Susceptibility Genes For Common Diseases Research Articles

Meta-Analysis of Genetic Association Studies

The object of this review is to help readers to understand meta-analysis of genetic association study. Genetic association studies are a powerful approach to identify susceptibility genes for common diseases. However, the results of these studies are not consistently reproducible. In order to overcome the limitations of individual studies, larger sample sizes or meta-analysis is required. Meta-analysis is a statistical tool for combining results of different studies on the same topic, thus increasing statistical strength and precision. Meta-analysis of genetic association studies combines the results from independent studies, explores the sources of heterogeneity, and identifies subgroups associated with the factor of interest. Meta-analysis of genetic association studies is an effective tool for garnering a greater understanding of complex diseases and potentially provides new insights into gene-disease associations.

Advances on gene-based association analysis

Genome-wide association studies (GWAS) have been widely used for hunting the susceptibility genes for common diseases in the past years; however, the abundant information for the disease mechanism based on the GWAS data has not been fully mined. Recently, some researchers focused on the biological network and pathway analysis for the GWAS data to explore the potential disease mechanism. Since genes are the basic units for the biological network and pathway, the genetic effects from all or part of single nucleotide polymorphisms (SNPs) in the genes should be integrated into genetic scores, which are so-called "gene-based association analysis". Gene-based association analysis takes into account some important factors such as genetic effects of the SNPs, the number of the SNPs in the genes and the linkage disequilibrium structure of the SNPs. In this review, we will focus on the progress, principle and application of gene-based association analysis.

Genetics of the Autoimmune Polyglandular Syndrome Type 3 Variant

Autoimmune thyroid diseases (AITD) and type 1 diabetes (T1D) are the most common autoimmune endocrine disorders. They occur frequently together in the same individual. This disease combination is denominated as autoimmune polyglandular syndrome type 3 variant (APS3v). This review aims to describe the genetic and pathological background of the syndrome. The joint susceptibility genes for AITD and T1D as well as the underlying pathogenetic mechanisms contributing to the development of autoimmunity are summarized. Family and population studies showed that the APS3v syndrome has a strong genetic background. Whole genome and candidate gene approaches identified several gene variations that are present in both AITD and T1D. Most important common disease susceptibility genes are human leucocyte antigen (chromosome 6), cytotoxic T-lymphocyte-associated antigen 4 (chromosome 2), protein tyrosine phosphatase nonreceptor type 22 (chromosome 1), forkhead box P3 (X chromosome), and the interleukin-2 receptor alpha/CD25 gene region (chromosome 10), all of which contributing to the susceptibility to APS3v. With respect to the underlying pathogenetic mechanisms, these genes are altogether involved in the immune regulation, in particular in the immunological synapse and T-cell activation. In addition to these common genes, there are further candidate genes with joint risk for AITD and T1D, in particular the v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 gene (chromosome 12) and C-type lectin domain family 16 member A gene (chromosome 16). The latter one might be involved in pathogen recognition. AITD and T1D share common susceptibility gene variants that possibly act pleiotropically as risk factors for the development of autoimmunity in APS3v. The functional consequences of the genetic variants as well as their interactions should be explored in greater detail. In particular, the functional consequences of the variants of forkhead box P3 predisposing to APS3v need to be elucidated. Finally, further large-scale genome-wide associations studies of single-nucleotide polymorphism variations capturing many thousand individual genetic profiles are warranted to identify further genes that are linked to the etiology of APS3v.

Methodological Issues in Molecular Genetic Studies of Mental Disorders

The development of methodologies for assaying genetic variation at high resolution throughout the genome has revolutionized the search for susceptibility genes for common diseases. This search, however, has been less successful in psychiatry than in other areas of medicine. It is hypothesized that the imprecision and uncertain validity of psychiatric diagnoses are major factors in this disappointing progress. Here we discuss the methodologies employed for genetic investigation of mental disorders, including phenotyping strategies, approaches to genetic mapping, and use of animal models of psychopathology.

Pharmacological effects of lipid-lowering drugs recapitulate with a larger amplitude the phenotypic effects of common variants within their target genes

A major expectation underlying the search for novel susceptibility genes for common diseases using genome-wide association studies (GWAS) is that these discoveries will lead to new drug targets. This claim has not been verified yet. Here, we tested the hypothesis that common single nucleotide polymorphisms (SNPs) within drug target genes are associated with the corresponding phenotypes, using a population-based GWAS dataset and lipid-lowering drugs as a test case. We examined the association between 36 genotyped and 193 imputed SNPs within four lipid-lowering drug target genes (HMGCR, PPARA, HM74A/GPR109A and CETP) and four non-lipid drug target genes (ACE, AGTR1, P2RY12, and ATP4B) and lipid phenotypes, blood pressure, and coronary artery disease in 5635 adult participants of the Lausanne, Switzerland, CoLaus study, genotyped using the Affymetrix 500K SNP chip technology. The phenotypes associated with SNPs within drug target genes recapitulated to a certain extent the pharmacological effects of the drug. The amplitude of the SNP effect was about 10 times smaller than the pharmacological effect of the corresponding drug. In particular, several CETP SNPs were associated with an elevation in HDL-cholesterol levels, yet a lower diastolic blood pressure, providing evidence that the blood pressure elevation induced by the CETP inhibitor torcetrapib is more likely compound specific than class specific. Pharmacological modulation of lipid-lowering drug targets recapitulates, and markedly amplifies, the phenotypic effects of common SNPs within these target genes. This data provides indirect evidence that, with certain limitations, large-scale GWAS represent a new tool for the discovery and the development of innovative drugs.

Failure to replicate the association between NRG1 and schizophrenia using Japanese large sample

Systematic linkage disequilibrium (LD) mapping of 8p12–21 in the Icelandic population identified neuregulin 1 (NRG1) as a prime candidate gene for schizophrenia. However, results of replication studies have been inconsistent, and no large sample analyses have been reported. Therefore, we designed this study with the aim of assessing this putative association between schizophrenia and NRG1 (especially HAPICE region and exon region) using a gene-based association approach in the Japanese population.This study was a two-stage association analysis with a different panel of samples, in which the significant association found in the first-set screening samples (1126 cases and 1022 controls) was further assessed in the confirmation samples (1262 cases and 1172 controls, and 166 trio samples). In the first-set scan, 60 SNPs (49 tagging SNPs from HapMap database, four SNPs from other papers, and seven SNPs detected in the mutation scan) were examined.One haplotype showed a significant association in the first-set screening samples (Global P-value=0.0244, uncorrected). However, we could not replicate this association in the following independent confirmation samples. Moreover, we could not find sufficient evidence for association of the haplotype identified as being significant in the first-set samples by imputing ungenotyped SNPs from HapMap database.These results indicate that the positionally and functionally attractive regions of NRG1 are unlikely to contribute to susceptibility to schizophrenia in the Japanese population. Moreover, the nature of our results support that two-stage analysis with large sample size is appropriate to examine the susceptibility genes for common diseases.

Interaction analysis of theCBLBandCTLA4genes in type 1 diabetes

Gene-gene interaction analyses have been suggested as a potential strategy to help identify common disease susceptibility genes. Recently, evidence of a statistical interaction between polymorphisms in two negative immunoregulatory genes, CBLB and CTLA4, has been reported in type 1 diabetes (T1D). This study, in 480 Danish families, reported an association between T1D and a synonymous coding SNP in exon 12 of the CBLB gene (rs3772534 G>A; minor allele frequency, MAF=0.24; derived relative risk, RR for G allele=1.78; P=0.046). Furthermore, evidence of a statistical interaction with the known T1D susceptibility-associated CTLA4 polymorphism rs3087243 (laboratory name CT60, G>A) was reported (P<0.0001), such that the CBLB SNP rs3772534 G allele was overtransmitted to offspring with the CTLA4 rs3087243 G/G genotype. We have, therefore, attempted to obtain additional support for this finding in both large family and case-control collections. In a primary analysis, no evidence for an association of the CBLB SNP rs3772534 with disease was found in either sample set (2162 parent-child trios, P=0.33; 3453 cases and 3655 controls, P=0.69). In the case-only statistical interaction analysis between rs3772534 and rs3087243, there was also no support for an effect (1994 T1D affected offspring, and 3215 cases, P=0.92). These data highlight the need for large, well-characterized populations, offering the possibility of obtaining additional support for initial observations owing to the low prior probability of identifying reproducible evidence of gene-gene interactions in the analysis of common disease-associated variants in human populations.

Profile of Tim Keith, Ph.D.

BioTechniquesVol. 41, No. 5 ScientistsOpen AccessProfile of Tim Keith, Ph.D.Vice President and Chief Scientific Officer, Genizon BioSciences, St. Laurent, QC, Canada and Waltham, MALynne LedermanLynne LedermanSearch for more papers by this authorPublished Online:21 May 2018https://doi.org/10.2144/000112301AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit In Love with GeneticsI've been in the biotechnology industry for more than 20 years and believe that now is the time for genetics to realize its full potential in the identification of disease genes and their translation into novel therapeutics.I have been privileged to have had the opportunity to work with some very influential people, key leaders, and top intellects in field of genetics, and to learn from them as much as possible. Much of my enthusiasm for science derives from the mentors that I have had. I fell in love with genetics during the first genetics course I ever took, at Tufts University where I did my undergraduate education, and decided at that time that's where I wanted to make a contribution to science.I received my Ph.D. at Harvard, where I worked with one of the preeminent population geneticists in the field, Richard Lewontin, and continued my postdoctoral studies with him. There were not many women at Harvard in the sciences when I began there, but by the time I completed my Ph.D. and postdoctoral fellowship, this was starting to change. I decided to transition from a pure academic focus into industry, where I could work on more practical applications. This took me from strict population genetics research using model organisms to human genetics with a real focus on human health.In 1986, I joined Collaborative Research, one of the first biotechnology organizations focused on developing the tools to make genetics a force in the future. A key mentor at Collaborative Research was Helen Donis-Keller, who was lead author on a 1987 Cell paper titled “A genetic linkage map of the human genome.” I was very fortunate to be in the right place at the right time, during an exciting period of rapid progress in human genetics, which solidified my commitment to this field.Over the past 20 years, Collaborative Research became Genome Therapeutics, and then Oscient Pharmaceuticals. During my tenure there, we forged several alliances with pharmaceutical companies to translate our disease gene discoveries into novel therapeutics. These programs were quite successful but somewhat limited in scope, because the tools needed to perform a comprehensive analysis of the genome in complex disorders were not available until very recently.As a continuation of my focus on identifying susceptibility genes for common diseases, I was enthusiastic to join Genizon BioSciences 2 years ago as their Chief Scientific Officer. Genizon utilizes a unique resource in its discovery efforts, the Quebec founder population, which descended from a few thousand French immigrants who arrived in Quebec between the early 1600s and the mid-1700s. Importantly, the population grew very rapidly in the next 300 years, but remained very homogeneous due to little intermarriage with the other residents of Quebec. As a result, this population is excellent for genetic studies, especially for complex diseases.Genizon is performing discovery research in more than 20 disease areas and has more than 36,000 DNA samples in its biobank. We've recently conducted genome-wide association studies in seven of our disease programs. Key to the success of these studies has been the unique resource of the Quebec founder population. I believe that we were the first company to complete a genome-wide association study for a complex disease and have partnered the results of this study on Crohn's disease with Genentech, which will be translating our studies into the development of novel therapeutics and companion diagnostics.While there were a lot of promises delivered by researchers in human genetics 5 to 10 years ago regarding the identification of disease genes to improve therapeutics and much progress was made, the scope of what we were able to do was quite limited. I am very enthusiastic about where the field is today. The completion of the human genome sequence and the HapMap project has facilitated the development of dense linkage disequilibrium-based genetic maps. These, combined with high-throughput low cost genotyping technology, have enabled cost-effective and comprehensive genome-wide studies to identify susceptibility genes for complex human diseases. There are still challenges to face in managing and analyzing vast amounts of data and selecting the best targets for therapeutic development, but we now have the tools and expertise to be successful.FiguresReferencesRelatedDetails Vol. 41, No. 5 Follow us on social media for the latest updates Metrics History Published online 21 May 2018 Published in print November 2006 Information© 2006 Author(s)PDF download

Asthma genetics 2006: the long and winding road to gene discovery

Asthma and atopy are complex phenotypes that are influenced by both genetic and environmental factors. A review of nearly 500 papers on disease association studies identified 25 genes that have been associated with an asthma or atopy phenotype in six or more populations. An additional 54 genes have been associated in 2-5 populations. Here, we discuss the methods that have been used to identify susceptibility genes for common diseases and overview the status of asthma genetic research. Finally, current challenges and future directions are discussed.

A sparse marker extension tree algorithm for selecting the best set of haplotype tagging single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) play a central role in the identification of susceptibility genes for common diseases. Recent empirical studies on human genome have revealed block-like structures, and each block contains a set of haplotype tagging SNPs (htSNPs) that capture a large fraction of the haplotype diversity. Herein, we present an innovative sparse marker extension tree (SMET) algorithm to select optimal htSNP set(s). SMET reduces the search space considerably (compared to full enumeration strategy), and therefore improves computing efficiency. We tested this algorithm on several datasets at three different genomic scales: (1) gene-wide (NOS3, CRP, IL6 PPARA, and TNF), (2) region-wide (a Whitehead Institute inflammatory bowel disease dataset and a UK Graves' disease dataset), and (3) chromosome-wide (chromosome 22) levels. SMET offers geneticists with greater flexibilities in SNP tagging than lossless methods with adjustable haplotype diversity coverage (phi). In simulation studies, we found that (1) an initial sample size of 50 individuals (100 chromosomes) or more is needed for htSNP selection; (2) the SNP tagging strategy is considerably more efficient when the underlying block structure is taken into account; and (3) htSNP sets at 80-90% phi are more cost-effective than the lossless sets in term of relative power, relative risk ratio estimation, and genotyping efforts. Our study suggests that the novel SMET algorithm is a valuable tool for association tests.

Investigating the utility of combining Φ29 whole genome amplification and highly multiplexed single nucleotide polymorphism BeadArray™ genotyping

BackgroundSustainable DNA resources and reliable high-throughput genotyping methods are required for large-scale, long-term genetic association studies. In the genetic dissection of common disease it is now recognised that thousands of samples and hundreds of thousands of markers, mostly single nucleotide polymorphisms (SNPs), will have to be analysed. In order to achieve these aims, both an ability to boost quantities of archived DNA and to genotype at low costs are highly desirable. We have investigated Φ29 polymerase Multiple Displacement Amplification (MDA)-generated DNA product (MDA product), in combination with highly multiplexed BeadArray™ genotyping technology. As part of a large-scale BeadArray genotyping experiment we made a direct comparison of genotyping data generated from MDA product with that from genomic DNA (gDNA) templates.ResultsEighty-six MDA product and the corresponding 86 gDNA samples were genotyped at 345 SNPs and a concordance rate of 98.8% was achieved. The BeadArray sample exclusion rate, blind to sample type, was 10.5% for MDA product compared to 5.8% for gDNA.ConclusionsWe conclude that the BeadArray technology successfully produces high quality genotyping data from MDA product. The combination of these technologies improves the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples.

Gene-environment interaction: a central concept in multifactorial diseases.

Unlike the rare and severe genetic defects that cause monogenic diseases, the genetic factors that modulate the individual susceptibility to multifactorial diseases (cardiovascular diseases, cancers, diabetes, etc.) are common, functionally different, forms of genes (polymorphisms), which generally have a modest effect at an individual level but, because of their high frequency in the population, can be associated with a high attributable risk. Environmental factors can reveal or facilitate the phenotypic expression of such susceptibility genes. Indeed, in common diseases genetic effects can be considerably amplified in the presence of triggering factors. There is now accumulating evidence that most of the susceptibility genes for common diseases do not have a primary aetiological role in predisposition to disease, but rather act as response modifiers to exogenous factors such as stress, environment, disease, drug intake. A better characterisation of the interactions between environmental and genetic factors constitute a key issue in the understanding of the pathogenesis of multifactorial diseases. The present paper will review three examples of gene-environment interactions in the field of CHD.

Genome-based pharmacogenetics and the pharmaceutical industry.

Pharmacogenetic capabilities have changed markedly since The SNP Consortium made a dense single-nucleotide polymorphism (SNP) map freely available in 2001. For more than 40 years, pharmacokinetics and pharmacodynamics of drug-metabolizing molecules were the focus of practical applications. Today, it is possible to use SNP-mapping technologies to create a genetic profile of each individual that can be used to identify patterns of susceptibility genes for common diseases as well as genetic risk/efficacy factors that are related to the effects of drugs.

APPLICATIONS OF PHARMACOGENETICS TO DRUG DEVELOPMENT: THE GLAXO WELLCOME EXPERIENCE*

The Genetics Directorate was established at Glaxo Wellcome (GW)in 1997. The goals of the Directorate are to identify susceptibility genes for common diseases with large unmet therapeutic need, apply genetic methods for the targeted development of medicines so that the right medicine is developed for the right patient, assist in translating gene discoveries into target selection, and represent genetics accurately internally within GW and externally (to laypersons, the medical community, the business community, government representatives, and regulatory agencies).As part of the goal of developing the right medication for the right patient, GW has added genetic research to its clinical drug studies in every major therapeutic area. GW worked closely with international ethicists experienced in the area of genetic research to develop the process and documents that are currently in use; these undergo frequent, rigorous review in light of the changing regulatory and legal environment and the needs of clinical investigators and patients. The addition of genetic research to clinical studies was accompanied by significant education efforts within GW and for study-site personnel, ethics committees, and regulatory authorities. Feedback from all those involved is an integral part of implementing GW's genetic research and is used to fine-tune the processes and protocol and consent document templates.A recently completed review of the approval rate from ethics committees in several countries has revealed trends in EC/IRB (Ethics Committees/Institutional Review Boards) questions and concerns about genetic research. This article will focus on the lessons learned from incorporating genetic research into clinical studies at over 1500 international sites and will include summaries of the feedback from investigators, EC/IRBs, and regulatory authorities. It also will include a discussion of the potential applications of SNP (single nucleotide polymorphism) map technologies to pharmacogenetics by increasing the ability to correlate patients' genetic information with their response to medicines.

Population choice in mapping genes for complex diseases.

The difficulty of identifying susceptibility genes for common diseases has polarized geneticists' views on what disease models are appropriate and how best to proceed once high-density genome maps become available. Different disease models have different implications for using linkage or linkage-disequilibrium-based approaches for mapping complex disease genes. We argue that the choice of study population is a critical factor when designing a study, and that genetically simplified isolates are more useful than diverse continental populations under most assumptions.