Quantitative Trait Linkage Analysis Research Articles

A QTL genome scan of the metabolic syndrome and its component traits.

BackgroundBecause high blood pressure, altered lipid levels, obesity, and diabetes so frequently occur together, they are sometimes collectively referred to as the metabolic syndrome. While there have been many studies of each metabolic syndrome trait separately, few studies have attempted to analyze them combined, i.e., as one composite variable, in quantitative trait linkage or association analysis. We used genotype and phenotype data from the Framingham Heart Study to perform a full-genome scan for quantitative trait loci underlying the metabolic syndrome.ResultsHeritability estimates for all of the covariate-adjusted and age- and gender-standardized individual traits, and the composite metabolic syndrome trait, were all fairly high (0.39–0.62), and the composite trait was among the highest at 0.61. The composite trait yielded no regions with suggestive linkage by Lander and Kruglyak's criteria, although there were several noteworthy regions for individual traits, some of which were also observed for the composite variable.ConclusionDespite its high heritability, the composite metabolic syndrome trait variable did not increase the power to detect or localize linkage peaks in this sample. However, this strategy and related methods of combining correlated individual traits deserve further investigation, particularly in settings with complex causal pathways.

Quantitative trait linkage analysis of longitudinal change in body weight.

One of the great strengths of the Framingham Heart Study data, provided for the Genetic Analysis Workshop 13, is the long-term survey of phenotypic data. We used this unique data to create new phenotypes representing the pattern of longitudinal change of the provided phenotypes, especially systolic blood pressure and body weight. We performed a linear regression of body weight and systolic blood pressure on age and took the slopes as new phenotypes for quantitative trait linkage analysis using the SOLAR package. There was no evidence for heritability of systolic blood pressure change. Heritability was estimated as 0.15 for adult life "body weight change", measured as the regression slope, and "body weight gain" (including only individuals with a positive regression slope), and as 0.22 for body weight "change up to 50" (regression slope of weight on age up to an age of 50). With multipoint analysis, two regions on the long arm of chromosome 8 showed the highest LOD scores of 1.6 at 152 cM for "body weight change" and of >1.9 around location 102 cM for "body weight gain" and "change up to 50". The latter two LOD scores almost reach the threshold for suggestive linkage. We conclude that the chromosome 8 region may harbor a gene acting on long-term body weight regulation, thereby contributing to the development of the metabolic syndrome.

Exploring pleiotropy using principal components.

A standard multivariate principal components (PCs) method was utilized to identify clusters of variables that may be controlled by a common gene or genes (pleiotropy). Heritability estimates were obtained and linkage analyses performed on six individual traits (total cholesterol (Chol), high and low density lipoproteins, triglycerides (TG), body mass index (BMI), and systolic blood pressure (SBP)) and on each PC to compare our ability to identify major gene effects. Using the simulated data from Genetic Analysis Workshop 13 (Cohort 1 and 2 data for year 11), the quantitative traits were first adjusted for age, sex, and smoking (cigarettes per day). Adjusted variables were standardized and PCs calculated followed by orthogonal transformation (varimax rotation). Rotated PCs were then subjected to heritability and quantitative multipoint linkage analysis. The first three PCs explained 73% of the total phenotypic variance. Heritability estimates were above 0.60 for all three PCs. We performed linkage analyses on the PCs as well as the individual traits. The majority of pleiotropic and trait-specific genes were not identified. Standard PCs analysis methods did not facilitate the identification of pleiotropic genes affecting the six traits examined in the simulated data set. In addition, genes contributing 20% of the variance in traits with over 0.60 heritability estimates could not be identified in this simulated data set using traditional quantitative trait linkage analyses. Lack of identification of pleiotropic and trait-specific genes in some cases may reflect their low contribution to the traits/PCs examined or more importantly, characteristics of the sample group analyzed, and not simply a failure of the PC approach itself.

Two loci affect angiotensin I-converting enzyme activity in baboons.

Serum LDL cholesterol (LDLC) concentrations and ACE activities are risk factors for the development of cardiovascular disease (CVD). However, the relationship between ACE and CVD susceptibility, and possible mechanisms of action, is controversial. With data on 622 pedigreed baboons, we used statistical genetic methods to determine the mode of inheritance of ACE activities and its relationship to LDLC on different diets. ACE activity was moderately heritable, and quantitative trait linkage analyses detected a quantitative trait locus (QTL) for ACE activity on the baboon homolog of human chromosome 17 (near the ACE structural locus, maximum multipoint lod=7.5, genomic P=0.000003). Bivariate analyses revealed that ACE activity was genetically correlated (rhoG) with LDLC response (LDLCRC) to a high-cholesterol diet (rhoG=0.30+/-0.13, P=0.01) but not to LDLC on a basal diet (rhoG=0.08+/-0.13). Bivariate genetic analyses indicated that a previously detected QTL for LDLCRC had significant (P=0.025) pleiotropic effects on ACE activity levels and accounted for the genetic correlation. Therefore, we have detected 2 putative loci that affect ACE activity in baboons, one of which also affects LDLC dietary response. The existence of at least 2 genes that affect ACE activity, one of which is diet-responsive, may help explain the lack of consistency among studies of the relationship between ACE and CVD.

Mapping of quantitative ultrasound of the calcaneus bone to chromosome 1 by genome-wide linkage analysis.

Quantitative ultrasound (QUS) may predict the risk of fracture independent of bone density. The aim of this study was to identify, using quantitative trait linkage analysis, chromosomal regions that might contain genes influencing variation in calcaneal ultrasound measures in a set of families from the general population. A genome-wide autosomal scan was conducted in 324 Caucasian families (1270 measured individuals) from the Framingham Osteoporosis Study, using a set of 401 Marshfield microsatellite markers with a 10 cM average density map. QUS measurements included broadband ultrasound attenuation (BUA), speed of sound (SOS), and quantitative ultrasound index (QUI). These phenotypes were regressed on age, age(2), body mass index, height, alcohol and caffeine consumption, smoking status, physical activity, and estrogen use in females, in each sex and generation separately. Adjusted QUS phenotypes demonstrated a strong heritability ranging from 0.45 (SOS) to 0.52 (BUA). By two-point variance components genome screening, phenotype-specific regions of possible linkage were identified on chromosomal regions 1p36.3 and 5p15.2. The maximum LOD score attained was 2.74 for BUA with D1S468 (4 cM) and 2.69 for SOS with D5S817 (23 cM). QUI, a linear combination of the SOS and BUA, showed linkage with both markers (LOD = 2.1 with D1S468 and LOD = 2.2 with D5S817). Results of two-point analysis were confirmed by multipoint linkage analysis only for BUA, with LOD = 2.4 at D1S468, but not for SOS or QUI. The results for QUS, adjusted for femoral and lumbar spinal bone mineral density, in addition to the above covariates, were virtually the same. In conclusion, our results suggest that there may be genetic determinants for BUA on 1p36.3. These results should encourage further investigations of the genetic source of QUS variability and candidate polymorphisms in this region.

Powerful Regression-Based Quantitative-Trait Linkage Analysis of General Pedigrees

We present a new method of quantitative-trait linkage analysis that combines the simplicity and robustness of regression-based methods and the generality and greater power of variance-components models. The new method is based on a regression of estimated identity-by-descent (IBD) sharing between relative pairs on the squared sums and squared differences of trait values of the relative pairs. The method is applicable to pedigrees of arbitrary structure and to pedigrees selected on the basis of trait value, provided that population parameters of the trait distribution can be correctly specified. Ambiguous IBD sharing (due to incomplete marker information) can be accommodated in the method by appropriate specification of the variance-covariance matrix of IBD sharing between relative pairs. We have implemented this regression-based method and have performed simulation studies to assess, under a range of conditions, estimation accuracy, type I error rate, and power. For normally distributed traits and in large samples, the method is found to give the correct type I error rate and an unbiased estimate of the proportion of trait variance accounted for by the additive effects of the locus-although, in cases where asymptotic theory is doubtful, significance levels should be checked by simulations. In large sibships, the new method is slightly more powerful than variance-components models. The proposed method provides a practical and powerful tool for the linkage analysis of quantitative traits.

Genome-wide linkage analysis assessing parent-of-origin effects in the inheritance of birth weight.

Family studies suggest that genetic variation may influence birth weight. We have assessed linkage of birth weight in a genome-wide scan in 269 Pima Indian siblings (334 sibling pairs, 92 families). As imprinting (expression of only a single copy of a gene depending on parent-of-origin), is commonly found in genes that affect fetal growth, we used a recently described modification of standard multipoint variance-component methods of linkage analysis of quantitative traits. This technique allows for comparison of linkage models that incorporate imprinting effects (in which the strength of linkage is expressed as LOD(IMP)) and models where parent-of-origin effects are not included (LOD(EQ)). Where significant evidence of linkage was present, separate contributions of alleles derived from father (LOD(FA)) or mother (LOD(MO)) to the imprinting model were estimated. Significant evidence of linkage was found on chromosome 11 (at map position 88 cM, LOD(IMP)=3.4) with evidence for imprinting (imprinting model superior, P<0.001). In this region, birth weight was linked predominantly to paternally derived alleles (LOD(FA)=4.1, LOD(MO)=0.0). An imprinted gene on chromosome 11 may influence birth weight in the Pima population. This chromosome contains one of the two major known clusters of imprinted genes in the human genome, lending biological plausibility to our findings.

Quantitative trait linkage analysis of lipid-related traits in familial type 2 diabetes: evidence for linkage of triglyceride levels to chromosome 19q.

Macrovascular disease is a major complication of type 2 diabetes. Epidemiological data suggest that the risk of macrovascular complications may predate the onset of hyperglycemia. Hypertriglyceridemia, low levels of HDL cholesterol, and an atherogenic profile characterize the insulin resistance/metabolic syndrome that is also prevalent among nondiabetic members of familial type 2 diabetic kindreds. To identify the genes for lipid-related traits, we first performed a 10-cM genome scan using 440 markers in 379 members of 19 multiplex families ascertained for two diabetic siblings (screening study). We then extended findings for three regions with initial logarithm of odds (LOD) scores >1.5 to an additional 23 families, for a total of 576 genotyped individuals (extended study). We found heritabilities for all lipid measures in the range of 0.31 to 0.52, similar to those reported by others in unselected families. However, we found the strongest evidence for linkage of triglyceride levels to chromosome 19q13.2, very close to the ApoC2/ApoE/ApoC1/ApoC4 gene cluster (LOD 2.56) in the screening study; the LOD increased to 3.16 in the extended study. Triglyceride-to-HDL cholesterol ratios showed slightly lower LOD scores (2.73, extended family) in this same location. Other regions with LOD scores >2.0 included HDL linkage to chromosome 1q21-q23, where susceptibility loci for both familial type 2 diabetes and familial combined hyperlipidemia have been mapped, and to chromosome 2q in the region of the NIDDM1 locus. Neither region showed stronger evidence for linkage in the extended studies, however. Our results suggest that genes in or near the ApoE/ApoC2/ApoC1/ApoC4 cluster on 19q13.2 may contribute to the commonly observed hypertriglyceridemia and low HDL seen in diabetic family members and their offspring, and thus may be a candidate locus for the insulin resistance syndrome.

Age- and stage-specific regulation patterns in the hematopoietic stem cell hierarchy

AbstractThe molecular mechanisms that regulate self-renewal and differentiation of very primitive hematopoietic stem and progenitor cells in vivo are still poorly understood. Despite the clinical relevance, even less is known about the mechanisms that regulate these cells in old animals. In a forward genetic approach, using quantitative trait linkage analysis in the mouse BXD recombinant inbred set, this study identified loci that regulate the genetic variation in the size of primitive hematopoietic cell compartments of young and old C57BL6 and DBA/2 animals. Linked loci were confirmed through the generation and analysis of congenic animals. In addition, a comparative linkage analysis revealed that the number of primitive hematopoietic cells and hematopoietic stem cells are regulated in a stage-specific and an age-specific manner.

The New Haseman-Elston Method and the Weighted Pairwise Correlation Statistic Are Variations on the Same Theme

Model free linkage analysis of quantitative traits has become increasingly popular in recent years. Its basic idea is to use the principle of similarity: Sib pairs that are genetically (dis)similar at a linked marker should be phenotypically (dis)similar and vice versa. The classical Haseman-Elston method, the new Haseman-Elston (nHE) method and the weighted pairwise correlation (WPC) method follow this principle. Similarities and differences between these approaches are discussed. It is proven that the nHE and the WPC statistic are identical under specific regularity conditions. It is furthermore demonstrated that the nHE and the WPC method are expected to yield similar results for a broad range of models. Finally, it is shown that it is impossible to a priori determine whether the nHE method or the WPC method has greater power to detect linkage under general genetic models.

Evidence for a susceptibility locus on chromosome 6q influencing phonological coding dyslexia.

A linkage study of 96 dyslexia families containing at least two affected siblings (totaling 877 individuals) has found evidence for a dyslexia susceptibility gene on chromosome 6q11.2-q12 (assigned the name DYX4). Using a qualitative phonological coding dyslexia (PCD) phenotype (affected, unaffected, or uncertain diagnoses), two-point parametric analyses found highly suggestive evidence for linkage between PCD and markers D6S254, D6S965, D6S280, and D6S251 (LOD(max) scores = 2.4 to 2.8) across an 11 cM region. Multipoint parametric analysis supported linkage of PCD to this region (peak HLOD = 1.6), as did multipoint nonparametric linkage analysis (P = 0.012). Quantitative trait linkage analyses of four reading measures (phonological awareness, phonological coding, spelling, and rapid automatized naming speed) also provided evidence for a dyslexia susceptibility locus on chromosome 6q. Using a variance-component approach, analysis of phonological coding and spelling measures resulted in peak LOD scores at D6S965 of 2.1 and 3.3, respectively, under 2 degrees of freedom. Furthermore, multipoint nonparametric quantitative trait sibpair analyses suggested linkage between the 6q region and phonological awareness, phonological coding, and spelling (P = 0.018, 0.017, 0.0005, respectively, for unweighted sibpairs < 18 years of age). Although conventional significance thresholds were not reached in the linkage analyses, the chromosome 6q11.2-q12 region clearly warrants investigation in other dyslexia family samples to attempt replication and confirmation of a dyslexia susceptibility gene in this region.

Family and genetic studies of indices of insulin sensitivity and insulin secretion in Pima Indians.

The present analyses were conducted to examine the extent to which insulin sensitivity and insulin secretion, assessed using simple indices derived from an oral glucose tolerance test, are influenced by genetic factors, and to assess whether these genetic factors overlap with those influencing susceptibility to type 2 diabetes in Pima Indians. Indices calculated from fasting and 2-h post-load insulin (I(0), I(120)) and glucose (G(0), G(120)) concentrations included insulin sensitivity index [ISI(0)=10(4)/(I(0).G(0))] and corrected insulin response [CIR(120)=I(120)/[G(120).(G(120)-70 mg/dl)]]. Heritability (h(2)) was determined using variance components methods in 1421 non-diabetic individuals from 446 sibships. Among 595 individuals in 186 sibships, genome-wide quantitative trait linkage analyses of ISI(0) and CIR(120) were conducted and affected-sibling analyses of diabetic siblings stratified by prediabetic measurements of ISI(0) and CIR(120) were also performed. Both ISI(0) (h(2)=0.37+/-0.06) and CIR(120) (h(2)=0.25+/-0.07) were moderately heritable. Modest evidence for linkage with CIR(120) (logarithm of odds (LOD)=1.6) was observed on chromosome 1q in a region previously shown to have linkage with young-onset diabetes in Pimas. When diabetic siblings were stratified by CIR(120), evidence for linkage in this region was strongest (LOD=1.5) among those with a low CIR(120). Additional regions with modest evidence for linkage with ISI(0) were observed on chromosomes 9p (LOD=2.0) and 14p (LOD=1.7). The present analyses suggest that insulin sensitivity and insulin secretion are influenced by genetic factors in Pima Indians. The linkage analyses suggest that the putative diabetes-susceptibility gene on chromosome 1q affects insulin secretion. Published in 2001 by John Wiley & Sons, Ltd.

Equivalence between Haseman-Elston and Variance-Components Linkage Analyses for Sib Pairs

The Haseman-Elston regression method offers a simpler alternative to variance-components (VC) models, for the linkage analysis of quantitative traits. However, even the "revisited" method, which uses the cross-product--rather than the squared difference--in sib trait values, is, in general, less powerful than VC models. In this report, we clarify the relative efficiencies of existing Haseman-Elston methods and show how a new Haseman-Elston method can be constructed to have power equivalent to that of VC models. This method uses as the dependent variable a linear combination of squared sums and squared differences, in which the weights are determined by the overall trait correlation between sibs in a population. We show how this method can be used for both the selection of maximally informative sib pairs for genotyping and the subsequent analysis of such selected samples.

Affected sibling pair linkage analysis of qualitative and quantitative traits for schizophrenia on chromosome 22 in a Chinese population.

We performed nonparametric linkage analysis on 136 families with two or more siblings with schizophrenia from Sichuan, southwestern China. In addition to categorical diagnosis, we used quantitative trait information from the Positive and Negative Symptom Scale and the modified Overt Aggression Scale. Categorical analysis using the diagnosis of schizophrenia and a maximum likelihood identity-by-descent method produced scores of close to 0 throughout the whole region tested. Multipoint analysis allowed exclusion of most markers with a relative risk of > 2, but did not exclude the possibility of a relative risk of < 1.5 for four of the markers. Our results provide no significant evidence for a locus for schizophrenia on chromosome 22. Quantitative linkage analysis using the PANSS-G scale score produced a maximum LOD score of approximately 1.2 with the marker D22S310, using either the Haseman-Elston method or maximum likelihood variance estimation with or without dominance. PANSS-N produced a maximum LOD score of 1.2 at the D22S283 locus. LOD score of about 1 are easily produced by chance. Thus, we conclude that under quantitative trait we also find no evidence of linkage between schizophrenia and markers on chromosome 22 in our Chinese sibling pair sample.

Power and robustness of a score test for linkage analysis of quantitative traits using identity by descent data on sib pairs.

Identification of genes involved in complex traits by traditional (lod score) linkage analysis is difficult due to many complicating factors. An unfortunate drawback of non-parametric procedures in general, though, is their low power to detect genetic effects. Recently, Dudoit and Speed [2000] proposed using a (likelihood-based) score test for detecting linkage with IBD data on sib pairs. This method uses the likelihood for theta, the recombination fraction between a trait locus and a marker locus, conditional on the phenotypes of the two sibs to test the null hypothesis of no linkage (theta = (1/2)). Although a genetic model must be specified, the approach offers several advantages. This paper presents results of simulation studies characterizing the power and robustness properties of this score test for linkage, and compares the power of the test to the Haseman-Elston and modified Haseman-Elston tests. The score test is seen to have impressively high power across a broad range of true and assumed models, particularly under multiple ascertainment. Assuming an additive model with a moderate allele frequency, in the range of p = 0.2 to 0.5, along with heritability H = 0.3 and a moderate residual correlation rho = 0.2 resulted in a very good overall performance across a wide range of trait-generating models. Generally, our results indicate that this score test for linkage offers a high degree of protection against wrong assumptions due to its strong robustness when used with the recommended additive model.

Assessment of Parent-of-Origin Effects in Linkage Analysis of Quantitative Traits

Methods are presented for incorporation of parent-of-origin effects into linkage analysis of quantitative traits. The estimated proportion of marker alleles shared identical by descent is first partitioned into a component derived from the mother and a component derived from the father. These parent-specific estimates of allele sharing are used in variance-components or Haseman-Elston methods of linkage analysis so that the effect of the quantitative-trait locus carried on the maternally derived chromosome is potentially different from the effect of the locus on the paternally derived chromosome. Statistics for linkage between trait and marker loci derived from either or both parents are then calculated, as are statistics for testing whether the effect of the maternally derived locus is equal to that of the paternally derived locus. Analyses of data simulated for 956 siblings from 263 nuclear families who had participated in a linkage study revealed that type I error rates for these statistics were generally similar to nominal values. Power to detect an imprinted locus was substantially increased when analyzed with a model allowing for parent-of-origin effects, compared with analyses that assumed equal effects; for example, for an imprinted locus accounting for 30% of the phenotypic variance, the expected LOD score was 4.5 when parent-of-origin effects were incorporated into the analysis, compared with 3.1 when these effects were ignored. The ability to include parent-of-origin effects within linkage analysis of quantitative traits will facilitate genetic dissection of complex traits.

Genetic linkage methods for quantitative traits.

We discuss methods for detecting genetic linkage for quantitative data. The usual LOD score method uses a pseudolikelihood formulation and has optimal power provided all parameters are correctly specified, but can lead to erroneous estimates of the location for the locus influencing a trait under misspecification of parameters describing the variance of the trait. Alternative methods, in which attention focuses upon modelling covariation among relatives as a function of genetic marker, similarity lead to unbiased estimates of the location and major gene heritability of the trait influencing locus. The Haseman-Elston approach uses a regression method to perform linkage analysis and its properties have been widely studied. This method is generally less powerful than variance components procedures, but the maximum likelihood-based variance components procedures require normality of the trait to ensure robustness of the genetic linkage tests (i.e. a correct false positive rate). When samples are non-randomly selected an ascertainment correction is generally required in order to obtain unbiased parameter estimates when applying variance components methods. For quantitative traits, ascertainment corrections usually condition either on the proband exceeding a threshold, or on the trait value of the proband. We summarize simulations that show that both approaches lead to similar efficiencies for estimating genetic effects. Finally, we discuss methods for analysing diseases that include time-to-onset information. A variety of methods are available for the linkage analysis of quantitative traits. Here, we have reviewed the most commonly used methods.

A bivariate Haseman-Elston method and application to the analysis of asthma-related phenotypes on chromosome 5q.

We propose a bivariate combination of different Haseman-Elston (HE) methods for model free linkage analysis of quantitative traits. Adjustments for correlations of phenotypes and sibship sizes > 2 are performed using generalized estimating equations (GEE). All calculations are carried out with freely available software packages. We illustrate the application of standard HE methods, the unified HE method, and our novel approach to asthma-associated quantitative traits from the COAG-Perth data set [Palmer et al., 1998, Am J Respir Crit Care Med 158:1825-30]. Our multipoint analyses provide evidence for linkage between log IgE levels adjusted for age, gender and antigen-specific IgE titers. Our results are consistent with previous findings that suggest the existence of loci regulating asthma-associated quantitative traits in the 5q31-33 chromosomal region. Simulation studies are required to compare the power of our novel bivariate HE with other HE approaches and the variance component method.

Practical application of residuals from survival models in quantitative trait linkage analysis.

A number of familial diseases have an age-of-onset component, which can be considered as a censored quantitative trait. However, few software resources are available for the use of time-to-event endpoints in linkage analysis. The purpose of this analysis was to examine the use of martingale residuals from Cox survival models as quantitative traits for familial diseases with variable age at onset. We used these residuals as quantitative traits in variance components linkage scans for the 50 replicates of the general population simulated data for chromosomes 6 and 7. The region on chromosome 6 containing markers D06G034 and D06G035 demonstrated evidence for linkage, consistent with the underlying genetic model. This analysis demonstrates the applicability of using martingale residuals as a quantitative trait in linkage analyses of diseases that depend on age of onset.

The effect of pedigree complexity on quantitative trait linkage analysis.

Due to the computational difficulties of performing linkage analysis on large complex pedigrees, most investigators resort to simplifying such pedigrees by some ad hoc strategy. In this paper, we suggest an analytical method to compare the power of various pedigree simplification schemes by using the asymptotic distribution of the likelihood-ratio statistic. We applied the method to the large Hutterine pedigree. Our results indicate that the breaking and reduction of inbreeding loops can greatly diminish the power to localize quantitative trait loci. We also present an efficient Monte Carlo method for estimating identity-by-descent allele sharing in large complex pedigrees. This method is used to facilitate a linkage analysis of serum IgE levels in the Hutterites without simplifying the pedigree.