Detection of SNP-SNP interactions in trios of parents with schizophrenic children

Evaluation of tree-based statistical learning methods for constructing genetic risk scores

Background

Genetic risk scores (GRS) summarize genetic features such as single nucleotide polymorphisms (SNPs) in a single statistic with respect to a given trait. So far, GRS are typically built using generalized linear models or regularized extensions. However, these linear methods are usually not able to incorporate gene-gene interactions or non-linear SNP-response relationships. Tree-based statistical learning methods such as random forests and logic regression may be an alternative to such regularized-regression-based methods and are investigated in this article. Moreover, we consider modifications of random forests and logic regression for the construction of GRS.

Results

In an extensive simulation study and an application to a real data set from a German cohort study, we show that both tree-based approaches can outperform elastic net when constructing GRS for binary traits. Especially a modification of logic regression called logic bagging could induce comparatively high predictive power as measured by the area under the curve and the statistical power. Even when considering no epistatic interaction effects but only marginal genetic effects, the regularized regression method lead in most cases to inferior results.

Conclusions

When constructing GRS, we recommend taking random forests and logic bagging into account, in particular, if it can be assumed that possibly unknown epistasis between SNPs is present. To develop the best possible prediction models, extensive joint hyperparameter optimizations should be conducted.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04634-w.

GADGETS: a genetic algorithm for detecting epistasis using nuclear families

MOTIVATION

Epistasis may play an etiologic role in complex diseases, but research has been hindered because identification of interactions among sets of single nucleotide polymorphisms (SNPs) requires exploration of immense search spaces. Current approaches using nuclear families accommodate at most several hundred candidate SNPs.

RESULTS

GADGETS detects epistatic SNP-sets by applying a genetic algorithm to case-parent or case-sibling data. To allow for multiple epistatic sets, island subpopulations of SNP-sets evolve separately under selection for evident joint relevance to disease risk. The software evaluates the identified SNP-sets via permutation testing and provides graphical visualization. GADGETS correctly identified epistatic SNP-sets in realistically simulated case-parent triads with 10 000 candidate SNPs, far more SNPs than competitors can handle, and it outperformed competitors in simulations with many fewer SNPs. Applying GADGETS to family-based oral-clefting data from dbGaP identified SNP-sets with possible epistatic effects on risk.

AVAILABILITY AND IMPLEMENTATION

GADGETS is part of the epistasisGA package at https://github.com/mnodzenski/epistasisGA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Association of ADH7 Gene Polymorphism with Schizophrenia in the Han Population of Northern China: a Case-Control Study

Schizophrenia is a serious neurodevelopmental disorder. Genetics is an important factor leading to schizophrenia, but its exact role is still unclear. Many studies have focused on neurotransmitters and regulators that participate in the processes mediated by these neurotransmitters. Alcohol dehydrogenase may not only catalyze the oxidation of retinol and ethanol but also be involved in a variety of neurotransmitter metabolic pathways. Therefore, our study investigated whether ADH7 gene variations in the Chinese Han population were associated with schizophrenia. Genomic DNA was extracted from a cohort of 275 schizophrenic patients (136 men and 139 women) and 313 healthy controls (160 men and 153 women) from the Northern Han Chinese population. The Hardy-Weinberg equilibrium test and linkage disequilibrium analysis were performed. Differences in genotypes, alleles, and haplotypes between the schizophrenic and control groups were determined using the chi-square test and correlation analysis. The distribution of the CC + TT genotype of rs284787 was statistically different between the case and control groups (p = 0.026, OR = 1.448); however, the difference disappeared after Bonferroni correction. Linkage analysis indicated that rs739147, rs284787, rs3805329, rs894369, rs3805331, and rs284786 were closely linked in one block. The haplotype analysis found no association between the composed haplotypes and the occurrence of schizophrenia. Our study showed that the ADH7 gene was not associated with the risk of schizophrenia. Additional studies with larger cohorts of different ethnicities are needed to validate our findings.

Gene-gene interaction among cell adhesion genes and risk of nonsyndromic cleft lip with or without cleft palate in Chinese case-parent trios

Background

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is a common birth defect with complex etiology. One strategy for studying the genetic risk factors of NSCL/P is to consider gene–gene interaction (G × G) among gene pathways having a role in craniofacial development. The present study aimed to investigate the G × G among cell adhesion gene pathway.

Methods

We carried out an interaction analysis of eight genes involved in cell adherens junctions among 806 NSCL/P Chinese case‐parent trios originally recruited for a genome‐wide association study (GWAS). Regression‐based approach was used to test for two‐way G × G interaction, while machine learning algorithm was run for exploring both two‐way and multi‐way interaction that may affect the risk of NSCL/P.

Results

A two‐way ACTN1 × CTNNB1 interaction reached the adjusted significance level. The single nucleotide polymorphisms pair composed of rs17252114 (CTNNB1) and rs1274944 (ACTN1) yielded a p value of .0002, and this interaction was also supported by the logic regression algorithm. Higher order interactions involving ACTN1, CTNNB1, and CDH1 were picked out by logic regression, suggesting a potential role in NSCL/P risk.

Conclusion

This study suggests for the first time evidence of both two‐way and multi‐way G × G interactions among cell adhesion genes contributing to the NSCL/P risk.

DISC1 gene polymorphisms and the risk of schizophrenia in an Iranian population: A preliminary study

BACKGROUND

Schizophrenia, schizoaffective disorder, and bipolar illness are common psychological disorders with high heritability and variable phenotypes. The disrupted in schizophrenia 1 ( DISC1) gene, on chromosome 1q42, has an essential role in neurite outgrowth and cell signaling. The purpose of this study was to investigate the association of three single-nucleotide polymorphisms (SNPs; rs6675281, rs2255340, and rs2738864) with schizophrenia disorder. These three SNPs were chosen as they had been used in most of the previous studies.

METHODS

In a case-control study of Iranian population for the first time 778 blood samples were collected including, 402 schizophrenic patients and 376 healthy controls. Genomic DNA was extracted from peripheral blood using DNA extraction kit (BioFlux Co). The genotypes of rs6675281, rs2255340, and rs2738864 were detected by nested allele-specific multiplex polymersae chain reaction (PCR).

RESULTS

Our data revealed that the three SNPs are significantly associated with schizophrenia (rs2255349 C>T: confidence interval (CI), 2.115 to 3.268; P = 0.0000 OR: 2.629; rs2738864 C>T: CI, 1.538 to 2.339; P = 0.0000 OR: 1.897; rs6675281 C>T: CI, 2.788 to 4.662; P = 0.0009241 OR: 3.605). Through applying the expectation-maximization (EM) algorithm, we calculated the haplotype frequency, and finally performed haplotype analysis with Bonferroni correction and data preprocessing methods and the results showed rs66875281 to have the highest association.

DISCUSSION

Our findings primarily showed that DISC1 gene polymorphisms contribute to schizophrenia risk and have a significant association with this disorder among Iranian population. The strategy was found to be easy, rapid, specific, and consistent for the co-occurring detection of the DISC1 polymorphisms. We could finally confirm that the polymorphisms are related to schizophrenia studied in Iranian population.

Identification of interactions of binary variables associated with survival time using survivalFS

Many medical studies aim to identify factors associated with a time to an event such as survival time or time to relapse. Often, in particular, when binary variables are considered in such studies, interactions of these variables might be the actual relevant factors for predicting, e.g., the time to recurrence of a disease. Testing all possible interactions is often not possible, so that procedures such as logic regression are required that avoid such an exhaustive search. In this article, we present an ensemble method based on logic regression that can cope with the instability of the regression models generated by logic regression. This procedure called survivalFS also provides measures for quantifying the importance of the interactions forming the logic regression models on the time to an event and for the assessment of the individual variables that take the multivariate data structure into account. In this context, we introduce a new performance measure, which is an adaptation of Harrel's concordance index. The performance of survivalFS and the proposed importance measures is evaluated in a simulation study as well as in an application to genotype data from a urinary bladder cancer study. Furthermore, we compare the performance of survivalFS and its importance measures for the individual variables with the variable importance measure used in random survival forests, a popular procedure for the analysis of survival data. These applications show that survivalFS is able to identify interactions associated with time to an event and to outperform random survival forests.

HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions

Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene-gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP-SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> SPOCK1) and (LINGO2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website ( http://statgen.snu.ac.kr/software/hiscom-ggi ).

Classical HLA alleles tag SNP in families from Antioquia with type 1 diabetes mellitus

Introduction: The HLA region strongly associates with autoimmune diseases, such as type 1 diabetes. An alternative way to test classical HLA alleles is by using tag SNP. A set of tag SNP for several classical HLA alleles has been reported as associated with susceptibility or resistance to this disease in Europeans. Objective: We aimed at validating the methodology based on tag SNP focused on the inference of classical HLA alleles, and at evaluating their association with type 1 diabetes mellitus in a sample of 200 families from Antioquia. Materials and methods: We studied a sample of 200 families from Antioquia. Each family had one or two children with T1D. We genotyped 13 SNPs using tetra-primer ARMS-PCR or PCRRFLP. In addition, we tested the validity of the tag SNP reported for Europeans in 60 individuals from a population of Colombians living in Medellín (CLM) from the 1000 Genomes Project database. Statistical analyses included the Hardy-Weinberg equilibrium, the transmission disequilibrium and the linkage disequilibrium tests. Results: The linkage disequilibrium was low in reported tag SNP and classical HLA alleles in this CLM population. Association analyses revealed both risk and protection factors to develop type 1 diabetes mellitus. Appropriate tag SNPs for the CLM population were determined by using the genotype information available in the 1000 Genome Project database. Conclusions: Although linkage disequilibrium patterns in this CLM population were different from those reported in Europeans, we did find strong evidence of the role of HLA in the development of type 1 diabetes mellitus in the study population.

Gene-gene interaction for nonsyndromic cleft lip with or without cleft palate in Chilean case-parent trios

OBJECTIVE

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is a birth defect for which several genes susceptibility genes been proposed. Consequently, it has been suggested that many of these genes belong to common inter-related pathways during craniofacial development gene-gene interaction. We evaluated the presence of gene-gene interaction for single nucleotide polymorphisms within interferon regulatory factor 6 (IRF6), muscle segment homeobox 1 (MSX1), bone morphogenetic protein 4 (BMP4) and transforming growth factor 3 (TGFB3) genes in NSCL/P risk in Chilean case-parent trios.

DESIGN

From previous studies, we retrieved genotypes for 13 polymorphic variants within these four genes in 152 case-parent trios. Using the trio package (R) we evaluate the gene-gen interaction in genetic markers pairs applying a 1°-of-freedom test (1df) and a confirmatory 4°-of-freedom (4df) test for epistasis followed by both a permutation test and a Benjamini-Hochberg test for multiple comparisons adjustment.

RESULTS

We found evidence of gene-gene interaction for rs6446693 (MSX1) and rs2268625 (TGFB3) (4df p = 0.024; permutation p = 0.015, Benjamini-Hochberg p = 0.001).

CONCLUSIONS

A significant gene-gene interaction was detected for rs6446693 (MSX1) and rs2268625 (TGFB3). This finding is concordant with research in animal models showing that MSX1 and TGFB3 are expressed in common molecular pathways acting in an epistatic manner during maxillofacial development.

The GABRB3 Polymorphism and its Association with Schizophrenia

The aim of this study was to explore whether schizophrenia occurrence is associated with polymorphisms in the 5' regulatory region of GABRB3 (gamma-aminobutyric acid type A receptor beta 3, subunit gene). The study included 324 patients with schizophrenia and 327 unaffected participants; all individuals were northern Han Chinese. Genotype and haplotype frequency distributions were compared for the 2 groups by means of PCR amplification and direct sequencing of the promoter region of GABRB3. The genotype distribution among control participants was in accordance with the Hardy-Weinberg equilibrium. Five common single-nucleotide polymorphism (SNP) sites were detected in the 5' promoter region of GABRB3: rs4243768, rs7171660, rs4363842, rs4906902, and rs8179184. Only rs8179184 and rs4906902 differed significantly in frequency between controls and cases (P < 0.05); this difference remained significant when only women in each group were compared. The 2 SNP sites showed linkage disequilibrium, resulting in 2 haplotypes: T-G and C-A. The frequency of C-A was significantly higher among patients with schizophrenia than among controls. Our findings suggest that rs4906902 and rs8179184 in the 5' promoter region of GABRB3 are associated with schizophrenia. The C-A haplotype may entail an increased risk of schizophrenia, and the onset of schizophrenia may be gender-specific.

Dopamine D1 receptor (DRD1) 5' region haplotypes significantly affect transcriptional activity in vitro

The role of dopamine D1 receptor (DRD1) gene promoter polymorphisms in schizophrenia remains controversial. We aimed to characterize the polymorphisms in the promoter region because little is known about the extent of variance in this region and potential roles in gene transcription activity. In a previous case-control study, we amplified and genotyped the polymorphisms of DRD1 gene. According to its haplotype estimation, we identified eight SNPs and confirmed ten different haplotypes by cloning and sequencing the fragment spanning -1990 to +10. The promoter activity of these haplotypes was analyzed using dual luciferase assays in SH-SY5Y and HEK293 cells. Compared with the reference haplotype, the constructed haplotypes containing different variation sites could significantly alter the luciferase activity. Additionally, the prediction of the transcription factor binding sites was performed. Our examination could provide the informative reference for the role of DRD1 gene promoter in schizophrenia.

Gene-Gene Interaction Among WNT Genes for Oral Cleft in Trios

Genome-wide association studies (GWAS) for nonsyndromic cleft lip with or without cleft palate (CL/P) have identified multiple genes as important in the etiology of this common birth defect. We performed a candidate gene/pathway analysis explicitly considering gene-gene (G × G) interaction to further explore the etiology of CL/P. Animal models have shown the WNT signaling pathway plays an important role in mid-facial development, and various genes in this pathway have been associated with nonsyndromic CL/P in previous studies. We propose a combined approach to search for possible G × G interactions using machine learning and regression-based methods to test for interactions between genes in the WNT family, and between these genes and other genes identified by GWAS in case-parent trios. Using this combined approach of regression-based and machine learning methods in CL/P case-parent trios, we found robust evidence of G × G interaction between markers in WNT5B and MAFB (empiric P-values = 0.0076 among Asian trios and P-values = 0.018 among European trios). Additional evidence for epistatic interaction between markers in WNT5A, IRF6, and C1orf107 was seen among Asian trios, and markers in the 8q24 region and WNT5B among European trios.

Detecting disease variants in case-parent trio studies using the bioconductor software package trio

Case-parent trio studies are commonly employed in genetics to detect variants underlying common complex disease risk. Both commercial and freely available software suites for genetic data analysis usually contain methods for case-parent trio designs. A user might, however, experience limitations with these packages, which can include missing functionality to extend the software if a desired analysis has not been implemented, and the inability to programmatically capture all the software versions used for low-level processing and high-level inference of genomic data, a critical consideration in particular for high-throughput experiments. Here, we present a software vignette (i.e., a manual with step by step instructions and examples to demonstrate software functionality) for reproducible genome-wide analyses of case-parent trio data using the open source Bioconductor package trio. The workflow for the practitioner uses data from previous genetic trio studies to illustrate functions for marginal association tests, assessment of parent-of-origin effects, power and sample size calculations, and functions to detect gene-gene and gene-environment interactions associated with disease.

Efficient simulation of epistatic interactions in case-parent trios

Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.

Performance of random forests and logic regression methods using mini-exome sequence data

Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways.

Glutathione pathway gene variation and risk of autism spectrum disorders

Despite evidence that autism is highly heritable with estimates of 15 or more genes involved, few studies have directly examined associations of multiple gene interactions. Since inability to effectively combat oxidative stress has been suggested as a mechanism of autism, we examined genetic variation 42 genes (308 single-nucleotide polymorphisms (SNPs)) related to glutathione, the most important antioxidant in the brain, for both marginal association and multi-gene interaction among 318 case–parent trios from The Autism Genetic Resource Exchange. Models of multi-SNP interactions were estimated using the trio Logic Regression method. A three-SNP joint effect was observed for genotype combinations of SNPs in glutaredoxin, glutaredoxin 3 (GLRX3), and cystathione gamma lyase (CTH); OR = 3.78, 95% CI: 2.36, 6.04. Marginal associations were observed for four genes including two involved in the three-way interaction: CTH, alcohol dehydrogenase 5, gamma-glutamylcysteine synthetase, catalytic subunit and GLRX3. These results suggest that variation in genes involved in counterbalancing oxidative stress may contribute to autism, though replication is necessary.

Importance measures for epistatic interactions in case-parent trios

Ensemble methods (such as Bagging and Random Forests) take advantage of unstable base learners (such as decision trees) to improve predictions, and offer measures of variable importance useful for variable selection. LogicFS has been proposed as such an ensemble learner for case-control studies when interactions of single nucleotide polymorphisms (SNPs) are of particular interest. LogicFS uses bootstrap samples of the data and employs the Boolean trees derived in logic regression as base learners to create ensembles of models that allow for the quantification of the contributions of epistatic interactions to the disease risk. In this article, we propose an extension of logicFS suitable for case-parent trio data, and derive an additional importance measure that is much less influenced by linkage disequilibrium between SNPs than the measure originally used in logicFS. We illustrate the performance of the novel procedure in simulation studies and in a case study of 461 case-parent trios with autistic children.

Logic regression and its extensions

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature.