive breeding and mapping techniques to identify multiple loci (and, subsequently, genes) contributing to the phenotypic difference between the tested strains. A prerequisite for either approach to studying variations in the traits of interest is the application of effective mouse cephalometric phenotype analysis and rapid DNA mapping techniques. These approaches will produce a wealth of new data on critical genes that influence the size and shape of the human face.

Diagnostic tools in linkage analysis for quantitative traits.

Diagnostic methods are key components in any good statistical analysis. Because of the similarities between the variance components approach and regression analysis with respect to the normality assumption, when performing quantitative genetic linkage analysis using variance component methods, one must check the normality assumption of the quantitative trait and outliers. Thus, the main purposes of this paper are to describe methods for testing the normality assumption, to describe various diagnostic methods for identifying outliers, and to discuss the issues that may arise when outliers are present when using variance components models in quantitative trait linkage analysis. Data from the Rochester Family Heart Study are used to illustrate the various diagnostic methods and related issues.

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.

Regression-Based Sib Pair Linkage Analysis for Binary Traits

The Haseman-Elston (HE) regression method offers a mathematically and computationally simpler alternative to variance-components (VC) models for the linkage analysis of quantitative traits. However, current versions of HE regression and VC models are not optimised for binary traits. Here, we present a modified HE regression and a liability-threshold VC model for binary-traits. The new HE method is based on the regression of a linear combination of the trait squares and the trait cross-product on the proportion of alleles identical by descent (IBD) at the putative locus, for sibling pairs. We have implemented both the new HE regression-based method and have performed analytic and simulation studies to assess its type 1 error rate and power under a range of conditions. These studies showed that the new HE method is well-behaved under the null hypothesis in large samples, is more powerful than both the original and the revisited HE methods, and is approximately equivalent in power to the liability-threshold VC model.

Mapping loci influencing blood pressure in the Framingham pedigrees using model-free LOD score analysis of a quantitative trait.

This paper presents a method of performing model-free LOD-score based linkage analysis on quantitative traits. It is implemented in the QMFLINK program. The method is used to perform a genome screen on the Framingham Heart Study data. A number of markers that show some support for linkage in our study coincide substantially with those implicated in other linkage studies of hypertension.Although the new method needs further testing on additional real and simulated data sets we can already say that it is straightforward to apply and may offer a useful complementary approach to previously available methods for the linkage analysis of quantitative traits.