scholarly journals Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping

PLoS Genetics ◽  
2015 ◽  
Vol 11 (6) ◽  
pp. e1005272 ◽  
Author(s):  
Chris Wallace ◽  
Antony J Cutler ◽  
Nikolas Pontikos ◽  
Marcin L Pekalski ◽  
Oliver S Burren ◽  
...  
2014 ◽  
Vol 36 (1) ◽  
pp. 2-10
Author(s):  
Song Qingfeng ◽  
Zhang Hongxing ◽  
Ma Yilong ◽  
Zhou Gangqiao

2015 ◽  
Author(s):  
Chris Wallace ◽  
Antony J Cutler ◽  
Nikolas Pontikos ◽  
Marcin L Pekalski ◽  
Oliver S Burren ◽  
...  

Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r2 =~ 0.3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4 + T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.


2001 ◽  
Vol 116 (5) ◽  
pp. 728-730 ◽  
Author(s):  
Francesca Capon ◽  
Sabrina Semprini ◽  
Giuseppe Novelli ◽  
Sergio Chimenti ◽  
Giuseppe Fabrizi ◽  
...  

Author(s):  
Wanson Choi ◽  
Yang Luo ◽  
Soumya Raychaudhuri ◽  
Buhm Han

Abstract Summary Fine-mapping human leukocyte antigen (HLA) genes involved in disease susceptibility to individual alleles or amino acid residues has been challenging. Using information regarding HLA alleles obtained from HLA typing, HLA imputation or HLA inference, our software expands the alleles to amino acid sequences using the most recent IMGT/HLA database and prepares a dataset suitable for fine-mapping analysis. Our software also provides useful functionalities, such as various association tests, visualization tools and nomenclature conversion. Availability and implementation https://github.com/WansonChoi/HATK.


Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 106-106
Author(s):  
Christine O’Keefe ◽  
Lukasz Gondek ◽  
Marcin Wlodarski ◽  
Judith Karp ◽  
Michael McDevitt ◽  
...  

Abstract Individual variability, including disease susceptibility, is determined by the interaction of inherited single base differences (single nucleotide polymorphisms, SNPs) and copy number variants (CNVs) of large genomic regions. A complex combination of these factors may result in a genetic background predisposing to disease. Regions of CNV account for approximately 12% of the human genome, including coding sequences and can range in size from kilobases to megabases. Recent studies have investigated the correlation between CNVs and complex conditions, including mental retardation, lupus and cardiovascular disease. While SNPs have been intensely investigated in many diseases, the influence of CNVs on disease susceptibility is only poorly understood. With the advent of high-throughput, high density array technology, global analysis of complex disease predisposition traits, including CNVs, can be performed. We have applied high-density SNP arrays (SNP-A) for the analysis of somatic chromosomal defects in various hematologic disorders. During our studies we noted a high frequency of germ-line CNVs, complicating our analysis of somatic defects. This observation lead us to the hypothesis that CNVs can themselves constitute predisposition factors to disease and chose to systematically investigate their type and frequency in myeloid disorders including aplastic anemia (AA; N=65), myelodysplastic syndrome (MDS; N=145) and primary and secondary (non-core binding factor) acute myeloid leukemia (AML; N=75). We performed whole genome scanning in patients and a cohort of healthy controls (N=79) using the Affymetrix 250K SNP array. We first identified and catalogued CNVs in controls; their frequency was compared to those reported in the Database of Genomic Variants (http://projects.tcag.ca/variation/) and found to be similar. The CNVs ranged in size from 245.6 Kb to 2.32 Mb (average 805.9 Kb) and were identified on all chromosomes except 5, 13, 16, 18 and 21. We next analyzed copy number changes in patients with myeloid disorders. Using controls (both our cohort and those in the literature) as a reference we determined the frequencies of recurrent CNVs in patients. For most of the CNVs the frequency was <10% within the individual patient groups, similar to what was seen in controls. Nonetheless, four regions (2 distinct loci in the pericentromeric region of 14q, pericentromeric 15q and a locus on 17q21.31) were identified in over 15% of samples studied. We then determined whether a distinct CNV is associated with specific disease risk. While for most CNVs the frequencies found in patients were similar to those in controls, two regions, 3q29 and 14q11.2, were more frequently encountered in patients with AML (3q29, 27/75 vs. 13/79 in controls, p=0.01; 14q11.2, 20/75 vs. 8/79 in controls, p=0.014). The region at 3q29 contains several genes and is a common breakpoint region for hematologic malignancies including MDS and AML, suggesting that this chromosomal area sensitive to physical rearrangement. The locus at 14q11.2 is a known hypervariable region, containing T cell receptor genes. In sum, in addition to SNPs, CNVs may be a part of complex genetic traits in patients with AA, MDS and AML and constitute disease predisposition factors. Beyond their potential role in disease, CNVs have to be excluded in SNP array-based analysis of somatic chromosomal lesions.


2007 ◽  
Vol 10 (6) ◽  
pp. 871-885 ◽  
Author(s):  
An Windelinckx ◽  
Robert Vlietinck ◽  
Jeroen Aerssens ◽  
Gaston Beunen ◽  
Martine A. I. Thomis

AbstractFine mapping of linkage peaks is one of the great challenges facing researchers who try to identify genes and genetic variants responsible for the variation in a certain trait or complex disease. Once the trait is linked to a certain chromosomal region, most studies use a candidate gene approach followed by a selection of polymorphisms within these genes, either based on their possibility to be functional, or based on the linkage disequilibrium between adjacent markers. For both candidate gene selection and SNP selection, several approaches have been described, and different software tools are available. However, mastering all these information sources and choosing between the different approaches can be difficult and time-consuming. Therefore, this article lists several of these in silico procedures, and the authors describe an empirical two-step fine mapping approach, in which candidate genes are prioritized using a bioinformatics approach (ENDEAVOUR), and the top genes are chosen for further SNP selection with a linkage disequilibrium based method (Tagger). The authors present the different actions that were applied within this approach on two previously identified linkage regions for muscle strength. This resulted in the selection of 331 polymorphisms located in 112 different candidate genes out of an initial set of 23,300 SNPs.


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