Haseman-Elston Method Research Articles

Familial genetic and environmental risk profile and high blood pressure event: a prospective cohort of cardio-metabolic and genetic study

Background and aims High blood pressure is the heritable risk factor for cardiovascular diseases. We investigated whether the presence of familial genetic and environmental risk factors are associated with increased risk of high blood pressure. Methods A total of 4,559 individuals from 401 families were included in this study. Familial aggregation analysis was carried out on systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI) and waist circumference (WC), and heritability was estimated for SBP and DBP. The association between familial risk factors and blood pressure traits including, incidence of hypertension, SBP and DBP was estimated separately using regression-based two-level Haseman-Elston (HE) method, with individual and familial BMI and WC as environmental exposures and familial genetic profile of known variants as genetic risk factors in 210 index families (≥2 hypertensive cases). Models were adjusted for the two nested sets of covariates. Results During a follow-up of 15 years, the SBP, DBP, BMI and WC were highly correlated in inter class of mother-offspring and intraclass of sister-sister with heritability of 30 and 25% for DBP and SBP, respectively. Among index families, those whose members with higher familial BMI or WC had significantly increased risk of hypertension and consistent, strong signals of rs2493134 (AGT) linked with SBP and DBP, rs976683 (NLGN1) linked with SBP and HTN, and epistasis of rs2021783 (TNXB) and known genetic variants linked with all blood pressure traits. Conclusions Findings from this study show that familial genetic and environmental risk profile increase risk for high blood pressure beyond the effect of the individuals’ own risk factors.

Rank Transformation in Haseman-Elston Regression Using Scores for Location-Scale Alternatives

The Haseman-Elston method is a simple regression approach for detecting genetic linkage to quantitative traits in sib-pair studies. Although this method and especially the new extended Haseman-Elston approach are quite robust, there might be some loss of power for non-normally distributed traits. We propose using rank transformation techniques, which either combine the information on a trend in locations and in scales or detect a trend only for a subset of the trait variables for genetically different sibs under linkage. As this rank transformation is based on linear regression, no exact grouping of identity by descent proportions has to be assumed. Simulation results indicate a gain in power compared to recently suggested nonparametric methods.

Family-based analysis of a myocardial infarction endophenotype: comparison of sampling designs

The power of linkage analysis of a quantitative disease endophenotype was compared for the following family selection designs: 1) Random samples: randomly chosen nuclear families, 2) "coronary artery calcification (CAC)" samples: selection of each nuclear family through a proband with abnormally high levels of the simulated quantitative endophenotype, CAC, and (3) "MI" samples: selection of each nuclear family through a disease affected proband, in this case a proband who had been simulated to have a myocardial infarction (MI) event.We assessed the power to detect linkage to five loci (two pairs of epistatic loci and one locus with an over-dominant allele) that were modeled as determinants of the simulated CAC levels. We did this using a Haseman-Elston regression-based linkage analysis of the adjusted CAC levels that considered each locus separately and then used a multiple regression extension of the Haseman-Elston method in which we considered the allele sharing at two true epistatic loci simultaneously and their interaction as possible factors related to the squared sibpair differences in adjusted CAC.Based on comparison of the mean square root of the LOD scores, there was no one sampling design that resulted in consistently greater power for these five loci. That is, we observed significant locus-by-sampling-design interaction (p < 0.0001). We noted however, that the largest average score was observed for the epistasis between tau3 and tau4 (mean > 1.8, SE = 0.06) in the MI-selected samples and the CAC-selected samples.

A General Approach for Sample Size and Power Calculations Based on the Haseman–Elston Method

Risch and Zhang (1995; Science 268: 1584-9) reported a simple sample size and power calculation approach for the Haseman-Elston method and based their computations on the null hypothesis of no genetic effect. We argue that the more reasonable null hypothesis is that of no recombination. For this null hypothesis, we provide a general approach for sample size and power calculations within the Haseman-Elston framework. We demonstrate the validity of our approach in a Monte-Carlo simulation study and illustrate the differences using data from published segregation analyses on body weight and heritability estimates on carotid artery artherosclerotic lesions.

Design and Analysis of Genetic Association Studies to Finely Map a Locus Identified by Linkage Analysis: Assessment of the Extent to Which an Association Can Account for the Linkage

Association studies are often used to finely map quantitative trait loci identified by linkage analysis. Once a polymorphism associated with the trait has been identified, it may be useful to conduct linkage analyses which adjust for this polymorphism to determine the extent to which the association accounts for the linkage signal. However, methods for conducting statistical significance tests for an observed reduction in the linkage signal are not well developed. In the present study we develop methods for assessment of the statistical significance of an observed reduction in the variance due to the linked locus, with variance components or with Haseman-Elston linkage methods. Simulations indicate that these methods have appropriate levels of type I error and that, like other association statistics, their power depends on the magnitude of linkage disequilibrium between functional and marker alleles and on the extent of similarity between the frequency of the functional allele and the frequency of the associated marker allele. These methods can help determine which association results are likely due to strong linkage disequilibrium with functional alleles and, thus, can facilitate the selection of small chromosomal regions for more extensive study.

A Bayesian genome-wide linkage analysis of quantitative traits for rheumatoid arthritis via perfect sampling

Rheumatoid arthritis is a complex disease caused by a combination of genetic, environmental, and hormonal factors, and their additive and/or non-additive effects. We performed a linkage analysis to provide evidence of rheumatoid factor IgM on linkage, based on Bayesian variable selection coupled with the new Haseman-Elston method. For statistical inferences to estimate unknown parameters, we utilized the perfect sampling algorithm, an emerging simulation technique that alleviates concerns over convergence and sampling mixing. Our methods provide powerful and conceptually simple approaches to simultaneous genome scans of main effects and all possible pairwise interactions. We apply them to the Genetic Analysis Workshop 15 data (Problem 2) provided by the North American Rheumatoid Arthritis Consortium (NARAC).

A nonparametric regression-based linkage scan of rheumatoid factor-IgM using sib-pair squared sums and differences.

Parametric linkage methods for quantitative trait locus mapping require explicit specification of the probability model of the quantitative trait and hence can lead to misleading linkage inferences when the model assumptions are not valid. Ghosh and Majumder developed a nonparametric regression method based on kernel-smoothing for linkage mapping of quantitative trait locus using squared differences in trait values of independent sib pairs, which is relatively more robust than parametric methods with respect to violations in distributional assumptions. In this study, we modify the above mentioned nonparametric regression method by considering local linear polynomials instead of the Nadaraya-Watson estimator and squared sums of sib-pair trait values in addition to squared differences to perform a genome-wide scan of rheumatoid factor-IgM levels on sib pairs in the Genetic Analysis Workshop 15 simulated data set. We obtain significant evidence of linkage very close to the quantitative trait locus controlling for RF-IgM. We find that the simultaneous use of squared differences and squared sums increases the power to detect linkage compared to using only squared differences. However, because of all the sib pairs are selected for rheumatoid arthritis, there is reduced variance of RF-IgM values, and empirical power to detect linkage is not very high. We also compare the performance of our method with two linear regression approaches: the classical Haseman-Elston method using squared sib-pair trait differences and its extension proposed by Elston et al. using mean-corrected sib-pair cross-products. We find that the proposed nonparametric method yields more power than the linear regression approaches.

Linkage of Genes to Total Lean Body Mass in Normal Women

Total lean body mass (LEAN-tot) is one of the three major components of body weight. Its deterioration is a risk factor for frailty. Despite this, there are few studies examining the contribution of genetic factors. Our objective was to examine the contribution of genetic factors for LEAN-tot variation, including a genome-wide search for the genes. Dual-energy x-ray absorptiometry measurements of LEAN-tot were obtained from each of the 3180 United Kingdom females (509 monozygotic and 1081 dizygotic twin pairs). Contribution of genetic factors was assessed using variance component analysis. A genome-wide linkage analysis was performed on the dizygotic twins using a modified version of the Haseman-Elston method. Age, body height, total fat, and bone mass were correlated with LEAN-tot, and commonly explained 52% of the LEAN-tot variation. The crude heritability estimate was 74.0 +/- 4.0%, after adjustment for the aforementioned factors; 65.2 +/- 4.6% was attributable to independent genetic effects. Significant (P < 0.001) genetic correlations were found between LEAN-tot and bone mass, and LEAN-tot and total fat. Adjusted only for age, LEAN-tot showed no significant linkage. After adjustment for all covariates, significant linkage (LOD = 4.49 and 3.62) was observed at chromosome 12q24.3 and 14q22.3, respectively. Additional peaks of interest were on 7p15.3-15.1 (LOD = 2.86) and 8p22 (LOD = 2.83). LEAN-tot measured by dual-energy x-ray absorptiometry is highly heritable, independent of other body measures. This first genomic search for genes associated with the lean component of body mass suggests significant linkage to quantitative trait loci on chromosomes 12 and 14.

Genome Scan for Loci Predisposing to Anxiety Disorders Using a Novel Multivariate Approach: Strong Evidence for a Chromosome 4 Risk Locus

We conducted a 10-centimorgan linkage autosomal genome scan in a set of 19 extended American pedigrees (219 subjects) ascertained through probands with panic disorder. Several anxiety disorders--including social phobia, agoraphobia, and simple phobia--in addition to panic disorder segregate in these families. In previous studies of this sample, linkage analyses were based separately on each of the individual categorical affection diagnoses. Given the substantial comorbidity between anxiety disorders and their probable shared genetic liability, it is clear that this method discards a considerable amount of information. In this article, we propose a new approach that considers panic disorder, simple phobia, social phobia, and agoraphobia as expressions of the same multivariate, putatively genetically influenced trait. We applied the most powerful multipoint Haseman-Elston method, using the grade of membership score generated from a fuzzy clustering of these phenotypes as the dependent variable in Haseman-Elston regression. One region on chromosome 4q31-q34, at marker D4S413 (with multipoint and single-point nominal P values < .00001), showed strong evidence of linkage (genomewide significance at P<.05). The same region is known to be the site of a neuropeptide Y receptor gene, NPY1R (4q31-q32), that was recently connected to anxiolytic-like effects in rats. Several other regions on four chromosomes (4q21.21-22.3, 5q14.2-14.3, 8p23.1, and 14q22.3-23.3) met criteria for suggestive linkage (multipoint nominal P values < .01). Family-by-family analysis did not show any strong evidence of heterogeneity. Our findings support the notion that the major anxiety disorders, including phobias and panic disorder, are complex traits that share at least one susceptibility locus. This method could be applied to other complex traits for which shared genetic-liability factors are thought to be important, such as substance dependencies.

A Bayesian genome screening of maximum number of drinks as an alcoholism phenotype with the new Haseman-Elston method

Common human disorders, such as alcoholism, may be the result of interactions of many genes as well as environmental risk factors. Therefore, it is important to incorporate gene × gene and gene × environment interactions in complex disease gene mapping. In this study, we applied a robust Bayesian genome screening method that can incorporate interaction effects to map genes underlying alcoholism through its application to the data of the Collaborative Studies on Genetics of Alcoholism provided by Genetic Analysis Workshop 14. Our Bayesian genome screening method uses the regression-based stochastic variable selection, coupled with the new Haseman-Elston method to identify markers linked to phenotypes of interest. Compared to traditional linkage methods based on single-gene disease models, our method allows for multilocus disease models for simultaneous screening including both main and interaction (epistatic) effects. It is conceptually simple and computationally efficient through the use of Gibbs sampler. We conducted genome-wide analysis and comparison between scans based on microsatellites and single-nucleotide polymorphisms. A total of 328 microsatellites and 11,560 single-nucleotide polymorphisms (by Affymetrix) on 22 autosomal chromosomes and sex chromosome were used.

A Bayesian approach for applying Haseman-Elston methods

The main goal of this paper is to couple the Haseman-Elston method with a simple yet effective Bayesian factor-screening approach. This approach selects markers by considering a set of multigenic models that include epistasis effects. The markers are ranked based on their marginal posterior probability. A significant improvement over our previously proposed Bayesian variable selection methodology is a simple Metropolis-Hasting algorithm that requires minimum tuning on the prior settings. The algorithm, however, is also flexible enough for us to easily incorporate our hypotheses and avoid computational pitfalls. We apply our approach to the microsatellite data of Collaborative Studies on Genetics of Alcoholism using the coded values for the ALDX1 variable as our response.

Haseman-Elston weighted by marker informativity.

In the Haseman-Elston approach the squared phenotypic difference is regressed on the proportion of alleles shared identical by descent (IBD) to map a quantitative trait to a genetic marker. In applications the IBD distribution is estimated and usually cannot be determined uniquely owing to incomplete marker information. At Genetic Analysis Workshop (GAW) 13, Jacobs et al. [BMC Genet 2003, 4(Suppl 1):S82] proposed to improve the power of the Haseman-Elston algorithm by weighting for information available from marker genotypes. The authors did not show, however, the validity of the employed asymptotic distribution. In this paper, we use the simulated data provided for GAW 14 and show that weighting Haseman-Elston by marker information results in increased type I error rates. Specifically, we demonstrate that the number of significant findings throughout the chromosome is significantly increased with weighting schemes. Furthermore, we show that the classical Haseman-Elston method keeps its nominal significance level when applied to the same data. We therefore recommend to use Haseman-Elston with marker informativity weights only in conjunction with empirical p-values. Whether this approach in fact yields an increase in power needs to be investigated further.

Genome‐Wide Scan for Loci Influencing Quantitative Immune Response Traits in the Belém Family Study: Comparison of Methods and Summary of Results

Here we report the results from a genome-wide linkage scan to identify genes and chromosomal regions that influence quantitative immune response traits, using multi-case leprosy and tuberculosis families from north-eastern Brazil. Total plasma IgE, antigen-specific IgG to Mycobacterium leprae soluble antigen (MLSA), M. tuberculosis soluble antigen (MTSA) and M. tuberculosis purified protein derivative (PPD), and antigen-specific lymphocyte proliferation (stimulation index or SI) and interferon-gamma (IFN-gamma) release to MLSA and PPD, were measured in 16 tuberculosis (184 individuals) and 21 leprosy (177 individuals) families. The individuals were genotyped at 382 autosomal microsatellite markers across the genome. The adjusted immune-response phenotypes were analysed using a variety of variance components and regression-based methods. These analyses highlighted a number of practical issues and problems with regard to implementation of the methods and, interestingly, differences were observed between several standard statistical and genetic analysis packages used. From this we determined that, for this set of traits in these pedigrees, significant p values for linkage using variance components analysis, supported by significance using the Visscher-Hopper modification of the Haseman-Elston method, provided the most compelling evidence for linkage. Using these criteria, linkage (5.8 x 10(-5) < p < 0.008) was seen for: total plasma IgE on chromosome 2; IgG to MLSA on chromosomes 8, 17 and 21; IgG to PPD on chromosome 12; SI to PPD on chromosome 1; IFN-gamma to MLSA on chromosomes 6, 7, 10, 12 and 14; and IFN-gamma to PPD on chromosomes 1, 16 and 19.

Linkage Analysis of Chromosome 1 with Essential Hypertension and Blood Pressure Quantitative Traits in Chinese Families

Summary Several recent studies have linked human chromosome 1p to essential hypertension (EH) or blood pressure (BP) levels. In an independent population of 148 hypertensive families from China we tested these findings. Thirty highly informative microsatellite markers spanning about 284 cM were genotyped. Qualitative linkage analysis was conducted using non-parametric linkage analysis implemented within the GENEHUNTER 2.0 software, whereas quantitative analysis was performed with the variance-component method integrated in the S.O.L.A.R. 1.7.4. software with an additional Haseman-Elston method using the SAGE/SIBPAL2 program. We observed suggestive linkage between D1S2890 (1p31, 80.9 cM) and hypertension using the multipoint non-parametric linkage analysis (NPL = 2.19, P = 0.01). In the quantitative analysis we didn't observe a significant excess of identity-by-descent allele sharing between the systolic blood pressure levels and the markers. However, the D1S207 microsatellite marker (1p21) which is located about 107 cM from the telomere of 1p showed weak linkage evidence with the diastolic blood pressure levels (LOD = 1.42). These findings suggest linkage of 1p31 with essential hypertension in the ethnic Chinese, and provide a potential clue for future studies involving candidate genes for hypertension.

P4-063 Very early onset familial Alzheimer's disease with lewy bodies without known causative mutations

A genome scan of the hoarding phenotype (a component of obsessive-compulsive disorder) was conducted on 77 sib pairs collected by the Tourette Syndrome Association International Consortium for Genetics (TSAICG). All sib pairs were concordant for a diagnosis of Gilles de la Tourette syndrome (GTS). However, the analyses reported here were conducted for hoarding as both a dichotomous trait and a quantitative trait. Not all sib pairs in the sample were concordant for hoarding. Standard linkage analyses were performed using GENEHUNTER and Haseman-Elston methods. In addition, novel analyses with a recursive-partitioning technique were employed. Significant allele sharing was observed for both the dichotomous and the quantitative hoarding phenotypes for markers at 4q34-35 (P=.0007), by use of GENEHUNTER, and at 5q35.2-35.3 (P=.000002) and 17q25 (P=.00002), by use of the revisited Haseman-Elston method. The 4q site is in proximity to D4S1625, which was identified by the TSAICG as a region linked to the GTS phenotype. The recursive-partitioning technique examined multiple markers simultaneously. Results suggest joint effects of specific loci on 5q and 4q, with an overall P value of .000003. Although P values were not adjusted for multiple comparison, nearly all were much smaller than the customary significance level of .0001 for genomewide scans.

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.

A Modified Revisited Haseman-Elston Method to Further Improve Power

The original and revisited Haseman-Elston methods are simple robust methods to detect linkage, but neither is uniformly optimal in terms of power. In this report, we propose a simple modification of the revisited Haseman-Elston method that retains the simplicity and robustness properties, but increases its power. We demonstrate theoretically that the modification can be more powerful than the optimally weighted Haseman-Elston method when the sibship mean can be correctly specified. We then examine the properties of this modification by simulation when the sibship mean is replaced by its best linear unbiased predictor. The simulation results indicate that this modification maintains good control over type I error, even in the case of larger sibships, and that the empirical power of this modification is similar to that of the optimally weighted Haseman-Elston method in most cases.

A Test of Linkage for Complex Discrete and Continuous Traits in Nuclear Families

This article presents a score test for genetic linkage in nuclear families which applies to any trait having a distribution belonging to the exponential family, which includes binary and normal distributions, and distributions which are skewed or have nonnormal kurtosis. The specific distribution need not be specified and the method applies to sibships of arbitrary size. Tests of complex genetic effects are given, including unspecified mode of inheritance or additive, dominant, overdominant, and recessive modes of inheritance, covariates, multiple-locus models, including gene-gene interactions, and gene-environment interactions. The relation of our method to the Haseman-Elston methods is studied theoretically and by simulation.

Locating disease genes using Bayesian variable selection with the Haseman-Elston method.

BackgroundWe applied stochastic search variable selection (SSVS), a Bayesian model selection method, to the simulated data of Genetic Analysis Workshop 13. We used SSVS with the revisited Haseman-Elston method to find the markers linked to the loci determining change in cholesterol over time. To study gene-gene interaction (epistasis) and gene-environment interaction, we adopted prior structures, which incorporate the relationship among the predictors. This allows SSVS to search in the model space more efficiently and avoid the less likely models.ResultsIn applying SSVS, instead of looking at the posterior distribution of each of the candidate models, which is sensitive to the setting of the prior, we ranked the candidate variables (markers) according to their marginal posterior probability, which was shown to be more robust to the prior. Compared with traditional methods that consider one marker at a time, our method considers all markers simultaneously and obtains more favorable results.ConclusionsWe showed that SSVS is a powerful method for identifying linked markers using the Haseman-Elston method, even for weak effects. SSVS is very effective because it does a smart search over the entire model space.

Further studies of the influence of apolipoprotein B alleles on glucose and lipid metabolism.

The effect of five genetic polymorphisms in the apolipoprotein B gene on parameters of lipid and glucose metabolism was assessed in 564 Danish mono- and dizygotic twins. Genotypes in apolipoprotein B T71I (ApaLI RFLP), A591V (AluI RFLP), L2712P (MvaI RFLP), R3611Q (MspI RFLP), and E4154K (EcoRI RFLP) were established using polymerase chain reaction and restriction enzyme digests. The effect of genotypes on lipid levels and on glucose, insulin, and HOMA (i.e., calculated parameters of beta-cell function and insulin resistance) was assessed by multivariate analyses of variance correcting for the effect of gender, age, glucose tolerance status, and body mass index. The effect of genotype on the risk of having impaired glucose metabolism was calculated by logistic regression analysis. Finally, linkage between allele sharing and physiological parameters was calculated by the new Haseman-Elston method. The allele frequencies of all five polymorphisms were similar to those previously reported for Caucasian populations. The L2711P (MvaI RFLP) polymorphism influenced LDL-cholesterol and LDL-to-HDL measures (p = 0.04 and 0.03, respectively), while the R3611Q (MspI RFLP) polymorphism had an effect on the insulin-to-glucose ratio (p = 0.04), and E4154K (EcoRI RFLP) influenced HOMAbeta (p = 0.04). Significant interactions were observed between genotype in T71I (ApaLI RFLP), A591V (AluI RFLP), R3611Q (MspI RFLP), and E4154K (EcoRI RFLP) and glucose tolerance on lipid-related parameters (0.03 < p < 0.004), and between genotype in L2712P (MvaI RFLP) and E4154K (EcoRI RFLP) and gender on lipid and glucose-related parameters (0.02 < p < 0.003). No genotypes were significantly associated with impaired glucose tolerance measured by logistic regression. Likewise, no effect of allele sharing in the five polymorphisms was seen in the dizygotic twins. The effect of the polymorphisms on lipid and glucose parameters could be mediated through linkage to genes with known effect on glucose metabolism or through free fatty acids exerting their effect on glucose metabolism.