John Blangero

Multipoint Quantitative-Trait Linkage Analysis in General Pedigrees

Multipoint linkage analysis of quantitative-trait loci (QTLs) has previously been restricted to sibships and small pedigrees. In this article, we show how variance-component linkage methods can be used in pedigrees of arbitrary size and complexity, and we develop a general framework for multipoint identity-by-descent (IBD) probability calculations. We extend the sib-pair multipoint mapping approach of Fulker et al. to general relative pairs. This multipoint IBD method uses the proportion of alleles shared identical by descent at genotyped loci to estimate IBD sharing at arbitrary points along a chromosome for each relative pair. We have derived correlations in IBD sharing as a function of chromosomal distance for relative pairs in general pedigrees and provide a simple framework whereby these correlations can be easily obtained for any relative pair related by a single line of descent or by multiple independent lines of descent. Once calculated, the multipoint relative-pair IBDs can be utilized in variance-component linkage analysis, which considers the likelihood of the entire pedigree jointly. Examples are given that use simulated data, demonstrating both the accuracy of QTL localization and the increase in power provided by multipoint analysis with 5-, 10-, and 20-cM marker maps. The general pedigree variance component and IBD estimation methods have been implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package.

Bivariate quantitative trait linkage analysis: Pleiotropy versus co-incident linkages

Power to detect linkage and localization of a major gene were compared in univariate and bivariate variance components linkage analysis of three related quantitative traits in general pedigrees. Although both methods demonstrated adequate power to detect loci of moderate effect, bivariate analysis improved both power and localization for correlated quantitative traits mapping to the same chromosomal region, regardless of whether co‐localization was the result of pleiotropy. Additionally, a test of pleiotropy versus co‐incident linkage was shown to have adequate power and a low error rate.

Joint Multipoint Linkage Analysis of Multivariate Qualitative and Quantitative Traits. I. Likelihood Formulation and Simulation Results

We describe a variance-components method for multipoint linkage analysis that allows joint consideration of a discrete trait and a correlated continuous biological marker (e.g., a disease precursor or associated risk factor) in pedigrees of arbitrary size and complexity. The continuous trait is assumed to be multivariate normally distributed within pedigrees, and the discrete trait is modeled by a threshold process acting on an underlying multivariate normal liability distribution. The liability is allowed to be correlated with the quantitative trait, and the liability and quantitative phenotype may each include covariate effects. Bivariate discrete-continuous observations will be common, but the method easily accommodates qualitative and quantitative phenotypes that are themselves multivariate. Formal likelihood-based tests are described for coincident linkage (i.e., linkage of the traits to distinct quantitative-trait loci [QTLs] that happen to be linked) and pleiotropy (i.e., the same QTL influences both discrete-trait status and the correlated continuous phenotype). The properties of the method are demonstrated by use of simulated data from Genetic Analysis Workshop 10. In a companion paper, the method is applied to data from the Collaborative Study on the Genetics of Alcoholism, in a bivariate linkage analysis of alcoholism diagnoses and P300 amplitude of event-related brain potentials.

Multipoint oligogenic linkage analysis of quantitative traits

We present an overview of pedigree‐based variance component linkage methods and discuss their extension to oligogenic inheritance. As an example, oligogenic linkage analyses were performed using the quantitative trait Q4 from the GAW10 simulated data set. A strategy involving sequential oligogenic analyses was found to have increased power to detect the three quantitative trait loci (QTL) influencing Q4 when compared to the classical marginal approach of requiring each locus to have a lod score ≥ 3. However, it is shown that requiring conditional lod scores ≥ 3 in the sequential analyses may be overly conservative and alternative criteria for the acceptance of multilocus models are discussed.

Power of variance component linkage analysis to detect quantitative trait loci

Expressions are derived for the sample size required to achieve a given power in variance component linkage analysis of a quantitative trait in unascertained samples. For simplicity an additive model, comprising effects due to a single QTL, residual additive genetic factors, and individual-specific random environmental variation, is considered. Equations are given relating sample size to trait heritability for sibpairs, sib trios, nuclear families having two and three sibs, and arbitrary relative pairs. The effects of nonzero residual additive genetic variance and parental information are discussed, and a scale relationship for sample sizes with sibships and nuclear families is derived. For larger sampling structures such as extended pedigrees the inheritance space is randomly sampled and the relevant equations are solved numerically. Comparative power curves are presented for sibships of size 2-4 and for an extended pedigree of 48 individuals. Simulation results for sibpairs confirm the validity of the theoretical results.

Comparison of variance components and sibpair-based approaches to quantitative trait linkage analysis in unselected samples

We compared the statistical performance of sibpair‐based and variance components approaches to multipoint linkage analysis of a quantitative trait in unselected samples. As a benchmark dataset, we used the simulated family data from Genetic Analysis Workshop 10 [Goldin et al., 1997], and each method was used to screen all 200 replications of the GAW10 genome for evidence of linkage to quantitative trait Q1. The sibpair and variance components methods were each applied to datasets comprising single‐sibpairs and complete sibships, and for further comparison we also applied the variance components method to the nuclear family and extended pedigree datasets. For each analysis, the unbiasedness and efficiency of parameter estimation, the power to detect linkage, and the Type I error rate were estimated empirically. Sibpair and variance components methods exhibited comparable performance in terms of the unbiasedness of the estimate of QTL location and the Type I error rate. Within the single‐sibpair and sibship sampling units, the variance components approach gave consistently superior power and efficiency of parameter estimation. Within each method, the statistical performance was improved by the use of the larger and more informative sampling units. Genet. Epidemiol. 16:113–134, 1999.

Power of variance component linkage analysis to detect epistasis

Variance component methods are now being used in linkage analysis to detect genes influencing complex diseases. These methods are easily extended to allow for simultaneous estimation of both the additive effects of multiple loci on phenotypic variation (conditional oligogenic analysis) and the additive interaction (epistatic) effects among loci. We performed linkage analyses on 200 of the simulated replicates in order to evaluate the power to detect the main effects of MG1and MG2 on Q1 as well as their interaction effects. The power to detect the main effect of MG1 was moderately good, although the power to detect MG2 and the MG1×MG2 interaction was poor.

Genome-Wide Linkage Scan for Genes Influencing Plasma Triglyceride Levels in the Veterans Administration Genetic Epidemiology Study

OBJECTIVE—Elevated plasma triglyceride concentration is a component of the insulin resistance syndrome and is commonly associated with type 2 diabetes, obesity, and coronary heart disease. The goal of our study was to perform a genome-wide linkage scan to identify genetic regions that influence variation in plasma triglyceride levels in families that are enriched with individuals with type 2 diabetes. RESEARCH DESIGN AND METHODS—We used phenotypic and genotypic data from 1,026 individuals distributed across 294 Mexican-American families, who were ascertained for type 2 diabetes, from the Veterans Administration Genetic Epidemiology Study (VAGES). Plasma triglyceride values were transformed, and a variance-components technique was used to conduct multipoint linkage analysis. RESULTS—After adjusting for the significant effects of sex and BMI, heritability for plasma triglycerides was estimated as 46 ± 7% (P < 0.0001). Multipoint linkage analysis yielded the strongest evidence for linkage of plasma triglycerides near marker D12S391 on chromosome 12p (logarithm of odds [LOD] = 2.4). Our linkage signal on chromosome 12p provides independent replication of a similar finding in another Mexican-American sample from the San Antonio Family Diabetes Study (SAFDS). Combined multipoint linkage analysis of the VAGES and SAFDS data yielded significant evidence for linkage of plasma triglycerides to a genetic location between markers GATA49D12 and D12S391 on 12p (LOD = 3.8, empirical P value = 2.0 × 10−5). This region on 12p harbors the gene-encoding adiponectin receptor 2 (AdipoR2), where we previously have shown that multiple single nucleotide polymorphisms are associated with plasma triglyceride concentrations in the SAFDS. In the present study, we provided suggestive evidence in favor of association for rs929434 with triglyceride concentrations in the VAGES. CONCLUSIONS—Collectively, these results provide strong evidence for a major locus on chromosome 12p that influences plasma triglyceride levels in Mexican Americans.

Genome-wide linkage scan for loci influencing plasma triglyceride levels

We conducted a genome-wide linkage scan to detect loci that influence the levels of fasting triglycerides in plasma. Fasting triglyceride levels were available at 4 time points (visits), 2 pre- and 2 post-fenofibrate intervention. Multipoint identity-by-descent (MIBD) matrices were derived from genotypes using IBDLD. Variance-component linkage analyses were then conducted using SOLAR (Sequential Oligogenic Linkage Analysis Routines). We found evidence of linkage (logarithm of odds [LOD] ≥3) at 5 chromosomal regions with triglyceride levels in plasma. The highest LOD scores were observed for linkage to the estimated genetic value (additive genetic component) of the log-normalized triglyceride levels in plasma. Our results suggest that a chromosome 10 locus at 37xa0cM (LODpreu2009=u20093.01, LODpostu2009=u20093.72) influences fasting triglyceride levels in plasma regardless of the fenofibrate intervention, and that loci in chromosomes 1 at 170xa0cM and 4 at 24xa0cM ceases to affect the triglyceride levels when fenofibrate is present, while the regions in chromosomes 6 at 136 to 162xa0cM and 11 at 39 to 40xa0cM appear to influence triglyceride levels in response to fenofibrate.

Heritability and genetic associations of triglyceride and HDL-C levels using pedigree-based and empirical kinships

The heritability of a phenotype is an estimation of the percent of variance in that phenotype that is attributable to additive genetic factors. Heritability is optimally estimated in family-based sample populations. Traditionally, this involves use of a pedigree-based kinship coefficient generated from the collected genealogical relationships between family members. An alternative, when dense genotype data are available, is to directly measure the empirical kinship between samples. This study compares the use of pedigree and empirical kinships in the GAW20 data set. Two phenotypes were assessed: triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and postintervention with the cholesterol-reducing drug fenofibrate. Using SOLAR (Sequential Oligogenic Linkage Analysis Routines), pedigree-based kinships and empirically calculated kinships (using IBDLD and LDAK) were used to calculate phenotype heritability. In addition, a genome-wide association study was conducted using each kinship model for each phenotype to identify genetic variants significantly associated with phenotypic variation. The variant rs247617 was significantly associated with HDL-C levels both pre- and post-fenofibrate intervention. Overall, the phenotype heritabilities calculated using pedigree based kinships or either of the empirical kinships generated using IBDLD or LDAK were comparable. Phenotype heritabilities estimated from empirical kinships generated using IBDLD were closest to the pedigree-based estimations. Given that there was not an appreciable amount of unknown relatedness between the pedigrees in this data set, a large increase in heritability in using empirical kinship was not expected, and our calculations support this. Importantly, these results demonstrate that when sufficient genotypic data are available, empirical kinship estimation is a practical alternative to using pedigree-based kinships.