HOWDY: an integrated database system for human genome research

Integrated genome sizing (IGS) approach for the parallelization of whole genome analysis

BACKGROUND

The use of whole genome sequence has increased recently with rapid progression of next-generation sequencing (NGS) technologies. However, storing raw sequence reads to perform large-scale genome analysis pose hardware challenges. Despite advancement in genome analytic platforms, efficient approaches remain relevant especially as applied to the human genome. In this study, an Integrated Genome Sizing (IGS) approach is adopted to speed up multiple whole genome analysis in high-performance computing (HPC) environment. The approach splits a genome (GRCh37) into 630 chunks (fragments) wherein multiple chunks can simultaneously be parallelized for sequence analyses across cohorts.

RESULTS

IGS was integrated on Maha-Fs (HPC) system, to provide the parallelization required to analyze 2504 whole genomes. Using a single reference pilot genome, NA12878, we compared the NGS process time between Maha-Fs (NFS SATA hard disk drive) and SGI-UV300 (solid state drive memory). It was observed that SGI-UV300 was faster, having 32.5 mins of process time, while that of the Maha-Fs was 55.2 mins.

CONCLUSIONS

The implementation of IGS can leverage the ability of HPC systems to analyze multiple genomes simultaneously. We believe this approach will accelerate research advancement in personalized genomic medicine. Our method is comparable to the fastest methods for sequence alignment.

Current research status, databases and application of single nucleotide polymorphism

Single Nucleotide Polymorphisms (SNPs) are the most frequent form of DNA variation in the genome. SNPs are genetic markers which are bi-allelic in nature and grow at a very fast rate. Current genomic databases contain information on several million SNPs. More than 6 million SNPs have been identified and the information is publicly available through the efforts of the SNP Consortium and others data bases. The NCBI plays a major role in facillating the identification and cataloging of SNPs through creation and maintenance of the public SNP database (dbSNP) by the biomedical community worldwide and stimulate many areas of biological research including the identification of the genetic components of disease. In this review article, we are compiling the existing SNP databases, research status and their application.

MutationView/KMcancerDB: a database for cancer gene mutations

It is known that cancers are caused by accumulated mutations in various genes and consequent functional alterations of proteins that are important for maintenance of normal cellular functions. The changes in nucleotide sequences and expression patterns of cancer-related genes are being extensively studied to better understand the mechanisms of tumorigenesis and to develop methods for DNA/protein [corrected] diagnosis and drug discovery. At present, a number of computer databases for molecular information on cancer-related genes are available publicly through the internet. These databases deal with familial cancer and sporadic cancer at the levels of germline mutation or somatic mutation, genomic or chromosomal abnormalities, and changes in the expression levels of relevant genes. Previously, we constructed a human gene mutation database named MutationView (http://mutview.dmb.med.keio.ac.jp/) and have accumulated mutation data for approximately 300 genes that are involved mainly in monogenic diseases. Forty-two genes are cancer-related and therefore a separate cancer database named KMcancerDB was constructed. MutationView/KMcancerDB utilizes a graphic display function for both queries and search results much more often than other existing databases, making the system quite user friendly. MutationView/KMcancerDB provides a highly sophisticated search function for all genes through a single internet URL. In the present paper, we briefly review various useful databases for cancer-related genes, and describe MutationView/KMcancerDB in more detail.

An information-driven approach to pharmacogenomics

Effective information management of the pharmacogenomics discipline presents many unique challenges. Genetic and genomic data generated via high-throughput methods need to be integrated with phenotypic data which are defined at multiscale levels, ranging from the molecular to the clinical level. Repositories storing these data are distributed and vary in terms of syntax and semantics which result in issues concerning data exchange and integration. The application of the emerging semantic web offers a promising solution to these interoperability issues.

Bayesian modelling of multivariate quantitative traits using seemingly unrelated regressions

We investigate a Bayesian approach to modelling the statistical association between markers at multiple loci and multivariate quantitative traits. In particular, we describe the use of Bayesian Seemingly Unrelated Regressions (SUR) whereby genotypes at the different loci are allowed to have non-simultaneous effects on the phenotypes considered with residuals from each regression assumed correlated. We present results from simulations showing that, under rather general conditions that are likely to hold in real situations, the Bayesian SUR approach has increased probability of selecting the true model compared to univariate analyses. Finally, we apply our methods to data from subjects genotyped for 12 SNPs in the apolipoprotein E (APOE) gene. Phenotypes relate to response to treatment with atorvastatin and include changes in total cholesterol, low-density lipoprotein cholesterol, and triglycerides. Missing genotype data are naturally accommodated in our Bayesian framework by imputing them using a nested haplotype phasing algorithm.

Current limitations of SNP data from the public domain for studies of complex disorders: a test for ten candidate genes for obesity and osteoporosis

BACKGROUND

Public SNP databases are frequently used to choose SNPs for candidate genes in the association and linkage studies of complex disorders. However, their utility for such studies of diseases with ethnic-dependent background has never been evaluated.

RESULTS

To estimate the accuracy and completeness of SNP public databases, we analyzed the allele frequencies of 41 SNPs in 10 candidate genes for obesity and/or osteoporosis in a large American-Caucasian sample (1,873 individuals from 405 nuclear families) by PCR-invader assay. We compared our results with those from the databases and other published studies. Of the 41 SNPs, 8 were monomorphic in our sample. Twelve were reported for the first time for Caucasians and the other 29 SNPs in our sample essentially confirmed the respective allele frequencies for Caucasians in the databases and previous studies. The comparison of our data with other ethnic groups showed significant differentiation between the three major world ethnic groups at some SNPs (Caucasians and Africans differed at 3 of the 18 shared SNPs, and Caucasians and Asians differed at 13 of the 22 shared SNPs). This genetic differentiation may have an important implication for studying the well-known ethnic differences in the prevalence of obesity and osteoporosis, and complex disorders in general.

CONCLUSION

A comparative analysis of the SNP data of the candidate genes obtained in the present study, as well as those retrieved from the public domain, suggests that the databases may currently have serious limitations for studying complex disorders with an ethnic-dependent background due to the incomplete and uneven representation of the candidate SNPs in the databases for the major ethnic groups. This conclusion attests to the imperative necessity of large-scale and accurate characterization of these SNPs in different ethnic groups.

SNP databases and pharmacogenetics: great start, but a long way to go

With the recent publication of the human genome project there has been an explosion of data available for pharmacogenetic research. Web-based databases containing information on single nucleotide polymorphisms (SNPs) are readily accessible to researchers, but there has been little comment on their utility. We used seven major international databases to identify SNPs in 74 genes involved in drug pathways. Very little overlap was seen among the databases, with only eight out of a putative 893 SNPs ( approximately 1%) common to the most commonly used databases. Problems with false positives, secondary to a high degree of homology in gene families, were also observed. These studies suggest researchers limiting their studies to one database would miss a great deal of information. Effort to update compilation databases, such as HGVbase, GeneSNP, PharmGKB, and HOWDY, and the aggressive removal of false positives from all databases is required if these resources are to facilitate the intended growth in pharmacogenetics research.