scholarly journals Automated Detection of Informative Combined Effects in Genetic Association Studies of Complex Traits

2003 ◽  
Vol 13 (8) ◽  
pp. 1952-1960
Author(s):  
Nadia Tahri-Daizadeh ◽  
David-Alexandre Tregouet ◽  
Viviane Nicaud ◽  
Nicolas Manuel ◽  
François Cambien ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Complex Traits ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Genetic Association Studies ◽  
Relevant Information ◽  
Cholesterol Concentration ◽  
Common Disease ◽  
Automated Method ◽  
Gene Variability

There is a growing body of evidence suggesting that the relationships between gene variability and common disease are more complex than initially thought and require the exploration of the whole polymorphism of candidate genes as well as several genes belonging to biological pathways. When the number of polymorphisms is relatively large and the structure of the relationships among them complex, the use of data mining tools to extract the relevant information is a necessity. Here, we propose an automated method for the detection of informative combined effects (DICE) among several polymorphisms (and nongenetic covariates) within the framework of association studies. The algorithm combines the advantages of the regressive approaches with those of data exploration tools. Importantly, DICE considers the problem of interaction between polymorphisms as an effect of interest and not as a nuisance effect. We illustrate the method with three applications on the relationship between (1) the P-selectin gene and myocardial infarction, (2) the cholesteryl ester transfer protein gene and plasma high-density-lipoprotein cholesterol concentration, and (3) genes of the renin-angiotensin-aldosterone system and myocardial infarction. The applications demonstrated that the method was able to recover results already found using other approaches, but in addition detected biologically sensible effects not previously described.

scholarly journals Serum irisin level in myocardial infarction patients with or without heart failure

2019 ◽  
Vol 97 (10) ◽  
pp. 932-938 ◽  
Author(s):  
Nashwa A. Abd El-Mottaleb ◽  
Heba M. Galal ◽  
Khaled M. El Maghraby ◽  
Aml I. Gadallah
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Troponin I ◽  
Density Lipoprotein ◽  
Left Ventricular ◽  
Lipoprotein Cholesterol ◽  
Cholesterol Concentration ◽  
Left Ventricular Ejection ◽  
Cardiac Markers ◽  
Tnf Α

This study aimed to assess serum irisin level in myocardial infarction (MI) with or without heart failure (HF) and the possible relation between irisin and cardiac markers, tumor necrosis factor-α (TNF-α) and lipid profile. Eighty-six subjects were included (33 patients had MI, 33 patients had MI with HF, and 20 controls). Body mass index (BMI), waist/hip ratio (WHR), systolic and diastolic blood pressure (SBP and DBP), heart rate, and left ventricular ejection fraction (LVEF) were measured. Blood samples were withdrawn on admission for measuring irisin, cardiac markers, TNF-α, total cholesterol (TC), triglycerides (TGs), low-density lipoprotein-cholesterol concentration (LDL-C), and high-density lipoprotein-cholesterol concentration (HDL-C). Patients with MI and HF had reduced serum irisin, LVEF, and HDL-C and higher levels of BMI, WHR, SBP, DBP, troponin-I, creatine kinase-MB (CK-MB), TNF-α, TC, TGs, and LDL-C compared with control. Negative correlations were observed between irisin and BMI, WHR, SBP, DBP, troponin-I, CK-MB, TNF-α, TC, TGs, and LDL-C. However, positive association was noticed between irisin and LVEF and HDL-C. Irisin might be a useful biomarker in diagnosis of MI with or without HF. It could have anti-inflammatory and hypolipidemic effects. Further studies are needed to elucidate the role of irisin as a promising prophylactic or therapeutic agent in cardiovascular diseases.

scholarly journals Recent advances in genetic predisposition to clinical acute lung injury

2009 ◽  
Vol 296 (5) ◽  
pp. L713-L725 ◽  
Author(s):  
Li Gao ◽  
Kathleen C. Barnes
Keyword(s):  
Acute Lung Injury ◽  
Lung Injury ◽  
Candidate Genes ◽  
Genetic Variants ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Special Focus ◽  
Disease Manifestation ◽  
The Impact

It has been well established that acute lung injury (ALI), and the more severe presentation of acute respiratory distress syndrome (ARDS), constitute complex traits characterized by a multigenic and multifactorial etiology. Identification and validation of genetic variants contributing to disease susceptibility and severity has been hampered by the profound heterogeneity of the clinical phenotype and the role of environmental factors, which includes treatment, on outcome. The critical nature of ALI and ARDS, compounded by the impact of phenotypic heterogeneity, has rendered the amassing of sufficiently powered studies especially challenging. Nevertheless, progress has been made in the identification of genetic variants in select candidate genes, which has enhanced our understanding of the specific pathways involved in disease manifestation. Identification of novel candidate genes for which genetic association studies have confirmed a role in disease has been greatly aided by the powerful tool of high-throughput expression profiling. This article will review these studies to date, summarizing candidate genes associated with ALI and ARDS, acknowledging those that have been replicated in independent populations, with a special focus on the specific pathways for which candidate genes identified so far can be clustered.

Parameters for reliable results in genetic association studies in common disease

10.1038/ng825 ◽  
2002 ◽  
Vol 30 (2) ◽  
pp. 149-150 ◽  
Author(s):  
Ingrid Dahlman ◽  
Iain A. Eaves ◽  
Roman Kosoy ◽  
V. Anne Morrison ◽  
Joanne Heward ◽  
...  
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Common Disease

scholarly journals Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

2021 ◽  
Author(s):  
Steven Gazal ◽  
Omer Weissbrod ◽  
Farhad Hormozdiari ◽  
Kushal Dey ◽  
Joseph Nasser ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Common Disease ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Functional Interpretation ◽  
Genome Wide

Although genome-wide association studies (GWAS) have identified thousands of disease-associated common SNPs, these SNPs generally do not implicate the underlying target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis, but it is unclear how these strategies should be applied in the context of interpreting common disease risk variants. We developed a framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk, leveraging polygenic analyses of disease heritability to define and estimate their precision and recall. We applied our framework to GWAS summary statistics for 63 diseases and complex traits (average N=314K), evaluating 50 S2G strategies. Our optimal combined S2G strategy (cS2G) included 7 constituent S2G strategies (Exon, Promoter, 2 fine-mapped cis-eQTL strategies, EpiMap enhancer-gene linking, Activity-By-Contact (ABC), and Cicero), and achieved a precision of 0.75 and a recall of 0.33, more than doubling the precision and/or recall of any individual strategy; this implies that 33% of SNP-heritability can be linked to causal genes with 75% confidence. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 7,111 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. Finally, we applied cS2G to genome-wide fine-mapping results for these traits (not restricted to GWAS loci) to rank genes by the heritability linked to each gene, providing an empirical assessment of disease omnigenicity; averaging across traits, we determined that the top 200 (1%) of ranked genes explained roughly half of the heritability linked to all genes. Our results highlight the benefits of our cS2G strategy in providing functional interpretation of GWAS findings; we anticipate that precision and recall will increase further under our framework as improved functional assays lead to improved S2G strategies. 

scholarly journals Statistical analysis of genetic interactions

2010 ◽  
Vol 92 (5-6) ◽  
pp. 443-459 ◽  
Author(s):  
NENGJUN YI
Keyword(s):  
Statistical Analysis ◽  
Complex Traits ◽  
Association Studies ◽  
Computer Software ◽  
Genetic Association Studies ◽  
Genetic Interactions ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genetic And Environmental Factors

SummaryMany common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene–gene and gene–environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships between these methods are also discussed. I conclude this review by highlighting some areas of future research.

Vagaries of genetic association studies in myocardial infarction

Blood ◽  
2003 ◽  
Vol 102 (4) ◽  
pp. 1558-1560 ◽  
Author(s):  
Pier M. Mannucci ◽  
Diego Ardissino ◽  
Piera A. Merlini ◽  
Flora Peyvandi
Keyword(s):  
Myocardial Infarction ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies

scholarly journals Retrospective analysis of main and interaction effects in genetic association studies of human complex traits

BMC Genetics ◽  
2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Qihua Tan ◽  
Lene Christiansen ◽  
Charlotte Brasch-Andersen ◽  
Jing Hua Zhao ◽  
Shuxia Li ◽  
...  
Keyword(s):  
Retrospective Analysis ◽  
Genetic Association ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Interaction Effects ◽  
Human Complex

scholarly journals Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Xi Long ◽  
Hong Xue
Keyword(s):  
Human Genome ◽  
Genetic Variants ◽  
Complex Traits ◽  
Genetic Variant ◽  
Phenotypic Diversity ◽  
Association Studies ◽  
Copy Number Variations ◽  
Receptor Interaction ◽  
Common Disease ◽  
Nucleotide Polymorphisms

Abstract Background Genetic variants, underlining phenotypic diversity, are known to distribute unevenly in the human genome. A comprehensive understanding of the distributions of different genetic variants is important for insights into genetic functions and disorders. Methods Herein, a sliding-window scan of regional densities of eight kinds of germline genetic variants, including single-nucleotide-polymorphisms (SNPs) and four size-classes of copy-number-variations (CNVs) in the human genome has been performed. Results The study has identified 44,379 hotspots with high genetic-variant densities, and 1135 hotspot clusters comprising more than one type of hotspots, accounting for 3.1% and 0.2% of the genome respectively. The hotspots and clusters are found to co-localize with different functional genomic features, as exemplified by the associations of hotspots of middle-size CNVs with histone-modification sites, work with balancing and positive selections to meet the need for diversity in immune proteins, and facilitate the development of sensory-perception and neuroactive ligand-receptor interaction pathways in the function-sparse late-replicating genomic sequences. Genetic variants of different lengths co-localize with retrotransposons of different ages on a “long-with-young” and “short-with-all” basis. Hotspots and clusters are highly associated with tumor suppressor genes and oncogenes (p < 10−10), and enriched with somatic tumor CNVs and the trait- and disease-associated SNPs identified by genome-wise association studies, exceeding tenfold enrichment in clusters comprising SNPs and extra-long CNVs. Conclusions In conclusion, the genetic-variant hotspots and clusters represent two-edged swords that spearhead both positive and negative genomic changes. Their strong associations with complex traits and diseases also open up a potential “Common Disease-Hotspot Variant” approach to the missing heritability problem.

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