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Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.

The INSIG2 rs7566605 polymorphism was identified for obesity (BMI> or =30 kg/m(2)) in one of the first genome-wide association studies, but replications were inconsistent. We collected statistics from 34 studies (n = 74,345), including general population (GP) studies, population-based studies with subjects selected for conditions related to a better health status ('healthy population', HP), and obesity studies (OB). We tested five hypotheses to explore potential sources of heterogeneity. The meta-analysis of 27 studies on Caucasian adults (n = 66,213) combining the different study designs did not support overall association of the CC-genotype with obesity, yielding an odds ratio (OR) of 1.05 (p-value = 0.27). The I(2) measure of 41% (p-value = 0.015) indicated between-study heterogeneity. Restricting to GP studies resulted in a declined I(2) measure of 11% (p-value = 0.33) and an OR of 1.10 (p-value = 0.015). Regarding the five hypotheses, our data showed (a) some difference between GP and HP studies (p-value = 0.012) and (b) an association in extreme comparisons (BMI> or =32.5, 35.0, 37.5, 40.0 kg/m(2) versus BMI

A SNP upstream of the INSIG2 gene, rs7566605, was recently found to be associated with obesity as measured by body mass index (BMI) by Herbert and colleagues. The association between increased BMI and homozygosity for the minor allele was first observed in data from a genome-wide association scan of 86,604 SNPs in 923 related individuals from the Framingham Heart Study offspring cohort. The association was reproduced in four additional cohorts, but was not seen in a fifth cohort. To further assess the general reproducibility of this association, we genotyped rs7566605 in nine large cohorts from eight populations across multiple ethnicities (total n = 16,969). We tested this variant for association with BMI in each sample under a recessive model using family-based, population-based, and case-control designs. We observed a significant (p < 0.05) association in five cohorts but saw no association in three other cohorts. There was variability in the strength of association evidence across examination cycles in longitudinal data from unrelated individuals in the Framingham Heart Study Offspring cohort. A combined analysis revealed significant independent validation of this association in both unrelated (p = 0.046) and family-based (p = 0.004) samples. The estimated risk conferred by this allele is small, and could easily be masked by small sample size, population stratification, or other confounders. These validation studies suggest that the original association is less likely to be spurious, but the failure to observe an association in every data set suggests that the effect of SNP rs7566605 on BMI may be heterogeneous across population samples.

Background: For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results. Results: In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available. Conclusions: The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others. [ABSTRACT FROM AUTHOR]

Background: Family-based association tests are important tools for investigating genetic risk factors of complex diseases. These tests are especially valuable for being robust to population structure. We introduce a tool, EFBAT, which performs exact family-based tests of association for X-chromosome and autosomal biallelic markers. Results: The program EFBAT extends a network algorithm previously applied to autosomal markers to include the X-chromosome and to perform tests of association under the null hypotheses "no association, no linkage" and "no association in the presence of linkage" under additive, dominant and recessive genetic models. These tests are valid regardless of patterns of missing familial data. Conclusion: The general framework for performing exact family-based association tests has been usefully extended to the X-chromosome, particularly for the hypothesis of "no association in the presence of linkage" and for different genetic models. [ABSTRACT FROM AUTHOR]

In order to assess the contribution of polymorphisms in the RANKL (TNFSF11), RANK (TNFRSF11A) and OPG (TNFRSF11B) genes to variations in bone mineral density (BMD), a population-based cohort with 1,120 extreme low hip BMD cases or extreme high hip BMD controls was genotyped on five SNPs. We further explored the associations between these genetic variations and forearm BMDs by genotyping 266 offspring and 309 available parents from 160 nuclear families. A family-based association test was used. Significantly positive associations were found for A163G polymorphisms in the promoter regions of the OPG gene, a missense substitution in exon 7 (Ala192Val) of the RANK gene and rs9594782 SNP in the 5′ UTR of the RANKL gene with BMD in men only. Men with TC/CC genotypes of the rs9594782 SNP had a 2.1 times higher risk of extremely low hip BMD ( P=0.004), and lower whole body BMD ( P<0.001). Subjects with the TC genotype of the Ala192Val polymorphism had a 40% reduced risk of having extremely low hip BMD ( P<0.01), and higher whole body BMD ( P<0.01). Subjects with the GG genotype of the A163G polymorphism had a 70% reduced risk of having extremely low hip BMD ( P<0.05), and higher whole body BMD ( P<0.01). Significant gene–gene interactions were also observed among the OPG, RANK and RANKL genes. Our findings suggest that genetic variation in genes involved in the RANKL/RANK/OPG bone remodeling pathway are strongly associated with BMD at different skeletal sites in adult men, but not in women. [ABSTRACT FROM AUTHOR]

In survival studies, information lost through censoring can be partially recaptured through repeated measures data which are predictive of survival. In addition, such data may be useful in removing bias in survival estimates, due to censoring which depends upon the repeated measures. Here we investigate joint models for survival T and repeated measurements , given a vector of covariates . Mixture models indexed as f(T∣) f(∣T,) are well suited for assessing covariate effects on survival time. Our objective is efficiency gains, using non-parametric models for in order to avoid introducing bias by misspecification of the distribution for . We model (T∣) as a piecewise exponential distribution with proportional hazards covariate effect. The component (∣T,) has a multinomial model. The joint likelihood for survival and longitudinal data is maximized, using the EM algorithm. The estimate of covariate effect is compared to the estimate based on the standard proportional hazards model and an alternative joint model based estimate. We demonstrate modest gains in efficiency when using the joint piecewise exponential joint model. In a simulation, the estimated efficiency gain over the standard proportional hazards model is 6.4 per cent. In clinical trial data, the estimated efficiency gain over the standard proportional hazards model is 10.2 per cent. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Nicotine is the major addictive substance in cigarettes, and genes involved in sensing nicotine are logical candidates for vulnerability to nicotine addiction. We studied six single-nucleotide polymorphisms (SNPs) in the CHRNA4 gene and four SNPs in the CHRNB2 gene with respect to nicotine dependence in a collection of 901 subjects (815 siblings and 86 parents) from 222 nuclear families with multiple nicotine-addicted siblings. The subjects were assessed for addiction by both the Fagerstrom Test for Nicotine Dependence (FTND) and the Revised Tolerance Questionnaire (RTQ). Because only 5.8% of female offspring were smokers, only male subjects were included in the final analyses (621 men from 206 families). Univariate (single-marker) family-based association tests (FBATs) demonstrated that variant alleles at two SNPs, rs1044396 and rs1044397, in exon 5 of the CHRNA4 gene were significantly associated with a protective effect against nicotine addiction as either a dichotomized trait or a quantitative phenotype (i.e., age-adjusted FTND and RTQ scores), which was consistent with the results of the global haplotype FBAT. Furthermore, the haplotype-specific FBAT showed a common (22.5%) CHRNA4 haplotype, GCTATA, which was significantly associated with both a protective effect against nicotine addiction as a dichotomized trait (Z = -3.04, P< .005) and significant decreases of age-adjusted FTND (Z = -3.31, P< .005 ) or RTQ scores (Z = -2.73, P< .006). Our findings provide strong evidence suggesting a common CHRNA4 haplotype might be protective against vulnerability to nicotine addiction in men. [ABSTRACT FROM AUTHOR]