An autosome-wide search using longitudinal data for loci linked to type 2 diabetes progression

A genome-wide screen was conducted for type 2 diabetes progression genes using measures of elevated fasting glucose levels as quantitative traits from the offspring enrolled in the Framingham Heart Study. We analyzed young (20–34 years) and old (≥ 35 years) subjects separately, using single-point and multipoint sibpair analysis, because of the possible differential impact of progression on the groups of interest. We observed significant linkage with change in fasting glucose levels on 1q25-32 (p = 5.21 × 10(-8)), 3p26.3-21.31 (p = 1 × 10(-11)), 8q23.1-24.13 (p = 2.94 × 10(-6)), 9p24.1-21.3 (p = 7 × 10(-7)), and 18p11.31-q22.1 (p < 10(-11)). The evidence for linkage on chromosomes 8 and 18 was consistent for the subset of study participants aged 43 through 55 years

Comparison of univariate and multivariate linkage analysis of traits related to hypertension

Complex traits are often manifested by multiple correlated traits. One example of this is hypertension (HTN), which is measured on a continuous scale by systolic blood pressure (SBP). Predisposition to HTN is predicted by hyperlipidemia, characterized by elevated triglycerides (TG), low-density lipids (LDL), and high-density lipids (HDL). We hypothesized that the multivariate analysis of TG, LDL, and HDL would be more powerful for detecting HTN genes via linkage analysis compared with univariate analysis of SBP. We conducted linkage analysis of four chromosomal regions known to contain genes associated with HTN using SBP as a measure of HTN in univariate Haseman-Elston regression and using the correlated traits TG, LDL, and HDL in multivariate Haseman-Elston regression. All analyses were conducted using the Framingham Heart Study data. We found that multivariate linkage analysis was better able to detect chromosomal regions in which the angiotensinogen, angiotensin receptor, guanine nucleotide-binding protein 3, and prostaglandin I2 synthase genes reside. Univariate linkage analysis only detected the AGT gene. We conclude that multivariate analysis is appropriate for the analysis of multiple correlated phenotypes, and our findings suggest that it may yield new linkage signals undetected by univariate analysis

Optimizing the evidence for linkage by permuting marker order

We developed a new marker-reordering algorithm to find the best order of fine-mapping markers for multipoint linkage analysis. The algorithm searches for the best order of fine-mapping markers such that the sum of the squared differences in identity-by-descent distribution between neighboring markers is minimized. To test this algorithm, we examined its effect on the evidence for linkage in the simulated and the Collaborative Studies on Genetics of Alcoholism (COGA) data. We found enhanced evidence for linkage with the reordered map at the true location in the simulated data (p-value decreased from 1.16 × 10(-9 )to 9.70 × 10(-10)). Analysis of the White population from the COGA data with the reordered map for alcohol dependence led to a significant change of the linkage signal (p = 0.0365 decreased to p = 0.0039) on chromosome 1 between marker D1S1592 and D1S1598. Our results suggest that reordering fine-mapping markers in candidate regions when the genetic map is uncertain can be a critical step when considering a dense map

Comparison of a unified analysis approach for family and unrelated samples with the transmission-disequilibrium test to study associations of hypertension in the Framingham Heart Study

Population stratification is one of the major causes of spurious associations in association studies. A unified association approach based on principal-component analysis can overcome the effect of population stratification, as well as make use of both family and unrelated samples combined to increase power (family-case-control, or FamCC). In this study, we compared FamCC and the transmission-disequilibrium test (TDT) using data on hypertension, systolic blood pressure, and diastolic blood pressure in the Framingham Heart Study. Our study indicated FamCC has reasonable type I error for both the unrelated sample and the family sample for all three traits. For these three traits, we found results from FamCC were inconsistent with those from the TDT. We discuss the reasons for this inconsistency. After correcting for multiple tests, we did not detect any significant single-nucleotide polymorphisms by either FamCC or the TDT

A method to correct for population structure using a segregation model

To overcome the "spurious" association caused by population stratification in population-based association studies, we propose a principal-component based method that can use both family and unrelated samples at the same time. More specifically, we adapt the multivariate logistic model, which is often used in segregation analysis and can allow for the family correlation structure, for association analysis. To correct the effect of hidden population structure, the first ten principal-components calculated from the matrix of marker genotype data are incorporated as covariates in the model. To test for the association, the marker of interest is also incorporated as a covariate in the model. We applied the proposed method to the second generation (i.e., the Offspring Cohort), in the Genetic Analysis Workshop 16 Framingham Heart Study 50 k data set to evaluate the performance of the method. Although there may have been difficulty in the convergence while maximizing the likelihood function as indicated by a flat likelihood, the distribution of the empirical p-values for the test statistic does show that the method has a correct type I error rate whenever the variance-covariance matrix of the estimates can be computed

The effect of multiple genetic variants in predicting the risk of type 2 diabetes

While recently performed genome-wide association studies have advanced the identification of genetic variants predisposing to type 2 diabetes (T2D), the potential application of these novel findings for disease prediction and prevention has not been well studied. Diabetes prediction and prevention have become urgent issues owing to the rapidly increasing prevalence of diabetes and its associated mortality, morbidity, and health care cost. New prediction approaches using genetic markers could facilitate early identification of high risk sub-groups of the population so that appropriate prevention methods could be effectively applied to delay, or even prevent, disease onset

Linkage analysis of alcohol dependence using both affected and discordant sib pairs

The basic idea of affected-sib-pair (ASP) linkage analysis is to test whether the inheritance pattern of a marker deviates from Mendelian expectation in a sample of ASPs. The test depends on an assumed Mendelian control distribution of the number of marker alleles shared identical by descent (IBD), i.e., 1/4, 1/2, and 1/4 for 2, 1, and 0 allele(s) IBD, respectively. However, Mendelian transmission may not always hold, for example because of inbreeding or meiotic drive at the marker or a nearby locus. A more robust and valid approach is to incorporate discordant-sib-pairs (DSPs) as controls to avoid possible false-positive results. To be robust to deviation from Mendelian transmission, here we analyzed Collaborative Study on the Genetics of Alcoholism data by modifying the ASP LOD score method to contrast the estimated distribution of the number of allele(s) shared IBD by ASPs with that by DSPs, instead of with the expected distribution under the Mendelian assumption. This strategy assesses the difference in IBD sharing between ASPs and the IBD sharing between DSPs. Further, it works better than the conventional LOD score ASP linkage method in these data in the sense of avoiding false-positive linkage evidence

Structural equation model-based genome scan for the metabolic syndrome

BACKGROUND: The metabolic syndrome is characterized by the clustering of several traits, including obesity, hypertension, decreased levels of HDL cholesterol, and increased levels of glucose and triglycerides. Because these traits cluster, there are likely common genetic factors involved. RESULTS: We used a multivariate structural equation model (SEM) approach to scan the genome for loci involved in the metabolic syndrome. We found moderate evidence for linkage on chromosomes 2, 3, 11, 13, and 15, and these loci appear to have different relative effects on the component traits of the metabolic syndrome. CONCLUSION: Our results suggest that the metabolic syndrome components, diabetes, obesity, and hypertension, are under the pleiotropic control of several loci

Studying genetic determinants of natural variation in human gene expression using Bayesian ANOVA

Standard genetic mapping techniques scan chromosomal segments for location of genetic linkage and association signals. The majority of these methods consider only correlations at single markers and/or phenotypes with explicit detailing of the genetic structure. These methods tend to be limited by their inability to consider the effect of large numbers of model variables jointly. In contrast, we propose a Bayesian analysis of variance (ANOVA) method to categorize individuals based on similarity of multidimensional profiles and attempt to analyze all variables simultaneously. Using Problem 1 of the Genetic Analysis Workshop 15 data set, we demonstrate the method's utility for joint analysis of gene expression levels and single-nucleotide polymorphism genotypes. We show that the method extracts similar information to that of previous genetic mapping analyses, and suggest extensions of the method for mining unique information not previously found