High-resolution melting analysis for SNP genotyping and mapping in tetraploid alfalfa (Medicago sativa L.)

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic polymorphism in plant genomes. SNP markers are valuable tools for genetic analysis of complex traits of agronomic importance, linkage and association mapping, genome-wide selection, map-based cloning, and marker-assisted selection. Current challenges for SNP genotyping in polyploid outcrossing species include multiple alleles per loci and lack of high-throughput methods suitable for variant detection. In this study, we report on a high-resolution melting (HRM) analysis system for SNP genotyping and mapping in outcrossing tetraploid genotypes. The sensitivity and utility of this technology is demonstrated by identification of the parental genotypes and segregating progeny in six alfalfa populations based on unique melting curve profiles due to differences in allelic composition at one or multiple loci. HRM using a 384-well format is a fast, consistent, and efficient approach for SNP discovery and genotyping, useful in polyploid species with uncharacterized genomes. Possible applications of this method include variation discovery, analysis of candidate genes, genotyping for comparative and association mapping, and integration of genome-wide selection in breeding programs.     

Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data

Background

High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits.

Results

We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight.

Conclusions

We developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest.

     

Disease-driven detection of differential inherited SNP modules from SNP network

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.     

Mining functional gene modules linked with rheumatoid arthritis using a SNP-SNP network

The identification of functional gene modules that are derived from integration of information from different types of networks is a powerful strategy for interpreting the etiology of complex diseases such as rheumatoid arthritis (RA). Genetic variants are known to increase the risk of developing RA. Here, a novel method, the construction of a genetic network, was used to mine functional gene modules linked with RA. A polymorphism interaction analy-sis (PIA) algorithm was used to obtain cooperating single nucleotide polymorphisms (SNPs) that contribute to RA disease. The acquired SNP pairs were used to construct a SNP-SNP network. Sub-networks defined by hub SNPs were then extracted and turned into gene modules by mapping SNPs to genes using dbSNP database. We per-formed Gene Ontology (GO) analysis on each gene module, and some GO terms enriched in the gene modules can be used to investigate clustered gene function for better understanding RA pathogenesis. This method was applied to the Genetic Analysis Workshop 15 (GAW 15) RA dataset. The results show that genes involved in func-tional gene modules, such as CD160 (rs744877) and RUNX1 (rs2051179), are especially relevant to RA, which is supported by previous reports. Furthermore, the 43 SNPs involved in the identified gene modules were found to be the best classifiers when used as variables for sample classification.     

Cuckoo search epistasis: a new method for exploring significant genetic interactions

The advent of high-throughput sequencing technology has resulted in the ability to measure millions of single-nucleotide polymorphisms (SNPs) from thousands of individuals. Although these high-dimensional data have paved the way for better understanding of the genetic architecture of common diseases, they have also given rise to challenges in developing computational methods for learning epistatic relationships among genetic markers. We propose a new method, named cuckoo search epistasis (CSE) for identifying significant epistatic interactions in population-based association studies with a case–control design. This method combines a computationally efficient Bayesian scoring function with an evolutionary-based heuristic search algorithm, and can be efficiently applied to high-dimensional genome-wide SNP data. The experimental results from synthetic data sets show that CSE outperforms existing methods including multifactorial dimensionality reduction and Bayesian epistasis association mapping. In addition, on a real genome-wide data set related to Alzheimer''s disease, CSE identified SNPs that are consistent with previously reported results, and show the utility of CSE for application to genome-wide data.     

A genome-wide screen of gene–gene interactions for rheumatoid arthritis susceptibility

The objective of the study was to identify interacting genes contributing to rheumatoid arthritis (RA) susceptibility and identify SNPs that discriminate between RA patients who were anti-cyclic citrullinated protein positive and healthy controls. We analyzed two independent cohorts from the North American Rheumatoid Arthritis Consortium. A cohort of 908 RA cases and 1,260 controls was used to discover pairwise interactions among SNPs and to identify a set of single nucleotide polymorphisms (SNPs) that predict RA status, and a second cohort of 952 cases and 1,760 controls was used to validate the findings. After adjusting for HLA-shared epitope alleles, we identified and replicated seven SNP pairs within the HLA class II locus with significant interaction effects. We failed to replicate significant pairwise interactions among non-HLA SNPs. The machine learning approach “random forest” applied to a set of SNPs selected from single-SNP and pairwise interaction tests identified 93 SNPs that distinguish RA cases from controls with 70% accuracy. HLA SNPs provide the most classification information, and inclusion of non-HLA SNPs improved classification. While specific gene–gene interactions are difficult to validate using genome-wide SNP data, a stepwise approach combining association and classification methods identifies candidate interacting SNPs that distinguish RA cases from healthy controls.     

Functional analysis of HapMap SNPs

Genome-wide association studies (GWAS) have successfully identified many genetic variants associated with complex diseases and traits. However, functional consequence of genetic variants studied in GWAS is not yet fully investigated, which would hinder the application of GWAS. We therefore performed a systematic functional analysis of HapMap SNPs, which have been most commonly used as the reference panel for GWAS. Our study highlights several characteristics of HapMap SNPs and identifies subsets of genetic variants with interesting functional implication. The results show that HapMap SNPs have good coverage within RefSeq genes, especially within known disease-related genes. On the other hand, only a small percentage of SNPs are non-synonymous SNPs while many SNPs are actually located at gene deserts. Moreover, many functionally important variants are not yet still interrogated. A redesigned SNP reference panel with additional functionally important variants would be useful to identify disease-causal variants in the future genome-wide studies.     

Selecting SNPs for association studies based on population frequencies: a novel interactive tool and its application to polygenic diseases

Common complex polygenic diseases as autoimmune diseases have not been completely understood on a molecular level. While many genes are known to be involved in the pathways responsible for the phenotype, explicit causes for the susceptibility of the disease remain to be elucidated. The susceptibility to disease is thought to be the result of genetic epistatic interactions between common polymorphic genes. This polymorphism is mostly caused by single nucleotide polymorphisms (SNPs). Human subpopulations are known to differ in the susceptibility to the diseases and generally in the distribution of single nucleotide polymorphisms. The here presented approach retrieves SNPs with the most divergent frequencies for selected human subpopulations to help defining properties for the experimental verification of SNPs within defined regions. A web-accessible program implementing this approach was evaluated for multiple sclerosis (MS), a common human polygenic disease. A link to a summary of data from "The SNP Consortium" (TSC) with sex-dependencies of SNPs is available. Associations of SNPs to genes, genetic markers and chromosomal loci are retrieved from the Ensembl project. This tool is recommended to be used in conjunction with microarray analyses or marker association studies that link genes or chromosomal loci to particular diseases.     

Fine-scale genetic mapping using independent component analysis

The aim of genetic mapping is to locate the loci responsible for specific traits such as complex diseases. These traits are normally caused by mutations at multiple loci of unknown locations and interactions. In this work, we model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process. Given this model, we propose a new fine-scale genetic mapping method based on independent component analysis. The proposed method outputs both independent associated groups of SNPs in addition to specific associated SNPs with the phenotype. It is applied to a clinical data set for the Schizophrenia disease with 368 individuals and 42 SNPs. It is also applied to a simulation study to investigate in more depth its performance. The obtained results demonstrate the novel characteristics of the proposed method compared to other genetic mapping methods. Finally, we study the robustness of the proposed method with missing genotype values and limited sample sizes.     

Identifying disease related sub-pathways for analysis of genome-wide association studies

Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.     

Pathway Analysis of GWAS Provides New Insights into Genetic Susceptibility to 3 Inflammatory Diseases

Although the introduction of genome-wide association studies (GWAS) have greatly increased the number of genes associated with common diseases, only a small proportion of the predicted genetic contribution has so far been elucidated. Studying the cumulative variation of polymorphisms in multiple genes acting in functional pathways may provide a complementary approach to the more common single SNP association approach in understanding genetic determinants of common disease. We developed a novel pathway-based method to assess the combined contribution of multiple genetic variants acting within canonical biological pathways and applied it to data from 14,000 UK individuals with 7 common diseases. We tested inflammatory pathways for association with Crohn''s disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) with 4 non-inflammatory diseases as controls. Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC). The generalisability of these predictive models was tested on an independent birth cohort from Northern Finland. Multiple canonical inflammatory pathways showed highly significant associations (p 10⁻³–10⁻²⁰) with CD, T1D and RA. Variable selection identified on average a set of 205 SNPs (149 genes) for T1D, 350 SNPs (189 genes) for RA and 493 SNPs (277 genes) for CD. The pattern of polymorphisms at these SNPS were found to be highly predictive of T1D (91% AUC) and RA (85% AUC), and weakly predictive of CD (60% AUC). The predictive ability of the T1D model (without any parameter refitting) had good predictive ability (79% AUC) in the Finnish cohort. Our analysis suggests that genetic contribution to common inflammatory diseases operates through multiple genes interacting in functional pathways.     

Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions

Background  

The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives.     

SNP-based pathway enrichment analysis for genome-wide association studies

Background  

Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs.     

Learning Gene Networks under SNP Perturbations Using eQTL Datasets

The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs) that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network identified computationally by our method under SNP perturbations is well supported by the results from experimental perturbation studies related to DNA replication stress response.     

Single-nucleotide polymorphism analysis by MALDI-TOF mass spectrometry

Single-nucleotide polymorphisms (SNPs) have great potential for use in genetic-mapping studies, which locate and characterize genes that are important in human disease and biological function. For SNPs to realize their full potential in genetic analysis, thousands of different SNP loci must be screened in a rapid, accurate and cost-effective manner. Matrix-assisted laser desorption-ionization-time-of-flight (MALDI-TOF) mass spectrometry is a promising tool for the high-throughput screening of SNPs, with future prospects for use in genetic analysis.     

Autoimmune Disease Classification by Inverse Association with SNP Alleles

With multiple genome-wide association studies (GWAS) performed across autoimmune diseases, there is a great opportunity to study the homogeneity of genetic architectures across autoimmune disease. Previous approaches have been limited in the scope of their analysis and have failed to properly incorporate the direction of allele-specific disease associations for SNPs. In this work, we refine the notion of a genetic variation profile for a given disease to capture strength of association with multiple SNPs in an allele-specific fashion. We apply this method to compare genetic variation profiles of six autoimmune diseases: multiple sclerosis (MS), ankylosing spondylitis (AS), autoimmune thyroid disease (ATD), rheumatoid arthritis (RA), Crohn''s disease (CD), and type 1 diabetes (T1D), as well as five non-autoimmune diseases. We quantify pair-wise relationships between these diseases and find two broad clusters of autoimmune disease where SNPs that make an individual susceptible to one class of autoimmune disease also protect from diseases in the other autoimmune class. We find that RA and AS form one such class, and MS and ATD another. We identify specific SNPs and genes with opposite risk profiles for these two classes. We furthermore explore individual SNPs that play an important role in defining similarities and differences between disease pairs. We present a novel, systematic, cross-platform approach to identify allele-specific relationships between disease pairs based on genetic variation as well as the individual SNPs which drive the relationships. While recognizing similarities between diseases might lead to identifying novel treatment options, detecting differences between diseases previously thought to be similar may point to key novel disease-specific genes and pathways.     

Shrunken methodology to genome-wide SNPs selection and construction of SNPs networks

Background

Recent development of high-resolution single nucleotide polymorphism (SNP) arrays allows detailed assessment of genome-wide human genome variations. There is increasing recognition of the importance of SNPs for medicine and developmental biology. However, SNP data set typically has a large number of SNPs (e.g., 400 thousand SNPs in genome-wide Parkinson disease data set) and a few hundred of samples. Conventional classification methods may not be effective when applied to such genome-wide SNP data.

Results

In this paper, we use shrunken dissimilarity measure to analyze and select relevant SNPs for classification problems. Examples of HapMap data and Parkinson disease (PD) data are given to demonstrate the effectiveness of the proposed method, and illustrate it has a potential to become a useful analysis tool for SNP data sets. We use Parkinson disease data as an example, and perform a whole genome analysis. For the 367440 SNPs with less than 1% missing percentage from all 22 chromosomes, we can select 357 SNPs from this data set. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and SNPs with significance of P < 0.01 are chosen to identify SNPs networks based on their P values. Here SNPs networks are constructed based on Gene Ontology knowledge, and therefore each SNP network plays a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to Parkinson disease.

Conclusions

Experimental results show that our approach is suitable to handle genetic variations, and provide useful knowledge in a genome-wide SNP study.

     

GCTA: a tool for genome-wide complex trait analysis

For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.