Validation of reported genetic risk factors for periodontitis in a large-scale replication study

2013 ◽  
Vol 40 (6) ◽  
pp. 563-572 ◽  
Author(s):  
Arne S. Schaefer ◽  
Gregor Bochenek ◽  
Thomas Manke ◽  
Michael Nothnagel ◽  
Christian Graetz ◽  
...  
Keyword(s):  
Risk Factors ◽  
Genetic Risk ◽  
Large Scale ◽  
Genetic Risk Factors ◽  
Replication Study

scholarly journals Nonvalidation of Reported Genetic Risk Factors for Acute Coronary Syndrome in a Large-Scale Replication Study

JAMA ◽  
2007 ◽  
Vol 297 (14) ◽  
pp. 1551 ◽  
Author(s):  
Thomas M. Morgan ◽  
Harlan M. Krumholz ◽  
Richard P. Lifton ◽  
John A. Spertus
Keyword(s):  
Risk Factors ◽  
Acute Coronary Syndrome ◽  
Genetic Risk ◽  
Large Scale ◽  
Genetic Risk Factors ◽  
Replication Study ◽  
Coronary Syndrome

scholarly journals Hypothesis-free identification of modulators of genetic risk factors

2015 ◽  
Author(s):  
Daria Zhernakova ◽  
Patrick Deelen ◽  
Martijn Vermaat ◽  
Maarten van Iterson ◽  
Michiel van Galen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Genetic Risk ◽  
Regulatory Networks ◽  
Large Scale ◽  
Cell Types ◽  
Genetic Risk Factors ◽  
Type I ◽  
Cell Type ◽  
Disease Etiology ◽  
Context Dependent

Genetic risk factors often localize in non-coding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying the association of genetic risk factors with disease. More mechanistic insights can be derived from knowledge of the context, such as cell type or the activity of signaling pathways, influencing the nature and strength of eQTLs. Here, we generated peripheral blood RNA-seq data from 2,116 unrelated Dutch individuals and systematically identified these context-dependent eQTLs using a hypothesis-free strategy that does not require prior knowledge on the identity of the modifiers. Out of the 23,060 significant cis-regulated genes (false discovery rate ≤ 0.05), 2,743 genes (12%) show context-dependent eQTL effects. The majority of those were influenced by cell type composition, revealing eQTLs that are particularly strong in cell types such as CD4+ T-cells, erythrocytes, and even lowly abundant eosinophils. A set of 145 cis-eQTLs were influenced by the activity of the type I interferon signaling pathway and we identified several cis-eQTLs that are modulated by specific transcription factors that bind to the eQTL SNPs. This demonstrates that large-scale eQTL studies in unchallenged individuals can complement perturbation experiments to gain better insight in regulatory networks and their stimuli.

scholarly journals AncestryDNA COVID-19 Host Genetic Study Identifies Three Novel Loci

2020 ◽  
Author(s):  
Genevieve H.L. Roberts ◽  
Danny S. Park ◽  
Marie V. Coignet ◽  
Shannon R. McCurdy ◽  
Spencer C. Knight ◽  
...  
Keyword(s):  
Risk Factors ◽  
Genetic Risk ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Risk Factors ◽  
Human Infection ◽  
Nasopharyngeal Swab ◽  
Genome Wide Association Studies ◽  
Health Crisis ◽  
Host Genetic

AbstractHuman infection with SARS-CoV-2, the causative agent of COVID-19, leads to a remarkably diverse spectrum of outcomes, ranging from asymptomatic to fatal. Recent reports suggest that both clinical and genetic risk factors may contribute to COVID-19 susceptibility and severity. To investigate genetic risk factors, we collected over 500,000 COVID-19 survey responses between April and May 2020 with accompanying genetic data from the AncestryDNA database. We conducted sex-stratified and meta-analyzed genome-wide association studies (GWAS) for COVID-19 susceptibility (positive nasopharyngeal swab test, ncases=2,407) and severity (hospitalization, ncases=250). The severity GWAS replicated associations with severe COVID-19 near ABO and SLC6A20 (P<0.05). Furthermore, we identified three novel loci with P<5×10−8. The strongest association was near IVNS1ABP, a gene involved in influenza virus replication1, and was associated only in males. The other two novel loci harbor genes with established roles in viral replication or immunity: SRRM1 and the immunoglobulin lambda locus. We thus present new evidence that host genetic variation likely contributes to COVID-19 outcomes and demonstrate the value of large-scale, self-reported data as a mechanism to rapidly address a health crisis.

Investigations of Genetic Risk Factors in MDS and AML Using High- Density 6.0 Affymetrix Arrays

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 638-638
Author(s):  
Bartlomiej P Przychodzen ◽  
Anna Malgorzata Jankowska ◽  
Sandra P Smieszek ◽  
Sanjay Ram Mohan ◽  
Ramon V. Tiu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Genetic Risk ◽  
Large Scale ◽  
Growth Regulation ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Snp Array ◽  
Genetic Risk Factors ◽  
Genetic Alterations

Abstract Genetic predisposition to MDS and AML is likely polygenic and may involve several low penetrance alleles which in concert with exogenous factors result in highly variable presentation, not easily amenable to genetic studies. With the advent of whole genome scanning (WGS) technologies utilizing various SNP array (SNP-A) platforms, large scale investigations in various disorders have been conducted. In hematological malignancies to date no systematic disease-association studies using SNP-A have been reported, likely due to lower prevalence of these conditions and a highly variable phenotype. We have applied SNP-A to conduct the first GWS in MDS and MDS-derived AML with the goal to identify possible low prevalence genetic variants that contribute to the pathogenesis of these conditions and explain individual disease risk. We have studied 189 patients with MDS and secondary AML as well 119 internal controls using SNP-A. Affymetrix GeneChip 6.0 (924644 SNP probes covering most of the known LD blocks) is designed to capture 67%-89% of SNP variation among Caucasians. Following exclusion of SNP’s with a call rate of &lt;95%, and those with serious violation of Hardy Weinberg equilibrium, single allele X2 statistics for all autosomal markers was performed. For the purpose of this study, SNP’s with minor allele frequency (MAF) &lt;10% and p&lt;0.001 after false discovery rate correction, were selected. Top 11 polymorphisms were chosen pointing directly to 4 genes or indirectly to informative loci through LD, informative genes include e.g., LAMC2, SGCE, FRAP1 and PTPRT. Remarkably, several informative LD blocks were also identified represented by multiple markers pointing to the presence of an informative polymorphisms in the corresponding regions. For example, 5/30 markers (all p&lt;8×10−4) including, rs2477436, rs503243, rs3768593, rs4651151 and rs549191 are part of an LD block spanning NMAT2 and LAMC2 loci. The corresponding minor variant frequencies were 6.6% and 37.6% in homozygous and heterozygous constellation, respectively (controls: 0% and 21.6%). Second potential locus identified in our study consisted of 4 markers, all of them located on SGCE gene (rs1357318, rs2037496, rs4330611, rs13225971; p&lt;1.9×10−4) with frequencies of homozygous variant in patients at 0.8% and 28.9% with heterozygous variant (controls 0% and 15.2%), respectively. FRAP1 (MTOR) gene was represented by singular rs3730380 marker (p=2.7×10−6), occurring at the heterozygous frequency of 17.8% vs. allelic frequency of 0% in controls. FRAP1 is a critical downstream effector of Akt involved in cell cycle regulation and angiogenesis being central regulator in PI3K/Akt/mTOR pathway. Genetic alterations of the pathway are frequent events in preneoplastic lesions and advanced cancers. Similarly, increased frequency of minor alleles of rs6030469 in PTPRT locus was found in homozygous and heterozygous constellation at 1.4% vs. 0% and 27.3% vs. 8.5% (p=4.80 × 10-5) in patients and controls, respectively. PTPRT gene was also found to be frequently mutated in cancer and is involved in growth regulation. For example, overexpression of PTPRT may lead to reduced expression of STAT3 target genes. In sum, our study constituting the first systematic approach of WGS to identify genetic risk factors in AMS and AML, suggests that several informative loci can be selected for delineation of the causative polymorphisms.

Genetic risk factors for cerebral infarction using data from a large-scale genetic epidemiological study: the Ohasama Study

2003 ◽  
Vol 3 (3) ◽  
pp. 150-153 ◽  
Author(s):  
Tomohiro Katsuya ◽  
Ken Sugimoto ◽  
Atsushi Hozawa ◽  
Takayoshi Ohkubo ◽  
Koichi Yamamoto ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cerebral Infarction ◽  
Epidemiological Study ◽  
Genetic Risk ◽  
Large Scale ◽  
Genetic Risk Factors ◽  
Using Data

How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases

2020 ◽  
Vol 26 (1) ◽  
pp. 23-37 ◽  
Author(s):  
Jason H. Moore ◽  
Randal S. Olson ◽  
Peter Schmitt ◽  
Yong Chen ◽  
Elisabetta Manduchi
Keyword(s):  
Risk Factors ◽  
Genetic Risk ◽  
Genetic Architecture ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Real Data ◽  
Genetic Risk Factors ◽  
Human Diseases ◽  
Nucleotide Polymorphisms ◽  
A Genome

Susceptibility to common human diseases such as cancer is influenced by many genetic and environmental factors that work together in a complex manner. The state of the art is to perform a genome-wide association study (GWAS) that measures millions of single-nucleotide polymorphisms (SNPs) throughout the genome followed by a one-SNP-at-a-time statistical analysis to detect univariate associations. This approach has identified thousands of genetic risk factors for hundreds of diseases. However, the genetic risk factors detected have very small effect sizes and collectively explain very little of the overall heritability of the disease. Nonetheless, it is assumed that the genetic component of risk is due to many independent risk factors that contribute additively. The fact that many genetic risk factors with small effects can be detected is taken as evidence to support this notion. It is our working hypothesis that the genetic architecture of common diseases is partly driven by non-additive interactions. To test this hypothesis, we developed a heuristic simulation-based method for conducting experiments about the complexity of genetic architecture. We show that a genetic architecture driven by complex interactions is highly consistent with the magnitude and distribution of univariate effects seen in real data. We compare our results with measures of univariate and interaction effects from two large-scale GWASs of sporadic breast cancer and find evidence to support our hypothesis that is consistent with the results of our computational experiment.

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