Genetic Comparison of a Croatian Isolate and CEPH European Founders

Human isolates have been postulated as a good resource for the identification of QTL due to reduced genetic diversity and a more homogeneous environment. Isolates may also have increased linkage disequilibrium (LD) due to small effective population size and, either loss or increase in frequency of alleles that are rare in the general population from which they originate. Here we investigate the difference in allele and genotype frequencies, LD and homozygous tracts between an isolate—several villages from the island of Vis in Croatia—and an outbred population of European origin: the Hapmap CEPH founders. Using the HumanHap300 v1 Genotyping BeadChip, we show that our population does not differ greatly from the reference CEU outbred population despite having a slightly higher proportion of monomorphic loci, a slightly higher long-range LD, and a greater proportion of individuals with long homozygous tracts. We conclude that genotyping arrays should perform equally well in our isolate as in outbred European populations for disease mapping studies and that SNP–trait associations discovered in our well-characterized Croatian isolate should be valid in the general European population from which they descend. Genet. Epidemiol. 34: 140–145, 2010. © 2009 Wiley-Liss, Inc

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

<div><p>Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.</p></div

Model Selection Approach Suggests Causal Association between 25-Hydroxyvitamin D and Colorectal Cancer

Vitamin D deficiency has been associated with increased risk of colorectal cancer (CRC), but causal relationship has not yet been confirmed. We investigate the direction of causation between vitamin D and CRC by extending the conventional approaches to allow pleiotropic relationships and by explicitly modelling unmeasured confounders.Plasma 25-hydroxyvitamin D (25-OHD), genetic variants associated with 25-OHD and CRC, and other relevant information was available for 2645 individuals (1057 CRC cases and 1588 controls) and included in the model. We investigate whether 25-OHD is likely to be causally associated with CRC, or vice versa, by selecting the best modelling hypothesis according to Bayesian predictive scores. We examine consistency for a range of prior assumptions.Model comparison showed preference for the causal association between low 25-OHD and CRC over the reverse causal hypothesis. This was confirmed for posterior mean deviances obtained for both models (11.5 natural log units in favour of the causal model), and also for deviance information criteria (DIC) computed for a range of prior distributions. Overall, models ignoring hidden confounding or pleiotropy had significantly poorer DIC scores.Results suggest causal association between 25-OHD and colorectal cancer, and support the need for randomised clinical trials for further confirmations

Common variation near CDKN1A, POLD3 and SHROOM2 influences colorectal cancer risk

We performed a meta-analysis of five genome-wide association studies to identify common variants influencing colorectal cancer (CRC) risk comprising 8,682 cases and 9,649 controls. Replication analysis was performed in case-control sets totaling 21,096 cases and 19,555 controls. We identified three new CRC risk loci at 6p21 (rs1321311, near CDKN1A; P = 1.14 × 10(-10)), 11q13.4 (rs3824999, intronic to POLD3; P = 3.65 × 10(-10)) and Xp22.2 (rs5934683, near SHROOM2; P = 7.30 × 10(-10)) This brings the number of independent loci associated with CRC risk to 20 and provides further insight into the genetic architecture of inherited susceptibility to CRC.Swedish Research Council et al.Manuscrip

Evaluation of Allele-Specific Somatic Changes of Genome-Wide Association Study Susceptibility Alleles in Human Colorectal Cancers

Tumors frequently exhibit loss of tumor suppressor genes or allelic gains of activated oncogenes. A significant proportion of cancer susceptibility loci in the mouse show somatic losses or gains consistent with the presence of a tumor susceptibility or resistance allele. Thus, allele-specific somatic gains or losses at loci may demarcate the presence of resistance or susceptibility alleles. The goal of this study was to determine if previously mapped susceptibility loci for colorectal cancer show evidence of allele-specific somatic events in colon tumors.We performed quantitative genotyping of 16 single nucleotide polymorphisms (SNPs) showing statistically significant association with colorectal cancer in published genome-wide association studies (GWAS). We genotyped 194 paired normal and colorectal tumor DNA samples and 296 paired validation samples to investigate these SNPs for allele-specific somatic gains and losses. We combined analysis of our data with published data for seven of these SNPs.No statistically significant evidence for allele-specific somatic selection was observed for the tested polymorphisms in the discovery set. The rs6983267 variant, which has shown preferential loss of the non-risk T allele and relative gain of the risk G allele in previous studies, favored relative gain of the G allele in the combined discovery and validation samples (corrected p-value = 0.03). When we combined our data with published allele-specific imbalance data for this SNP, the G allele of rs6983267 showed statistically significant evidence of relative retention (p-value = 2.06×10(-4)).Our results suggest that the majority of variants identified as colon cancer susceptibility alleles through GWAS do not exhibit somatic allele-specific imbalance in colon tumors. Our data confirm previously published results showing allele-specific imbalance for rs6983267. These results indicate that allele-specific imbalance of cancer susceptibility alleles may not be a common phenomenon in colon cancer

Evaluation of the linkage-disequilibrium method for the estimation of effective population size when generations overlap:an empirical case

Example of calculation of long-term contributions. (DOCX 14 kb

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A)

Recurrent Coding Sequence Variation Explains only A Small Fraction of the Genetic Architecture of Colorectal Cancer

Whilst common genetic variation in many non-coding genomic regulatory regions are known to impart risk of colorectal cancer (CRC), much of the heritability of CRC remains unexplained. To examine the role of recurrent coding sequence variation in CRC aetiology, we genotyped 12,638 CRCs cases and 29,045 controls from six European populations. Single-variant analysis identified a coding variant (rs3184504) in SH2B3 (12q24) associated with CRC risk (OR = 1.08, P = 3.9 × 10-7), and novel damaging coding variants in 3 genes previously tagged by GWAS efforts; rs16888728 (8q24) in UTP23 (OR = 1.15, P = 1.4 × 10-7); rs6580742 and rs12303082 (12q13) in FAM186A (OR = 1.11, P = 1.2 × 10-

Improved genetic profiling of anthropometric traits using a big data approach

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach, the use of which has traditionally been limited due to computational requirements and lack of sufficiently large individual level genotyped cohorts, but which improve the prediction of multiple medically relevant phenotypes using the same panel of SNPs. As a proof of principle, we used a shared panel of 319,038 common SNPs with MAF > 0.05 to train the prediction models in 114,264 unrelated White-British individuals for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given the captured heritable component. For height, this represents an improvement in prediction accuracy of up to 68% (184% more phenotypic variance explained) over SNPs reported to be robustly associated with height in a previous GWAS meta-analysis of similar size. Across-population predictions in White non-British individuals were similar to those in White-British whilst those in Asian and Black individuals were informative but less accurate. We estimate that the genotyping of circa 500,000 unrelated individuals will yield predictions between 66% and 82% of the SNP-heritability captured by common variants in our array. Prediction accuracies did not improve when including rarer SNPs or when fitting multiple traits jointly in multivariate models