Haplotype sharing correlation analysis using family data: a comparison with family-based association test in the presence of allelic heterogeneity

Statistical analysis of rare sequence variants: an overview of collapsing methods

With the advent of novel sequencing technologies, interest in the identification of rare variants that influence common traits has increased rapidly. Standard statistical methods, such as the Cochrane-Armitage trend test or logistic regression, fail in this setting for the analysis of unrelated subjects because of the rareness of the variants. Recently, various alternative approaches have been proposed that circumvent the rareness problem by collapsing rare variants in a defined genetic region or sets of regions. We provide an overview of these collapsing methods for association analysis and discuss the use of permutation approaches for significance testing of the data-adaptive methods.

A novel approach for haplotype-based association analysis using family data

Background

Haplotype-based approaches have been extensively studied for case-control association mapping in recent years. It has been shown that haplotype methods can provide more consistent results comparing to single-locus based approaches, especially in cases where causal variants are not typed. Improved power has been observed by clustering similar or rare haplotypes into groups to reduce the degrees of freedom of association tests. For family-based association studies, one commonly used strategy is Transmission Disequilibrium Tests (TDT), which examine the imbalanced transmission of alleles/haplotypes to affected and normal children. Many extensions have been developed to deal with general pedigrees and continuous traits.

Results

In this paper, we propose a new haplotype-based association method for family data that is different from the TDT framework. Our approach (termed F_HapMiner) is based on our previous successful experiences on haplotype inference from pedigree data and haplotype-based association mapping. It first infers diplotype pairs of each individual in each pedigree assuming no recombination within a family. A phenotype score is then defined for each founder haplotype. Finally, F_HapMiner applies a clustering algorithm on those founder haplotypes based on their similarities and identifies haplotype clusters that show significant associations with diseases/traits. We have performed extensive simulations based on realistic assumptions to evaluate the effectiveness of the proposed approach by considering different factors such as allele frequency, linkage disequilibrium (LD) structure, disease model and sample size. Comparisons with single-locus and haplotype-based TDT methods demonstrate that our approach consistently outperforms the TDT-based approaches regardless of disease models, local LD structures or allele/haplotype frequencies.

Conclusion

We present a novel haplotype-based association approach using family data. Experiment results demonstrate that it achieves significantly higher power than TDT-based approaches.

A haplotype similarity based transmission/disequilibrium test under founder heterogeneity

Taking advantage of increasingly available high-density single nucleotide polymorphisms (SNP) markers across the genome, various types of transmission/disequilibrium tests (TDT) using haplotype information have been developed. A practical challenge arising in such studies is the possibility that transmitted haplotypes have inherited disease-causing mutations from different ancestral chromosomes, or do not bear any disease-causing mutations (founder heterogeneity). To reduce the loss of signal strength due to founder heterogeneity, we propose an SP-TDT test that combines a sequential peeling procedure with the haplotype similarity based TDT method. The proposed SP-TDT method is applicable to any size of nuclear family with or without ambiguous phase information. Simulation studies suggest that the SP-TDT method has the correct type I error rate in stratified populations, and enhanced power compared with some existing haplotype similarity based TDT methods. Finally, we apply the proposed method to study the association of the leptin gene with obesity from the National Heart, Lung, and Blood Institute Family Heart Study.

TreeDT: tree pattern mining for gene mapping

We describe TreeDT, a novel association-based gene mapping method. Given a set of disease-associated haplotypes and a set of control haplotypes, TreeDT predicts likely locations of a disease susceptibility gene. TreeDT extracts, essentially in the form of haplotype trees, information about historical recombinations in the population: A haplotype tree constructed at a given chromosomal location is an estimate of the genealogy of the haplotypes. TreeDT constructs these trees for all locations on the given haplotypes and performs a novel disequilibrium test on each tree: Is there a small set of subtrees with relatively high proportions of disease-associated chromosomes, suggesting shared genetic history for those and a likely disease gene location? We give a detailed description of TreeDT and the tree disequilibrium tests, we analyze the algorithm formally, and we evaluate its performance experimentally on both simulated and real data sets. Experimental results demonstrate that TreeDT has high accuracy on difficult mapping tasks and comparisons to other methods (EATDT, HPM, TDT) show that TreeDT is very competitive.

Haplotype sharing correlation of alcohol dependence on chromosomes 1-6 in 93 nuclear families

Haplotype data contain signatures of ancestral alleles and increased information for mapping genes associated with complex traits. The motivation of this paper is to test the feasibility of a recently developed haplotype reconstruction algorithm and to perform haplotype-sharing correlation (HSC) analysis in nuclear families using data provided by the Genetic Analysis Workshop 14 and the Collaborative Study of the Genetics of Alcoholism. As an exemplary analysis, haplotype data on chromosomes 1–6 were reconstructed from genotype data in 93 nuclear families by minimizing both the recombinants in within-family haplotypes and the tree distance in between-family haplotypes. HSC analysis was performed using the best set of reconstructed haplotypes, and chromosome-wide significance was evaluated using a permutation procedure. Three markers were found to have significant haplotype associations with DSM-IV alcohol dependence that exceeded the 0.05 level of chromosome-wide significance: marker rs895941 at 36.7 cM on chromosome 3 (p = 0.03), marker rs1631833 at 109.1 cM on chromosome 4 (p = 0.008), and marker rs953887 at 74.2 cM on chromosome 6 (p = 0.02). These results indicated the usefulness of HSC analysis and provided further evidence on chromosome regions associated with alcohol dependence.

Using tree-based recursive partitioning methods to group haplotypes for increased power in association studies

Motivated by the increasing availability of high-density single nucleotide polymorphism (SNP) markers across the genome, various haplotype-based methods have been developed for candidate gene association studies, and even for genome-wide association studies. Although haplotype approaches dramatically reduce the multiple comparisons problem (as compared to single SNP analysis), even the number of existing haplotypes is relatively large, which increases the degrees of freedom and decreases the power for the corresponding test statistic. Grouping haplotypes is a way to reduce the degrees of freedom. We propose a procedure that uses a tree-based recursive partitioning algorithm to group haplotypes into a small number of clusters, and conducts the association test based on groups of haplotypes instead of individual haplotypes. The method can be used for both population-based and family-based association studies, with known or ambiguous phase information. Simulation studies suggest that the proposed method has the right type I error rate, and is more powerful than some existing haplotype-based tests.

Haplotype Sharing Analysis Using Mantel Statistics

OBJECTIVE

The potential value of haplotypes has attracted widespread interest in the mapping of complex traits. Haplotype sharing methods take the linkage disequilibrium information between multiple markers into account, and may have good power to detect predisposing genes. We present a new approach based on Mantel statistics for spacetime clustering, which is developed in order to improve the power of haplotype sharing analysis for gene mapping in complex disease.

METHODS

The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes for case-only and case-control studies. The genetic similarity is measured as the shared length between haplotypes around a putative disease locus. The phenotypic similarity is measured as the mean-corrected cross-product based on the respective phenotypes. We analyzed two tests for statistical significance with respect to type I error: (1) assuming asymptotic normality, and (2) using a Monte Carlo permutation procedure. The results were compared to the chi(2) test for association based on 3-marker haplotypes.

RESULTS

The results of the type I error rates for the Mantel statistics using the permutational procedure yielded pointwise valid tests. The approach based on the assumption of asymptotic normality was seriously liberal.

CONCLUSION

Power comparisons showed that the Mantel statistics were better than or equal to the chi(2) test for all simulated disease models.

Haplotypes and haplotype-tagging single-nucleotide polymorphism: Presentation Group 8 of Genetic Analysis Workshop 14

Moderately dense maps of single-nucleotide polymorphism (SNP) markers across the human genome for both the simulated data set and data from the Collaborative Study of the Genetics of Alcoholism were available at Genetic Analysis Workshop 14 for the first time. This allowed examination of various novel and existing methods for haplotype analyses. Three contributors applied Mantel statistics in different ways for both linkage and association analysis by using the shared length between two haplotypes at a marker locus as a measure of genetic similarity. The results indicate that haplotype-sharing based on Mantel statistics can be a powerful approach and needs further methodological evaluation. Four contributors investigated haplotype-tagging SNP (htSNP) selection procedures, two contributors examined the use of multilocus haplotypes compared to single loci in association tests, and two contributors compared the accuracy of various methods for reconstructing haplotypes and estimating haplotype frequencies for both pedigree data and data from unrelated individuals. For all three different tasks, software packages and procedures gave similar results in regions of high linkage disequilibrium (LD). However, they were not as consistent in regions of moderate to low LD. One coalescence-based approach for estimating haplotype frequencies, coupled with a Markov chain Monte Carlo technique, outperformed the other haplotype frequency estimation methods in regions of low LD. In conclusion, regardless of the task, results were similar in chromosomal regions of high LD. However, based on the differing results observed here, methodological improvements are required for chromosomal regions of low to moderate LD.