Gad Kimmel

Estimating Local Ancestry in Admixed Populations

Large-scale genotyping of SNPs has shown a great promise in identifying markers that could be linked to diseases. One of the major obstacles involved in performing these studies is that the underlying population substructure could produce spurious associations. Population substructure can be caused by the presence of two distinct subpopulations or a single pool of admixed individuals. In this work, we focus on the latter, which is significantly harder to detect in practice. New advances in this research direction are expected to play a key role in identifying loci that are different among different populations and are still associated with a disease. We evaluated current methods for inference of population substructure in such cases and show that they might be quite inaccurate even in relatively simple scenarios. We therefore introduce a new method, LAMP (Local Ancestry in adMixed Populations), which infers the ancestry of each individual at every single-nucleotide polymorphism (SNP). LAMP computes the ancestry structure for overlapping windows of contiguous SNPs and combines the results with a majority vote. Our empirical results show that LAMP is significantly more accurate and more efficient than existing methods for inferring locus-specific ancestries, enabling it to handle large-scale datasets. We further show that LAMP can be used to estimate the individual admixture of each individual. Our experimental evaluation indicates that this extension yields a considerably more accurate estimate of individual admixture than state-of-the-art methods such as STRUCTURE or EIGENSTRAT, which are frequently used for the correction of population stratification in association studies.

Tag SNP selection in genotype data for maximizing SNP prediction accuracy

MOTIVATION The search for genetic regions associated with complex diseases, such as cancer or Alzheimers disease, is an important challenge that may lead to better diagnosis and treatment. The existence of millions of DNA variations, primarily single nucleotide polymorphisms (SNPs), may allow the fine dissection of such associations. However, studies seeking disease association are limited by the cost of genotyping SNPs. Therefore, it is essential to find a small subset of informative SNPs (tag SNPs) that may be used as good representatives of the rest of the SNPs. RESULTS We define a new natural measure for evaluating the prediction accuracy of a set of tag SNPs, and use it to develop a new method for tag SNPs selection. Our method is based on a novel algorithm that predicts the values of the rest of the SNPs given the tag SNPs. In contrast to most previous methods, our prediction algorithm uses the genotype information and not the haplotype information of the tag SNPs. Our method is very efficient, and it does not rely on having a block partition of the genomic region. We compared our method with two state-of-the-art tag SNP selection algorithms on 58 different genotype datasets from four different sources. Our method consistently found tag SNPs with considerably better prediction ability than the other methods. AVAILABILITY The software is available from the authors on request.

Inference of locus-specific ancestry in closely related populations

A characterization of the genetic variation of recently admixed populations may reveal historical population events, and is useful for the detection of single nucleotide polymorphisms (SNPs) associated with diseases through association studies and admixture mapping. Inference of locus-specific ancestry is key to our understanding of the genetic variation of such populations. While a number of methods for the inference of locus-specific ancestry are accurate when the ancestral populations are quite distant (e.g. African–Americans), current methods incur a large error rate when inferring the locus-specific ancestry in admixed populations where the ancestral populations are closely related (e.g. Americans of European descent). Results: In this work, we extend previous methods for the inference of locus-specific ancestry by the incorporation of a refined model of recombination events. We present an efficient dynamic programming algorithm to infer the locus-specific ancestries in this model, resulting in a method that attains improved accuracies; the improvement is most significant when the ancestral populations are closely related. An evaluation on a wide range of scenarios, including admixtures of the 52 population groups from the Human Genome Diversity Project demonstrates that locus-specific ancestry can indeed be accurately inferred in these admixtures using our method. Finally, we demonstrate that imputation methods can be improved by the incorporation of locus-specific ancestry, when applied to admixed populations. Availability: The implementation of the WINPOP model is available as part of the LAMP package at http://lamp.icsi.berkeley.edu/lamp Contact: heran@icsi.berkeley.edu

Pediatric onset crohn's colitis is characterized by genotype-dependent age-related susceptibility

Background: Pediatric onset Crohns disease (CD) is associated with more colitis and less ileitis compared with adult onset CD. Differences in disease site by age may suggest a different genotype, or different host responses such as decreased ileal susceptibility or increased susceptibility of the colon. Methods: We evaluated 721 pediatric onset CD patients from 3 cohorts with a high allele frequency of NOD2/CARD15 mutations. Children with isolated upper intestinal disease were excluded. The remaining 678 patients were evaluated for interactions between age of onset, NOD2/CARD15, and disease location. Results: We found an age‐related tendency for isolated colitis. Among pediatric onset patients without NOD2/CARD15 mutations, colitis without ileal involvement was significantly more common in first‐decade onset patients (P = 4.57 × 10−5, odds ratio [OR] 2.76, 95% confidence interval [CI] 1.72–4.43). This was not true for colonic disease with ileal involvement (P = 0.35), or for isolated colitis in patients with NOD2/CARD15 mutations (P = 0.61). Analysis of 229 patients with ileal or ileocolonic disease and a NOD2/CARD15 mutation disclosed that ileocolitis was more prevalent through age 10, while isolated ileitis was more prevalent above age 10 (P = 0.016). NOD2/CARD15 mutations were not associated with age of onset. Conclusions: In early‐onset pediatric CD, children with NOD2/CARD15 mutations demonstrate more ileocolitis and less isolated ileitis. Young children without NOD2/CARD15 mutations have an isolated colonic disease distribution, suggesting that this phenotype is associated with genes that lead to a specific phenotype of early‐onset disease. (Inflamm Bowel Dis 2007)

A Randomization Test for Controlling Population Stratification in Whole-Genome Association Studies

Population stratification can be a serious obstacle in the analysis of genomewide association studies. We propose a method for evaluating the significance of association scores in whole-genome cohorts with stratification. Our approach is a randomization test akin to a standard permutation test. It conditions on the genotype matrix and thus takes into account not only the population structure but also the complex linkage disequilibrium structure of the genome. As we show in simulation experiments, our method achieves higher power and significantly better control over false-positive rates than do existing methods. In addition, it can be easily applied to whole-genome association studies.

A fast method for computing high-significance disease association in large population-based studies.

Because of rapid progress in genotyping techniques, many large-scale, genomewide disease-association studies are now under way. Typically, the disorders examined are multifactorial, and, therefore, researchers seeking association must consider interactions among loci and between loci and other factors. One of the challenges of large disease-association studies is obtaining accurate estimates of the significance of discovered associations. The linkage disequilibrium between SNPs makes the tests highly dependent, and dependency worsens when interactions are tested. The standard way of assigning significance (P value) is by a permutation test. Unfortunately, in large studies, it is prohibitively slow to compute low P values by this method. We present here a faster algorithm for accurately calculating low P values in case-control association studies. Unlike with several previous methods, we do not assume a specific distribution of the traits, given the genotypes. Our method is based on importance sampling and on accounting for the decay in linkage disequilibrium along the chromosome. The algorithm is dramatically faster than the standard permutation test. On data sets mimicking medium-to-large association studies, it speeds up computation by a factor of 5,000-100,000, sometimes reducing running times from years to minutes. Thus, our method significantly increases the problem-size range for which accurate, meaningful association results are attainable.

GEVALT: An integrated software tool for genotype analysis

BackgroundGenotype information generated by individual and international efforts carries the promise of revolutionizing disease studies and the association of phenotypes with alleles and haplotypes. Given the enormous amounts of public genotype data, tools for analyzing, interpreting and visualizing these data sets are of critical importance to researchers. In past works we have developed algorithms for genotypes phasing and tag SNP selection, which were shown to be quick and accurate. Both algorithms were available until now only as batch executables.ResultsHere we present GEVALT (GEnotype Visualization and ALgorithmic Tool), a software package designed to simplify and expedite the process of genotype analysis, by providing a common interface to several tasks relating to such analysis. GEVALT combines the strong visual abilities of Haploview with our quick and powerful algorithms for genotypes phasing (GERBIL), tag SNP selection (STAMPA) and permutation testing for evaluating significance of association. All of the above are provided in a visually appealing and interactive interface.ConclusionGEVALT is an integrated viewer that uses state of the art phasing and tag SNP selection algorithms. By streamlining the application of GERBIL and STAMPA together with strong visualization for assessment of the results, GEVALT makes the algorithms accessible to the broad community of researchers in genetics.

ATM haplotypes and breast cancer risk in Jewish high-risk women

While genetic factors clearly play a role in conferring breast cancer risk, the contribution of ATM gene mutations to breast cancer is still unsettled. To shed light on this issue, ATM haplotypes were constructed using eight SNPs spanning the ATM gene region (142 kb) in ethnically diverse non-Ashkenazi Jewish controls (n=118) and high-risk (n=142) women. Of the 28 haplotypes noted, four were encountered in frequencies of 5% or more and accounted for 85% of all haplotypes. Subsequently, ATM haplotyping of high-risk, non-Ashkenazi Jews was performed on 66 women with breast cancer and 76 asymptomatic. One SNP (rs228589) was significantly more prevalent among breast cancer cases compared with controls (P=4 × 10−9), and one discriminative ATM haplotype was significantly more prevalent among breast cancer cases (33.3%) compared with controls (3.8%), (P⩽10−10). There was no significant difference in the SNP and haplotype distribution between asymptomatic high-risk and symptomatic women as a function of disease status. We conclude that a specific ATM SNP and a specific haplotype are associated with increased breast cancer risk in high-risk non-Ashkenazi Jews.

Maximum likelihood resolution of multi-block genotypes

We present a new algorithm for the problems of genotype phasing and block partitioning. Our algorithm is based on a new stochastic model, and on the novel concept of probabilistic common haplotypes. We formulate the goals of genotype resolving and block partitioning as a maximum likelihood problem, and solve it by an EM algorithm. When applied to real biological SNP data, our algorithm outperforms two state of the art phasing algorithms. Our algorithm is also considerably more sensitive and accurate than a previous method in predicting and identifying disease association.

Risk Factors for Perianal Crohn's Disease: The Role of Genotype, Phenotype, and Ethnicity

OBJECTIVES:Perianal disease (PD) is a frequent complication of Crohns disease (CD). The lack of association between PD and development of intestinal penetrating disease may suggest that PD is a distinct phenotype with specific genetic or clinical risk factors. This study was undertaken to evaluate the role of genotype, clinical, and demographic characteristics with PD.METHODS:Phenotypic data on 121 CD patients with PD and 179 patients without PD were carefully characterized. The patients were genotyped for disease-associated OCTN1/2 and NOD2/CARD15 variants and the TNF-α promoter polymorphisms. Analysis was performed to evaluate the differences in phenotype and genotype frequencies between the PD group and the non-PD group.RESULTS:PD was associated with rectal involvement (odds ratio [OR] 2.27, 95% CI 1.32–3.91) and with Sephardic (non-Ashkenazi) Jewish ethnicity (OR 1.71, 95% CI 1.02–2.9). No association was found among the studied OCTN, NOD2, TNF-α variants and the risk for PD.CONCLUSIONS:The strongest factor associated with PD is rectal inflammation. OCTN1/2, NOD2/CARD15, and TNF-α promoter variants do not play a role in the risk to PD in the Jewish Israeli population. The association of ethnicity with PD may suggest that there are as yet unknown genetic variants that are associated with PD.