Optimization Methods for Genotype Data Analysis in Epidemiological Studies

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Variants in dehydroepiandrosterone sulfotransferase (SULT2A1) are involved in the regulation of dehydroepiandrosterone (DHEA)/DHEA-Sulfate (DHEAS) levels in the polycystic ovary syndrome

The β2-adrenoceptor gene and hypertension: is it the promoter or the coding region or neither?

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

Genetic association studies have been a popular approach for assessing the association between common Single Nucleotide Polymorphisms (SNPs) and complex diseases. However, other genomic data involved in the mechanism from SNPs to disease, e.g., gene expressions, are usually neglected in these association studies. In this paper, we propose to exploit gene expression information to more powerfully test the association between SNPs and diseases by jointly modeling the relations among SNPs, gene expressions and diseases. We propose a variance component test for the total effect of SNPs and a gene expression on disease risk. We cast the test within the causal mediation analysis framework with the gene expression as a potential mediator. For eQTL SNPs, the use of gene expression information can enhance power to test for the total effect of a SNP-set, which are the combined direct and indirect effects of the SNPs mediated through the gene expression, on disease risk. We show that the test statistic under the null hypothesis follows a mixture of χ2 distributions, which can be evaluated analytically or empirically using the resampling-based perturbation method. We construct tests for each of three disease models that is determined by SNPs only, SNPs and gene expression, or includes also their interactions. As the true disease model is unknown in practice, we further propose an omnibus test to accommodate different underlying disease models. We evaluate the finite sample performance of the proposed methods using simulation studies, and show that our proposed test performs well and the omnibus test can almost reach the optimal power where the disease model is known and correctly specified. We apply our method to re-analyze the overall effect of the SNP-set and expression of the ORMDL3 gene on the risk of asthma.

Advances in high‐throughput genotyping technologies have enabled the efficient systematic screening of single‐nucleotide polymorphisms (SNPs) throughout the human genome for disease associations in genome‐wide association studies (GWAS). Many human complex diseases have been interrogated by GWAS; these include autoimmune and chronic inflammatory diseases, neurological and neuropsychiatric diseases, cardiovascular diseases and cancers among others. Presently, more than 60 cancer GWAS have been published and numerous novel SNPs and genetic loci were found to be associated with various cancers such as breast, prostate and colorectal cancers. These SNP associations were supported by robust statistical evidence. The GWAS of cancers have so far focused only on SNPs. Other germline genetic variations such as small insertions and deletions (indels), copy number variations (CNVs) and tandem repeat polymorphisms should also be investigated for their associations with cancers in the future. Key Concepts: Cancer is similar to other complex diseases in that it is a consequence of complex interactions between multiple genetic factors and environmental exposures. The genetic susceptibility of cancer has been previously established by epidemiological, twin and familial aggregation studies. The implication of a genetic component in the aetiology of cancer has driven enormous research endeavours to identify the genetic risk factors. The candidate gene association and linkage analysis approaches to identifying these genetic risk factors has, however, generated inconsistent results in the past. Notable advances have been achieved in recent years by genome‐wide association studies (GWAS) – where several hundred–thousand single nucleotide polymorphisms (SNPs) throughout the human genome are interrogated for disease associations. Numerous novel genetic loci or genes have been implicated in a variety of cancers by GWAS. This significant progress in dissecting the germline genetic component of cancers is credited to the development of high‐throughput genotyping technologies, which has enabled GWAS to be done more efficiently on thousands of samples. Similar to other complex diseases, most of the GWAS‐identified SNPs for cancers have small effect sizes (OR <1.5) and collectively only account for a small fraction of the heritability of cancers. Several practical steps to further uncover additional genetic loci for cancers include (a) extending GWAS in different populations, (b) meta‐analysis, (c) subgroup analysis and (d) a second tier of replication. In addition, other approaches should also be considered such as investigating rare variants or less common SNPs, targeted resequencing of the GWAS‐identified loci, copy number variations and homozygosity mapping analysis.

IL28B single nucleotide polymorphisms in the treatment of hepatitis C

With an increased emphasis on genotyping of single nucleotide polymorphisms (SNPs) in disease association studies, the genotyping platform of choice is constantly evolving. In addition, the development of more specific SNP assays and appropriate genotype validation applications is becoming increasingly critical to elucidate ambiguous genotypes. In this study, we have used SNP specific Locked Nucleic Acid (LNA) hybridization probes on a real-time PCR platform to genotype an association cohort and propose three criteria to address ambiguous genotypes. Based on the kinetic properties of PCR amplification, the three criteria address PCR amplification efficiency, the net fluorescent difference between maximal and minimal fluorescent signals and the beginning of the exponential growth phase of the reaction. Initially observed SNP allelic discrimination curves were confirmed by DNA sequencing (n = 50) and application of our three genotype criteria corroborated both sequencing and observed real-time PCR results. In addition, the tested Caucasian association cohort was in Hardy-Weinberg equilibrium and observed allele frequencies were very similar to two independently tested Caucasian association cohorts for the same tested SNP. We present here a novel approach to effectively determine ambiguous genotypes generated from a real-time PCR platform. Application of our three novel criteria provides an easy to use semi-automated genotype confirmation protocol.

Genetic counseling in direct-to-consumer exome sequencing: a case report.

<div>Abstract<p>Background: Inherited risk of pancreatic cancer has been associated with mutations in several genes, including <i>BRCA2, CDKN2A</i> (p16), <i>PRSS1</i>, and <i>PALB2</i>. We hypothesized that common variants in these genes, single nucleotide polymorphisms (SNP), may also influence risk for pancreatic cancer development.</p><p>Methods: A clinic-based case-control study in non-Hispanic white persons compared 1,143 patients with pancreatic adenocarcinoma with 1,097 healthy controls. Twenty-eight genes directly and indirectly involved in the Fanconi/BRCA pathway (includes <i>BRCA1, BRCA2</i>, and <i>PALB2</i>) were identified and 248 tag SNPs were selected. In addition, 11 SNPs in <i>CDKN2A, PRSS1</i>, and <i>PRSS2</i> were selected. Association studies were done at the gene level by principal components analysis, whereas recursive partitioning analysis was used to investigate pathway effects. At the individual SNP level, adjusted additive, dominant, and recessive models were investigated, and gene-environment interactions were also assessed.</p><p>Results: Gene level analyses showed no significant association of any genes with altered pancreatic cancer risk. Multiple single SNP analyses showed associations, which will require replication. Exploratory pathway analyses by recursive partitioning showed no association between SNPs and risk for pancreatic cancer.</p><p>Conclusion: In a candidate gene and pathway SNP association study analysis, common variations in the Fanconi/BRCA pathway and other candidate familial pancreatic cancer genes are not associated with risk for pancreatic cancer. Cancer Epidemiol Biomarkers Prev 2009;18(9):2549–52)</p></div>

On May 31 this year, James Watson received the DNA sequence of his full genome in a ceremony at the Baylor College of Medicine in Houston (TX, USA). It marked the end of a two‐month project by 454 Life Sciences, a biotech company in Branford (CT, USA) specializing in DNA sequencing, which generated the raw data from a blood sample given by Watson. Baylor College then verified whether the sequence encompassed the entire genome and, on July 6, Watson posted his genome—excluding a portion including the ApoE gene, which he had asked to be redacted—online at the GenBank database maintained by the US National Center for Biology Information (Bethesda, MD, USA). Watson, the co‐discoverer of the double helical structure of DNA and the father of the Human Genome Project, must have expected to be the first person to make his genome publicly available. He was not. Nine days earlier, on June 27, J. Craig Venter, the inventor of the ‘shot‐gun’ sequencing strategy, who once acknowledged a love–hate relationship with his former boss Watson, had posted his own complete genome online prior to a publication (Levy et al , 2007). There was no official race between Venter and Watson to be the first. The timing of the publications of their genomes was the result of two unrelated projects. But what is the rationale for making the genome data of noted geneticists publicly available in the first place? Is it pure scientific research or genetic exhibitionism? Or has science finally embraced celebrity culture? The media hoopla surrounding the sequencing of Watson's genome has already had some commentators worrying that genome sequencing could become the next must‐have for the rich and privileged (Check, 2007). However, beyond the publicity, it is only a matter of time until genome sequencing will be affordable for most …

In population-based genetic association studies, one tests association between a genetic marker and a phenotype (trait). A common genetic marker is a single-nucleotide polymorphism (SNP), which consists of two alleles. Each person has one of the three genotypes formed by the two alleles. The two most common types of phenotype in genetic association studies are binary (discrete) and quantitative (continuous) traits. Case-control is a typical binary trait, and blood pressure is an example of a quantitative trait. The true disease loci, or functional loci, contributing to the variations of a trait are often unknown. A large number of SNPs, about half to one million, are genotyped in genome-wide association studies (GWASs) for many common and complex diseases. When a SNP is in linkage disequilibrium with a disease locus, the association between the SNP and the trait can be tested. When a SNP has strong association with the trait, it may indicate that the disease locus is nearby. Follow-up studies, including regional haplotype analysis, meta-analysis, imputation and resequencing, may provide more evidence about the underlying disease loci.

High-throughput single nucleotide polymorphism (SNP) genotyping technologies make massive genotype data, with a large number of individuals, publicly available. Accessibility of genetic data makes genome-wide association studies for complex diseases possible. The susceptibility to complex diseases can be predicted through the analysis of the genetic data and prospective patients can be helped to make informed decisions. With the development of DNA microarray technique, it is possible to access the human genetic information related to specific diseases, but most disease association studies are based on genotypes. This paper uses a combinatorial method to analyze the haplotype data for Crohn's disease and search disease-associated factors for given case/control samples. A Linear programming based method has been applied to publicly available genotype data on Crohn's disease for association study and achieved a promising result.

Genetic changes that may be associated with complex diseases are tried to be determined by means of many genome-wide association studies. Single Nucleotide Polymorphisms (SNPs) are used primarily in these studies since they comprise a large part of these genetic changes. Statistical importance of the genome-wide association study is directly related to the number of individuals and SNPs. However, it is still very costly and time-consuming to genotype all SNPs inside the candidate area for many individuals in very large-scale association studies. For this reason, with a small error, it is necessary to select an appropriate subset of all SNPs that will represent the rest of SNPs. These selected SNPs are called tag SNPs or haplotype tag SNPs (tag SNPs or htSNPs). It is essential in tag SNP selection to determine minimum tag SNP set with very good prediction accuracy. In this study, while Clonal Selection Algorithm (CLONALG) was used as tag SNP selection method, a new method named CLONSim, in which similarity association between SNPs was used as the prediction method for the rest of SNPs was proposed. The proposed method was compared with BPSO (Binary Particle Swarm Optimization) and CLONTagger methods with parameter optimization using datasets of different sizes. Experiment results showed that the proposed method could identify tag SNPs significantly faster.

Hundreds of common genetic variants related to the risk of human disease, such as diabetes, hypertension, bipolar, and Crohn’s disease, have been successfully discovered by Genomewide Association Studies (GWAS) (Barret et al., 2008; Hindorff, 2009; Thomas et al., 1991; WTCCC, 2007). Current GWAS are based on the strategy of linkage disequilibrium (LD) mapping, in which a sufficient number of single nucleotide polymorphism (SNP) markers are selectively genotyped to capture the genetic variation of the whole genome. However, there are two major issues related to the results of GWAS. First, the results only explain a small fraction of the heritability of complex diseases. One of the reasons may be that many functional variants, in particular rare variants, which are not directly genotyped in GWAS, have a weak LD with SNP markers, and hence are missed by GWAS (Iyengar et al., 2004; Manolio et al., 2009). Second, the identified associations in GWAS are often inconsistent between different populations. The reason for this may be the varied LD structures between markers and underlying causal variants among populations, resulting in associations can only be observed in specific populations.