Genome-wide association interaction analysis for Alzheimer's disease

Gusareva, Elena S.; Carrasquillo, Minerva M.; Bellenguez, Céline; Cuyvers, Elise; Colon, Samuel; Graff‐Radford, Neill R.; Petersen, Ronald C.; Dickson, Dennis W.; John, Jestinah Mahachie; Bessonov, Kyrylo; Broeckhoven, Christine Van; Harold, Denise; Williams, Julie; Amouyel, Philippe; Sleegers, Kristel; Ertekin-Taner, Nilüfer; Lambert, Jean‐Charles; Steen, Kristel Van

doi:10.1016/j.neurobiolaging.2014.05.014

Cited by 59 publications

(47 citation statements)

References 25 publications

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“…Epistatic interactions have been shown to contribute to quantitative traits such as gene expression and lipid levels as well as disease risk, for example in relation to diabetes and Alzheimer’s disease (Gusareva et al 2014; Hemani et al 2014; Lopez-Rios et al 2011; Ma et al 2014). Furthermore, epistasis has been proposed as one of the mechanisms to fill the heritability gap (Wang et al 2012b; Zuk et al 2012).…”

Section: Why Does Gwas Fail To Close the Gap?mentioning

confidence: 99%

Missing heritability of common diseases and treatments outside the protein-coding exome

et al. 2014

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Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the ‘missing heritability’. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an ‘off-well’ state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common ‘normal’ variants remain hidden in the noise. Second, epistasis (dynamic gene-gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene-gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.

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Section: Why Does Gwas Fail To Close the Gap?mentioning

confidence: 99%

Missing heritability of common diseases and treatments outside the protein-coding exome

et al. 2014

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“…A novel method of incorporating the entire family structure to reduce bias was used in a family-based GWAS, which generated strong evidence for a novel association of PLXNA4 with AD [62]. The first genome-wide epistasis study for AD was performed using GWAS data with a protocol for exhaustive epistasis screening, which revealed an AD-associated interacting SNP-pair of the KHDRBS2 and the CRYL1 genes [63].…”

Section: Other Forms Of Gwass In Admentioning

confidence: 99%

An Overview of Genome-Wide Association Studies in Alzheimer’s Disease

Shen

Jia

2016

Neurosci. Bull.

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Genome-wide association studies (GWASs) have revealed a plethora of putative susceptibility genes for Alzheimer's disease (AD). With the sole exception of the APOE gene, these AD susceptibility genes have not been unequivocally validated in independent studies. No single novel functional risk genetic variant has been identified. In this review, we evaluate recent GWASs of AD, and discuss their significance, limitations, and challenges in the investigation of the genetic spectrum of AD.

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“…1, analytical blocks 2a and 2b). This threshold is smaller than that is commonly used in main effects Gwa contexts, to have a good compromise between information gain and control of computational burden [see for instance Gusareva et al (2014a) for applications]. an extensive simulation study to identify the most optimal threshold for GwaI studies is underway.…”

Section: Ld Pruningmentioning

confidence: 99%

“…Hence, multilocus genotype (MLG) regression-based association interaction analyses will generate for each SNP pair eight multilocus genotypes (while the reference category is homozygous for the major alleles at both SNPs). as a consequence, when several such MLG studies are performed, the study-specific 8 effect sizes per SNP pair can be meta-analyzed (e.g., Gusareva et al 2014a). alternatively, p values from the appropriate 4 degrees of freedom likelihood ratio tests are combined, at the risk of losing refined information on the within multilocus architecture.…”

Section: Data Pooling and Meta-analysis In The Context Of Epistasis Smentioning

confidence: 99%

Practical aspects of genome-wide association interaction analysis

Gusareva

Steen

2014

Hum Genet

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Large-scale epistasis studies can give new clues to system-level genetic mechanisms and a better understanding of the underlying biology of human complex disease traits. Though many novel methods have been proposed to carry out such studies, so far only a few of them have demonstrated replicable results. Here, we propose a minimal protocol for genome-wide association interaction (GWAI) analysis to identify gene-gene interactions from large-scale genomic data. The different steps of the developed protocol are discussed and motivated, and encompass interaction screening in a hypothesis-free and hypothesis-driven manner. In particular, we examine a wide range of aspects related to epistasis discovery in the context of complex traits in humans, hereby giving practical recommendations for data quality control, variant selection or prioritization strategies and analytic tools, replication and meta-analysis, biological validation of statistical findings and other related aspects. The minimal protocol provides guidelines and attention points for anyone involved in GWAI analysis and aims to enhance the biological relevance of GWAI findings. At the same time, the protocol improves a better assessment of strengths and weaknesses of published GWAI methodologies.

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Genome-wide association interaction analysis for Alzheimer's disease

Cited by 59 publications

References 25 publications

Missing heritability of common diseases and treatments outside the protein-coding exome

Missing heritability of common diseases and treatments outside the protein-coding exome

An Overview of Genome-Wide Association Studies in Alzheimer’s Disease

Practical aspects of genome-wide association interaction analysis

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