Clustering of haplotypes based on phylogeny: how good a strategy for association testing?

Haplotype based testing for a better understanding of the selective architecture

BACKGROUND

The identification of genomic regions affected by selection is one of the most important goals in population genetics. If temporal data are available, allele frequency changes at SNP positions are often used for this purpose. Here we provide a new testing approach that uses haplotype frequencies instead of allele frequencies.

RESULTS

Using simulated data, we show that compared to SNP based test, our approach has higher power, especially when the number of candidate haplotypes is small or moderate. To improve power when the number of haplotypes is large, we investigate methods to combine them with a moderate number of haplotype subsets. Haplotype frequencies can often be recovered with less noise than SNP frequencies, especially under pool sequencing, giving our test an additional advantage. Furthermore, spurious outlier SNPs may lead to false positives, a problem usually not encountered when working with haplotypes. Post hoc tests for the number of selected haplotypes and for differences between their selection coefficients are also provided for a better understanding of the underlying selection dynamics. An application on a real data set further illustrates the performance benefits.

CONCLUSIONS

Due to less multiple testing correction and noise reduction, haplotype based testing is able to outperform SNP based tests in terms of power in most scenarios.

Bayesian quantitative trait locus mapping using inferred haplotypes

We describe a fast hierarchical Bayesian method for mapping quantitative trait loci by haplotype-based association, applicable when haplotypes are not observed directly but are inferred from multiple marker genotypes. The method avoids the use of a Monte Carlo Markov chain by employing priors for which the likelihood factorizes completely. It is parameterized by a single hyperparameter, the fraction of variance explained by the quantitative trait locus, compared to the frequentist fixed-effects model, which requires a parameter for the phenotypic effect of each combination of haplotypes; nevertheless it still provides estimates of haplotype effects. We use simulation to show that the method matches the power of the frequentist regression model and, when the haplotypes are inferred, exceeds it for small QTL effect sizes. The Bayesian estimates of the haplotype effects are more accurate than the frequentist estimates, for both known and inferred haplotypes, which indicates that this advantage is independent of the effect of uncertainty in haplotype inference and will hold in comparison with frequentist methods in general. We apply the method to data from a panel of recombinant inbred lines of Arabidopsis thaliana, descended from 19 inbred founders.

Global haplotype partitioning for maximal associated SNP pairs

BACKGROUND

Global partitioning based on pairwise associations of SNPs has not previously been used to define haplotype blocks within genomes. Here, we define an association index based on LD between SNP pairs. We use the Fisher's exact test to assess the statistical significance of the LD estimator. By this test, each SNP pair is characterized as associated, independent, or not-statistically-significant. We set limits on the maximum acceptable proportion of independent pairs within all blocks and search for the partitioning with maximal proportion of associated SNP pairs. Essentially, this model is reduced to a constrained optimization problem, the solution of which is obtained by iterating a dynamic programming algorithm.

RESULTS

In comparison with other methods, our algorithm reports blocks of larger average size. Nevertheless, the haplotype diversity within the blocks is captured by a small number of tagSNPs. Resampling HapMap haplotypes under a block-based model of recombination showed that our algorithm is robust in reproducing the same partitioning for recombinant samples. Our algorithm performed better than previously reported models in a case-control association study aimed at mapping a single locus trait, based on simulation results that were evaluated by a block-based statistical test. Compared to methods of haplotype block partitioning, we performed best on detection of recombination hotspots.

CONCLUSION

Our proposed method divides chromosomes into the regions within which allelic associations of SNP pairs are maximized. This approach presents a native design for dimension reduction in genome-wide association studies. Our results show that the pairwise allelic association of SNPs can describe various features of genomic variation, in particular recombination hotspots.

Power comparisons between similarity-based multilocus association methods, logistic regression, and score tests for haplotypes

Recently, a genomic distance-based regression for multilocus associations was proposed (Wessel and Schork [2006] Am. J. Hum. Genet. 79:792-806) in which either locus or haplotype scoring can be used to measure genetic distance. Although it allows various measures of genomic similarity and simultaneous analyses of multiple phenotypes, its power relative to other methods for case-control analyses is not well known. We compare the power of traditional methods with this new distance-based approach, for both locus-scoring and haplotype-scoring strategies. We discuss the relative power of these association methods with respect to five properties: (1) the marker informativity; (2) the number of markers; (3) the causal allele frequency; (4) the preponderance of the most common high-risk haplotype; (5) the correlation between the causal single-nucleotide polymorphism (SNP) and its flanking markers. We found that locus-based logistic regression and the global score test for haplotypes suffered from power loss when many markers were included in the analyses, due to many degrees of freedom. In contrast, the distance-based approach was not as vulnerable to more markers or more haplotypes. A genotype counting measure was more sensitive to the marker informativity and the correlation between the causal SNP and its flanking markers. After examining the impact of the five properties on power, we found that on average, the genomic distance-based regression that uses a matching measure for diplotypes was the most powerful and robust method among the seven methods we compared.

Gains in power for exhaustive analyses of haplotypes using variable-sized sliding window strategy: a comparison of association-mapping strategies

Linkage disequilibrium (LD)-based association mapping is often performed by analyzing either individual SNPs or block-based multi-SNP haplotypes. Sliding windows of several fixed sizes (in terms of SNP numbers) were also applied to a few simulated or real data sets. In comparison, exhaustively testing based on variable-sized sliding windows (VSW) of all possible sizes of SNPs over a genomic region has the best chance to capture the optimum markers (single SNPs or haplotypes) that are most significantly associated with the traits under study. However, the cost is the increased number of multiple tests and computation. Here, a strategy of VSW of all possible sizes is proposed and its power is examined, in comparison with those using only haplotype blocks (BLK) or single SNP loci (SGL) tests. Critical values for statistical significance testing that account for multiple testing are simulated. We demonstrated that, over a wide range of parameters simulated, VSW increased power for the detection of disease variants by approximately 1-15% over the BLK and SGL approaches. The improved performance was more significant in regions with high recombination rates. In an empirical data set, VSW obtained the most significant signal and identified the LRP5 gene as strongly associated with osteoporosis. With the use of computational techniques such as parallel algorithms and clustering computing, it is feasible to apply VSW to large genomic regions or those regions preliminarily identified by traditional SGL/BLK methods.

hapConstructor: automatic construction and testing of haplotypes in a Monte Carlo framework

Summary: Haplotypes carry important information that can direct investigators towards underlying susceptibility variants, and hence multiple tagging single nucleotide polymorphisms (tSNPs) are usually studied in candidate gene association studies. However, it is often unknown which SNPs should be included in haplotype analyses, or which tests should be performed for maximum power. We have developed a program, hapConstructor, which automatically builds multi-locus SNP sets to test for association in a case-control framework. The multi-SNP sets considered need not be contiguous; they are built based on significance. An important feature is that the missing data imputation is carried out based on the full data, for maximal information and consistency. HapConstructor is implemented in a Monte Carlo framework and naturally extends to allow for significance testing and false discovery rates that account for the construction process and to related individuals. HapConstructor is a useful tool for exploring multi-locus associations in candidate genes and regions.

Availability: http://www-genepi.med.utah.edu/Genie

Contact:ryan.abo@hsc.utah.edu

Haplotype-association analysis

Association methods based on linkage disequilibrium (LD) offer a promising approach for detecting genetic variations that are responsible for complex human diseases. Although methods based on individual single nucleotide polymorphisms (SNPs) may lead to significant findings, methods based on haplotypes comprising multiple SNPs on the same inherited chromosome may provide additional power for mapping disease genes and also provide insight on factors influencing the dependency among genetic markers. Such insights may provide information essential for understanding human evolution and also for identifying cis-interactions between two or more causal variants. Because obtaining haplotype information directly from experiments can be cost prohibitive in most studies, especially in large scale studies, haplotype analysis presents many unique challenges. In this chapter, we focus on two main issues: haplotype inference and haplotype-association analysis. We first provide a detailed review of methods for haplotype inference using unrelated individuals as well as related individuals from pedigrees. We then cover a number of statistical methods that employ haplotype information in association analysis. In addition, we discuss the advantages and limitations of different methods.

Generalized genomic distance-based regression methodology for multilocus association analysis

Large-scale, multilocus genetic association studies require powerful and appropriate statistical-analysis tools that are designed to relate genotype and haplotype information to phenotypes of interest. Many analysis approaches consider relating allelic, haplotypic, or genotypic information to a trait through use of extensions of traditional analysis techniques, such as contingency-table analysis, regression methods, and analysis-of-variance techniques. In this work, we consider a complementary approach that involves the characterization and measurement of the similarity and dissimilarity of the allelic composition of a set of individuals' diploid genomes at multiple loci in the regions of interest. We describe a regression method that can be used to relate variation in the measure of genomic dissimilarity (or "distance") among a set of individuals to variation in their trait values. Weighting factors associated with functional or evolutionary conservation information of the loci can be used in the assessment of similarity. The proposed method is very flexible and is easily extended to complex multilocus-analysis settings involving covariates. In addition, the proposed method actually encompasses both single-locus and haplotype-phylogeny analysis methods, which are two of the most widely used approaches in genetic association analysis. We showcase the method with data described in the literature. Ultimately, our method is appropriate for high-dimensional genomic data and anticipates an era when cost-effective exhaustive DNA sequence data can be obtained for a large number of individuals, over and above genotype information focused on a few well-chosen loci.