The HAP webserver: Tools for the Discovery of Genetic Basis of Human Disease HYUN MIN KANG Computer Science and Engineering University of California, San Diego 1. Introduction Understanding the structure of human variation is important for understanding the genetic basis of human diseases. Recent advances in high-throughput genotyping technology generating a tremendous amount of high density single nucleotide polymorphism(SNP) data holds great promise for discovering genetic risk factors associated with disease. In order to identify association between disease and variations in an individual’s chromosome, the genotype data must be phased into haplotypes. Based on HAP, which is a very efficient tool for haplotype resolution based on imprefect phylogeny, HAP webserver provides an integrated method to reconstruct haplotype structure and to identify genetic variants associated with complex phenotypes which can give insight into the genetic factors of complex diseases. Our methods leverage interplay between genotype phasing, haplotype phylogeny, association analysis, and functional SNPs prediction. Our methods leverage new insights into the structure of human variation which allows us to observe phenotype associations directly from genotype and phenotype data. We demonstrate our methods via an analysis of two genes implicated in hypertension. Our methods are easily accessible via the webserver, providing complete results of association analysis including graphical visualizations. We expect that our methods will facilitate current association studies. NOAH ZAITLEN Bioinformatics Program University of California, San Diego TAURIN TAN-ATICHAT Electrical and Computer Engineering University of California, San Diego EDWARD SHYU Computer Science and Engineering University of California, San Diego GRACE SHAW Computer Science and Engineering University of California, San Diego DAFNA BITTON Computer Science and Engineering University of Calfornia, San Diego ELAD HAZAN Department of Computer Science Princeton University ERAN HALPERIN International Computer- Science Institute, Berkeley ELEAZAR ESKIN Computer Science and Engineering University of California, San Diego 2. HAP – haplotype resolution HAP is a haplotype analysis system which is aimed in helping geneticists perform disease association studies. The main feature of HAP is a phasing method which is based on the assumption of imperfect phylogeny. The phasing method is very efficient, which allows HAP to work with very large data sets, and to perform other operations such as finding a partition of the region into blocks of limited diversity or performing association tests on each of these block with in vitro experiments already published. HAP takes as input a set of genotypes over a region, taken form a population, and returns the haplotype phase of each of the individual’s genotypes. From our studies, we observed that HAP is very accurate when the number of individual taken is at least a couple of dozens. In addition to phasing, HAP also produces a partition of the region into blocks of correlated SNPs. The block partition of the haplotypes is such that it minimizes the number of tag SNPs. HAP leverages a new insight into the underlying structure of haplotypes which shows that SNPs are organized in highly correlated “blocks”(Daly et al 01, Patil et al 01). HAP has shown to have competitive accuracy compared to the state of the art sofrwares(such as PHASE, HAPLOTYPER). On the other hand, HAP is extremely fast and can be used on very large datasets. Recently, HAP is successfully used in revealing whole genome haplotype structure. (Hinds et al. 05) Figure 2  Predicted CHGA phylogeny Each symbol denotes a haplotype variants of CHGA promoter. Each haplotype variant is classified into one of three groups: ancestral, common, or recent haplotype. A solid line denotes mutant, and dashed lines denotes recombination. This figure is automatically generated by our webserver. CHGA HAPLOTYPE ID NUCLEOTIDE AT POSITIONSTATISTICAL TESTS Linear Regression Unpaired t-test Mann- Whitney Jonckheere- Terpstra A GATTGTCC.948(+).963 (+).969 (+).963 (+) B AATTGTCC.977(  ).999 (  ).996 (  ) C GACGATAC.175 (  ).209 (  ).505 (  ).485 (  ) D GATTGCCC.999 (  ).990 (+).983 (+).997 (+) E GTTTGCCT.004 (+)**.011 (+)* F GACGATCC.836 (  ).978 (  ).986 (  ) Table 1  Haplotype analysis between CHGA promoter region and CHGA plasma levels : Statistical p-values for the association between the haplotypes in CHGA promoter region and CHGA plasma levels in 221 African Americans over various statistical tests. Each haplotype ID and its sequence is identical to that of Figure 2. The p-values are evaluated by permutation tests with 10 5 times of random shuffling of phenotypes. The p-values are also adjusted to multiple comparisons, thus no further conservative adjustments are required. The plus or minus sign next to each p-value denotes whether the haplotype variant shows positive or negative effect on the phenotype for each statistical test. Single and double asterisks by the p-value denotes that the p-value is less than 0.05 and 0.01, respectively. This table is automatically generated by our webserver. Figure 4  CHGA association visualization A histogram of CHGA levels grouped by the number of copies of the haplotypes E in Table 1. The x- axis represents plasma levels, and y-axis represents the fraction of individuals with given plasma level. It can be observed that there are significant association for the haplotype to increase plasma level. This figure is automatically generated by our webserver. Figure 5  CHGA functional SNPs prediction Results of predicting how each SNP contributes to the association identified in Table 1. The y-axis is a score that represents the degree of functional contribution. The SNP at the position -89 makes the highest functional contribution, and those at positions -1014,-988,-462 share the second highest score. This results is consistent to the in vitro experiments previously published. This figure is automatically generated by our webserver. 3. Inferring Phylogenetic Relationships between Haplotypes Recent studies have shown that within short regions, there is limited genetic variability, and only a small number of haplotypes account for the entire population. In a typical region of 20kb, three or four common haplotypes account for 80% of the population. Futhermore, most rare variants appear to be minor variants of common haplotypes. Using these results, phylogeny is inferred by identifying most likely ancestors for the each of the rare haplotypes given the frequent ones. Then, ancestral haplotypes are found by searching for similar common variants. Figure 1  HAP webserver (a) HAP is used in revealing whole genome haplotype structure. The article “Whole-Genome Patterns of Common DNA Variation in Three Human Populations” is published on the cover of Science. (b) The screenshot of HAP webserver main page, available at 4. Identifying Association via Statistical Tests Leveraging haplotype structure Quantitative phenotypes & Dose-effects Nonparametric Tests Covariates Figure 3  Linkage disequilibrium plot Results of of running HAP webserver with linkage disequilibrium data. The example data is available via webserver. The axis represent SNP positions. The red regions indicate high disequilibrium while the blue indicates low disequilibrium. 5. Functional SNPs Prediction Once associated haplotypes are identified using rigorous statistical tests, our methods provide a method for estimating the likelihood of each SNP contributing the association. To make this prediction, we iterate over several groupings of the haplotypes to attempt to isolate the functional SNPs. The outcome of the second step is a score distribution over the SNPs estimating how likely each SNP is to be functional. 6. Whole Genome Association Studies with HDL Mouse Phenome Database Figure 6  HDL Phenotype The association test results for the level of HDL cholesterol in the different mouse strains. Figure 7  Random The association test results for randomly permuted HDL phenotype in figure 6.