Genome-wide Association Study Strategy Research Articles

Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.

The Power of Inbreeding: NGS-Based GWAS of Rice Reveals Convergent Evolution during Rice Domestication

Low-coverage whole-genome sequencing is an effective strategy for genome-wide association studies in humans, due to the availability of large reference panels for genotype imputation. However, it is unclear whether this strategy can be utilized in other species without reference panels. Using simulations, we show that this approach is even more relevant in inbred species such as rice (Oryza sativa L.), which are effectively haploid, allowing easy haplotype construction and imputation-based genotype calling, even without the availability of large reference panels. We sequenced 203 rice varieties with well-characterized phenotypes from the United States Department of Agriculture Rice Mini-Core Collection at an average depth of 1.5× and used the data for mapping three traits. For the first two traits, amylose content and seed length, our approach leads to direct identification of the previously identified causal SNPs in the major-effect loci. For the third trait, pericarp color, an important trait underwent selection during domestication, we identified a new major-effect locus. Although known loci can explain color variation in the varieties of two main subspecies of Asian domesticated rice, japonica and indica, the new locus identified is unique to another domesticated rice subgroup, aus, and together with existing loci, can fully explain the major variation in pericarp color in aus. Our discovery of a unique genetic basis of white pericarp in aus provides an example of convergent evolution during rice domestication and suggests that aus may have a domestication history independent of japonica and indica.

Analysis of genetic differentiation and genomic variation to reveal potential regions of importance during maize improvement.

BackgroundExploring genetic differentiation and genomic variation is important for both the utilization of heterosis and the dissection of the genetic bases of complex traits.MethodsWe integrated 1857 diverse maize accessions from America, Africa, Europe and Asia to investigatetheir genetic differentiation, genomic variation using 43,252 high-quality single-nucleotide polymorphisms(SNPs),combing GWAS and linkage analysis strategy to exploring the function of relevant genetic segments.ResultsWe uncovered many more subpopulations that recently or historically formed during the breeding process. These patterns are represented by the following lines: Mo17, GB, E28, Ye8112, HZS, Shen137, PHG39, B73, 207, A634, Oh43, Reid Yellow Dent, and the Tropical/subtropical (TS) germplasm. A total of 85 highly differentiated regions with a DEST of more than 0.2 were identified between the TS and temperate subpopulations. These regions comprised 79 % of the genetic variation, and most were significantly associated with adaptive traits. For example, the region containing the SNP tag PZE.108075114 was highly differentiated, and this region was significantly associated with flowering time (FT)-related traits, as supported by a genome-wide association study (GWAS) within the interval of FT-related quantitative trait loci (QTL). This region was also closely linked to zcn8 and vgt1, which were shown to be involved in maize adaptation. Most importantly, 197 highly differentiated regions between different subpopulation pairs were located within an FT- or plant architecture-related QTL.ConclusionsHere we reported that 700–1000 SNPs were necessary needed to robustly estimate the genetic differentiation of a naturally diverse panel. In addition, 13 subpopulations were observed in maize germplasm, 85 genetic regions with higher differentiation between TS and temperate maize germplasm, 197 highly differentiated regions between different subpopulation pairs, which contained some FT- related QTNs/QTLs/genes supported by GWAS and linkage analysis, and these regions were expected to play important roles in maize adaptation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-015-0646-7) contains supplementary material, which is available to authorized users.

Discovering susceptibility genes for allergic rhinitis and allergy using a genome-wide association study strategy.

Allergic rhinitis and allergy are complex conditions, in which both genetic and environmental factors contribute to the pathogenesis. Genome-wide association studies (GWASs) employing common single-nucleotide polymorphisms have accelerated the search for novel and interesting genes, and also confirmed the role of some previously described genes which may be involved in the cause of allergic rhinitis and allergy. The aim of this review is to provide an overview of the genetic basis of allergic rhinitis and the associated allergic phenotypes, with particular focus on GWASs. The last decade has been marked by the publication of more than 20 GWASs of allergic rhinitis and the associated allergic phenotypes. Allergic diseases and traits have been shown to share a large number of genetic susceptibility loci, of which IL33/IL1RL1, IL-13-RAD50 and C11orf30/LRRC32 appear to be important for more than two allergic phenotypes. GWASs have further reflected the genetic heterogeneity underlying allergic phenotypes. Large-scale genome-wide association strategies are underway to discover new susceptibility variants for allergic rhinitis and allergic phenotypes. Characterization of the underlying genetics provides us with an insight into the potential targets for future studies and the corresponding interventions.

Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data.

With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS). However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden. This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach. The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered. Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments. Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects. Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits.

An improved robust association test for GWAS with multiple diseases

Abstract In a previous study, we proposed a new design and analysis strategy for Genome-wide Association Studies (GWAS) with multiple diseases but no controls. We have proposed to use an overall chi-square test to test for the association between an SNP with any one of the diseases. The overall chi-square test is not sensitive to the underlying model assumption; however, it does not use the information about the trend among the relative risks of the three genotypes. In this study, we propose a new overall test based on the chi-square partition method. The overall p -value of the proposed approach can be estimated by combining independent p -values from the more powerful one-sided tests which incorporate the trend among the relative risks. Simulation study and real data application show that the proposed test is more powerful and robust.

Genome-Wide Association Study Reveals Host Genetic Factors for Liver Diseases.

A number of disease-associated genetic markers for common liver diseases have been identified using genome-wide association studies (GWASs). The GWAS strategy is based on genome-wide single-nucleotide polymorphism typing technologies, which are now commercially available, accompanied by statistical methods to identify host genetic factors that are associated with target diseases or complex genetic traits. One of the most striking features of the GWAS strategy is the ability to identify unexpected disease-associated genetic markers across the entire human genome. Here, we describe the technological aspects of the GWAS strategy with examples from actual GWAS reports related to hepatitis research, including drug response for patients with chronic hepatitis C, susceptibility to primary biliary cirrhosis, and hepatitis-B-related hepatocellular carcinoma.

Research progress on genome-wide association studies of colorectal cancer

Colorectal cancer (CRC), one of the most common malignant tumors, is caused both by environmental and genetic factors. Genetic factor plays an important role in the pathogenesis of CRC. Genome-wide association study (GWAS) is a new tool of genetic research. A series of susceptibility genes and loci of the complex diseases has been identified with GWAS strategy. In this article, the research progress on GWAS of CRC is reviewed, and the advantages and limitations of GWAS study as well as the prospective of its application are discussed.

Genome-wide association study of total number born and number born alive in pigsusing both compressed mixed linear model and Bayes model

GWAS (Genome-wide association study) strategy has been extensively used for identification of economical trait loci in livestock animals. Using Illumina's PorcineSNP60 BeadChip, a GWA study of 820 commercial pigs with reproductive traits recorded was performed. The PCA analysis showed that there was no significant population stratification. Two different statistical models Compressed Mixed Linear Model(GAPIT program package)and Bayes CPi (GenSel software) were used to implement GWAS on total number born and number born alive of the first and second parity. To compare the most significant 50 SNPs from each method, a total of 31 and 20 coincided SNPs for total number born in the first parity were identified, and there were 20 coincided SNPs for number born alive in the first parityinthe results of bothmethods. The most significant SNPs were also significant in the results of the other method. The most significantly associated regions for total number born in the first parity were located on SSC1, 2, 3, 7, 13, 16, and 18. The most significantly associated regions for number born alive in the first parity were locatedon SSC1, 3, 4, 13, and 16. There were 5 common regions significantly associated with bothtraits on SSC1, 3, 13, and 16. The most significantly associated regions forbothtotal number born and number born alive for the second parity were mainly located on six common regions on SSC7, 10, 12, 13, 14, and 16.

Design and analysis of multiple diseases genome-wide association studies without controls

In genome-wide association studies (GWAS), multiple diseases with shared controls is one of the case–control study designs. If data obtained from these studies are appropriately analyzed, this design can have several advantages such as improving statistical power in detecting associations and reducing the time and cost in the data collection process. In this paper, we propose a study design for GWAS which involves multiple diseases but without controls. We also propose corresponding statistical data analysis strategy for GWAS with multiple diseases but no controls. Through a simulation study, we show that the statistical association test with the proposed study design is more powerful than the test with single disease sharing common controls, and it has comparable power to the overall test based on the whole dataset including the controls. We also apply the proposed method to a real GWAS dataset to illustrate the methodologies and the advantages of the proposed design. Some possible limitations of this study design and testing method and their solutions are also discussed. Our findings indicate that the proposed study design and statistical analysis strategy could be more efficient than the usual case–control GWAS as well as those with shared controls.

DOCK4 and CEACAM21 as novel schizophrenia candidate genes in the Jewish population

It is well accepted that schizophrenia has a strong genetic component. Several genome-wide association studies (GWASs) of schizophrenia have been published in recent years; most of them population based with a case-control design. Nevertheless, identifying the specific genetic variants which contribute to susceptibility to the disorder remains a challenging task. A family-based GWAS strategy may be helpful in the identification of schizophrenia susceptibility genes since it is protected against population stratification, enables better accounting for genotyping errors and is more sensitive for identification of rare variants which have a very low frequency in the general population. In this project we implemented a family-based GWAS of schizophrenia in a sample of 107 Jewish-Israeli families. We found one genome-wide significant association in the intron of the DOCK4 gene (rs2074127, p value=1.134×10⁻⁷) and six additional nominally significant association signals with p<1×10⁻⁵. One of the top single nucleotide polymorphisms (p<1×10⁻⁵) which is located in the predicted intron of the CEACAM21 gene was significantly replicated in independent family-based sample of Arab-Israeli origin (rs4803480: p value=0.002; combined p value=9.61×10⁻⁸), surviving correction for multiple testing. Both DOCK4 and CEACAM21 are biologically reasonable candidate genes for schizophrenia although generalizability of the association of DOCK4 with schizophrenia should be investigated in further studies. In addition, gene-wide significant associations were found within three schizophrenia candidate genes: PGBD1, RELN and PRODH, replicating previously reported associations. By application of a family-based strategy to GWAS, our study revealed new schizophrenia susceptibility loci in the Jewish-Israeli population.

Strategies for Pathway Analysis from GWAS Data

Genome-wide association studies (GWAS) are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p values are declared statistically significant due to issues arising from multiple testing and type I error concerns. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, as they are difficult to separate from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios.

Identification of new schizophrenia susceptibility loci in an ethnically homogeneous, family‐based, Arab‐Israeli sample

While the use of population-based samples is a common strategy in genome-wide association studies (GWASs), family-based samples have considerable advantages, such as robustness against population stratification and false-positive associations, better quality control, and the possibility to check for both linkage and association. In a genome-wide linkage study of schizophrenia in Arab-Israeli families with multiple affected individuals, we previously reported significant evidence for a susceptibility locus at chromosome 6q23.2-q24.1 and suggestive evidence at chromosomes 10q22.3-26.3, 2q36.1-37.3 and 7p21.1-22.3. To identify schizophrenia susceptibility genes, we applied a family-based GWAS strategy in an enlarged, ethnically homogeneous, Arab-Israeli family sample. We performed genome-wide single nucleotide polymorphism (SNP) genotyping and single SNP transmission disequilibrium test association analysis and found genome-wide significant association (best value of P=1.22×10(-11)) for 8 SNPs within or near highly reasonable functional candidate genes for schizophrenia. Of particular interest are a group of SNPs within and flanking the transcriptional factor LRRFIP1 gene. To determine replicability of the significant associations beyond the Arab-Israeli population, we studied the association of the significant SNPs in a German case-control validation sample and found replication of associations near the UGT1 subfamily and EFHD1 genes. Applying an exploratory homozygosity mapping approach as a complementary strategy to identify schizophrenia susceptibility genes in our Arab Israeli sample, we identified 8 putative disease loci. Overall, this GWAS, which emphasizes the important contribution of family based studies, identifies promising candidate genes for schizophrenia.

Advances in genome-wide association study of coronary heart disease

As a new effective strategy for researching complex diseases, genome-wide association study (GWAS) has being developed rapidly in recent years worldwide. The world genomic study has been taken into a new stage, since a series of disease related genes or variants have been identified by GWAS strategy. Coronary heart disease (CHD) is a complex disease that is caused by both environmental and genetic factors, and has become one of the leading causes of death and disability worldwide. With the application of GWAS strategy, researchers from all over the world have identified many susceptibility loci or regions of CHD that were unable to be identified by candidate gene case-control study. The present paper reviewed the important progresses worldwide attained in GWAS of CHD in recent years. Then it is also expounded the challenges we are facing nowadays in GWAS as well as the future study direction. The information outlined in this paper provides us a valuable guidance upon further exploration into genetic mechanism of CHD.

Perils in the Use of Linkage Disequilibrium for Fine Gene Mapping: Simple Insights from Population Genetics

It is generally believed that genome-wide association (GWA) studies stand a good chance for finding susceptibility genes for common complex diseases. Although the results thus far have been somewhat promising, there are still many inherent difficulties and many initial associations do not get replicated. The common strategy in GWA studies has been that of selecting the most statistically significant single nucleotide polymorphisms with the hope that these will be very physically close to causal variants because of strong linkage disequilibrium (LD). Using simple ideas from population genetics, this commentary explains why this strategy can be misleading. It argues that there is an intrinsic problem in the way LD is currently used for fine-mapping. This is because most of the metrics that are currently used to measure LD are inadequate, as they do not take into account evolutionary variables that shape the LD structure of the human genome. Recent research on another metric, based on Malécot's model for isolation by distance, holds considerable promise for GWA studies and merits more serious consideration by geneticists.

A two-step multiple-marker strategy for genome-wide association studies

Genome-wide association studies raise study-design and analytical issues that are still being debated. Among them, stands the issue of reducing the number of markers to be genotyped without loss of efficiency in identifying trait loci, which can reduce the cost of studies and minimize the multiple testing problem. With this aim, we proposed a two-step strategy based on two analytical methods suited to examine sets of markers rather than single markers: the local score, which screens the genome to select candidate regions in Step 1, and FBAT-LC, a multiple-marker family-based association test used to obtain significance levels of regions at step 2. The performance of this strategy was evaluated on all replicates of Genetic Analysis Workshop 15 Problem 3 simulated data, using the answers to that problem. Overall, seven of the nine generated trait loci were detected in at least 87% of the replicates using a framework designed to handle either association with the disease or association with the severity of disease. This multiple-marker strategy was compared to the single-marker approach. By considering regions instead of single markers, this strategy minimizes the multiple testing problem and the number of false-positive results.

Two-Stage Designs in Case–Control Association Analysis

DNA pooling is a cost-effective approach for collecting information on marker allele frequency in genetic studies. It is often suggested as a screening tool to identify a subset of candidate markers from a very large number of markers to be followed up by more accurate and informative individual genotyping. In this article, we investigate several statistical properties and design issues related to this two-stage design, including the selection of the candidate markers for second-stage analysis, statistical power of this design, and the probability that truly disease-associated markers are ranked among the top after second-stage analysis. We have derived analytical results on the proportion of markers to be selected for second-stage analysis. For example, to detect disease-associated markers with an allele frequency difference of 0.05 between the cases and controls through an initial sample of 1000 cases and 1000 controls, our results suggest that when the measurement errors are small (0.005), approximately 3% of the markers should be selected. For the statistical power to identify disease-associated markers, we find that the measurement errors associated with DNA pooling have little effect on its power. This is in contrast to the one-stage pooling scheme where measurement errors may have large effect on statistical power. As for the probability that the disease-associated markers are ranked among the top in the second stage, we show that there is a high probability that at least one disease-associated marker is ranked among the top when the allele frequency differences between the cases and controls are not <0.05 for reasonably large sample sizes, even though the errors associated with DNA pooling in the first stage are not small. Therefore, the two-stage design with DNA pooling as a screening tool offers an efficient strategy in genomewide association studies, even when the measurement errors associated with DNA pooling are nonnegligible. For any disease model, we find that all the statistical results essentially depend on the population allele frequency and the allele frequency differences between the cases and controls at the disease-associated markers. The general conclusions hold whether the second stage uses an entirely independent sample or includes both the samples used in the first stage and an independent set of samples.