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In this analysis, we investigate the contributions that linkage-based methods, such as identical-by-descent mapping, can make to association mapping to identify rare variants in next-generation sequencing data. First, we identify regions in which cases share more segments identical-by-descent around a putative causal variant than do controls. Second, we use a two-stage mixed-effect model approach to summarize the single-nucleotide polymorphism data within each region and include them as covariates in the model for the phenotype. We assess the impact of linkage disequilibrium in determining identical-by-descent states between individuals by using markers with and without linkage disequilibrium for the first part and the impact of imputation in testing for association by using imputed genome-wide association studies or raw sequence markers for the second part. We apply the method to next-generation sequencing longitudinal family data from Genetic Association Workshop 18 and identify a significant region at chromosome 3: 40249244-41025167 (p-value = 2.3 × 10-3). [ABSTRACT FROM AUTHOR]

Our aim is to develop methods for mapping genes related to age at onset in general pedigrees. We propose two score tests, one derived from a gamma frailty model with pairwise likelihood and one derived from a log-normal frailty model with approximated likelihood around the null random effect. The score statistics are weighted nonparametric linkage statistics, with weights depending on the age at onset. These tests are correct under the null hypothesis irrespective of the weight used. They are simple, robust, computationally fast, and can be applied to large, complex pedigrees. We apply these methods to simulated data and to the Genetic Analysis Workshop 16 Framingham Heart Study data set. We investigate the time to the first of three events: hard coronary heart disease, diabetes, or death from any cause. We use a two-step procedure. In the first step, we estimate the population parameters under the null hypothesis of no linkage. In the second step, we apply the score tests, using the population parameters estimated in the first step. [ABSTRACT FROM AUTHOR]

Background: In this paper, we propose a one degree of freedom test for association between a candidate gene and a binary trait. This method is a generalization of Terwilliger's likelihood ratio statistic and is especially powerful for the situation of one associated haplotype. As an alternative to the likelihood ratio statistic, we derive a score statistic, which has a tractable expression. For haplotype analysis, we assume that phase is known. Results: By means of a simulation study, we compare the performance of the score statistic to Pearson's chi-square statistic and the likelihood ratio statistic proposed by Terwilliger. We illustrate the method on three candidate genes studied in the Leiden Thrombophilia Study. Conclusion: We conclude that the statistic follows a chi square distribution under the null hypothesis and that the score statistic is more powerful than Terwilliger's likelihood ratio statistic when the associated haplotype has frequency between 0.1 and 0.4 and has a small impact on the studied disorder. With regard to Pearson's chi-square statistic, the score statistic has more power when the associated haplotype has frequency above 0.2 and the number of variants is above five. [ABSTRACT FROM AUTHOR]

Background: The apparent dominant model of colorectal cancer (CRC) inheritance in several large families, without mutations in known CRC susceptibility genes, suggests the presence of so far unidentified genes with strong or moderate effect on the development of CRC. Linkage analysis could lead to identification of susceptibility genes in such families. In comparison to classical linkage analysis with multi-allelic markers, single nucleotide polymorphism (SNP) arrays have increased information content and can be processed with higher throughput. Therefore, SNP arrays can be excellent tools for linkage analysis. However, the vast number of SNPs on the SNP arrays, combined with large informative pedigrees (e.g. >35-40 bits), presents us with a computational complexity that is challenging for existing statistical packages or even exceeds their capacity. We therefore setup a procedure for linkage analysis in large pedigrees and validated the method by genotyping using SNP arrays of a colorectal cancer family with a known MLH1 germ line mutation. Methods: Quality control of the genotype data was performed in Alohomora, Mega2 and SimWalk2, with removal of uninformative SNPs, Mendelian inconsistencies and Mendelian consistent errors, respectively. Linkage disequilibrium was measured by SNPLINK and Merlin. Parametric linkage analysis using two flanking markers was performed using MENDEL. For multipoint parametric linkage analysis and haplotype analysis, SimWalk2 was used. Results: On chromosome 3, in the MLH1-region, a LOD score of 1.9 was found by parametric linkage analysis using two flanking markers. On chromosome 11 a small region with LOD 1.1 was also detected. Upon linkage disequilibrium removal, multipoint linkage analysis yielded a LOD score of 2.1 in the MLH1 region, whereas the LOD score dropped to negative values in the region on chromosome 11. Subsequent haplotype analysis in the MLH1 region perfectly matched the mutation status of the family members. Conclusion: We developed a workflow for linkage analysis in large families using high-density SNP arrays and validated this workflow in a family with colorectal cancer. Linkage disequilibrium has to be removed when using SNP arrays, because it can falsely inflate the LOD score. Haplotype analysis is adequate and can predict the carrier status of the family members. [ABSTRACT FROM AUTHOR]

We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × 10(-12)).

Background: Assessment of the clinical significance of unclassified variants (UVs) identified in BRCA1 and BRCA2 is very important for genetic counselling. The analysis of co-segregation of the variant with the disease in families is a powerful tool for the classification of these variants. Statistical methods have been described in literature but these methods are not always easy to apply in a diagnostic setting., Methods: We have developed an easy to use method which calculates the likelihood ratio (LR) of an UV being deleterious, with penetrance as a function of age of onset, thereby avoiding the use of liability classes. The application of this algorithm is publicly available http://www.msbi.nl/cosegregation. It can easily be used in a diagnostic setting since it requires only information on gender, genotype, present age and/or age of onset for breast and/or ovarian cancer., Results: We have used the algorithm to calculate the likelihood ratio in favour of causality for 3 UVs in BRCA1 (p.M18T, p.S1655F and p.R1699Q) and 5 in BRCA2 (p.E462G p.Y2660D, p.R2784Q, p.R3052W and p.R3052Q). Likelihood ratios varied from 0.097 (BRCA2, p.E462G) to 230.69 (BRCA2, p.Y2660D). Typing distantly related individuals with extreme phenotypes (i.e. very early onset cancer or old healthy individuals) are most informative and give the strongest likelihood ratios for or against causality., Conclusion: Although co-segregation analysis on itself is in most cases insufficient to prove pathogenicity of an UV, this method simplifies the use of co-segregation as one of the key features in a multifactorial approach considerably.

We incorporate population effects of sex and antibodies directed against cyclic citrullinated peptides (anti-CCP) into the linkage analysis of rheumatoid arthritis (RA) with microsatellites data provided by the North American Rheumatoid Arthritis Consortium in Genetic Analysis Workshop 15.The method stems from a generalized linear mixed model that incorporates the marginal population effects of important covariates. The resulting test for linkage is based on a score test in a pseudo-likelihood of this model. The mathematical derivation is given elsewhere but the test has a simple and appealing form: it assigns weights to excess identity-by-descent sharing between pairs of related individuals depending on the individual-specific values of the covariates and phenotypes.Although RA is three times more prevalent in women than in men, the weights derived for male-male, female-male, and female-female affected sib pairs turn out to be very similar and the sex-adjusted analysis hardly differs from an unadjusted analysis. High anti-CCP levels are known to strongly predict RA. Our test assigns very small weights to pairs whose anti-CCP levels are high for the two siblings, sib pairs with two low anti-CCP levels are those most contributing to the evidence for linkage. Comparison of the unadjusted and the anti-CCP-adjusted analyses identifies persisting peaks mapping to regions that can be attributed to a 'dimension' of RA independent of anti-CCP.