GEVALT: An integrated software tool for genotype analysis

BACKGROUND: Genotype 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. RESULTS: Here 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. CONCLUSION: GEVALT 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

A specific RAD51 haplotype increases breast cancer risk in Jewish non-Ashkenazi high-risk women

While the precise genes involved in determining familial breast cancer risk in addition to BRCA1/2 are mostly unknown, one strong candidate is RAD51. Jewish non-Ashkenazi women at high-risk for breast/ovarian cancer and ethnically matched controls were genotyped using four single nucleotide polymorphisms spanning the RAD51 genomic region, and the resulting haplotypes were constructed using the GERBIL algorithm. A total of 314 individuals were genotyped: 184 non-Ashkenazi high-risk women (119 with breast cancer), and 130 unaffected, average-risk ethnically matched controls. Using GEBRIL, three frequent haplotypes were constructed. One of the haplotypes (TGTA -coined haplotype 3) was present in 7.3% (19/260 haplotypes) of controls (n = 130) and in 16.8% (40/238 haplotypes) of high-risk breast cancer patients (n = 119, P = 0.001). A specific RAD51 haplotype is more prevalent among non-Ashkenazi Jewish high-risk women than in average-risk population

Analysis of MicroRNA Expression in Embryonic Developmental Toxicity Induced by MC-RR

As cynobacterial blooms frequently occur in fresh waters throughout the world, microcystins (MCs) have caused serious damage to both wildlife and human health. MCs are known to have developmental toxicity, however, the possible molecular mechanism is largely unknown. This is the first toxicological study to integrate post-transcriptomic, proteomic and bioinformatics analysis to explore molecular mechanisms for developmental toxicity of MCs in zebrafish. After being microinjected directly into embryos, MC-RR dose-dependently decreased survival rates and increased malformation rates of embryos, causing various embryo abnormalities including loss of vascular integrity and hemorrhage. Expressions of 31 microRNAs (miRNAs) and 78 proteins were significantly affected at 72 hours post-fertilisation (hpf). Expressions of miR-430 and miR-125 families were also significantly changed. The altered expressions of miR-31 and miR-126 were likely responsible for the loss of vascular integrity. MC-RR significantly reduced the expressions of a number of proteins involved in energy metabolism, cell division, protein synthesis, cytoskeleton maintenance, response to stress and DNA replication. Bioinformatics analysis shows that several aberrantly expressed miRNAs and proteins (involved in various molecular pathways) were predicted to be potential MC-responsive miRNA-target pairs, and that their aberrant expressions should be the possible molecular mechanisms for the various developmental defects caused by MC-RR

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

The Incomplete Perfect Phylogeny Haplotype Problem

this paper we address the IPPH proble

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

Accepted (Day Month Year)

The problem of resolving genotypes into haplotypes, under the perfect phylogeny model, has been under intensive study recently. All studies so far handled missing data entries in a heuristic manner. We prove that the perfect phylogeny haplotype problem is NPcomplete when some of the data entries are missing, even when the phylogeny is rooted. We define a biologically motivated probabilistic model for genotype generation and for the way missing data occur. Under this model, we provide an algorithm, which takes an expected polynomial time. In tests on simulated data, our algorithm quickly resolves the genotypes under high rates of missing entries