A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS on each of the estimated phenotypes identified 28 genome-wide significant variants at 13 loci across the 12 estimated phenotypes, one of which was novel (in DAOA) and had not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover novel biological mechanisms influencing quantitative traits.

Blood DNA methylation profiling identifies cathepsin Z dysregulation in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is characterised by pulmonary vascular remodelling causing premature death from right heart failure. Established DNA variants influence PAH risk, but susceptibility from epigenetic changes is unknown. We addressed this through epigenome-wide association study (EWAS), testing 865,848 CpG sites for association with PAH in 429 individuals with PAH and 1226 controls. Three loci, at Cathepsin Z (CTSZ, cg04917472), Conserved oligomeric Golgi complex 6 (COG6, cg27396197), and Zinc Finger Protein 678 (ZNF678, cg03144189), reached epigenome-wide significance (p < 10-7) and are hypermethylated in PAH, including in individuals with PAH at 1-year follow-up. Of 16 established PAH genes, only cg10976975 in BMP10 shows hypermethylation in PAH. Hypermethylation at CTSZ is associated with decreased blood cathepsin Z mRNA levels. Knockdown of CTSZ expression in human pulmonary artery endothelial cells increases caspase-3/7 activity (p < 10-4). DNA methylation profiles are altered in PAH, exemplified by the pulmonary endothelial function modifier CTSZ, encoding protease cathepsin Z.

51st European Mathematical Genetics Meeting (EMGM) 2023.

NA.

A Combined Effect of Polygenic Scores and Environmental Factors on Individual Differences in Depression Level.

The risk of depression could be evaluated through its multifactorial nature using the polygenic score (PGS) approach. Assuming a "clinical continuum" hypothesis of mental diseases, a preliminary assessment of individuals with elevated risk for developing depression in a non-clinical group is of high relevance. In turn, epidemiological studies suggest including social/lifestyle factors together with PGS to address the "missing heritability" problem. We designed regression models, which included PGS using 27 SNPs and social/lifestyle factors to explain individual differences in depression levels in high-education students from the Volga-Ural region (VUR) of Eurasia. Since issues related to population stratification in PGS scores may lead to imprecise variant effect estimates, we aimed to examine a sensitivity of PGS calculated on summary statistics of depression and neuroticism GWAS from Western Europeans to assess individual proneness to depression levels in the examined sample of Eastern Europeans. A depression score was assessed using the revised version of the Beck Depression Inventory (BDI) in 1065 young adults (age 18-25 years, 79% women, Eastern European ancestry). The models based on weighted PGS demonstrated higher sensitivity to evaluate depression level in the full dataset, explaining up to 2.4% of the variance (p = 3.42 × 10-7); the addition of social parameters enhanced the strength of the model (adjusted r2 = 15%, p < 2.2 × 10-16). A higher effect was observed in models based on weighted PGS in the women group, explaining up to 3.9% (p = 6.03 × 10-9) of variance in depression level assuming a combined SNPs effect and 17% (p < 2.2 × 10-16)-with the addition of social factors in the model. We failed to estimate BDI-measured depression based on summary statistics from Western Europeans GWAS of clinical depression. Although regression models based on PGS from neuroticism (depression-related trait) GWAS in Europeans were associated with a depression level in our sample (adjusted r2 = 0.43%, p = 0.019-for unweighted model), the effect was mainly attributed to the inclusion of social/lifestyle factors as predictors in these models (adjusted r2 = 15%, p < 2.2 × 10-16-for unweighted model). In conclusion, constructed PGS models contribute to a proportion of interindividual variability in BDI-measured depression in high-education students, especially women, from the VUR of Eurasia. External factors, including the specificity of rearing in childhood, used as predictors, improve the predictive ability of these models. Implementation of ethnicity-specific effect estimates in such modeling is important for individual risk assessment.

Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes.

OBJECTIVE: Depression is a common comorbidity of type 2 diabetes. We assessed the causal relationships and shared genetics between them. RESEARCH DESIGN AND METHODS: We applied two-sample, bidirectional Mendelian randomization (MR) to assess causality between type 2 diabetes and depression. We investigated potential mediation using two-step MR. To identify shared genetics, we performed 1) genome-wide association studies (GWAS) separately and 2) multiphenotype GWAS (MP-GWAS) of type 2 diabetes (19,344 case subjects, 463,641 control subjects) and depression using major depressive disorder (MDD) (5,262 case subjects, 86,275 control subjects) and self-reported depressive symptoms (n = 153,079) in the UK Biobank. We analyzed expression quantitative trait locus (eQTL) data from public databases to identify target genes in relevant tissues. RESULTS: MR demonstrated a significant causal effect of depression on type 2 diabetes (odds ratio 1.26 [95% CI 1.11-1.44], P = 5.46 × 10-4) but not in the reverse direction. Mediation analysis indicated that 36.5% (12.4-57.6%, P = 0.0499) of the effect from depression on type 2 diabetes was mediated by BMI. GWAS of type 2 diabetes and depressive symptoms did not identify shared loci. MP-GWAS identified seven shared loci mapped to TCF7L2, CDKAL1, IGF2BP2, SPRY2, CCND2-AS1, IRS1, CDKN2B-AS1. MDD has not brought any significant association in either GWAS or MP-GWAS. Most MP-GWAS loci had an eQTL, including single nucleotide polymorphisms implicating the cell cycle gene CCND2 in pancreatic islets and brain and the insulin signaling gene IRS1 in adipose tissue, suggesting a multitissue and pleiotropic underlying mechanism. CONCLUSIONS: Our results highlight the importance to prevent type 2 diabetes at the onset of depressive symptoms and the need to maintain a healthy weight in the context of its effect on depression and type 2 diabetes comorbidity.

Abdominal obesity is a more important causal risk factor for pancreatic cancer than overall obesity.

Obesity and type 2 diabetes (T2D) are associated with increased risk of pancreatic cancer. Here we assessed the relationship between pancreatic cancer and two distinct measures of obesity, namely total adiposity, using BMI, versus abdominal adiposity, using BMI adjusted waist-to-hip ratio (WHRadjBMI) by utilising polygenic scores (PGS) and Mendelian randomisation (MR) analyses. We constructed z-score weighted PGS for BMI and WHRadjBMI using publicly available data and tested for their association with pancreatic cancer defined in UK biobank (UKBB). Using publicly available summary statistics, we then performed bi-directional MR analyses between the two obesity traits and pancreatic cancer. PGSBMI was significantly (multiple testing-corrected) associated with pancreatic cancer (OR[95%CI] = 1.0804[1.025-1.14], P = 0.0037). The significance of association declined after T2D adjustment (OR[95%CI] = 1.073[1.018-1.13], P = 0.00904). PGSWHRadjBMI association with pancreatic cancer was at the margin of statistical significance (OR[95%CI] = 1.047[0.99-1.104], P = 0.086). T2D adjustment effectively lost any suggestive association of PGSWHRadjBMI with pancreatic cancer (OR[95%CI] = 1.039[0.99-1.097], P = 0.14). MR analyses showed a nominally significant causal effect of WHRadjBMI on pancreatic cancer (OR[95%CI] = 1.00095[1.00011-1.0018], P = 0.027) but not for BMI on pancreatic cancer. Overall, we show that abdominal adiposity measured using WHRadjBMI, may be a more important causal risk factor for pancreatic cancer compared to total adiposity, with T2D being a potential driver of this relationship.

Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes.

DNA methylation variations are prevalent in human obesity but evidence of a causative role in disease pathogenesis is limited. Here, we combine epigenome-wide association and integrative genomics to investigate the impact of adipocyte DNA methylation variations in human obesity. We discover extensive DNA methylation changes that are robustly associated with obesity (N = 190 samples, 691 loci in subcutaneous and 173 loci in visceral adipocytes, P < 1 × 10-7). We connect obesity-associated methylation variations to transcriptomic changes at >500 target genes, and identify putative methylation-transcription factor interactions. Through Mendelian Randomisation, we infer causal effects of methylation on obesity and obesity-induced metabolic disturbances at 59 independent loci. Targeted methylation sequencing, CRISPR-activation and gene silencing in adipocytes, further identifies regional methylation variations, underlying regulatory elements and novel cellular metabolic effects. Our results indicate DNA methylation is an important determinant of human obesity and its metabolic complications, and reveal mechanisms through which altered methylation may impact adipocyte functions.

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes.

We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, PFDR = 3.40 × 10-5), CCR5 rs333 (OR = 1.99, PFDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, PFDR = 2.64 × 10-4), TCF7L2 rs114758349 (OR = 1.77, PFDR = 9.37 × 10-5), and CCL2 rs1024611 (OR = 1.38, PFDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7-87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10-6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = -17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.

Machine Learning to Advance Human Genome-Wide Association Studies.

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist's perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis.

Osteoporosis (OP) is a multifactorial bone disease belonging to the metabolic osteopathies group. Using the polygenic score (PGS) approach, we combined the effects of bone mineral density (BMD) DNA loci, affecting osteoporosis pathogenesis, based on GEFOS/GENOMOS consortium GWAS meta-analysis. We developed models to predict the risk of low fractures in women from the Volga-Ural region of Russia with efficacy of 74% (AUC = 0.740; OR (95% CI) = 2.9 (2.353-3.536)), as well as the formation of low BMD with efficacy of 79% (AUC = 0.790; OR (95% CI) = 3.94 (2.993-5.337)). In addition, we propose a model that predicts fracture risk and low BMD in a comorbid condition with 85% accuracy (AUC = 0.850; OR (95% CI) = 6.6 (4.411-10.608)) in postmenopausal women.

MicroRNA Processing Pathway-Based Polygenic Score for Clear Cell Renal Cell Carcinoma in the Volga-Ural Region Populations of Eurasian Continent.

The polygenic scores (PGSs) are developed to help clinicians in distinguishing individuals at high risk of developing disease outcomes from the general population. Clear cell renal cell carcinoma (ccRCC) is a complex disorder that involves numerous biological pathways, one of the most important of which is responsible for the microRNA biogenesis machinery. Here, we defined the biological-pathway-specific PGS in a case-control study of ccRCC in the Volga-Ural region of the Eurasia continent. We evaluated 28 DNA SNP variants, located in microRNA biogenesis genes, in 464 individuals with clinically diagnosed ccRCC and 1042 individuals without the disease. Individual genetic risks were defined using the SNP-variant effects derived from the ccRCC association analysis. The final weighted and unweighted PGS models were based on 21 SNPs, and 7 SNPs were excluded due to high LD. In our dataset, microRNA-machinery-weighted PGS revealed 1.69-fold higher odds (95% CI [1.51-1.91]) for ccRCC risk in individuals with ccRCC compared with controls with a p-value of 2.0 × 10-16. The microRNA biogenesis pathway weighted PGS predicted the risk of ccRCC with an area under the curve (AUC) = 0.642 (95%nCI [0.61-0.67]). Our findings indicate that DNA variants of microRNA machinery genes modulate the risk of ccRCC in Volga-Ural populations. Moreover, larger powerful genome-wide association studies are needed to reveal a wider range of genetic variants affecting microRNA processing. Biological-pathway-based PGSs will advance the development of innovative screening systems for future stratified medicine approaches in ccRCC.

Bifidobacterium Is Enriched in Gut Microbiome of Kashmiri Women with Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) is a very common endocrine condition in women in India. Gut microbiome alterations were shown to be involved in PCOS, yet it is remarkably understudied in Indian women who have a higher incidence of PCOS as compared to other ethnic populations. During the regional PCOS screening program among young women, we recruited 19 drug naive women with PCOS and 20 control women at the Sher-i-Kashmir Institute of Medical Sciences, Kashmir, North India. We profiled the gut microbiome in faecal samples by 16S rRNA sequencing and included 40/58 operational taxonomic units (OTUs) detected in at least 1/3 of the subjects with relative abundance (RA) ≥ 0.1%. We compared the RAs at a family/genus level in PCOS/non-PCOS groups and their correlation with 33 metabolic and hormonal factors, and corrected for multiple testing, while taking the variation in day of menstrual cycle at sample collection, age and BMI into account. Five genera were significantly enriched in PCOS cases: Sarcina, Megasphaera, and previously reported for PCOS Bifidobacterium, Collinsella and Paraprevotella confirmed by different statistical models. At the family level, the relative abundance of Bifidobacteriaceae was enriched, whereas Peptococcaceae was decreased among cases. We observed increased relative abundance of Collinsella and Paraprevotella with higher fasting blood glucose levels, and Paraprevotella and Alkalibacterium with larger hip, waist circumference, weight, and Peptococcaceae with lower prolactin levels. We also detected a novel association between Eubacterium and follicle-stimulating hormone levels and between Bifidobacterium and alkaline phosphatase, independently of the BMI of the participants. Our report supports that there is a relationship between gut microbiome composition and PCOS with links to specific reproductive health metabolic and hormonal predictors in Indian women.

Relationship between glucose homeostasis and obesity in early life-a study of Italian children and adolescents.

Epidemic obesity is the most important risk factor for prediabetes and type 2 diabetes (T2D) in youth as it is in adults. Obesity shares pathophysiological mechanisms with T2D and is likely to share part of the genetic background. We aimed to test if weighted genetic risk scores (GRSs) for T2D, fasting glucose (FG) and fasting insulin (FI) predict glycaemic traits and if there is a causal relationship between obesity and impaired glucose metabolism in children and adolescents. Genotyping of 42 SNPs established by genome-wide association studies for T2D, FG and FI was performed in 1660 Italian youths aged between 2 and 19 years. We defined GRS for T2D, FG and FI and tested their effects on glycaemic traits, including FG, FI, indices of insulin resistance/beta cell function and body mass index (BMI). We evaluated causal relationships between obesity and FG/FI using one-sample Mendelian randomization analyses in both directions. GRS-FG was associated with FG (beta = 0.075 mmol/l, SE = 0.011, P = 1.58 × 10-11) and beta cell function (beta = -0.041, SE = 0.0090 P = 5.13 × 10-6). GRS-T2D also demonstrated an association with beta cell function (beta = -0.020, SE = 0.021 P = 0.030). We detected a causal effect of increased BMI on levels of FI in Italian youths (beta = 0.31 ln (pmol/l), 95%CI [0.078, 0.54], P = 0.0085), while there was no effect of FG/FI levels on BMI. Our results demonstrate that the glycaemic and T2D risk genetic variants contribute to higher FG and FI levels and decreased beta cell function in children and adolescents. The causal effects of adiposity on increased insulin resistance are detectable from childhood age.

The Role of the European Society of Human Genetics in Delivering Genomic Education.

The European Society of Human Genetics (ESHG) was founded in 1967 as a professional organisation for members working in genetics in clinical practice, research and education. The Society seeks the integration of scientific research and its implementation into clinical practice and the education of specialists and the public in all areas of medical and human genetics. The Society works to do this through many approaches, including educational sessions at the annual conference; training courses in general and specialist areas of genetics; an online resource of educational materials (EuroGEMS); and a mentorship scheme. The ESHG Education Committee is implementing new approaches to expand the reach of its educational activities and portfolio. With changes in technology, appreciation of the utility of genomics in healthcare and the public's and patients' increased awareness of the role of genomics, this review will summarise how the ESHG is adapting to deliver innovative educational activity.

Functional Effects In Silico Prediction for Androgen Receptor Ligand-Binding Domain Novel I836S Mutation.

Over 1000 mutations are described in the androgen receptor (AR) gene. Of those, about 600 were found in androgen insensitivity syndrome (AIS) patients, among which 400 mutations affect the ligand-binding domain (LBD) of the AR protein. Recently, we reported a novel missense mutation c.2507T>G I836S (ClinVarID: 974911) in a patient with complete AIS (CAIS) phenotype. In the present study, we applied a set of computational approaches for the structural analysis of the ligand-binding domains in a wild-type and mutant AR to evaluate the functional impact of the novel I836S mutation. We revealed that the novel I836S substitution leads to a shorter existence time of the ligand's gating tunnel and internal cavity, occurring only in the presence of S836 phosphorylation. Additionally, the analysis of phosphorylation of the 836 mutant residues explained the negative impact on AR homodimerization, since monomer surface changes indirectly impacted the binding site. Our analyses provide evidence that I836S causes disruptions of AR protein functionality and development of CAIS clinical features in patients.