Ultrafast genome-wide scan for SNP–SNP interactions in common complex disease

Prabhu, Snehit; Pe’er, Itsik

doi:10.1101/gr.137885.112

Cited by 104 publications

(106 citation statements)

References 54 publications

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“…CACNA2D4 is part of the CACN gene family responsible for encoding voltagedependent calcium channels; its deletion was implicated recently in the risk for late-onset BP (21); another study showed it to interact significantly with another calcium subunit gene, RYR2, in BP (42). These genes are closely related to CACNA1A (whose direct association was not found in the current study), one of the genes more strongly associated with the risk of BP (64).…”

Section: Ica Derived Dmnsmentioning

confidence: 85%

“…Interleukin family genes (IL3RA, IL-18, IL-12, IL-10, and IL1A/B/RA), representing a group of cytokines expressed in white blood cells, may play an important role in SZ (83,84). Although "regulation of smooth muscle contraction" was a top enriched pathway, upon examining the encompassed genes, they were primarily Ca 2+ channel/voltage regulation genes (including CACNA1C, CACNB2, and CACNA2D4), their strong interactors such as RYR2/3, and second-messenger molecules such as PKC and PLC-β1 that are crucial in smooth muscle contraction but also act as important cellular signaling molecules whose functions are linked strongly to psychiatric disorders (21,29,42).…”

Section: Ica Derived Dmnsmentioning

confidence: 99%

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Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia

Meda

Ruaño

Windemuth

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

218

159

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The brain's default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/ or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging-genetic relationships. ICA identified three DMNs. Hypoconnectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.genetics | BSNIP | architecture | molecular S chizophrenia (SZ) and psychotic bipolar disorder (PBP) are common, serious, heritable, genetically complex illnesses, sharing multiple characteristics, including risk genes and abnormalities in cognition, neural function, and brain structure (1-4). However, despite recent advances, their underlying biological mechanisms are largely undetermined and may be shared across the two diagnostic groups. Recent large-scale analyses have used various statistical informatics strategies to dissect these biological underpinnings better (5, 6). A recent study using a pathwayenrichment strategy showed that genes involved in neuronal cell adhesion, synaptic formation, and cell signaling are overrepresented in SZ and bipolar disorder (BP) (6). Another study using an informatics-based approach identified several cohesive genetic networks related to axon guidance, neuronal cell mobility, and synaptic functioning as key players in schizophrenia (5).Although risk for psychotic illnesses is driven in small part by highly penetrant, often private mutations such as copy number variants, substantial...

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Section: Ica Derived Dmnsmentioning

confidence: 85%

Section: Ica Derived Dmnsmentioning

confidence: 99%

Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia

Meda

Ruaño

Windemuth

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

218

159

View full text Add to dashboard Cite

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“…Studies such as these are able to ignore multiple-hypothesis correction issues due to their focus on known associated regions but do not have the potential to reveal novel loci. Other studies examining genomewide joint testing of all pairs of SNPs, including those with statistical interaction terms, pay such severe multiple-hypothesis correction penalties that many loci found via standard marginal approaches would not reach genome-wide significance (Prabhu and Pe'er 2012;Arkin et al 2014) and are computationally expensive, although effect methods for reducing the computational burden have been explored (Prabhu and Pe'er 2012;Wang et al 2015). Slavin and Elston (Slavin et al 2011) proposed testing all adjacent pairs and applied their approach in the Wellcome Trust Case Control Consortium (WTCCC) seven disease study.…”

mentioning

confidence: 99%

Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies

et al. 2016

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There is mounting evidence that complex human phenotypes are highly polygenic, with many loci harboring multiple causal variants, yet most genetic association studies examine each SNP in isolation. While this has led to the discovery of thousands of disease associations, discovered variants account for only a small fraction of disease heritability. Alternative multi-SNP methods have been proposed, but issues such as multiple-testing correction, sensitivity to genotyping error, and optimization for the underlying genetic architectures remain. Here we describe a local joint-testing procedure, complete with multiple-testing correction, that leverages a genetic phenomenon we call linkage masking wherein linkage disequilibrium between SNPs hides their signal under standard association methods. We show that local joint testing on the original Wellcome Trust Case Control Consortium (WTCCC) data set leads to the discovery of 22 associated loci, 5 more than the marginal approach. These loci were later found in follow-up studies containing thousands of additional individuals. We find that these loci significantly increase the heritability explained by genome-wide significant associations in the WTCCC data set. Furthermore, we show that local joint testing in a cis-expression QTL (eQTL) study of the gEUVADIS data set increases the number of genes containing significant eQTL by 10.7% over marginal analyses. Our multiplehypothesis correction and joint-testing framework are available in a python software package called Jester, available at github.com/ brielin/Jester.KEYWORDS statistical genetics; heritability; epistasis; polygenicity; joint testing G ENETIC association studies typically take a marginal approach to analysis, investigating each SNP in isolation of all other SNPs for association with a phenotype of interest. While this method has led to the discovery of thousands of loci associated with hundreds of phenotypes (Welter et al. 2014;Eicher et al. 2015), it fails to capture the additional signal available when multiple SNPs representing independent genetic signals are examined simultaneously or when SNPs are imperfectly imputed (Wood et al. 2011). Furthermore, the hidden heritability, the difference between the heritability due to genome-wide significant associations and heritability due to genotyped variants, remains substantial (Eichler et al. 2010). In this work we investigate a local joint-testing approach to analysis of genetic data sets in which pairs of variants from the same locus are examined simultaneously for association with a phenotype. The motivation for our approach comes from the mounting evidence that complex traits are highly polygenic (Visscher et al. 2012), that causal variants are not evenly distributed across the genome (Gusev et al. 2013), that known associated loci often harbor multiple causal variants (Udler et al. 2009;Fellay et al. 2010;Trynka et al. 2011;Wood et al. 2011;Liu et al. 2012;Patsopoulos et al. 2013;Pickrell 2014), and that the underlying causal variants can be in link...

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“…We then compute the difference in the average trait value between the two groups to determine whether the pair of SNPs is significantly associated with the trait. Finding an association between a trait and a pair of SNPs is called the ''pairwise association test,'' and recently, several different methods have been proposed for pairwise association tests (Evans et al, 2006;Zhang et al, 2010;Prabhu and Pe'er, 2012;Yang et al, 2009;Millstein et al, 2006;Ljungberg et al, 2004).…”

Section: Introduction Gmentioning

confidence: 99%

“…Most of these methods are only applicable to case/control data. These methods include TEAM (Zhang et al, 2010), which uses a dynamic programming approach to identify significant pairs, SIXPAC (Prabhu and Pe'er, 2012), which utilizes a novel randomization technique, BOOST (Wan et al, 2010), which utilizes a Boolean representation of data and a screening stage to filter out most nonsignificant SNP interactions, and RAPID (Brinza et al, 2010), which utilizes geometric properties of the v 2 statistic to identify significant pairs. However, none of these methods are applicable to quantitative traits.…”

Section: Introduction Gmentioning

confidence: 99%

Gene–Gene Interactions Detection Using a Two-stage Model

Wang

Sul

Snir

et al. 2015

Journal of Computational Biology

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Genome-wide association studies (GWAS) have discovered numerous loci involved in genetic traits. Virtually all studies have reported associations between individual single nucleotide polymorphisms (SNPs) and traits. However, it is likely that complex traits are influenced by interaction of multiple SNPs. One approach to detect interactions of SNPs is the brute force approach which performs a pairwise association test between a trait and each pair of SNPs. The brute force approach is often computationally infeasible because of the large number of SNPs collected in current GWAS studies. We propose a two-stage model, Threshold-based Efficient Pairwise Association Approach (TEPAA), to reduce the number of tests needed while maintaining almost identical power to the brute force approach. In the first stage, our method performs the single marker test on all SNPs and selects a subset of SNPs that achieve a certain significance threshold. In the second stage, we perform a pairwise association test between traits and pairs of the SNPs selected from the first stage. The key insight of our approach is that we derive the joint distribution between the association statistics of a single SNP and the association statistics of pairs of SNPs. This joint distribution allows us to provide guarantees that the statistical power of our approach will closely approximate the brute force approach. We applied our approach to the Northern Finland Birth Cohort data and achieved 63 times speedup while maintaining 99% of the power of the brute force approach.

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Ultrafast genome-wide scan for SNP–SNP interactions in common complex disease

Cited by 104 publications

References 54 publications

Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia

Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia

Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies

Gene–Gene Interactions Detection Using a Two-stage Model

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