Search For Susceptibility Loci Research Articles

Genetics of rheumatic fever and rheumatic heart disease.

Rheumatic heart disease (RHD) is a complication of group A streptococcal infection that results from a complex interaction between the genetic make-up of the host, the infection itself and several other environmental factors, largely reflecting poverty. RHD is estimated to affect 33.4 million people and results in 10.5 million disability-adjusted life-years lost globally. The disease has long been considered heritable but still little is known about the host genetic factors that increase or reduce the risk of developing RHD. In the 1980s and 1990s, several reports linked the disease to the human leukocyte antigen (HLA) locus on chromosome 6, followed in the 2000s by reports implicating additional candidate regions elsewhere in the genome. Subsequently, the search for susceptibility loci has been reinvigorated by the use of genome-wide association studies (GWAS) through which millions of variants can be tested for association in thousands of individuals. Early findings implicate not only HLA, particularly the HLA-DQA1 to HLA-DQB1 region, but also the immunoglobulin heavy chain locus, including the IGHV4-61 gene segment, on chromosome 14. In this Review, we assess the emerging role of GWAS in assessing RHD, outlining both the advantages and disadvantages of this approach. We also highlight the potential use of large-scale, publicly available data and the value of international collaboration to facilitate comprehensive studies that produce findings that have implications for clinical practice.

Enrichment of B cell receptor signaling and epidermal growth factor receptor pathways in monoclonal gammopathy of undetermined significance: a genome-wide genetic interaction study

BackgroundRecent identification of 10 germline variants predisposing to monoclonal gammopathy of undetermined significance (MGUS) explicates genetic dependency of this asymptomatic precursor condition with multiple myeloma (MM). Yet much of genetic burden as well as functional links remain unexplained. We propose a workflow to expand the search for susceptibility loci with genome-wide interaction and for subsequent identification of genetic clusters and pathways.MethodsPolygenic interaction analysis on 243 cases/1285 controls identified 14 paired risk loci belonging to unique chromosomal bands which were then replicated in two independent sets (case only study, 82 individuals; case/control study 236 cases/ 2484 controls). Further investigation on gene-set enrichment, regulatory pathway and genetic network was carried out with stand-alone in silico tools separately for both interaction and genome-wide association study-detected risk loci.ResultsIntronic-PREX1 (20q13.13), a reported locus predisposing to MM was confirmed to have contribution to excess MGUS risk in interaction with SETBP1, a well-established candidate predisposing to myeloid malignancies. Pathway enrichment showed B cell receptor signaling pathway (P < 5.3 × 10− 3) downstream to allograft rejection pathway (P < 5.6 × 10− 4) and autoimmune thyroid disease pathway (P < 9.3 × 10− 4) as well as epidermal growth factor receptor regulation pathway (P < 2.4 × 10− 2) to be differentially regulated. Oncogene ALK and CDH2 were also identified to be moderately interacting with rs10251201 and rs16966921, two previously reported risk loci for MGUS.ConclusionsWe described novel pathways and variants potentially causal for MGUS. The methodology thus proposed to facilitate our search streamlines risk locus-based interaction, genetic network and pathway enrichment analyses.

Population-based association and gene by environment interactions in Genetic Analysis Workshop 18.

In the past decade, genome-wide association studies have been successful in identifying genetic loci that play a role in many complex diseases. Despite this, it has become clear that for many traits, investigation of single common variants does not give a complete picture of the genetic contribution to the phenotype. Therefore a number of new approaches are currently being investigated to further the search for susceptibility loci or regions. We summarize the contributions to Genetic Analysis Workshop 18 (GAW18) that concern this search using methods for population-based association analysis. Many of the members of our GAW18 working group made use of data types that have only recently become available through the use of next-generation sequencing technologies, with many focusing on the investigation of rare variants instead of or in combination with common variants. Some contributors used a haplotype-based approach, which to date has been used relatively infrequently but may become more important for analyzing rare variant association data. Others analyzed gene-gene or gene-environment interactions, where novel statistical approaches were needed to make the best use of the available information without requiring an excessive computational burden. GAW18 provided participants with the chance to make use of state-of-the-art data, statistical techniques, and technology. We report here some of the experiences and conclusions that were reached by workshop participants who analyzed the GAW18 data as a population-based association study.

Genome-Wide High-Density SNP Linkage Search for Glioma Susceptibility Loci: Results from the Gliogene Consortium

Gliomas, which generally have a poor prognosis, are the most common primary malignant brain tumors in adults. Recent genome-wide association studies have shown that inherited susceptibility plays a role in the development of glioma. Although first-degree relatives of patients exhibit a two-fold increased risk of glioma, the search for susceptibility loci in familial forms of the disease has been challenging because the disease is relatively rare, fatal, and heterogeneous, making it difficult to collect sufficient biosamples from families for statistical power. To address this challenge, the Genetic Epidemiology of Glioma International Consortium (Gliogene) was formed to collect DNA samples from families with two or more cases of histologically confirmed glioma. In this study, we present results obtained from 46 U.S. families in which multipoint linkage analyses were undertaken using nonparametric (model-free) methods. After removal of high linkage disequilibrium single-nucleotide polymorphism, we obtained a maximum nonparametric linkage score (NPL) of 3.39 (P = 0.0005) at 17q12-21.32 and the Z-score of 4.20 (P = 0.000007). To replicate our findings, we genotyped 29 independent U.S. families and obtained a maximum NPL score of 1.26 (P = 0.008) and the Z-score of 1.47 (P = 0.035). Accounting for the genetic heterogeneity using the ordered subset analysis approach, the combined analyses of 75 families resulted in a maximum NPL score of 3.81 (P = 0.00001). The genomic regions we have implicated in this study may offer novel insights into glioma susceptibility, focusing future work to identify genes that cause familial glioma.

A screening methodology based on Random Forests to improve the detection of gene–gene interactions

The search for susceptibility loci in gene-gene interactions imposes a methodological and computational challenge for statisticians because of the large dimensionality inherent to the modelling of gene-gene interactions or epistasis. In an era in which genome-wide scans have become relatively common, new powerful methods are required to handle the huge amount of feasible gene-gene interactions and to weed out false positives and negatives from these results. One solution to the dimensionality problem is to reduce data by preliminary screening of markers to select the best candidates for further analysis. Ideally, this screening step is statistically independent of the testing phase. Initially developed for small numbers of markers, the Multifactor Dimensionality Reduction (MDR) method is a nonparametric, model-free data reduction technique to associate sets of markers with optimal predictive properties to disease. In this study, we examine the power of MDR in larger data sets and compare it with other approaches that are able to identify gene-gene interactions. Under various interaction models (purely and not purely epistatic), we use a Random Forest (RF)-based prescreening method, before executing MDR, to improve its performance. We find that the power of MDR increases when noisy SNPs are first removed, by creating a collection of candidate markers with RFs. We validate our technique by extensive simulation studies and by application to asthma data from the European Committee of Respiratory Health Study II.

Genome-wide linkage analyses of hereditary prostate cancer families with colon cancer provide further evidence for a susceptibility locus on 15q11–q14

The search for susceptibility loci in hereditary prostate cancer (HPC) is challenging because of locus and disease heterogeneity. One approach to reduce disease heterogeneity is to stratify families on the basis of the occurrence of multiple cancer types. This method may increase the power for detecting susceptibility loci, including those with pleiotropic effects. We have completed a genome-wide SNP linkage analysis of 96 HPC families, each of which has one or more first-degree relatives with colon cancer (CCa), and further analyzed the subset of families with two or more CCa cases (n = 27). When only a prostate cancer (PCa) phenotype was considered to be affected, we observed suggestive evidence for linkage (LOD ≥1.86) at 15q14, 18q21 and 19q13 in all families, and at 1p32 and 15q11-q14 in families with two or more CCa cases. When both the PCa and CCa phenotypes were considered affected, suggestive evidence for linkage was observed at 11q25, 15q14 and 18q21 in all families, and at 1q31, 11q14 and 15q11-14 in families with two or more CCa cases. The strongest linkage signal was identified at 15q14 when both PCa and CCa phenotypes were considered to be affected in families with two or more CCa cases (recessive HLOD = 3.88). These results provide further support for the presence of HPC susceptibility loci on chromosomes 11q14, 15q11-q14 and 19q13 and highlight loci at 1q31, 11q, 15q11-14 and 18q21 as having possible pleiotropic effects. This study shows the benefit of using a comprehensive family cancer history to create more genetically homogenous subsets of HPC families for linkage analyses.

Recent advances in coeliac disease genetics

Around 1 in 100 individuals of European descent have coeliac disease. An immune response in the small intestine is generated to fragments of “gluten”, storage proteins found in wheat, rye...

Dense genome-wide SNP linkage scan in 301 hereditary prostate cancer families identifies multiple regions with suggestive evidence for linkage

The search for susceptibility loci in hereditary prostate cancer (HPC) has proven challenging due to genetic and disease heterogeneity. Multiple risk loci have been identified to date, however few loci have been replicated across independent linkage studies. In addition, most previous analyses have been hampered by the relatively poor information content provided by microsatellite scans. To overcome these issues, we have performed linkage analyses on members of 301 HPC families genotyped using the Illumina SNP linkage panel IVb. The information content for this panel, averaged over all pedigrees and all chromosomes, was 86% (range 83-87% over chromosomes). Analyses were also stratified on families according to disease aggressiveness, age at diagnosis and number of affected individuals to achieve more genetically homogeneous subsets. Suggestive evidence for linkage was identified at 7q21 (HLOD = 1.87), 8q22 (KCLOD = 1.88) and 15q13-q14 (HLOD = 1.99) in 289 Caucasian families, and nominal evidence for linkage was identified at 2q24 (LOD = 1.73) in 12 African American families. Analysis of more aggressive prostate cancer phenotypes provided evidence for linkage to 11q25 (KCLOD = 2.02), 15q26 (HLOD = 1.99) and 17p12 (HLOD = 2.13). Subset analyses according to age at diagnosis and number of affected individuals also identified several regions with suggestive evidence for linkage, including a KCLOD of 2.82 at 15q13-q14 in 128 Caucasian families with younger ages at diagnosis. The results presented here provide further evidence for a prostate cancer susceptibility locus on chromosome 15q and demonstrate the power of utilizing high information content SNP scans in combination with homogenous collections of large prostate cancer pedigrees.

Gene polymorphism in IgA nephropathy

SUMMARY: IgA nephropathy (IgAN) is the most common primary glomerulonephritis in the world. Since its original description in 1968, a large body of clinical, epidemiological and immunological studies of its pathogenesis has emerged. However, the basic molecular mechanisms of abnormal mesangial IgA deposition have remained obscure. In recent years, much clinical and experimental evidence has indicated the presence of genetic factors in the development and progression of IgAN. The search for susceptibility loci has centred on the major histocompatibility complex (MHC) using disease association in family and population studies. In addition, genes outside the realm of the MHC have been reported. The evidence for genetic factors in the onset and progression of IgAN is reviewed in this paper. The major loci influencing disease susceptibility include HLA‐DR4, ‐DQw7 and ‐DQβ, C4 null and T‐cell receptor Cα genes, whereas those affecting the natural course of IgAN include HLA‐DQβ, angiotensin‐converting enzyme D/D, T‐cell receptor Cβ and endothelial cell nitric oxide synthase 4a alleles.

The different approaches to the genetic analysis of autoimmune thyroid disease.

Graves' disease and Hashimoto's thyroiditis are organ-specific autoimmune disorders of multifactorial aetiology with a polygenic mode of inheritance. Familial clustering and twin studies provide evidence for a genetic predisposition. Three main approaches have been used in the search for susceptibility loci: population-based case-control studies, classical linkage analysis, and intrafamilial linkage disequilibrium. Case-control studies are a sensitive method of gene detection and the collection of subjects is resource-efficient. However, they require prior knowledge of a candidate gene and are prone to inconsistent results due to false positives that may arise from population mismatch. Linkage analysis is a powerful tool for detecting 'major' genes that does not require a candidate gene and is, therefore, a means of genome screening. This method, however, has limited power to detect genes of 'modest' effect, and the collection of sibpairs and multiple family members may be difficult. Intrafamilial linkage disequilibrium analysis is more sensitive than classical linkage analysis, requires only one affected offspring, and eliminates population mismatch. This approach has confirmed linkage disequilibrium of the HLA region with Graves' disease, previously not detected by linkage analysis. Knowledge of a candidate locus is required, however, and this method cannot, therefore, at present be used for genome screening. It is likely that a combination of all three approaches will be required to identify susceptibility loci for autoimmune thyroid disease.

1.1 Genome wide search for susceptibility loci in essential hypertension

1.1 Genome wide search for susceptibility loci in essential hypertension

A7 Search for susceptibility loci to hypertension in a region on human chromosome 20Q, the homologous region to rat chromosome 3

A7 Search for susceptibility loci to hypertension in a region on human chromosome 20Q, the homologous region to rat chromosome 3