Functional assessment of the "two-hit" model for neurodevelopmental defects in Drosophila and X. laevis.

We previously identified a deletion on chromosome 16p12.1 that is mostly inherited and associated with multiple neurodevelopmental outcomes, where severely affected probands carried an excess of rare pathogenic variants compared to mildly affected carrier parents. We hypothesized that the 16p12.1 deletion sensitizes the genome for disease, while "second-hits" in the genetic background modulate the phenotypic trajectory. To test this model, we examined how neurodevelopmental defects conferred by knockdown of individual 16p12.1 homologs are modulated by simultaneous knockdown of homologs of "second-hit" genes in Drosophila melanogaster and Xenopus laevis. We observed that knockdown of 16p12.1 homologs affect multiple phenotypic domains, leading to delayed developmental timing, seizure susceptibility, brain alterations, abnormal dendrite and axonal morphology, and cellular proliferation defects. Compared to genes within the 16p11.2 deletion, which has higher de novo occurrence, 16p12.1 homologs were less likely to interact with each other in Drosophila models or a human brain-specific interaction network, suggesting that interactions with "second-hit" genes may confer higher impact towards neurodevelopmental phenotypes. Assessment of 212 pairwise interactions in Drosophila between 16p12.1 homologs and 76 homologs of patient-specific "second-hit" genes (such as ARID1B and CACNA1A), genes within neurodevelopmental pathways (such as PTEN and UBE3A), and transcriptomic targets (such as DSCAM and TRRAP) identified genetic interactions in 63% of the tested pairs. In 11 out of 15 families, patient-specific "second-hits" enhanced or suppressed the phenotypic effects of one or many 16p12.1 homologs in 32/96 pairwise combinations tested. In fact, homologs of SETD5 synergistically interacted with homologs of MOSMO in both Drosophila and X. laevis, leading to modified cellular and brain phenotypes, as well as axon outgrowth defects that were not observed with knockdown of either individual homolog. Our results suggest that several 16p12.1 genes sensitize the genome towards neurodevelopmental defects, and complex interactions with "second-hit" genes determine the ultimate phenotypic manifestation.

KIBRA and APOE Gene Variants Affect Brain Functional Network Connectivity in Healthy Older People.

Genetic factors play a critical role in the development of Alzheimer's disease (AD). Kidney and brain expressed protein (KIBRA) and apolipoprotein E (APOE) are involved in episodic memory performance and AD. However, the interactions between KIBRA and APOE on brain functional network connectivity (FNC) remain unknown in healthy older people. Using independent component analysis, we systematically investigated additive and epistatic interactions of KIBRA rs1707045 and APOE on FNC in 170 healthy older Chinese people of Han ethnicity. We found significant additive KIBRA-APOE interactions on brain FNC in the right medial prefrontal cortex, the posterior cingulate cortex in the default-mode network, and the dorsal anterior cingulate cortex in the salience network. We also found significant epistatic KIBRA-APOE interactions on brain FNC in the left superior frontal gyrus and left angular gyrus in default-mode network. No significant KIBRA-APOE interactions were detected in other brain resting-state networks. These findings suggest that healthy older people have additive and epistatic interactions of KIBRA and APOE gene variants, which modulate brain FNC and may partly elucidate their association with episodic memory performance and AD.

Multiple-Criteria Decision Analysis-Based Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions.

Gene-gene interactions (GGIs) are important markers for determining susceptibility to a disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting GGIs and primarily adopts the correct classification rate (CCR) to assess the quality of a GGI. However, CCR measurement alone may not successfully detect certain GGIs because of potential model preferences and disease complexities. In this study, multiple-criteria decision analysis (MCDA) based on MDR was named MCDA-MDR and proposed for detecting GGIs. MCDA facilitates MDR to simultaneously adopt multiple measures within the two-way contingency table of MDR to assess GGIs; the CCR and rule utility measure were employed. Cross-validation consistency was adopted to determine the most favorable GGIs among the Pareto sets. Simulation studies were conducted to compare the detection success rates of the MDR-only-based measure and MCDA-MDR, revealing that MCDA-MDR had superior detection success rates. The Wellcome Trust Case Control Consortium dataset was analyzed using MCDA-MDR to detect GGIs associated with coronary artery disease, and MCDA-MDR successfully detected numerous significant GGIs (p < 0.001). MCDA-MDR performance assessment revealed that the applied MCDA successfully enhanced the GGI detection success rate of the MDR-based method compared with MDR alone.

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.

Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed "dropout," which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.

Epistatic effects between pairs of the growth hormone secretagogue receptor 1a, growth hormone, growth hormone receptor, non-SMC condensin I complex, subunit G and stearoyl-CoA desaturase genes on carcass, price-related and fatty acid composition traits in Japanese Black cattle.

Growth hormone secretagogue receptor 1a (GHSR1a), growth hormone (GH), growth hormone receptor (GHR), non-SMC condensin I complex, subunit G (NCAPG) and stearoyl-CoA desaturase (SCD), are known to play important roles in growth and lipid metabolisms. Single and epistatic effects of the five genes on carcass, price-related and fatty acid (FA) composition traits were analyzed in a commercial Japanese Black cattle population of Ibaraki Prefecture. A total of 650 steers and 116 heifers for carcass and price-related traits, and 158 steers for FA composition traits were used in this study. Epistatic effects between pairs of the five genes were found in several traits. Alleles showing strain-specific differences in the five genes had significant single and epistatic effects in some traits. The data suggest that a TG-repeat polymorphism of the GHSR1a.5'UTR-(TG)n locus plays a central role in gene-gene epistatic interaction of FA composition traits in the adipose tissue of Japanese Black cattle.

A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium.

Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70,917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46,450 breast cancer cases and 42,461 controls of European origin as part of a multi-consortium project (COGS). First, SNPs were preselected based on evidence (P < 0.01) of a per-allele main effect, and all two-way combinations of those were evaluated by a per-allele (1 d.f.) test for interaction using logistic regression. Second, all 2.5 billion possible two-SNP combinations were evaluated using Boolean operation-based screening and testing, and SNP pairs with the strongest evidence of interaction (P < 10(-4)) were selected for more careful assessment by logistic regression. Under the first approach, 3277 SNPs were preselected, but an evaluation of all possible two-SNP combinations (1 d.f.) identified no interactions at P < 10(-8). Results from the second analytic approach were consistent with those from the first (P > 10(-10)). In summary, we observed little evidence of two-way SNP interactions in breast cancer susceptibility, despite the large number of SNPs with potential marginal effects considered and the very large sample size. This finding may have important implications for risk prediction, simplifying the modelling required. Further comprehensive, large-scale genome-wide interaction studies may identify novel interacting loci if the inherent logistic and computational challenges can be overcome.

Epistatic interactions of AKT1 on human medial temporal lobe biology and pharmacogenetic implications.

AKT1 controls important processes in medial temporal lobe (MTL) development and plasticity, but the impact of human genetic variation in AKT1 on these processes is not known in healthy or disease states. Here, we report that an AKT1 variant (rs1130233) previously associated with AKT1 protein expression, prefrontal function and schizophrenia, affects human MTL structure and memory function. Further, supporting AKT1's role in transducing hippocampal neuroplasticity and dopaminergic processes, we found epistasis with functional polymorphisms in BDNF and COMT--genes also implicated in MTL biology related to AKT1. Consistent with prior predictions that these biologic processes relate to schizophrenia, we found epistasis between the same AKT1, BDNF and COMT functional variants on schizophrenia risk, and pharmacogenetic interactions of AKT1 with the effects on cognition and brain volume measures by AKT1 activators in common clinical use--lithium and sodium valproate. Our findings suggest that AKT1 affects risk for schizophrenia and accompanying cognitive deficits, at least in part through specific genetic interactions related to brain neuroplasticity and development, and that these AKT1 effects may be pharmacologically modulated in patients.

Bias correction for estimated QTL effects using the penalized maximum likelihood method.

A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a QTL effect to that of a scaled inverse chi-square prior. The results of Monte Carlo simulation experiments show that the improved method increases the power from 25 to 88% in the detection of two closely linked QTL of equal size in opposite directions and from 60 to 80% in the identification of QTL with small effects (0.5% of the total phenotypic variance). We used the improved method to detect QTL responsible for the barley kernel weight trait using 145 doubled haploid lines developed in the North American Barley Genome Mapping Project. Application of the proposed method to other shrinkage estimation of QTL effects is discussed.

Bayesian epistasis association mapping via SNP imputation.

Genetic mutations may interact to increase the risk of human complex diseases. Mapping of multiple interacting disease loci in the human genome has recently shown promise in detecting genes with little main effects. The power of interaction association mapping, however, can be greatly influenced by the set of single nucleotide polymorphism (SNP) genotyped in a case-control study. Previous imputation methods only focus on imputation of individual SNPs without considering their joint distribution of possible interactions. We present a new method that simultaneously detects multilocus interaction associations and imputes missing SNPs from a full Bayesian model. Our method treats both the case-control sample and the reference data as random observations. The output of our method is the posterior probabilities of SNPs for their marginal and interacting associations with the disease. Using simulations, we show that the method produces accurate and robust imputation with little overfitting problems. We further show that, with the type I error rate maintained at a common level, SNP imputation can consistently and sometimes substantially improve the power of detecting disease interaction associations. We use a data set of inflammatory bowel disease to demonstrate the application of our method.

Nonparametric Bayesian variable selection with applications to multiple quantitative trait loci mapping with epistasis and gene-environment interaction.

The joint action of multiple genes is an important source of variation for complex traits and human diseases. However, mapping genes with epistatic effects and gene-environment interactions is a difficult problem because of relatively small sample sizes and very large parameter spaces for quantitative trait locus models that include such interactions. Here we present a nonparametric Bayesian method to map multiple quantitative trait loci (QTL) by considering epistatic and gene-environment interactions. The proposed method is not restricted to pairwise interactions among genes, as is typically done in parametric QTL analysis. Rather than modeling each main and interaction term explicitly, our nonparametric Bayesian method measures the importance of each QTL, irrespective of whether it is mostly due to a main effect or due to some interaction effect(s), via an unspecified function of the genotypes at all candidate QTL. A Gaussian process prior is assigned to this unknown function. In addition to the candidate QTL, nongenetic factors and covariates, such as age, gender, and environmental conditions, can also be included in the unspecified function. The importance of each genetic factor (QTL) and each nongenetic factor/covariate included in the function is estimated by a single hyperparameter, which enters the covariance function and captures any main or interaction effect associated with a given factor/covariate. An initial evaluation of the performance of the proposed method is obtained via analysis of simulated and real data.

Emotional distress in parents of psychotic patients is modified by serotonin transporter gene (5-HTTLPR)--brain-derived neurotrophic factor gene interactions.

Caregiving of a family member with psychotic disorder is considered among the most significant stressors and relatives of a sufferer experienced psychological and physical burden that may be the cause of neurotic states. There is growing evidence that sensitivity of individuals to depressogenic effects of stressful factor is moderated by genetic variants of serotonin transporter (SERT) and brain-derived neurotrophic factors (BDNF). We examined the association of the 5-HTTLPR SERT and Val66Met BDNF polymorphisms with signs of depression and anxiety measured with the Minnesota Multiphasic Personality Inventory (MMPI) in 235 unaffected parents of patients with major psychosis and 102 age-matched controls. A significant effect of the SERT-BDNF interaction on Depression and Psychasthenia scales was found in the group of parents, but not in the control group. Carriers of the Val/Val x SS variant scored higher as compared to other allelic combinations. The results obtained revealed that the SERT-BDNF interactions might moderate the level of anxiety and depression caused by caregiving status in parents of psychotic patients.

The cost of multiple drug resistance in Pseudomonas aeruginosa.

The spread of bacterial antibiotic resistance mutations is thought to be constrained by their pleiotropic fitness costs. Here we investigate the fitness costs of resistance in the context of the evolution of multiple drug resistance (MDR), by measuring the cost of acquiring streptomycin resistance mutations (StrepR) in independent strains of the bacterium Pseudomonas aeruginosa carrying different rifampicin resistance (RifR) mutations. In the absence of antibiotics, StrepR mutations are associated with similar fitness costs in different RifR genetic backgrounds. The cost of StrepR mutations is greater in a rifampicin-sensitive (RifS) background, directly demonstrating antagonistic epistasis between resistance mutations. In the presence of rifampicin, StrepR mutations have contrasting effects in different RifR backgrounds: StrepR mutations have no detectable costs in some RifR backgrounds and massive fitness costs in others. Our results clearly demonstrate the importance of epistasis and genotype-by-environment interactions for the evolution of MDR.

Genetic risk factors in recurrent venous thromboembolism: A multilocus, population-based, prospective approach.

BACKGROUND: Recurrent venous thromboembolism (VTE) is a common, complex disorder; however, genetic factors have been suggested to play a role in the disease development. We therefore conducted a multi-locus genetic study examining the potential associations of candidate gene variants in inflammation, thrombosis, coagulation, and lipid metabolism pathways, individually or interactively, with risk of recurrent VTE. METHODS: Using DNA samples collected at baseline in the Prevention of Recurrent Venous Thromboembolism trial (PREVENT), we genotyped 86 candidate genes polymorphisms among 43 individuals who subsequently developed recurrent VTE and among 396 individuals who remained free of recurrent event over a mean follow-up period of 2.1 years to prospectively determine whether these gene polymorphisms contribute to the risk of recurrent VTE. RESULTS: Using a single-marker 'uncorrected' analysis, CCR5 A(-2459)G [rs1799864], MMP3 5A(-1171)6A [rs3025058] and PON1 gln192arg [rs662] gene variants were associated with increased risk, and CETP C(-629)A [rs1800775] gene variant with reduced risk of recurrent VTE, respectively. Furthermore, potentially important gene-gene-interactions were detected by the Monte Carlo Markov chain Logic Regression method. CONCLUSIONS: Although the present findings are hypothesis-generating and require confirmation in an independent investigation, our study provides a practical example of detecting epistasis in common, complex diseases.