Genetic association studies

FSH receptor N680S genotype-guided gonadotropin choice increases cumulative pregnancy and live birth rates after in vitro fertilization

Objective

This study aimed to compare cumulative [fresh and frozen embryo transfers from one ovarian stimulation (OS) cycle] pregnancy and live birth rates in women for whom the choice between recombinant FSH (rFSH) and urinary FSH (uFSH) for OS was linked to FSH receptor (FSHR) N680S genotype and compared these to non-genotyped controls.

Methods

To define the optimal combination of FSH type and FSHR genotype, 475 women were allocated to either the rFSH group or to the uFSH group for OS. The number of aspirated oocytes, cumulative pregnancy rates, and live birth rates in the first OS cycle were determined. Subsequently, their FSHR N680S (rs6166) variant was analyzed. Clinical data were backed up by in vitro experiments, in which COS-1 cells were transfected with homozygous FSHR variants and stimulated with either uFSH or rFSH. cAMP was measured to evaluate receptor activity. Thereafter, a sub-cohort of 221 who received optimal FSH treatment in relation to their FSHR genotype was selected from the total cohort of 475 women. Cumulative pregnancy and live birth rates were compared between 991 non-genotyped controls and these 221 women. Binary logistic regression was used to explore the odds ratios (ORs) and 95% confidence intervals (CIs) for cumulative pregnancy and live birth rates in the first OS cycle among genotyped and optimally treated women, with the non-genotyped cohort set as the reference. Adjustment was made for age, body mass index, and method of fertilization.

Results

The combined clinical and in vitro data indicated that uFSH was the optimal choice for FSHR N680S S-allele carriers, whereas rFSH was the hormone of choice for asparagine (NN) subjects. The sub-cohort consisting of uFSH-treated S-carriers together with rFSH-treated NN-carriers had a significantly higher chance of pregnancy (51% vs. 40%; OR: 1.40, 95% CI 1.12-1.75, p=0.003) and live birth (40% vs. 29%; OR: 1.55, 95% CI 1.23-1.96, p<0.001) compared to non-genotyped women, in whom the choice of hormone was based on a standard clinical evaluation.

Conclusion

A significantly increased chance of pregnancy and live birth can be achieved by a genotype-guided approach. While the administration of uFSH should be the choice for S-carriers, rFSH is beneficial for NN-carrying women.

MassArray Genotyping as a Selection Tool for Extending the Shelf-Life of Fresh Gilthead Sea Bream and European Seabass

In modern aquaculture, genomics-driven breeding programs have emerged as powerful tools for optimizing fish quality. This study focused on two emblematic Mediterranean fish species, the European seabass (Dicentrarchus labrax) and the gilthead sea bream (Sparus aurata), with a primary aim of exploring the genetic basis of white muscle/fillet degradation in fresh fish following harvest. We identified 57 and 44 missense SNPs in gilthead sea bream and European seabass, respectively, located within genes encoding for endogenous proteases responsible for fillet quality. These SNPs were cherry-picked based on their strategic location within the catalytic/regulatory domains of endogenous proteases that are expressed in the white muscle. Using MassArray technology, we successfully associated differentiated enzymatic activity of those endogenous proteases post-harvest as a phenotypic trait with genetic polymorphism of six SNPs in gilthead sea bream and nine in European seabass. These findings can be valuable attributes in selective breeding programs toward the extension of freshness and shelf life of these species. The integration of MassArray technology into breeding programs offers a cost-effective strategy for harnessing the potential of these genetic variants to enhance the overall quality of the final product. Recognizing that fresh fish perishability is a challenge, extending shelf-life is pivotal in reducing losses and production costs.

Interleukin Variants Are Associated with the Development and Progression of IgA Nephropathy: A Candidate-Gene Association Study and Meta-Analysis

The interleukin-1 gene cluster encodes cytokines, which modulate mesangial cell proliferation and matrix expansion, both constituting central factors in the development and progression of immunoglobulin A nephropathy (IgAN). A candidate-gene study was performed to examine the association of polymorphisms of the interleukin-1 gene cluster with the risk of progressive IgAN. To gain deeper insights into the involvement of interleukin genes in IgAN, a meta-analysis of genetic association studies (GAS) that examine the association between interleukin variants and IgAN was conducted. Association study: The case-control study consisted of 121 unrelated Caucasians with sporadic, histologically diagnosed IgAN and of 246 age- and sex-matched healthy controls. Persistent proteinuria (>2 g/24 h) and/or impaired kidney function (serum creatinine > 1.5 mg/dL) defined progressive (n = 67) vs. non-progressive (n = 54) IgAN cases. Genotypes were assessed for two promoter-region single-nucleotide polymorphisms, C-899T (rs1800587) in IL1A and C-511T (rs16944) in IL1B, and for one penta-allelic variable-length tandem repeat polymorphism (VNTR 86 bp intron 2) in IL1RN. The association of these variants with the susceptibility of IgAN and the development of progressive IgAN (healthy status, IgAN, progressive IgAN) was tested using the generalized odds ratio (ORG) metric. Linkage disequilibrium and haplotype analysis were also performed. Meta-analysis: We included in the meta-analysis 15 studies investigating association between 14 interleukin variants harbored in eight different genes and IgAN. The ORG was used to evaluate the association between interleukin variants and IgAN using random effects models. The present case-control study revealed association of IL1B C-511T (rs16944) with the progression of IgAN (p = 0.041; ORG = 2.11 (1.09-4.07)). On haplotype analysis, significant results were derived for the haplotypes C-C-1 (p = 0.005; OR = 0.456 (0.261~0.797)) and C-T-2 (p = 0.003; OR = 4.208 (1.545-11.50)). Regarding association and meta-analysis results, variants in IL1B (rs1143627 and rs16944), IL1RN (rs928940, rs439154, and rs315951) and IL10 (rs1800871) were associated with IgAN based on either genotype or allele counts. Genetic variants and haplotypes in the IL1B, IL1RN, and IL10 genes might contribute to an increased risk for development and progression of IgAN.

Learning the kernel for rare variant genetic association test

Introduction: Compared to Genome-Wide Association Studies (GWAS) for common variants, single-marker association analysis for rare variants is underpowered. Set-based association analyses for rare variants are powerful tools that capture some of the missing heritability in trait association studies. Methods: We extend the convex-optimized SKAT (cSKAT) test set procedure which learns from data the optimal convex combination of kernels, to the full Generalised Linear Model (GLM) setting with arbitrary non-genetic covariates. We call this extended cSKAT (ecSKAT) and show that the resulting optimization problem is a quadratic programming problem that can be solved with no additional cost compared to cSKAT. Results: We show that a modified objective is related to an upper bound for the p-value through a decreasing exponential term in the objective function, indicating that optimizing this objective function is a principled way of learning the combination of kernels. We evaluate the performance of the proposed method on continuous and binary traits using simulation studies and illustrate its application using UK Biobank Whole Exome Sequencing data on hand grip strength and systemic lupus erythematosus rare variant association analysis. Discussion: Our proposed ecSKAT method enables correcting for important confounders in association studies such as age, sex or population structure for both quantitative and binary traits. Simulation studies showed that ecSKAT can recover sensible weights and achieve higher power across different sample sizes and misspecification settings. Compared to the burden test and SKAT method, ecSKAT gives a lower p-value for the genes tested in both quantitative and binary traits in the UKBiobank cohort.

Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data

Teaching statistics through engaging applications to contemporary large-scale datasets is essential to attracting students to the field. To this end, we developed a hands-on, week-long workshop for senior high-school or junior undergraduate students, without prior knowledge in statistical genetics but with some basic knowledge in data science, to conduct their own genome-wide association study (GWAS). The GWAS was performed for open source gene expression data, using publicly available human genetics data. Assisted by a detailed instruction manual, students were able to obtain ∼ 1.4 million p-values from a real scientific study, within several days. This early motivation kept students engaged in learning the theories that support their results, including regression, data visualization, results interpretation, and large-scale multiple hypothesis testing. To further their learning motivation by emphasizing the personal connection to this type of data analysis, students were encouraged to make short presentations about how GWAS has provided insights into the genetic basis of diseases that are present in their friends or families. The appended open source, step-by-step instruction manual includes descriptions of the datasets used, the software needed, and results from the workshop. Additionally, scripts used in the workshop are archived on Github and Zenodo to further enhance reproducible research and training.

CYP19A1 TC/CC Polymorphism, along with Deletion of GSTM1 and GSTT1 Genes, Strongly Influences Female Infertility Risk

Oxidative stress has a fundamental role in the pathophysiology of various conditions, like infertility. This case-control study was performed to assess the potential role of CYP19A1, GSTM1, and GSTT1 in modifying individual predisposition to female infertility. Genotyping of 201 women with established infertility and 161 fertile female controls was performed, and statistical associations were analyzed. For carriers of GSTM1 null genotype along with CYP19A1 C allele, there is a significant association with female infertility risk (OR 7.023; 95% CI (3.627-13.601; p < 0.001), and, also for carriers of GSTT1 null genotype along with the CYP19A1 TC/CC genotype (OR 24.150; 95% CI (11.148-52.317; p < 0.001). A positive association with female infertility risk for carriers of the C allele in CYP19A1 and null genotypes in GTSM1 (OR 11.979; 95% CI (4.570-31.400; p < 0.001) or GSTT1 (OR 13.169; 95% CI (4.518-38.380; p < 0.001) was found. When both GSTs are deleted, the risk of developing female infertility is significant, independently of the CYP19A1 genotype; when all the presumed high-risk genotypes are present, we found a significant association with female infertility risk (OR 47,914; 95% CI (14,051-163,393; p < 0.001).

New loci for refractive errors and ocular biometric parameters in young Chinese Han adults

Myopia has become a major public health issue with an increasing prevalence. There are still individuals who experience similar environmental risk factors and, yet, remain non-myopic. Thus, there might be genetic factors protecting people from myopia. Considering the opposite ocular characteristics of primary angle closure glaucoma (PACG) to myopia and possible common pathway between them, we propose that certain risk genes for PACG might act as a protective factor for myopia. In this study, 2,678 young adults were genotyped for 37 targeted single nucleotide polymorphisms. Compared with emmetropia, rs1401999 (allele C: OR=0.795, P=0.03; genotype in dominant model: OR=0.759, P=0.02) and rs1258267 (allele A: OR=0.824, P=0.03; genotype in dominant model: OR=0.603, P=0.01) were associated with low to moderate myopia and high myopia, respectively. Genotype under recessive model of rs11024102 was correlated with myopia (OR=1.456, P=0.01), low to moderate myopia (OR=1.443, P=0.02) and high myopia (OR=1.453, P=0.02). However, these associations did not survive Bonferroni correction. Moreover, rs1401999, rs1258267, and rs11024102 showed associations with certain ocular biometric parameters in different groups. Our study suggests that ABCC5, CHAT and PLEKHA7 might be associated with refractive errors by contributing to the regulation of ocular biometry, in terms of uncorrected results and their biological functions.

Sparse group variable selection for gene-environment interactions in the longitudinal study

Penalized variable selection for high-dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G  × $\times $  E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual levels. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program, we conduct G  × $\times $  E study by using high-dimensional single nucleotide polymorphism data as genetic factors and the longitudinal trait, forced expiratory volume in 1 s, as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.

DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks

Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process of embedding messages is called steganography and it is used for hiding and watermarking data to protect intellectual property. In contrast, steganalysis is a group of algorithms that serves to detect hidden information from covert media. Various methods have been developed to detect messages embedded in conventional covert channels. However, conventional steganalysis algorithms are mostly limited to common covert media. Most common detection approaches, such as frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography and are easily bypassed by recently developed steganography techniques. To address the limitations of conventional approaches, a sequence-learning-based malicious DNA sequence analysis method based on neural networks has been proposed. The proposed method learns intrinsic distributions and identifies distribution variations using a classification score to predict whether a sequence is to be a coding or non-coding sequence. Based on our experiments and results, we have developed a framework to safeguard security against DNA steganography.

Polymorphisms at CYP enzymes, NR1I2 and NR1I3 in association with virologic response to antiretroviral therapy in Brazilian HIV-positive individuals

Virologic failure of antiretroviral therapy (ART) may be explained by single nucleotide polymorphisms (SNPs) in drug absorption and metabolism genes. Here, we characterized the associations between polymorphisms in cytochrome P450 enzymes' genes CYP2B6 and CYP3A4/A5, nuclear receptor genes NR1I2/3, and initial ART efficacy among 203 HIV-positive individuals from Rio de Janeiro. Association between SNPs and virologic control was evaluated after 6 and 12 months of follow-up using Cox regression models. The SNP rs2307424 (NR1I3) was associated with increased virologic response after 12 months of treatment, while rs1523127 (NR1I2), rs3003596, and rs2502815 (NR1I3) were associated with decreased response. Increased virologic response after 12 months (_adjHR = 1.54; p = 0.02) was also observed among carriers of the NR1I3 haplotype rs2502815G-rs3003596A-rs2307424A versus the reference haplotype G-A-G. Our results suggest that NR1I2 and NR1I3 variants are associated with virologic responses to ART among Brazilians.

Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection

In high-throughput genetics studies, an important aim is to identify gene–environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.

Meta-Analysis and Bioinformatics Detection of Susceptibility Genes in Diabetic Nephropathy

The latest meta-analysis of genome-wide linkage studies (GWLS) identified nine cytogenetic locations suggestive of a linkage with diabetic nephropathy (DN) due to type 1 diabetes mellitus (T1DM) and seven locations due to type 2 diabetes mellitus (T2DM). In order to gain biological insight about the functional role of the genes located in these regions and to prioritize the most significant genetic loci for further research, we conducted a gene ontology analysis with an over representation test for the functional annotation of the protein coding genes. Protein analysis through evolutionary relationships (PANTHER) version 16.0 software and Cytoscape with the relevant plugins were used for the gene ontology analysis, and the overrepresentation test and STRING database were used for the construction of the protein network. The findings of the over-representation test highlight the contribution of immune related molecules like immunoglobulins, cytokines, and chemokines with regard to the most overrepresented protein classes, whereas the most enriched signaling pathways include the VEGF signaling pathway, the Cadherin pathway, the Wnt pathway, the angiogenesis pathway, the p38 MAPK pathway, and the EGF receptor signaling pathway. The common section of T1DM and T2DM results include the significant over representation of immune related molecules, and the Cadherin and Wnt signaling pathways that could constitute potential therapeutic targets for the treatment of DN, irrespective of the type of diabetes.

Serpin Family E Member 1 Tag Single-Nucleotide Polymorphisms in Patients with Diabetic Nephropathy: An Association Study and Meta-Analysis Using a Genetic Model-Free Approach

BACKGROUND

Many lines of evidence highlight the genetic contribution on the development of diabetic nephropathy (DN). One of the studied genes is SERPINE1 whose the role in the risk of developing DN remains questionable. In order to elucidate the contribution of SERPINE1 in DN progression in the context of type 2 diabetes mellitus (T2DM), we conducted an association study and meta-analysis of SERPINE1 genetic variants.

MATERIALS AND METHODS

A total of 190 patients with DN, 150 T2DM (type 2 diabetes mellitus) patients without DN and 238 healthy controls were recruited. We selected five tag single-nucleotide polymorphisms (SNPs) from the HapMap. The generalized odds ratio (ORG) was calculated to estimate the risk on DN development. Subgroup analyses based on ethnicity and type of diabetes were also performed.

RESULTS

Both the present association study regarding SERPINE1 SNPs (rs2227667, rs2070682, rs1050813, rs2227690, rs2227692) did not found any significant association between SERPINE1 variants and DN and the meta-analysis of variant 4G>5G (rs1799889) did not also reveal a significant association between 4G>5G variant and DN in main and subgroup analyses.

DISCUSSION

In conclusion, the present association study and meta-analysis provides strong evidence that SERPINE1 genetic variant 4G>5G is not implicated in the risk or development of DN in Caucasians. Further studies in other populations remain to further investigate the role of this variant in the course of DN.

Assortative mating and within-spouse pair comparisons

Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias.

Genotype-phenotype correlation and interaction of 4q25, 15q14 and MIPEP variants with myopia in southern Chinese population

BACKGROUND/AIMS

To determine the association and interaction of genome-wide association study-reported variants for Asian populations with myopia and ocular biometric parameters in southern Chinese population.

METHODS

Totally, 1462 unrelated Han Chinese subjects were recruited with complete ophthalmic examinations, including 1196 myopia and 266 control subjects. A total of nine variants were selected for TaqMan genotyping. The genetic association, joint additive effect and genotype-phenotype correlation were investigated.

RESULTS

The 4q25 variant rs10034228 (p=0.002, OR=0.56) and MIPEP variant rs9318086 (p=0.004, OR=1.62) were found to be significantly associated with myopia as well as different severity of myopia. Moreover, 15q14 variant rs524952 (p=0.015, OR=1.49) also showed mild association with myopia and high myopia. However, there was no significant association of CTNND2, vasoactive intestinal peptide receptor 2 and syntrophin beta 1 variants with myopia. Joint additive analysis revealed that the subjects carrying 6 risk alleles of the 3 associated variants were 10-fold higher risk predisposed to high myopia. Genotype-phenotype correlation analysis revealed that high myopia subjects carrying 4q25 rs10034228 T allele showed thicker central corneal thickness, whereas high myopia subjects carrying 15q14 rs524952 A allele were associated with longer axial length and larger curvature ratio.

CONCLUSION

This study revealed significant association of 4q25, 15q14 and MIPEP variants with myopia and different severity of myopia in southern Chinese population, joint additively enhancing 10-fold of risk predisposing to high myopia. The correlation of these associated variants with axial length and corneal parameters suggests their contribution to the refractive status in high myopia subjects.

Genetic variations at 10q26 regions near FGFR2 gene and its association with non-syndromic cleft lip with or without cleft palate

OBJECTIVES

In our study, we focussed on three SNPs in the non-coding regions near FGFR2 gene, as studies on non-coding variants in the genome are the novel trends to identify the susceptible loci for nonsyndromic cleft lip with or without cleft palate (NSCL/P). FGFR2 gene is selected as a candidate gene based on knock out animal models and the role played in syndromic forms of clefting. FGFR2 gene also plays an important role in FGF signaling pathway during craniofacial development.

METHODS

In the present study 148 case-parent triads were assessed for three SNPs rs10749408, rs11199874 and rs10788165 near FGFR2 gene by using TaqMan allelic discrimination method. Transmission disequilibrium test (TDT) was used to find the allelic association. Linkage disequilibrium (LD) between the markers was analysed using Haploview program 4.2. Haplotype transmission effects were estimated using FAMHAP package. The possible parent-of-origin effects were assessed by likelihood based approach.

RESULTS

TDT analysis of three SNPs failed to show significant transmission disortion from heterozygous parents to the affected child and are not associated with NSCL/P. Linkage disequilibrium analysis showed strong LD between rs11199874 and rs10788165 SNPs. In the haplotype TDT analysis, GG haplotype of rs11199874-rs10788165 showed significant undertransmission to affected child. No significant parent-of-origin effects were observed.

CONCLUSION

The present study on noncoding variants near FGFR2 gene is not associated with NSCL/P. As the numbers of triads included in the study are less, further studies are needed including large sample size to find association with NSCL/P.

Innovative approach to identify multigenomic and environmental interactions associated with birth defects in family-based hybrid designs

Genes, including those with transgenerational effects, work in concert with behavioral, environmental, and social factors via complex biological networks to determine human health. Understanding complex relationships between causal factors underlying human health is an essential step towards deciphering biological mechanisms. We propose a new analytical framework to investigate the interactions between maternal and offspring genetic variants or their surrogate single nucleotide polymorphisms (SNPs) and environmental factors using family-based hybrid study design. The proposed approach can analyze diverse genetic and environmental factors and accommodate samples from a variety of family units, including case/control-parental triads, and case/control-parental dyads, while minimizing potential bias introduced by population admixture. Comprehensive simulations demonstrated that our innovative approach outperformed the log-linear approach, the best available method for case-control family data. The proposed approach had greater statistical power and was capable to unbiasedly estimate the maternal and child genetic effects and the effects of environmental factors, while controlling the Type I error rate against population stratification. Using our newly developed approach, we analyzed the associations between maternal and fetal SNPs and obstructive and conotruncal heart defects, with adjustment for demographic and lifestyle factors and dietary supplements. Fourteen and 11 fetal SNPs were associated with obstructive and conotruncal heart defects, respectively. Twenty-seven and 17 maternal SNPs were associated with obstructive and conotruncal heart defects, respectively. In addition, maternal body mass index was a significant risk factor for obstructive defects. The proposed approach is a powerful tool for interrogating the etiological mechanism underlying complex traits.

Genetic Architecture of Depression: Where Do We Stand Now?

The research of depression genetics has been occupied by historical candidate genes which were tested by candidate gene association studies. However, these studies were mostly not replicable. Thus, genetics of depression have remained elusive for a long time. As research moves from candidate gene association studies to GWAS, the hypothesis-free non-candidate gene association studies in genome-wide level, this trend will likely change. Despite the fact that the earlier GWAS of depression were not successful, the recent GWAS suggest robust findings for depression genetics. These altogether will catalyze a new wave of multidisciplinary research to pin down the neurobiology of depression.

Potpourri: An Epistasis Test Prioritization Algorithm via Diverse SNP Selection

Genome-wide association studies (GWAS) explain a fraction of the underlying heritability of genetic diseases. Investigating epistatic interactions between two or more loci help to close this gap. Unfortunately, the sheer number of loci combinations to process and hypotheses prohibit the process both computationally and statistically. Epistasis test prioritization algorithms rank likely epistatic single nucleotide polymorphism (SNP) pairs to limit the number of tests. However, they still suffer from very low precision. It was shown in the literature that selecting SNPs that are individually correlated with the phenotype and also diverse with respect to genomic location leads to better phenotype prediction due to genetic complementation. Here, we propose that an algorithm that pairs SNPs from such diverse regions and ranks them can improve prediction power. We propose an epistasis test prioritization algorithm that optimizes a submodular set function to select a diverse and complementary set of genomic regions that span the underlying genome. The SNP pairs from these regions are then further ranked w.r.t. their co-coverage of the case cohort. We compare our algorithm with the state of the art on three GWAS and show that (1) we substantially improve precision (from 0.003 to 0.652) while maintaining the significance of selected pairs, (2) decrease the number of tests by 25-fold, and (3) decrease the runtime by 4-fold. We also show that promoting SNPs from regulatory/coding regions improves the performance (up to 0.8). Potpourri is available at http:/ciceklab.cs.bilkent.edu.tr/potpourri.

Causal inference in genetic trio studies

We introduce a method to draw causal inferences-inferences immune to all possible confounding-from genetic data that include parents and offspring. Causal conclusions are possible with these data because the natural randomness in meiosis can be viewed as a high-dimensional randomized experiment. We make this observation actionable by developing a conditional independence test that identifies regions of the genome containing distinct causal variants. The proposed digital twin test compares an observed offspring to carefully constructed synthetic offspring from the same parents to determine statistical significance, and it can leverage any black-box multivariate model and additional nontrio genetic data to increase power. Crucially, our inferences are based only on a well-established mathematical model of recombination and make no assumptions about the relationship between the genotypes and phenotypes. We compare our method to the widely used transmission disequilibrium test and demonstrate enhanced power and localization.

TNFSF4 is a risk factor to systemic lupus erythematosus in a Latin American population

OBJECTIVE

The aim of this study was to examine the association of three TNFSF4 single nucleotide variants (SNVs) with systemic lupus erythematosus (SLE) susceptibility in Mexican patients.

METHODS

Genotypes of the TNFSF4 rs1234315T/C, rs2205960G/T, and rs704840T/G SNVs were determined using a TaqMan assay. In our study, we included 395 patients with SLE and 500 controls.

RESULTS

Our information shows a significant difference in the allelic and genotypic frequency of the three TNFSF4 SNVs between cases and controls. Thus, our data showed an association between TNFSF4 rs1234315T/C (T vs. C, OR 1.40, p = 0.00087), rs2205960G/T (G vs. T, OR 1.32, p = 0.0037), and rs704840T/G (T vs. G, OR 1.41, p = 0.0003) and SLE susceptibility in Mexican subjects. Besides, we conducted a meta-analysis to determine the role of TNFSF4 rs2205960G/T and SLE susceptibility; our results showed that this variant is a risk factor for SLE in Latin Americans and Asians.

CONCLUSION

Our results show that TNFSF4 rs1234315T/C, rs2205960G/T, and rs704840T/G are risk factors to SLE in Mexicans. This is the first study to document an association between TNFSF4 rs704840T/G and SLE in a Latin American population. In addition, our meta-analysis showed that TNFSF4 rs2205960G/T is a risk factor for Asians and Latin Americans. Key Point • The TNFSF4 rs1234315T/C, rs2205960G/T, and rs704849T/G SNVs are risk factors to SLE in patients from Mexico.

Design efficiency in genetic association studies

Selecting the best design for genetic association studies requires careful deliberation; different study designs can be used to scan for different genetic effects, and each design has its own set of strengths and limitations. A variety of family and unrelated control configurations are amenable to genetic association analyses, including the case-control design, case-parent triads, and case-parent triads in combination with unrelated controls or control-parent triads. Ultimately, the goal is to choose the design that achieves the highest statistical power using the lowest cost. For given parameter values and genotyped individuals, designs can be compared directly by computing the power. However, a more informative and general design comparison can be achieved by studying the relative efficiency, defined as the ratio of variances of two different parameter estimators, corresponding to two separate designs. Using log-linear modeling, we derive the relative efficiency from the asymptotic variance of the parameter estimators and relate it to the concept of Pitman efficiency. The relative efficiency takes into account the fact that different designs impose different costs relative to the number of genotyped individuals. We show that while optimal efficiency for analyses of regular autosomal effects is achieved using the standard case-control design, the case-parent triad design without unrelated controls is efficient when searching for parent-of-origin effects. Due to the potential loss of efficiency, maternal genes should generally not be adjusted for in an initial genome-wide association study scan of offspring genes but instead checked post hoc. The relative efficiency calculations are implemented in our R package Haplin.

TOLLIP deficiency is associated with increased resistance to Legionella pneumophila pneumonia

Legionella pneumophila (Lp) is a flagellated, intracellular bacterium that can cause Legionnaires' disease (LD). Lp activates multiple innate immune receptors, and TOLLIP dampens MyD88-dependent signaling and may influence susceptibility to LD. We evaluated the effect of TOLLIP on innate immunity, pneumonia severity, and LD susceptibility in mouse lungs and human populations. To accomplish this, we evaluated the effect of TOLLIP on lung-specific Lp control and immune response and associated a common functional TOLLIP variant with Lp-induced innate immune responses and LD susceptibility in humans. After aerosol Lp infection, Tollip-/- mice demonstrated significantly fewer bacterial colony-forming unit and increased cytokine responses from BAL fluid. Tollip-/- macrophages also suppressed intracellular Lp replication in a flagellin-independent manner. The presence of a previously characterized, functionally active SNP associated with decreased TOLLIP mRNA transcript in monocytes was associated with increased TNF and IL-6 secretion after Lp stimulation of PBMC ex vivo. This genotype was separately associated with decreased LD susceptibility (309 controls, 88 cases, p = 0.008, OR 0.36, 95% CI 0.16-0.76) in a candidate gene association study. These results suggest that TOLLIP decreases lung-specific TLR responses to increase LD susceptibility in human populations. Better understanding of TOLLIP may lead to novel immunomodulatory therapies.

Identification of Novel CXCL12 Genetic Polymorphisms Associated with Type 2 Diabetes Mellitus: A Chinese Sib-Pair Study

Aims: To discover possible relationships between CXCL12 single nucleotide polymorphisms (SNPs) and type 2 diabetes mellitus (T2DM) and its risk factors. Methods: The present sib-pair study was conducted in a rural community of Beijing, China. SNPs rs2297630, rs1746048, and rs1801157 located within or nearby the CXCL12 gene were genotyped using the allele-specific polymerase chain reaction method. Haseman-Elston regression was used to investigate linkages between these SNPs and T2DM. A generalized estimating equation logistic regression model was used to discover associations between the SNPs, T2DM, and its risk factors. Results: A total of 3171 participants were recruited, comprising 2277 sib pairs. After Bonferroni correction (α = 0.016), rs2297630 was found to be significantly linked to (p = 0.003) and associated with T2DM (AA vs. GG/GA: OR = 2.26, 95% CI: 1.31-3.88, p = 0.003). There were interactions between rs2297630 and dyslipidemia (p < 0.001) and between rs1746048 and hypertension (p = 0.011). Compared to dyslipidemia-free subjects with rs2297630 GG/GA genotypes, dyslipidemia patients with rs2297630 AA had a higher risk of T2DM (OR = 4.15, 95% CI: 2.24-7.67, p < 0.001). Compared to hypertension-free subjects with rs1746048 CC genotypes, hypertension-free subjects with rs1746048 CT/TT had a decreased risk of T2DM (OR = 0.77, 95% CI: 0.60-0.99, p = 0.045). Conclusions: A novel linkage and association was found between rs2297630 and T2DM. Moreover, novel interactions were found between rs2297630 and dyslipidemia as well as rs1746048 and hypertension. These findings will help identify individuals at higher risk of developing T2DM.

Human pain genetics database: a resource dedicated to human pain genetics research

The Human Pain Genetics Database (HPGDB) is a comprehensive variant-focused inventory of genetic contributors to human pain. After curation, the HPGDB currently includes 294 studies reporting associations between 434 distinct genetic variants and various pain phenotypes. Variants were then submitted to a comprehensive analysis. First, they were validated in an independent high-powered replication cohort by testing the association of each variant with 10 different pain phenotypes (n = 1320-26,973). One hundred fifty-five variants replicated successfully (false discovery rate 20%) in at least one pain phenotype, and the association P values of the HPGDB variants were significantly lower compared with those of random controls. Among the 155 replicated variants, 21 had been included in the HPGDB because of their association with analgesia-related and 13 with nociception-related phenotypes, confirming analgesia and nociception as pathways of vulnerability for pain phenotypes. Furthermore, many genetic variants were associated with multiple pain phenotypes, and the strength of their association correlated between many pairs of phenotypes. These genetic variants explained a considerable amount of the variance between different pairs of pain phenotypes, indicating a shared genetic basis among pain phenotypes. In addition, we found that HPGDB variants show many pleiotropic associations, indicating that genetic pathophysiological mechanisms are also shared among painful and nonpainful conditions. Finally, we demonstrated that the HPGDB data set is significantly enriched for functional variants that modify gene expression, are deleterious, and colocalize with open chromatin regions. As such, the HPGDB provides a validated data set that represents a valuable resource for researchers in the human pain field.

IMI - Myopia Genetics Report

The knowledge on the genetic background of refractive error and myopia has expanded dramatically in the past few years. This white paper aims to provide a concise summary of current genetic findings and defines the direction where development is needed. We performed an extensive literature search and conducted informal discussions with key stakeholders. Specific topics reviewed included common refractive error, any and high myopia, and myopia related to syndromes. To date, almost 200 genetic loci have been identified for refractive error and myopia, and risk variants mostly carry low risk but are highly prevalent in the general population. Several genes for secondary syndromic myopia overlap with those for common myopia. Polygenic risk scores show overrepresentation of high myopia in the higher deciles of risk. Annotated genes have a wide variety of functions, and all retinal layers appear to be sites of expression. The current genetic findings offer a world of new molecules involved in myopiagenesis. As the missing heritability is still large, further genetic advances are needed. This Committee recommends expanding large-scale, in-depth genetic studies using complementary big data analytics, consideration of gene-environment effects by thorough measurement of environmental exposures, and focus on subgroups with extreme phenotypes and high familial occurrence. Functional characterization of associated variants is simultaneously needed to bridge the knowledge gap between sequence variance and consequence for eye growth.

Multivariate Methods for Meta-Analysis of Genetic Association Studies

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Gonadotropin receptor variants are linked to cumulative live birth rate after in vitro fertilization

Purpose

The objective was to investigate if the gonadotropin receptor variants N680S (N: asparagine, S: serine, rs6166) in the follicle-stimulating hormone receptor (FSHR) and N312S (rs2293275) in the luteinizing hormone/human chorionic gonadotropin receptor (LHCGR) predicted cumulative live birth rate after in vitro fertilization (IVF).

Methods

A total of 665 women were consecutively enrolled for IVF during the period 2007–2016. Inclusion criteria were < 40 years of age, body mass index < 30 kg/m², non-smoking, regular menstruation cycle of 21–35 days, and bilateral ovaries. A blood sample was drawn for endocrine hormonal analysis and for DNA extraction with subsequent genotyping of the FSHR N680S and LHCGR N312S polymorphisms. Statistical analyses were done on all completed IVF cycles.

Results

Women homozygous for S in both receptors combined (4S) had significantly higher live birth rate compared to those with other receptor variants when combining the first three IVF cycles (OR = 2.00, 95% CI [1.02, 3.92], p = 0.043). Cumulatively higher chance of live birth rate, during all IVF cycles, was also evident (HR = 1.89, 95% CI [1.00, 3.57], p = 0.049).

Conclusions

Gonadotropin receptor variants are promising candidates for the prediction of the possibility to have a baby to take home after IVF treatment.

Association of interleukin 6, interleukin 7 receptor alpha, and interleukin 12B gene polymorphisms with multiple sclerosis

Pro-inflammatory and anti-inflammatory cytokines have been shown to play a crucial role in the pathophysiology of multiple sclerosis (MS). We investigated the association between interleukin (IL) IL6-174 G/C (rs1800795), IL7RA C/T (rs6897932), and IL-12B A1188C (rs3212227) gene polymorphisms (SNPs) and MS. The study consisted of 297 unrelated MS patients and 135 healthy individuals. In IL6-174G/C (rs1800795), a significant association between the C allele and MS risk [OR 1.41, 95% CI (1.05-1.92); P = 0.025] was found. Carriage of genotypes CC and CG were more common in MS patients [OR 1.58, 95% CI (1.04-2.39); P = 0.031] and also in female MS patients [OR 1.68, 95% CI (1.02-2.79); P = 0.043]. However, after applying Bonferroni's correction the differences did not remain significant. No significant association between the IL7RA C/T (rs6897932) and IL12B A1188C (rs3212227) gene polymorphisms and MS susceptibility was observed. Regarding IL-12B A1188C (rs3212227), a significant association between the CC genotype and MS progression, expressed as MSSS, was demonstrated in the female MS group. Our results indicate that the distribution of IL6-174G/C (rs1800795) SNP was marginally associated with MS susceptibility. We also showed that IL-12B A1188C (rs3212227) can contribute to the progression of the disease in the Czech population.

Exercise and weight loss interventions and miRNA expression in women with breast cancer

PURPOSE

Obesity and weight gain are associated with comorbidities including a higher risk of tumor recurrence and cancer-related deaths among breast cancer (BC) survivors; however, the underlying mechanisms linking obesity and cancer are poorly understood. Given the lack of clinically validated BC biomarkers, obesity and weight-loss studies utilize serum biomarkers as the intermediary outcomes of tumor recurrence. Studies have indicated microRNAs (miRNA)s are reliable biomarkers for cancer. We hypothesized that miRNA expression correlates with obesity and weight loss amongst BC survivors. This would yield insight into the biological pathways by which this association occurs, enabling more precise development of therapeutics.

PATIENTS AND METHODS

We correlated baseline body mass index (BMI) with serum miRNA expression in 121 BC survivors enrolled in the Hormones and Physical Exercise (HOPE) trial. We then analyzed expression of the 35 most abundant miRNAs from HOPE in a six-month randomized controlled weight-loss trial (Lifestyle, Exercise, and Nutrition; LEAN) in 100 BC survivors. Ingenuity pathway analysis (IPA) software was used to identify biological pathway targets of the BMI-associated and intervention-responsive miRNAs using predictive biomarkers.

RESULTS

Pearson correlations in HOPE identified eight miRNAs associated with BMI, including miR-191-5p (r = - 0.22, p = 0.016) and miR-122-5p (r = 0.25, p = 0.0048). In the LEAN validation study, levels of miR-191-5p significantly increased during the six-month intervention (p = 0.082). Ingenuity Pathway Analysis identified "Estrogen-mediated S-phase entry" (HOPE p = 0.003; LEAN p < 0.001) and "Molecular mechanisms of cancer" (HOPE p = 0.02; LEAN p < 0.001) as the top canonical pathways that significantly correlated with BMI-associated and intervention-responsive miRNAs and contain obesity and cancer-relevant genes including the E2F family of transcription factors and CCND1, which have been implicated in sporadic BC.

CONCLUSION

While the association between obesity and BC recurrence and mortality has been demonstrated in the literature, mechanisms underlying the link between weight gain and cancer are unclear. Using two independent clinical trials, we identified novel miRNAs associative to BMI and weight loss that contribute to the development of cancer. Predictive modeling of miRNA targets identified multiple canonical pathways associated with cancer, highlighting potential mechanisms explaining the link between BMI and increased cancer risk.

Key Concepts in Genetic Epidemiology

Genetic epidemiology is a discipline closely allied to traditional epidemiology that deals with the analysis of the familial distribution of traits. It emerged in the mid-1980s bringing together approaches and techniques developed in mathematical and quantitative genetics, medical and population genetics, statistics and epidemiology. The purpose of this chapter is to familiarize the reader with key concepts in genetic epidemiology as applied at present to unveil the familial and genetic determinants of disease and the joint effects of genes and environmental exposures.

Genetic polymorphisms of the 5HT receptors are not related with depression in temporal lobe epilepsy caused by hippocampal sclerosis

BACKGROUND

Temporal lobe epilepsy caused by hippocampal sclerosis (TLE-HS) is the most frequent form of drug-resistant epilepsy in adults. Mood disorders are the most frequent psychiatric comorbidities observed in these patients. Common pathophysiological mechanisms of epilepsy and psychiatric comorbidities include abnormalities in the serotonin pathway. The primary goal of this study was to determine the possible association between polymorphisms of genes encoding the serotonin receptors 5HT1A (rs6295), 5HT1B (rs6296), and 5HT2C (rs6318) and the presence of mood disorders in patients with TLE-HS. Our secondary goal was to evaluate the possible association between these variants and susceptibility to develop seizures in TLE-HS.

METHODS

We assessed 119 patients with TLE-HS, with and without psychiatric comorbidities; 146 patients with major depressive disorder; and 113 healthy volunteers. Individuals were genotyped for the rs6295, rs6296, and rs6318 polymorphisms.

RESULTS

No difference was observed between the group with TLE-HS, healthy controls, and the group with major depressive disorder without epilepsy regarding the polymorphisms that were evaluated. There was no correlation between rs6318, rs6295, rs6296, and epilepsy-related factors and history of psychiatric comorbidities.

CONCLUSIONS

Our work suggests that the studied polymorphisms were not related to the presence of TLE, psychiatric comorbidities in TLE, and epilepsy-related factors.

Introduction to Epidemiological Studies

The basic epidemiological study designs are cross-sectional, case-control, and cohort studies. Cross-sectional studies provide a snapshot of a population by determining both exposures and outcomes at one time point. Cohort studies identify the study groups based on the exposure and, then, the researchers follow up study participants to measure outcomes. Case-control studies identify the study groups based on the outcome, and the researchers retrospectively collect the exposure of interest. The present chapter discusses the basic concepts, the advantages, and disadvantages of epidemiological study designs and their systematic biases, including selection bias, information bias, and confounding.

Prioritizing tests of epistasis through hierarchical representation of genomic redundancies

Epistasis is defined as a statistical interaction between two or more genomic loci in terms of their association with a phenotype of interest. Epistatic loci that are identified using data from Genome-Wide Association Studies (GWAS) provide insights into the interplay among multiple genetic factors, with applications including assessment of susceptibility to complex diseases, decision making in precision medicine, and gaining insights into disease mechanisms. Since the number of genomic loci assayed by GWAS is extremely large (usually in the order of millions), identification of epistatic loci is a statistically difficult and computationally intensive problem. Even when only pairwise interactions are considered, the size of the search space ranges from hundreds of millions to billions of locus pairs. The large number of statistical tests performed also makes sufficient type one error correction imperative. Consequently, efficient algorithms are required to filter the tests that are performed and evaluate large GWAS data sets in a reasonable amount of computation time. It has been observed that many pairwise tests are redundant due to correlations in their genotype values across samples, known as linkage disequilibrium. However, algorithms that have been developed for efficient identification of epistatic loci do not systematically exploit linkage disequilibrium. Here, we propose a new algorithm for fast epistasis detection based on hierarchical representation of linkage disequilibrium (LinDen). We utilize redundancies in genotype patterns between neighboring loci to generate a hierarchical structure and execute a branch-and-bound search to prioritize loci testing based on approximations of a test statistic for pairs of locus groups. The hierarchical organization of tests performed by LinDen allows for efficient scaling based on the screened loci. We test LinDen comprehensively on three data sets obtained from the Wellcome Trust Case Control Consortium: type two diabetes, psoriasis, and hypertension. Our results show that, as compared other state-of-the-art tools for fast epistasis detection, LinDen drastically reduces the number of tests performed while discovering statistically significant locus pairs. LinDen is implemented in C++ and is available as open source at http://compbio.case.edu/linden/.

A Functional Toll-Interacting Protein Variant Is Associated with Bacillus Calmette-Guérin-Specific Immune Responses and Tuberculosis

RATIONALE

The molecular mechanisms that regulate tuberculosis susceptibility and bacillus Calmette-Guérin (BCG)-induced immunity are mostly unknown. However, induction of the adaptive immune response is a critical step in host control of Mycobacterium tuberculosis. Toll-interacting protein (TOLLIP) is a ubiquitin-binding protein that regulates innate immune responses, including Toll-like receptor signaling, which initiate adaptive immunity. TOLLIP variation is associated with susceptibility to tuberculosis, but the mechanism by which it regulates tuberculosis immunity is poorly understood.

OBJECTIVES

To identify functional TOLLIP variants and evaluate the role of TOLLIP variation on innate and adaptive immune responses to mycobacteria and susceptibility to tuberculosis.

METHODS

We used human cellular immunology approaches to characterize the role of a functional TOLLIP variant on monocyte mRNA expression and M. tuberculosis-induced monocyte immune functions. We also examined the association of TOLLIP variation with BCG-induced T-cell responses and susceptibility to latent tuberculosis infection.

MEASUREMENTS AND MAIN RESULTS

We identified a functional TOLLIP promoter region single-nucleotide polymorphism, rs5743854, which was associated with decreased TOLLIP mRNA expression in infant monocytes. After M. tuberculosis infection, TOLLIP-deficient monocytes demonstrated increased IL-6, increased nitrite, and decreased bacterial replication. The TOLLIP-deficiency G/G genotype was associated with decreased BCG-specific IL-2⁺ CD4⁺ T-cell frequency and proliferation. This genotype was also associated with increased susceptibility to latent tuberculosis infection.

CONCLUSIONS

TOLLIP deficiency is associated with decreased BCG-specific T-cell responses and increased susceptibility to tuberculosis. We hypothesize that the heightened antibacterial monocyte responses after vaccination of TOLLIP-deficient infants are responsible for decreased BCG-specific T-cell responses. Activating TOLLIP may provide a novel adjuvant strategy for BCG vaccination.

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

The genetic background of Parkinson's disease: current progress and future prospects

Almost two decades of genetic research in Parkinson's disease (PD) have remarkably increased our knowledge regarding the genetic basis of PD with numerous genes and genetic loci having been found to cause familial PD or affect the risk for PD. Approximately 5-10% of PD patients have monogenic forms of the disease, exhibiting a classical Mendelian type of inheritance, however, the majority PD cases are sporadic, probably caused by a combination of genetic and environmental risk factors. Nowadays, six genes, alpha synuclein, LRRK2, VPS35, Parkin, PINK1 and DJ-1, have definitely been associated with an autosomal dominant or recessive PD mode of inheritance. The advent of genome-wide association studies (GWAS) and the implementation of new technologies, like next generation sequencing (NGS) and exome sequencing has undoubtedly greatly aided the identification on novel risk variants for sporadic PD. In this review, we will summarize the current progress and future prospects in the field of PD genetics.

Cannabis and a lower BMI in psychosis: What is the role of AKT1?

Cannabis use has been associated with favorable outcomes on metabolic risk factors. The cause of this relation is still unknown. In this study we investigated whether this effect is mediated by the AKT1 gene, as activation of the related enzyme by cannabis may cause metabolic changes. Six Single Nucleotide Polymorphisms (SNPs) of the AKT1 gene (rs1130214, rs1130233, rs2494732, rs2498784, rs3730358, and rs3803300) of patients with psychotic disorders (n=623) were related to Body Mass Index (BMI), levels of glycosylated hemoglobin (HBA1c) and total metabolic risk. Next, mediation analysis was performed with BMI as outcome, cannabis as predictor, and AKT1 as mediator. Cannabis use was inversely related to BMI but not with levels of HBA1c and total metabolic risk. Moreover, out of 6 AKT1 SNPs, rs2494732 was associated with cannabis use, but AKT1 did not mediate the effect of cannabis on BMI. In conclusion, cannabis use is likely to be associated with a lower BMI in patients with a psychotic disorder. Moreover, AKT1 risk alleles may increase the incidence of cannabis use in patients with a psychotic disorder, but AKT1 does not appear to mediate the effect of cannabis on BMI.

Is maternal trait anxiety a risk factor for late preterm and early term deliveries?

Background

Anxiety is associated with preterm deliveries in general (before week 37 of pregnancy), but is that also true for late preterm (weeks 34/0–36/6) and early term deliveries (weeks 37/0–38/6)? We aim to examine this association separately for spontaneous and provider-initiated deliveries.

Methods

Participants were pregnant women from the Norwegian Mother and Child Cohort Study (MoBa), which has been following 95 200 pregnant women since 1999. After excluding pregnancies with serious health complications, 81 244 participants remained. National ultrasound records were used to delineate late preterm, early term, and full-term deliveries, which then were subdivided into spontaneous and provider-initiated deliveries. We measured trait anxiety based on two ratings of the anxiety items on the Symptom Checklist-8 (Acta Psychiatr Scand 87:364–7, 1993). Trait anxiety was transformed into categorizing the score at the mean and at ± 2 standard deviations.

Results

Trait anxiety was substantially associated with late preterm and early term deliveries after adjusting for confounders. In the whole sample, women with the highest anxiety scores (+2 standard deviations) were more likely [(odds ratio (OR) = 1.7; 95 % confidence-interval (CI) 1.3-2.0)] to delivering late preterm than women with the lowest anxiety scores. Their odds of delivering early term were also high (OR = 1.4; CI 1.3-1.6). Women with spontaneous deliveries and the highest anxiety scores had higher odds (OR = 1.4; CI 1.1-1.8) of delivering late preterm and early term (OR = 1.3; CI = 1.3-1.5). The corresponding odds for women with provider-initiated deliveries were OR = 1.7 (CI = 1.2-2.4) for late preterm and OR = 1.3 for early term (CI = 1.01-1.6). Irrespective of delivery onset, women with provider-initiated deliveries had higher levels of anxiety than women delivering spontaneously. However, women with high anxiety were equally likely to have provider-initiated or spontaneous deliveries.

Conclusions

This study is the first to show substantial associations between high levels of trait anxiety and late preterm delivery. Increased attention should be given to the mechanism underlying this association, including factors preceding the pregnancy. In addition, acute treatment should be offered to women displaying high levels of anxiety throughout pregnancy to avoid suffering for the mother and the child.

Data and programs in support of network analysis of genes and their association with diseases

The network-based approaches that were employed in order to depict the relationships between human genetic diseases and their associated genes are described. Towards this direction, monopartite disease-disease and gene-gene networks were constructed from bipartite gene-disease association networks. The latter were created by collecting and integrating data from three diverse resources, each one with different content, covering from rare monogenic disorders to common complex diseases. Moreover, topological and clustering graph analyses were performed. The methodology and the programs presented in this article are related to the research article entitled “Network analysis of genes and their association with diseases” [1].

Network analysis of genes and their association with diseases

A plethora of network-based approaches within the Systems Biology universe have been applied, to date, to investigate the underlying molecular mechanisms of various human diseases. In the present study, we perform a bipartite, topological and clustering graph analysis in order to gain a better understanding of the relationships between human genetic diseases and the relationships between the genes that are implicated in them. For this purpose, disease-disease and gene-gene networks were constructed from combined gene-disease association networks. The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases. This data pluralism enabled us to uncover important associations between diseases with unrelated phenotypic manifestations but with common genetic origin. For our analysis, the topological attributes and the functional implications of the individual networks were taken into account and are shortly discussed. We believe that some observations of this study could advance our understanding regarding the etiology of a disease with distinct pathological manifestations, and simultaneously provide the springboard for the development of preventive and therapeutic strategies and its underlying genetic mechanisms.