Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs

Genome-wide association studies identify loci controlling specialized seed metabolites in Arabidopsis

Plants synthesize specialized metabolites to facilitate environmental and ecological interactions. During evolution, plants diversified in their potential to synthesize these metabolites. Quantitative differences in metabolite levels of natural Arabidopsis (Arabidopsis thaliana) accessions can be employed to unravel the genetic basis for metabolic traits using genome-wide association studies (GWAS). Here, we performed metabolic GWAS on seeds of a panel of 315 A. thaliana natural accessions, including the reference genotypes C24 and Col-0, for polar and semi-polar seed metabolites using untargeted ultra-performance liquid chromatography-mass spectrometry. As a complementary approach, we performed quantitative trait locus (QTL) mapping of near-isogenic introgression lines between C24 and Col-0 for specific seed specialized metabolites. Besides common QTL between seeds and leaves, GWAS revealed seed-specific QTL for specialized metabolites, indicating differences in the genetic architecture of seeds and leaves. In seeds, aliphatic methylsulfinylalkyl and methylthioalkyl glucosinolates associated with the ALKENYL HYDROXYALKYL PRODUCING loci (GS-ALK and GS-OHP) on chromosome 4 containing alkenyl hydroxyalkyl producing 2 (AOP2) and 3 (AOP3) or with the GS-ELONG locus on chromosome 5 containing methylthioalkyl malate synthase (MAM1) and MAM3. We detected two unknown sulfur-containing compounds that were also mapped to these loci. In GWAS, some of the annotated flavonoids (kaempferol 3-O-rhamnoside-7-O-rhamnoside, quercetin 3-O-rhamnoside-7-O-rhamnoside) were mapped to transparent testa 7 (AT5G07990), encoding a cytochrome P450 75B1 monooxygenase. Three additional mass signals corresponding to quercetin-containing flavonols were mapped to UGT78D2 (AT5G17050). The association of the loci and associating metabolic features were functionally verified in knockdown mutant lines. By performing GWAS and QTL mapping, we were able to leverage variation of natural populations and parental lines to study seed specialized metabolism. The GWAS data set generated here is a high-quality resource that can be investigated in further studies.

A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis

The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across ≈ 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value p < 5 × 10 - 8 53 ). We further partitioned the significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12x as large as that explained by the GWAS effects on average (range: 0.59 to 43.89). Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.

Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

Network Aggregation to Enhance Results Derived from Multiple Analytics

The more complex data are, the higher the number of possibilities to extract partial information from those data. These possibilities arise by adopting different analytic approaches. The heterogeneity among these approaches and in particular the heterogeneity in results they produce are challenging for follow-up studies, including replication, validation and translational studies. Furthermore, they complicate the interpretation of findings with wide-spread relevance. Here, we take the example of statistical epistasis networks derived from genome-wide association studies with single nucleotide polymorphisms as nodes. Even though we are only dealing with a single data type, the epistasis detection problem suffers from many pitfalls, such as the wide variety of analytic tools to detect them, each highlighting different aspects of epistasis and exhibiting different properties in maintaining false positive control. To reconcile different network views to the same problem, we considered 3 network aggregation methods and discussed their performance in the context of epistasis network aggregation. We furthermore applied a latent class method as best performer to real-life data on inflammatory bowel disease (IBD) and highlighted its benefits to increase our understanding about IBD underlying genetic architectures.

High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies

Genome-wide association studies (GWAS) are a common approach for systematic discovery of single nucleotide polymorphisms (SNPs) which are associated with a given disease. Univariate analysis approaches commonly employed may miss important SNP associations that only appear through multivariate analysis in complex diseases. However, multivariate SNP analysis is currently limited by its inherent computational complexity. In this work, we present a computational framework that harnesses supercomputers. Based on our results, we estimate a three-way interaction analysis on 1.1 million SNP GWAS data requiring over 5.8 years on the full "Avoca" IBM Blue Gene/Q installation at the Victorian Life Sciences Computation Initiative. This is hundreds of times faster than estimates for other CPU based methods and four times faster than runtimes estimated for GPU methods, indicating how the improvement in the level of hardware applied to interaction analysis may alter the types of analysis that can be performed. Furthermore, the same analysis would take under 3 months on the currently largest IBM Blue Gene/Q supercomputer "Sequoia" at the Lawrence Livermore National Laboratory assuming linear scaling is maintained as our results suggest. Given that the implementation used in this study can be further optimised, this runtime means it is becoming feasible to carry out exhaustive analysis of higher order interaction studies on large modern GWAS.

EPIQ-efficient detection of SNP-SNP epistatic interactions for quantitative traits

MOTIVATION

Gene-gene interactions are of potential biological and medical interest, as they can shed light on both the inheritance mechanism of a trait and on the underlying biological mechanisms. Evidence of epistatic interactions has been reported in both humans and other organisms. Unlike single-locus genome-wide association studies (GWAS), which proved efficient in detecting numerous genetic loci related with various traits, interaction-based GWAS have so far produced very few reproducible discoveries. Such studies introduce a great computational and statistical burden by necessitating a large number of hypotheses to be tested including all pairs of single nucleotide polymorphisms (SNPs). Thus, many software tools have been developed for interaction-based case-control studies, some leading to reliable discoveries. For quantitative data, on the other hand, only a handful of tools exist, and the computational burden is still substantial.

RESULTS

We present an efficient algorithm for detecting epistasis in quantitative GWAS, achieving a substantial runtime speedup by avoiding the need to exhaustively test all SNP pairs using metric embedding and random projections. Unlike previous metric embedding methods for case-control studies, we introduce a new embedding, where each SNP is mapped to two Euclidean spaces. We implemented our method in a tool named EPIQ (EPIstasis detection for Quantitative GWAS), and we show by simulations that EPIQ requires hours of processing time where other methods require days and sometimes weeks. Applying our method to a dataset from the Ludwigshafen risk and cardiovascular health study, we discovered a pair of SNPs with a near-significant interaction (P = 2.2 × 10(-13)), in only 1.5 h on 10 processors.

AVAILABILITY

https://github.com/yaarasegre/EPIQ

Discovering epistasis in large scale genetic association studies by exploiting graphics cards

Despite the enormous investments made in collecting DNA samples and generating germline variation data across thousands of individuals in modern genome-wide association studies (GWAS), progress has been frustratingly slow in explaining much of the heritability in common disease. Today's paradigm of testing independent hypotheses on each single nucleotide polymorphism (SNP) marker is unlikely to adequately reflect the complex biological processes in disease risk. Alternatively, modeling risk as an ensemble of SNPs that act in concert in a pathway, and/or interact non-additively on log risk for example, may be a more sensible way to approach gene mapping in modern studies. Implementing such analyzes genome-wide can quickly become intractable due to the fact that even modest size SNP panels on modern genotype arrays (500k markers) pose a combinatorial nightmare, require tens of billions of models to be tested for evidence of interaction. In this article, we provide an in-depth analysis of programs that have been developed to explicitly overcome these enormous computational barriers through the use of processors on graphics cards known as Graphics Processing Units (GPU). We include tutorials on GPU technology, which will convey why they are growing in appeal with today's numerical scientists. One obvious advantage is the impressive density of microprocessor cores that are available on only a single GPU. Whereas high end servers feature up to 24 Intel or AMD CPU cores, the latest GPU offerings from nVidia feature over 2600 cores. Each compute node may be outfitted with up to 4 GPU devices. Success on GPUs varies across problems. However, epistasis screens fare well due to the high degree of parallelism exposed in these problems. Papers that we review routinely report GPU speedups of over two orders of magnitude (>100x) over standard CPU implementations.

Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma

Synthetic lethal interactions in cancer hold the potential for successful combined therapies, which would avoid the difficulties of single molecule-targeted treatment. Identification of interactions that are specific for human tumors is an open problem in cancer research. This work aims at deciphering synthetic sick or lethal interactions directly from somatic alteration, expression and survival data of cancer patients. To this end, we look for pairs of genes and their alterations or expression levels that are "avoided" by tumors and "beneficial" for patients. Thus, candidates for synthetic sickness or lethality (SSL) interaction are identified as such gene pairs whose combination of states is under-represented in the data. Our main methodological contribution is a quantitative score that allows ranking of the candidate SSL interactions according to evidence found in patient survival. Applying this analysis to glioblastoma data, we collect 1,956 synthetic sick or lethal partners for 85 abundantly altered genes, most of which show extensive copy number variation across the patient cohort. We rediscover and interpret known interaction between TP53 and PLK1, as well as provide insight into the mechanism behind EGFR interacting with AKT2, but not AKT1 nor AKT3. Cox model analysis determines 274 of identified interactions as having significant impact on overall survival in glioblastoma, which is more informative than a standard survival predictor based on patient's age.

GWIS--model-free, fast and exhaustive search for epistatic interactions in case-control GWAS

Background

It has been hypothesized that multivariate analysis and systematic detection of epistatic interactions between explanatory genotyping variables may help resolve the problem of "missing heritability" currently observed in genome-wide association studies (GWAS). However, even the simplest bivariate analysis is still held back by significant statistical and computational challenges that are often addressed by reducing the set of analysed markers. Theoretically, it has been shown that combinations of loci may exist that show weak or no effects individually, but show significant (even complete) explanatory power over phenotype when combined. Reducing the set of analysed SNPs before bivariate analysis could easily omit such critical loci.

Results

We have developed an exhaustive bivariate GWAS analysis methodology that yields a manageable subset of candidate marker pairs for subsequent analysis using other, often more computationally expensive techniques. Our model-free filtering approach is based on classification using ROC curve analysis, an alternative to much slower regression-based modelling techniques. Exhaustive analysis of studies containing approximately 450,000 SNPs and 5,000 samples requires only 2 hours using a desktop CPU or 13 minutes using a GPU (Graphics Processing Unit). We validate our methodology with analysis of simulated datasets as well as the seven Wellcome Trust Case-Control Consortium datasets that represent a wide range of real life GWAS challenges. We have identified SNP pairs that have considerably stronger association with disease than their individual component SNPs that often show negligible effect univariately. When compared against previously reported results in the literature, our methods re-detect most significant SNP-pairs and additionally detect many pairs absent from the literature that show strong association with disease. The high overlap suggests that our fast analysis could substitute for some slower alternatives.

Conclusions

We demonstrate that the proposed methodology is robust, fast and capable of exhaustive search for epistatic interactions using a standard desktop computer. First, our implementation is significantly faster than timings for comparable algorithms reported in the literature, especially as our method allows simultaneous use of multiple statistical filters with low computing time overhead. Second, for some diseases, we have identified hundreds of SNP pairs that pass formal multiple test (Bonferroni) correction and could form a rich source of hypotheses for follow-up analysis.

Availability

A web-based version of the software used for this analysis is available at http://bioinformatics.research.nicta.com.au/gwis.

The advantages and limitations of trait analysis with GWAS: a review

Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.

Cost-effective GPU-grid for genome-wide epistasis calculations

BACKGROUND

Until recently, genotype studies were limited to the investigation of single SNP effects due to the computational burden incurred when studying pairwise interactions of SNPs. However, some genetic effects as simple as coloring (in plants and animals) cannot be ascribed to a single locus but only understood when epistasis is taken into account [1]. It is expected that such effects are also found in complex diseases where many genes contribute to the clinical outcome of affected individuals. Only recently have such problems become feasible computationally.

OBJECTIVES

The inherently parallel structure of the problem makes it a perfect candidate for massive parallelization on either grid or cloud architectures. Since we are also dealing with confidential patient data, we were not able to consider a cloud-based solution but had to find a way to process the data in-house and aimed to build a local GPU-based grid structure.

METHODS

Sequential epistatsis calculations were ported to GPU using CUDA at various levels. Parallelization on the CPU was compared to corresponding GPU counterparts with regards to performance and cost.

RESULTS

A cost-effective solution was created by combining custom-built nodes equipped with relatively inexpensive consumer-level graphics cards with highly parallel GPUs in a local grid. The GPU method outperforms current cluster-based systems on a price/performance criterion, as a single GPU shows speed performance comparable up to 200 CPU cores.

CONCLUSION

The outlined approach will work for problems that easily lend themselves to massive parallelization. Code for various tasks has been made available and ongoing development of tools will further ease the transition from sequential to parallel algorithms.

A comparison of internal model validation methods for multifactor dimensionality reduction in the case of genetic heterogeneity

BACKGROUND

Determining the genes responsible for certain human traits can be challenging when the underlying genetic model takes a complicated form such as heterogeneity (in which different genetic models can result in the same trait) or epistasis (in which genes interact with other genes and the environment). Multifactor Dimensionality Reduction (MDR) is a widely used method that effectively detects epistasis; however, it does not perform well in the presence of heterogeneity partly due to its reliance on cross-validation for internal model validation. Cross-validation allows for only one "best" model and is therefore inadequate when more than one model could cause the same trait. We hypothesize that another internal model validation method known as a three-way split will be better at detecting heterogeneity models.

RESULTS

In this study, we test this hypothesis by performing a simulation study to compare the performance of MDR to detect models of heterogeneity with the two different internal model validation techniques. We simulated a range of disease models with both main effects and gene-gene interactions with a range of effect sizes. We assessed the performance of each method using a range of definitions of power.

CONCLUSIONS

Overall, the power of MDR to detect heterogeneity models was relatively poor, especially under more conservative (strict) definitions of power. While the overall power was low, our results show that the cross-validation approach greatly outperformed the three-way split approach in detecting heterogeneity. This would motivate using cross-validation with MDR in studies where heterogeneity might be present. These results also emphasize the challenge of detecting heterogeneity models and the need for further methods development.

GLIDE: GPU-based linear regression for detection of epistasis

Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year's time to complete the same task.