GSA-SNP: a general approach for gene set analysis of polymorphisms

Nam, Dougu; Kim, Jin; Kim, Seon‐Young; Kim, Sang Soo

doi:10.1093/nar/gkq428

Cited by 129 publications

(139 citation statements)

References 31 publications

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“…[108][109][110][111][112] These analytic algorithms seek to identify a sets of genes whose variants collectively demonstrate strong association with a trait of interest even if the component SNPs individually exhibit relatively modest or nonsignificant association. 105 To identify novel associations between established biological mechanisms and CAD, we recently performed a 2-stage pathwaybased gene-set enrichment analysis of 16 GWAS data sets for CAD that included >25 000 subjects with CAD and >66 000 controls 105 using the i-GSEA4GWAS tool 112 and the Reactome pathway database.…”

Section: Beyond the Single Snpmentioning

confidence: 99%

Genetics of Coronary Artery Disease

McPherson

Tybjærg‐Hansen

2016

Circulation Research

327

223

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Genetic factors contribute importantly to the risk of coronary artery disease (CAD), and in the past decade, there has been major progress in this area. The tools applied include genome-wide association studies encompassing >200 000 individuals complemented by bioinformatic approaches, including 1000 Genomes imputation, expression quantitative trait locus analyses, and interrogation of Encyclopedia of DNA Elements, Roadmap, and other data sets. close to 60 common SNPs (minor allele frequency>0.05) associated with CAD risk and reaching genomewide significance (P<5×10 −8 ) have been identified. Furthermore, a total of 202 independent signals in 109 loci have achieved a false discovery rate (q<0.05) and together explain 28% of the estimated heritability of CAD. These data have been used successfully to create genetic risk scores that can improve risk prediction beyond conventional risk factors and identify those individuals who will benefit most from statin therapy. Such information also has important applications in clinical medicine and drug discovery by using a Mendelian randomization approach to interrogate the causal nature of many factors found to associate with CAD risk in epidemiological studies. In contrast to genome-wide association studies, whole-exome sequencing has provided valuable information directly relevant to genes with known roles in plasma lipoprotein metabolism but has, thus far, failed to identify other rare coding variants linked to CAD. Overall, recent studies have led to a broader understanding of the genetic architecture of CAD and demonstrate that it largely derives from the cumulative effect of multiple common risk alleles individually of small effect size rather than rare variants with large effects on CAD risk. Despite this success, there has been limited progress in understanding the function of the novel loci; the majority of which are in noncoding regions of the genome. (Circ Res. 2016;118:564-578.

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Section: Beyond the Single Snpmentioning

confidence: 99%

Genetics of Coronary Artery Disease

McPherson

Tybjærg‐Hansen

2016

Circulation Research

327

223

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“…For the other GWAS for which raw genotype data were not available (the Psychiatric Genomics Consortium (PGC) traits, International League Against Epilepsy (ILAE) Consortium on Complex Epilepsies -see Supplementary Table 9, and the non-cognitive control GWAS datasets of waist-hip ratio, fasting glucose homeostasis, glucose challenge homeostasis, systolic blood pressure and diastolic blood pressure -see Supplementary Table 6) the default HapMap population was used to control for LD in the VEGAS analysis. The GWAS-enrichment statistic was calculated for a given module from the gene-based association P-values (from VEGAS) using the Z-test based bootstrapping method 73 (one-sided) where, for each network, 100,000 random gene sets of same size as the network were sampled from the list of all hippocampus expressed genes (n=9,616). P-values of enrichment for the Discovery cohort were considered significant if they passed false discovery rate correction for the number of modules tested, as indicated in each case.…”

Section: Gwas-enrichment Analysismentioning

confidence: 99%

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

et al. 2015

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2Genetic determinants of cognition are poorly characterized and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here, we used a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities including memory. Using exome sequence data from 6,871 trios, we find that M3 genes are also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease genes in the developed human brain, and provide empirical support for a convergent generegulatory network influencing cognition and neurodevelopmental disease.Cognition refers to human mental abilities such as memory, attention, processing speed, reasoning and executive function. Performance on cognitive tasks varies between individuals and is highly heritable 1 and polygenic 2,3 . However, to date, progress in identifying molecular genetic contributions to healthy human cognitive abilities has been limited 4,5 .A distinction can be made between cognitive domains such as the ability to apply acquired knowledge and learned skills (so called crystallized abilities) and fluid cognitive abilities such as the capacity to establish new memories, reason in novel situations or perform cognitive tasks accurately and quickly 6 . Notably, within individuals, performance on different measures of cognitive ability tend to be positively correlated such that people who do well in one domain, such as memory, tend to do well in other domains 7 . Seemingly disparate domains of cognitive ability also show high levels of genetic correlation in twin studies, typically in excess of 0.6 8 , and analyses using genome-wide similarity between unrelated individuals 3 (genome-wide complex trait analysis, GCTA) has also demonstrated substantial genetic correlation between diverse cognitive and learning abilities 9,10 . These studies suggest genes that influence human cognition may exert pleiotropic effects across diverse cognitive domains, such that genes regulating one cognitive ability might influence other cognitive abilities.Since impairment of cognitive function is a core clinical feature of many neurodevelopmental diseases including schizophrenia 11 , autism 12 , epilepsy 13 and intellectual disability (by definition), we sought to investigate gene-regulatory networks for human cognition and to determine their relationship to neurodevelopmental disease. An overview of our experimental design is provided in Supplementary Fig. 1. RESULTS Gene co-expression network a...

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“…Nam 60 Two step Both GSA-SNP: gene-level test based on SNP with minimum P-value (or second best), followed by GS test using either a Z-test statistic, maxmean test statistic, or GSEA.…”

Section: Softwarementioning

confidence: 99%

Gene set analysis of SNP data: benefits, challenges, and future directions

2011

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The last decade of human genetic research witnessed the completion of hundreds of genome-wide association studies (GWASs). However, the genetic variants discovered through these efforts account for only a small proportion of the heritability of complex traits. One explanation for the missing heritability is that the common analysis approach, assessing the effect of each singlenucleotide polymorphism (SNP) individually, is not well suited to the detection of small effects of multiple SNPs. Gene set analysis (GSA) is one of several approaches that may contribute to the discovery of additional genetic risk factors for complex traits. Complex phenotypes are thought to be controlled by networks of interacting biochemical and physiological pathways influenced by the products of sets of genes. By assessing the overall evidence of association of a phenotype with all measured variation in a set of genes, GSA may identify functionally relevant sets of genes corresponding to relevant biomolecular pathways, which will enable more focused studies of genetic risk factors. This approach may thus contribute to the discovery of genetic variants responsible for some of the missing heritability. With the increased use of these approaches for the secondary analysis of data from GWAS, it is important to understand the different GSA methods and their strengths and weaknesses, and consider challenges inherent in these types of analyses. This paper provides an overview of GSA, highlighting the key challenges, potential solutions, and directions for ongoing research.

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GSA-SNP: a general approach for gene set analysis of polymorphisms

Cited by 129 publications

References 31 publications

Genetics of Coronary Artery Disease

Genetics of Coronary Artery Disease

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

Gene set analysis of SNP data: benefits, challenges, and future directions

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