The genetic regulation of the gastric transcriptome is associated with metabolic and obesity-related traits and diseases

Tissue-specific gene expression and gene regulation lead to a better understanding of tissue-specific physiology and pathophysiology. We analyzed the transcriptome and genetic regulatory profiles of two distinct gastric sites, corpus and antrum, to identify tissue-specific gene expression and its regulation. Gastric corpus and antrum mucosa biopsies were collected during routine gastroscopies from up to 431 healthy individuals. We obtained genotype and transcriptome data and performed transcriptome profiling and expression quantitative trait locus (eQTL) studies. We further used data from genome-wide association studies (GWAS) of various diseases and traits to partition their heritability and to perform transcriptome-wide association studies (TWAS). The transcriptome data from corpus and antral mucosa highlights the heterogeneity of gene expression in the stomach. We identified enriched pathways revealing distinct and common physiological processes in gastric corpus and antrum. Furthermore, we found an enrichment of the single nucleotide polymorphism (SNP)-based heritability of metabolic, obesity-related, and cardiovascular traits and diseases by considering corpus- and antrum-specifically expressed genes. Particularly, we could prioritize gastric-specific candidate genes for multiple metabolic traits, like NQO1 which is involved in glucose metabolism, MUC1 which contributes to purine and protein metabolism or RAB27B being a regulator of weight and body composition. Our findings show that gastric corpus and antrum vary in their transcriptome and genetic regulatory profiles indicating physiological differences which are mostly related to digestion and epithelial protection. Moreover, our findings demonstrate that the genetic regulation of the gastric transcriptome is linked to biological mechanisms associated with metabolic, obesity-related, and cardiovascular traits and diseases. NEW & NOTEWORTHY We analyzed the transcriptomes and genetic regulatory profiles of gastric corpus and for the first time also of antrum mucosa in 431 healthy individuals. Through tissue-specific gene expression and eQTL analyses, we uncovered unique and common physiological processes across both primary gastric sites. Notably, our findings reveal that stomach-specific eQTLs are enriched in loci associated with metabolic traits and diseases, highlighting the pivotal role of gene expression regulation in gastric physiology and potential pathophysiology.

Meta-analysis of ACE inhibitor-induced angioedema identifies novel risk locus

BACKGROUND

Angioedema is a rare but potentially life-threatening adverse drug reaction in patients receiving angiotensin-converting enzyme inhibitors (ACEis). Research suggests that susceptibility to ACEi-induced angioedema (ACEi-AE) involves both genetic and nongenetic risk factors. Genome- and exome-wide studies of ACEi-AE have identified the first genetic risk loci. However, understanding of the underlying pathophysiology remains limited.

OBJECTIVE

We sought to identify further genetic factors of ACEi-AE to eventually gain a deeper understanding of its pathophysiology.

METHODS

By combining data from 8 cohorts, a genome-wide association study meta-analysis was performed in more than 1000 European patients with ACEi-AE. Secondary bioinformatic analyses were conducted to fine-map associated loci, identify relevant genes and pathways, and assess the genetic overlap between ACEi-AE and other traits. Finally, an exploratory cross-ancestry analysis was performed to assess shared genetic factors in European and African-American patients with ACEi-AE.

RESULTS

Three genome-wide significant risk loci were identified. One of these, located on chromosome 20q11.22, has not been implicated previously in ACEi-AE. Integrative secondary analyses highlighted previously reported genes (BDKRB2 [bradykinin receptor B2] and F5 [coagulation factor 5]) as well as biologically plausible novel candidate genes (PROCR [protein C receptor] and EDEM2 [endoplasmic reticulum degradation enhancing alpha-mannosidase like protein 2]). Lead variants at the risk loci were found with similar effect sizes and directions in an African-American cohort.

CONCLUSIONS

The present results contributed to a deeper understanding of the pathophysiology of ACEi-AE by (1) providing further evidence for the involvement of bradykinin signaling and coagulation pathways and (2) suggesting, for the first time, the involvement of the fibrinolysis pathway in this adverse drug reaction. An exploratory cross-ancestry comparison implicated the relevance of the associated risk loci across diverse ancestries.

Phenome-wide Mendelian randomisation analysis of 378,142 cases reveals risk factors for eight common cancers

For many cancers there are only a few well-established risk factors. Here, we use summary data from genome-wide association studies (GWAS) in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify potentially causal relationships for over 3,000 traits. Our outcome datasets comprise 378,142 cases across breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, as well as 485,715 controls. We complement this analysis by systematically mining the literature space for supporting evidence. In addition to providing supporting evidence for well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we also find sex steroid hormones, plasma lipids, and telomere length as determinants of cancer risk. A number of the molecular factors we identify may prove to be potential biomarkers. Our analysis, which highlights aetiological similarities and differences in common cancers, should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app to visualise findings.

Imatinib treatment and longitudinal growth in pediatric patients with chronic myeloid leukemia: Influence of demographic, pharmacological, and genetic factors in the German CML-PAED cohort

In children and adolescents, impaired growth due to tyrosine kinase inhibitor therapy remains an insufficiently studied adverse effect. This study examines demographic, pharmacological, and genetic factors associated with impaired longitudinal growth in a uniform pediatric cohort treated with imatinib. We analyzed 94 pediatric patients with chronic myeloid leukemia (CML) diagnosed in the chronic phase and treated with imatinib for >12 months who participated in the Germany-wide CML-PAEDII study between February 2006 and February 2021. During imatinib treatment, significant height reduction occurred, with medians of -0.35 standard deviation score (SDS) at 12 months and -0.76 SDS at 24 months. Cumulative height SDS change (Δheight SDS) showed a more pronounced effect in prepubertal patients during the first year but were similar between prepubertal and pubertal subgroups by the second year (-0.55 vs. -0.50). From months 12 to 18 on imatinib, only 18% patients achieved individually longitudinal growth adequate to the growth standard (Δheight SDS≥0). When patients were divided into two subgroups based on median Δheight SDS (classifier Δheight SDS > or ≤-0.37) after one year on imatinib therapy, cohort 1 (Δheight SDS extending -0.37) showed younger age at diagnosis, a higher proportion of prepubertal children, but also better treatment response and higher imatinib serum levels. Exploring the association of growth parameters with pharmacokinetically relevant single nucleotide polymorphisms, known for affecting imatinib response, showed no correlation. This retrospective study provides new insights into imatinib-related growth impairment. We emphasize the importance of optimizing treatment strategies for pediatric patients to realize their maximum growth potential.

Single-cell analysis reveals the spatial-temporal expression of genes associated with esophageal malformations

Understanding the molecular mechanisms of congenital diseases is challenging due to their occurrence within specific developmental stages. Esophageal malformations are examples of such conditions, characterized by abnormalities in the development of esophagus during embryogenesis. These developmental malformations encompass a range of anomalies, including esophageal atresia, and tracheoesophageal fistula. Here, we investigated the preferential expression of 29 genes that are implicated in such malformations and their immediate interactome (a total of 67 genes). We conducted our analyses across several single-cell atlases of embryonic development, encompassing approximately 150,000 cells from the mouse foregut, 180,000 cells from human embryos, and 500,000 cells from 24 human organs. Our study, spanning diverse mesodermal and endodermal cell populations and early developmental stages, shows that the genes associated with esophageal malformations show their highest cell-type specific expression in lateral plate mesoderm cells and at the developmental stage of E8.75-E9.0 days. In human embryos, these genes show a significant cell-type specific expression among subpopulations of epithelial cells, fibroblasts and progenitor cells including basal cells. Notably, members of the forkhead-box family of transcription factors, namely FOXF1, FOXC1, and FOXD1, as well as the SRY-box transcription factor, SOX2, demonstrate the most significant preferential expression in both mouse and human embryos. Overall, our findings provide insights into the temporal and cellular contexts contributing to esophageal malformations.

Ability of a polygenic risk score to refine colorectal cancer risk in Lynch syndrome

BACKGROUND

Polygenic risk scores (PRSs) have been used to stratify colorectal cancer (CRC) risk in the general population, whereas its role in Lynch syndrome (LS), the most common type of hereditary CRC, is still conflicting. We aimed to assess the ability of PRS to refine CRC risk prediction in European-descendant individuals with LS.

METHODS

1465 individuals with LS (557 MLH1, 517 MSH2/EPCAM, 299 MSH6 and 92 PMS2) and 5656 CRC-free population-based controls from two independent cohorts were included. A 91-SNP PRS was applied. A Cox proportional hazard regression model with 'family' as a random effect and a logistic regression analysis, followed by a meta-analysis combining both cohorts were conducted.

RESULTS

Overall, we did not observe a statistically significant association between PRS and CRC risk in the entire cohort. Nevertheless, PRS was significantly associated with a slightly increased risk of CRC or advanced adenoma (AA), in those with CRC diagnosed <50 years and in individuals with multiple CRCs or AAs diagnosed <60 years.

CONCLUSION

The PRS may slightly influence CRC risk in individuals with LS in particular in more extreme phenotypes such as early-onset disease. However, the study design and recruitment strategy strongly influence the results of PRS studies. A separate analysis by genes and its combination with other genetic and non-genetic risk factors will help refine its role as a risk modifier in LS.

Analysis of 72,469 UK Biobank exomes links rare variants to male-pattern hair loss

Male-pattern hair loss (MPHL) is common and highly heritable. While genome-wide association studies (GWAS) have generated insights into the contribution of common variants to MPHL etiology, the relevance of rare variants remains unclear. To determine the contribution of rare variants to MPHL etiology, we perform gene-based and single-variant analyses in exome-sequencing data from 72,469 male UK Biobank participants. While our population-level risk prediction suggests that rare variants make only a minor contribution to general MPHL risk, our rare variant collapsing tests identified a total of five significant gene associations. These findings provide additional evidence for previously implicated genes (EDA2R, WNT10A) and highlight novel risk genes at and beyond GWAS loci (HEPH, CEPT1, EIF3F). Furthermore, MPHL-associated genes are enriched for genes considered causal for monogenic trichoses. Together, our findings broaden the MPHL-associated allelic spectrum and provide insights into MPHL pathobiology and a shared basis with monogenic hair loss disorders.

Gene-based burden scores identify rare variant associations for 28 blood biomarkers

BACKGROUND

A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling.

METHODS

We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined).

RESULTS

Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models.

CONCLUSION

This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level.

Assessing the performance of European-derived cardiometabolic polygenic risk scores in South-Asians and their interplay with family history

BACKGROUND & AIMS

We aimed to assess the performance of European-derived polygenic risk scores (PRSs) for common metabolic diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D) in the South Asian (SAS) individuals in the UK Biobank. Additionally, we studied the interaction between PRS and family history (FH) in the same population.

METHODS

To calculate the PRS, we used a previously published model derived from the EUR population and applied it to the individuals of SAS ancestry from the UKB study. Each PRS was adjusted according to an individual's genotype location in the principal components (PC) space to derive an ancestry adjusted PRS (aPRS). We calculated the percentiles based on aPRS and stratified individuals into three aPRS categories: low, intermediate, and high. Considering the intermediate-aPRS percentile as a reference, we compared the low and high aPRS categories and generated the odds ratio (OR) estimates. Further, we measured the combined role of aPRS and first-degree family history (FH) in the SAS population.

RESULTS

The risk of developing severe obesity for SAS individuals was almost twofold higher for individuals with high aPRS than for those with intermediate aPRS, with an OR of 1.95 (95% CI = 1.71-2.23, P < 0.01). At the same time, the risk of severe obesity was lower in the low-aPRS group (OR = 0.60, CI = 0.53-0.67, P < 0.01). Results in the same direction were found in the EUR data, where the low-PRS group had an OR of 0.53 (95% CI = 0.51-0.56, P < 0.01) and the high-PRS group had an OR of 2.06 (95% CI = 2.00-2.12, P < 0.01). We observed similar results for CAD and T2D. Further, we show that SAS individuals with a familial history of CAD and T2D with high-aPRS are associated with a higher risk of these diseases, implying a greater genetic predisposition.

CONCLUSION

Our findings suggest that CAD, obesity, and T2D GWAS summary statistics generated predominantly from the EUR population can be potentially used to derive aPRS in SAS individuals for risk stratification. With future GWAS recruiting more SAS participants and tailoring the PRSs towards SAS ancestry, the predictive power of PRS is likely to improve further.

AI-based multi-PRS models outperform classical single-PRS models

Polygenic risk scores (PRS) calculate the risk for a specific disease based on the weighted sum of associated alleles from different genetic loci in the germline estimated by regression models. Recent advances in genetics made it possible to create polygenic predictors of complex human traits, including risks for many important complex diseases, such as cancer, diabetes, or cardiovascular diseases, typically influenced by many genetic variants, each of which has a negligible effect on overall risk. In the current study, we analyzed whether adding additional PRS from other diseases to the prediction models and replacing the regressions with machine learning models can improve overall predictive performance. Results showed that multi-PRS models outperform single-PRS models significantly on different diseases. Moreover, replacing regression models with machine learning models, i.e., deep learning, can also improve overall accuracy.

Dissecting the genetic heterogeneity of gastric cancer

BACKGROUND

Gastric cancer (GC) is clinically heterogenous according to location (cardia/non-cardia) and histopathology (diffuse/intestinal). We aimed to characterize the genetic risk architecture of GC according to its subtypes. Another aim was to examine whether cardia GC and oesophageal adenocarcinoma (OAC) and its precursor lesion Barrett's oesophagus (BO), which are all located at the gastro-oesophageal junction (GOJ), share polygenic risk architecture.

METHODS

We did a meta-analysis of ten European genome-wide association studies (GWAS) of GC and its subtypes. All patients had a histopathologically confirmed diagnosis of gastric adenocarcinoma. For the identification of risk genes among GWAS loci we did a transcriptome-wide association study (TWAS) and expression quantitative trait locus (eQTL) study from gastric corpus and antrum mucosa. To test whether cardia GC and OAC/BO share genetic aetiology we also used a European GWAS sample with OAC/BO.

FINDINGS

Our GWAS consisting of 5816 patients and 10,999 controls highlights the genetic heterogeneity of GC according to its subtypes. We newly identified two and replicated five GC risk loci, all of them with subtype-specific association. The gastric transcriptome data consisting of 361 corpus and 342 antrum mucosa samples revealed that an upregulated expression of MUC1, ANKRD50, PTGER4, and PSCA are plausible GC-pathomechanisms at four GWAS loci. At another risk locus, we found that the blood-group 0 exerts protective effects for non-cardia and diffuse GC, while blood-group A increases risk for both GC subtypes. Furthermore, our GWAS on cardia GC and OAC/BO (10,279 patients, 16,527 controls) showed that both cancer entities share genetic aetiology at the polygenic level and identified two new risk loci on the single-marker level.

INTERPRETATION

Our findings show that the pathophysiology of GC is genetically heterogenous according to location and histopathology. Moreover, our findings point to common molecular mechanisms underlying cardia GC and OAC/BO.

FUNDING

German Research Foundation (DFG).

First genome-wide association study for lymphatic filariasis in a West African population points to an HLA-mediated disease pathophysiology

OBJECTIVES

Lymphatic filariasis (LF) represents a parasitic disease caused by filarial nematodes. While some infected individuals present an asymptomatic course, others suffer severe chronic lymphatic pathology, including lymphoedema (LE), hydrocele, and elephantiasis. Several studies have shown that host genetic factors influence LF susceptibility and chronic pathology. The present study aimed to conduct the first genome-wide association study (GWAS) to systematically determine LF susceptibility.

METHODS

We analyzed genome-wide single-nucleotide polymorphism (SNP) data from 1,459 LF cases and 1,492 asymptomatic controls of West African (Ghanaian) descent.

RESULTS

We identified two independent genome-wide significant associated genetic variants near the genes HLA-DQB2 (rs7742085) and HLA-DQA1 (rs4959107) contributing to LF and/or LE susceptibility (P < 5.0 × 10^-8, odds ratios (ORs) > 1.30). We also observed suggestive evidence of LF associations (P < 1.0 × 10^-6) at two non-HLA loci, near the genes ZFHX4-AS1 (rs79562145) and CHP2 (rs12933387). In contrast, we could not replicate any previously reported LF associations drawn from candidate gene association studies. On the polygenic level, we show that our GWAS data explain 24-42% of LF heritability, depending on an assumed population prevalence of 0.5-5.0%.

CONCLUSION

Our findings point to an involvement of HLA-mediated immune mechanisms in LF pathophysiology.

Risk factors for eight common cancers revealed from a phenome-wide Mendelian randomisation analysis of 378,142 cases and 485,715 controls

For many cancers there are few well-established risk factors. Summary data from genome-wide association studies (GWAS) can be used in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify causal relationships. We performed a MR-PheWAS of breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, comprising 378,142 cases and 485,715 controls. To derive a more comprehensive insight into disease aetiology we systematically mined the literature space for supporting evidence. We evaluated causal relationships for over 3,000 potential risk factors. In addition to identifying well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we provide evidence for specific factors, including dietary intake, sex steroid hormones, plasma lipids and telomere length as determinants of cancer risk. We also implicate molecular factors including plasma levels of IL-18, LAG-3, IGF-1, CT-1, and PRDX1 as risk factors. Our analyses highlight the importance of risk factors that are common to many cancer types but also reveal aetiological differences. A number of the molecular factors we identify have the potential to be biomarkers. Our findings should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app (https://mrcancer.shinyapps.io/mrcan/) to visualise findings.

GWAS meta-analysis of 16 790 patients with Barrett's oesophagus and oesophageal adenocarcinoma identifies 16 novel genetic risk loci and provides insights into disease aetiology beyond the single marker level

OBJECTIVE

Oesophageal cancer (EC) is the sixth leading cause of cancer-related deaths. Oesophageal adenocarcinoma (EA), with Barrett's oesophagus (BE) as a precursor lesion, is the most prevalent EC subtype in the Western world. This study aims to contribute to better understand the genetic causes of BE/EA by leveraging genome wide association studies (GWAS), genetic correlation analyses and polygenic risk modelling.

DESIGN

We combined data from previous GWAS with new cohorts, increasing the sample size to 16 790 BE/EA cases and 32 476 controls. We also carried out a transcriptome wide association study (TWAS) using expression data from disease-relevant tissues to identify BE/EA candidate genes. To investigate the relationship with reported BE/EA risk factors, a linkage disequilibrium score regression (LDSR) analysis was performed. BE/EA risk models were developed combining clinical/lifestyle risk factors with polygenic risk scores (PRS) derived from the GWAS meta-analysis.

RESULTS

The GWAS meta-analysis identified 27 BE and/or EA risk loci, 11 of which were novel. The TWAS identified promising BE/EA candidate genes at seven GWAS loci and at five additional risk loci. The LDSR analysis led to the identification of novel genetic correlations and pointed to differences in BE and EA aetiology. Gastro-oesophageal reflux disease appeared to contribute stronger to the metaplastic BE transformation than to EA development. Finally, combining PRS with BE/EA risk factors improved the performance of the risk models.

CONCLUSION

Our findings provide further insights into BE/EA aetiology and its relationship to risk factors. The results lay the foundation for future follow-up studies to identify underlying disease mechanisms and improving risk prediction.

Risk factors for eight common cancers revealed from a phenome-wide Mendelian randomisation analysis of 378,142 cases and 485,715 controls

For many cancers there are few well-established risk factors. Summary data from genome-wide association studies (GWAS) can be used in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify causal relationships. We performed a MR-PheWAS of breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, comprising 378,142 cases and 485,715 controls. To derive a more comprehensive insight into disease aetiology we systematically mined the literature space for supporting evidence. We evaluated causal relationships for over 3,000 potential risk factors. In addition to identifying well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we provide evidence for specific factors, including dietary intake, sex steroid hormones, plasma lipids and telomere length as determinants of cancer risk. We also implicate molecular factors including plasma levels of IL-18, LAG-3, IGF-1, CT-1, and PRDX1 as risk factors. Our analyses highlight the importance of risk factors that are common to many cancer types but also reveal aetiological differences. A number of the molecular factors we identify have the potential to be biomarkers. Our findings should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app (https://mrcancer.shinyapps.io/mrcan/) to visualise findings.

Genomic analyses of hair from Ludwig van Beethoven

Ludwig van Beethoven (1770-1827) remains among the most influential and popular classical music composers. Health problems significantly impacted his career as a composer and pianist, including progressive hearing loss, recurring gastrointestinal complaints, and liver disease. In 1802, Beethoven requested that following his death, his disease be described and made public. Medical biographers have since proposed numerous hypotheses, including many substantially heritable conditions. Here we attempt a genomic analysis of Beethoven in order to elucidate potential underlying genetic and infectious causes of his illnesses. We incorporated improvements in ancient DNA methods into existing protocols for ancient hair samples, enabling the sequencing of high-coverage genomes from small quantities of historical hair. We analyzed eight independently sourced locks of hair attributed to Beethoven, five of which originated from a single European male. We deemed these matching samples to be almost certainly authentic and sequenced Beethoven's genome to 24-fold genomic coverage. Although we could not identify a genetic explanation for Beethoven's hearing disorder or gastrointestinal problems, we found that Beethoven had a genetic predisposition for liver disease. Metagenomic analyses revealed furthermore that Beethoven had a hepatitis B infection during at least the months prior to his death. Together with the genetic predisposition and his broadly accepted alcohol consumption, these present plausible explanations for Beethoven's severe liver disease, which culminated in his death. Unexpectedly, an analysis of Y chromosomes sequenced from five living members of the Van Beethoven patrilineage revealed the occurrence of an extra-pair paternity event in Ludwig van Beethoven's patrilineal ancestry.

Clinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence

BACKGROUND AND AIMS

Summarised in polygenic risk scores (PRS), the effect of common, low penetrant genetic variants associated with colorectal cancer (CRC), can be used for risk stratification.

METHODS

To assess the combined impact of the PRS and other main factors on CRC risk, 163,516 individuals from the UK Biobank were stratified as follows: 1. carriers status for germline pathogenic variants (PV) in CRC susceptibility genes (APC, MLH1, MSH2, MSH6, PMS2), 2. low (< 20%), intermediate (20-80%), or high PRS (> 80%), and 3. family history (FH) of CRC. Multivariable logistic regression and Cox proportional hazards models were applied to compare odds ratios and to compute the lifetime incidence, respectively.

RESULTS

Depending on the PRS, the CRC lifetime incidence for non-carriers ranges between 6 and 22%, compared to 40% and 74% for carriers. A suspicious FH is associated with a further increase of the cumulative incidence reaching 26% for non-carriers and 98% for carriers. In non-carriers without FH, but high PRS, the CRC risk is doubled, whereas a low PRS even in the context of a FH results in a decreased risk. The full model including PRS, carrier status, and FH improved the area under the curve in risk prediction (0.704).

CONCLUSION

The findings demonstrate that CRC risks are strongly influenced by the PRS for both a sporadic and monogenic background. FH, PV, and common variants complementary contribute to CRC risk. The implementation of PRS in routine care will likely improve personalized risk stratification, which will in turn guide tailored preventive surveillance strategies in high, intermediate, and low risk groups.

Genetic prediction of male pattern baldness based on large independent datasets

Genetic prediction of male pattern baldness (MPB) is important in science and society. Previous genetic MPB prediction models were limited by sparse marker coverage, small sample size, and/or data dependency in the different analytical steps. Here, we present novel models for genetic prediction of MPB based on a large set of markers and large independent subsample sets drawn among 187,435 European subjects. We selected 117 SNP predictors within 85 distinct loci from a list of 270 previously MPB-associated SNPs in 55,573 males of the UK Biobank Study (UKBB). Based on these 117 SNPs with and without age as additional predictor, we trained, by use of different methods, prediction models in a non-overlapping subset of 104,694 UKBB males and tested them in a non-overlapping subset of 26,177 UKBB males. Estimates of prediction accuracy were similar between methods with AUC ranges of 0.725-0.728 for severe, 0.631-0.635 for moderate, 0.598-0.602 for slight, and 0.708-0.711 for no hair loss with age, and slightly lower without, while prediction of any versus no hair loss gave 0.690-0.711 with age and slightly lower without. External validation in an early-onset enriched MPB dataset from the Bonn Study (N = 991) showed improved prediction accuracy without considering age such as AUC of 0.830 for no vs. any hair loss. Because of the large number of markers and the large independent datasets used for the different analytical steps, the newly presented genetic prediction models are the most reliable ones currently available for MPB or any other human appearance trait.

Identification of de novo variants in nonsyndromic cleft lip with/without cleft palate patients with low polygenic risk scores

BACKGROUND

Nonsyndromic cleft lip with/without cleft palate (nsCL/P) is a congenital malformation of multifactorial etiology. Research has identified >40 genome-wide significant risk loci, which explain less than 40% of nsCL/P heritability. Studies show that some of the hidden heritability is explained by rare penetrant variants.

METHODS

To identify new candidate genes, we searched for highly penetrant de novo variants (DNVs) in 50 nsCL/P patient/parent-trios with a low polygenic risk for the phenotype (discovery). We prioritized DNV-carrying candidate genes from the discovery for resequencing in independent cohorts of 1010 nsCL/P patients of diverse ethnicities and 1574 population-matched controls (replication). Segregation analyses and rare variant association in the replication cohort, in combination with additional data (genome-wide association data, expression, protein-protein-interactions), were used for final prioritization.

CONCLUSION

In the discovery step, 60 DNVs were identified in 60 genes, including a variant in the established nsCL/P risk gene CDH1. Re-sequencing of 32 prioritized genes led to the identification of 373 rare, likely pathogenic variants. Finally, MDN1 and PAXIP1 were prioritized as top candidates. Our findings demonstrate that DNV detection, including polygenic risk score analysis, is a powerful tool for identifying nsCL/P candidate genes, which can also be applied to other multifactorial congenital malformations.

A statistical boosting framework for polygenic risk scores based on large-scale genotype data

Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To improve the predictive performance of PRS, it is desirable to fit multivariable models directly on the genetic data. Due to the large and high-dimensional data, a direct application of existing methods is often not feasible and new efficient algorithms are required to overcome the computational burden regarding efficiency and memory demands. We develop an adapted component-wise L 2-boosting algorithm to fit genotype data from large cohort studies to continuous outcomes using linear base-learners for the genetic variants. Similar to the snpnet approach implementing lasso regression, the proposed snpboost approach iteratively works on smaller batches of variants. By restricting the set of possible base-learners in each boosting step to variants most correlated with the residuals from previous iterations, the computational efficiency can be substantially increased without losing prediction accuracy. Furthermore, for large-scale data based on various traits from the UK Biobank we show that our method yields competitive prediction accuracy and computational efficiency compared to the snpnet approach and further commonly used methods. Due to the modular structure of boosting, our framework can be further extended to construct PRS for different outcome data and effect types-we illustrate this for the prediction of binary traits.

Common Germline Risk Variants Impact Somatic Alterations and Clinical Features across Cancers

Aggregation of genome-wide common risk variants, such as polygenic risk score (PRS), can measure genetic susceptibility to cancer. A better understanding of how common germline variants associate with somatic alterations and clinical features could facilitate personalized cancer prevention and early detection. We constructed PRSs from 14 genome-wide association studies (median n = 64,905) for 12 cancer types by multiple methods and calibrated them using the UK Biobank resources (n = 335,048). Meta-analyses across cancer types in The Cancer Genome Atlas (n = 7,965) revealed that higher PRS values were associated with earlier cancer onset and lower burden of somatic alterations, including total mutations, chromosome/arm somatic copy-number alterations (SCNA), and focal SCNAs. This contrasts with rare germline pathogenic variants (e.g., BRCA1/2 variants), showing heterogeneous associations with somatic alterations. Our results suggest that common germline cancer risk variants allow early tumor development before the accumulation of many somatic alterations characteristic of later stages of carcinogenesis.

SIGNIFICANCE

Meta-analyses across cancers show that common germline risk variants affect not only cancer predisposition but the age of cancer onset and burden of somatic alterations, including total mutations and copy-number alterations.

Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets

In Genetics, gene sets are grouped in collections concerning their biological function. This often leads to high-dimensional, overlapping, and redundant families of sets, thus precluding a straightforward interpretation of their biological meaning. In Data Mining, it is often argued that techniques to reduce the dimensionality of data could increase the maneuverability and consequently the interpretability of large data. In the past years, moreover, we witnessed an increasing consciousness of the importance of understanding data and interpretable models in the machine learning and bioinformatics communities. On the one hand, there exist techniques aiming to aggregate overlapping gene sets to create larger pathways. While these methods could partly solve the large size of the collections' problem, modifying biological pathways is hardly justifiable in this biological context. On the other hand, the representation methods to increase interpretability of collections of gene sets that have been proposed so far have proved to be insufficient. Inspired by this Bioinformatics context, we propose a method to rank sets within a family of sets based on the distribution of the singletons and their size. We obtain sets' importance scores by computing Shapley values; Making use of microarray games, we do not incur the typical exponential computational complexity. Moreover, we address the challenge of constructing redundancy-aware rankings where, in our case, redundancy is a quantity proportional to the size of intersections among the sets in the collections. We use the obtained rankings to reduce the dimension of the families, therefore showing lower redundancy among sets while still preserving a high coverage of their elements. We finally evaluate our approach for collections of gene sets and apply Gene Sets Enrichment Analysis techniques to the now smaller collections: As expected, the unsupervised nature of the proposed rankings allows for unremarkable differences in the number of significant gene sets for specific phenotypic traits. In contrast, the number of performed statistical tests can be drastically reduced. The proposed rankings show a practical utility in bioinformatics to increase interpretability of the collections of gene sets and a step forward to include redundancy-awareness into Shapley values computations.

A Genome-Wide Association Study into the Aetiology of Congenital Solitary Functioning Kidney

Congenital solitary functioning kidney (CSFK) is a birth defect that occurs in 1:1500 children and predisposes them to kidney injury. Its aetiology is likely multifactorial. In addition to known monogenic causes and environmental risk factors, common genetic variation may contribute to susceptibility to CSFK. We performed a genome-wide association study among 452 patients with CSFK and two control groups of 669 healthy children and 5363 unaffected adults. Variants in two loci reached the genome-wide significance threshold of 5 × 10^-8, and variants in 30 loci reached the suggestive significance threshold of 1 × 10^-5. Of these, an identified locus with lead single nucleotide variant (SNV) rs140804918 (odds ratio 3.1, p-value = 1.4 × 10^-8) on chromosome 7 was most promising due to its close proximity to HGF, a gene known to be involved in kidney development. Based on their known molecular functions, both KCTD20 and STK38 could explain the suggestive significant association with lead SNV rs148413365 on chromosome 6. Our findings need replication in an independent cohort of CSFK patients before they can be established definitively. However, our analysis suggests that common variants play a role in CSFK aetiology. Future research could enhance our understanding of the molecular mechanisms involved.

Transcriptomics and miRNomics data integration in lymphoblastoid cells highlights the key role of immune-related functions in lithium treatment response in Bipolar disorder

BACKGROUND

Bipolar Disorder (BD) is a complex mental disease characterized by recurrent episodes of mania and depression. Lithium (Li) represents the mainstay of BD pharmacotherapy, despite the narrow therapeutic index and the high variability in treatment response. However, although several studies have been conducted, the molecular mechanisms underlying Li therapeutic effects remain unclear.

METHODS

In order to identify molecular signatures and biological pathways associated with Li treatment response, we conducted transcriptome and miRNome microarray analyses on lymphoblastoid cell lines (LCLs) from 20 patients diagnosed with BD classified as Li responders (n = 11) or non-responders (n = 9).

RESULTS

We found 335 mRNAs and 77 microRNAs (miRNAs) significantly modulated in BD responders versus non-responders. Interestingly, pathway and network analyses on these differentially expressed molecules suggested a modulatory effect of Li on several immune-related functions. Indeed, among the functional molecular nodes, we found NF-κB and TNF. Moreover, networks related to these molecules resulted overall inhibited in BD responder patients, suggesting anti-inflammatory properties of Li. From the integrative analysis between transcriptomics and miRNomics data carried out using miRComb R package on the same samples from patients diagnosed with BD, we found 97 significantly and negatively correlated mRNA-miRNA pairs, mainly involved in inflammatory/immune response.

CONCLUSIONS

Our results highlight that Li exerts modulatory effects on immune-related functions and that epigenetic mechanisms, especially miRNAs, can influence the modulation of different genes and pathways involved in Li response. Moreover, our data suggest the potentiality to integrate data coming from different high-throughput approaches as a tool to prioritize genes and pathways.

Diverse ancestry whole-genome sequencing association study identifies TBX5 and PTK7 as susceptibility genes for posterior urethral valves

Posterior urethral valves (PUV) are the commonest cause of end-stage renal disease in children, but the genetic architecture of this rare disorder remains unknown. We performed a sequencing-based genome-wide association study (seqGWAS) in 132 unrelated male PUV cases and 23,727 controls of diverse ancestry, identifying statistically significant associations with common variants at 12q24.21 (p=7.8 × 10^-12; OR 0.4) and rare variants at 6p21.1 (p=2.0 × 10^-8; OR 7.2), that were replicated in an independent European cohort of 395 cases and 4151 controls. Fine mapping and functional genomic data mapped these loci to the transcription factor TBX5 and planar cell polarity gene PTK7, respectively, the encoded proteins of which were detected in the developing urinary tract of human embryos. We also observed enrichment of rare structural variation intersecting with candidate cis-regulatory elements, particularly inversions predicted to affect chromatin looping (p=3.1 × 10^-5). These findings represent the first robust genetic associations of PUV, providing novel insights into the underlying biology of this poorly understood disorder and demonstrate how a diverse ancestry seqGWAS can be used for disease locus discovery in a rare disease.

Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning: Dementia Praecox Revisited

Importance

The behavioral and cognitive symptoms of severe psychotic disorders overlap with those seen in dementia. However, shared brain alterations remain disputed, and their relevance for patients in at-risk disease stages has not been explored so far.

Objective

To use machine learning to compare the expression of structural magnetic resonance imaging (MRI) patterns of behavioral-variant frontotemporal dementia (bvFTD), Alzheimer disease (AD), and schizophrenia; estimate predictability in patients with bvFTD and schizophrenia based on sociodemographic, clinical, and biological data; and examine prognostic value, genetic underpinnings, and progression in patients with clinical high-risk (CHR) states for psychosis or recent-onset depression (ROD).

Design, Setting, and Participants

This study included 1870 individuals from 5 cohorts, including (1) patients with bvFTD (n = 108), established AD (n = 44), mild cognitive impairment or early-stage AD (n = 96), schizophrenia (n = 157), or major depression (n = 102) to derive and compare diagnostic patterns and (2) patients with CHR (n = 160) or ROD (n = 161) to test patterns' prognostic relevance and progression. Healthy individuals (n = 1042) were used for age-related and cohort-related data calibration. Data were collected from January 1996 to July 2019 and analyzed between April 2020 and April 2022.

Main Outcomes and Measures

Case assignments based on diagnostic patterns; sociodemographic, clinical, and biological data; 2-year functional outcomes and genetic separability of patients with CHR and ROD with high vs low pattern expression; and pattern progression from baseline to follow-up MRI scans in patients with nonrecovery vs preserved recovery.

Results

Of 1870 included patients, 902 (48.2%) were female, and the mean (SD) age was 38.0 (19.3) years. The bvFTD pattern comprising prefrontal, insular, and limbic volume reductions was more expressed in patients with schizophrenia (65 of 157 [41.2%]) and major depression (22 of 102 [21.6%]) than the temporo-limbic AD patterns (28 of 157 [17.8%] and 3 of 102 [2.9%], respectively). bvFTD expression was predicted by high body mass index, psychomotor slowing, affective disinhibition, and paranoid ideation (R2 = 0.11). The schizophrenia pattern was expressed in 92 of 108 patients (85.5%) with bvFTD and was linked to the C9orf72 variant, oligoclonal banding in the cerebrospinal fluid, cognitive impairment, and younger age (R2 = 0.29). bvFTD and schizophrenia pattern expressions forecasted 2-year psychosocial impairments in patients with CHR and were predicted by polygenic risk scores for frontotemporal dementia, AD, and schizophrenia. Findings were not associated with AD or accelerated brain aging. Finally, 1-year bvFTD/schizophrenia pattern progression distinguished patients with nonrecovery from those with preserved recovery.

Conclusions and Relevance

Neurobiological links may exist between bvFTD and psychosis focusing on prefrontal and salience system alterations. Further transdiagnostic investigations are needed to identify shared pathophysiological processes underlying the neuroanatomical interface between the 2 disease spectra.

Statistical learning for sparser fine-mapped polygenic models: The prediction of LDL-cholesterol

Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser models for genome-wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine-mapping is independently applied for blocks of variants in linkage disequilibrium, where informative variants are retrieved by using variable selection methods including boosting with probing and stochastic searches with the Adaptive Subspace method. Finally, joint prediction models with the selected variants are derived using statistical boosting. In contrast to alternative approaches relying on univariate summary statistics from genome-wide association studies, our three-step approach enables to select and fit multivariable regression models on large-scale genotype data. Based on UK Biobank data, we develop prediction models for LDL-cholesterol as a continuous trait. Additionally, we consider a recent scalable algorithm for the Lasso. Results show that statistical learning approaches based on fine-mapping of genetic signals result in a competitive prediction performance compared to classical polygenic risk approaches, while yielding sparser risk models.

Chemokine receptor 4 expression on blood T lymphocytes predicts severity of major depressive disorder

BACKGROUND

Chemokines and their receptors regulate inflammatory processes in major depressive disorder (MDD). Here, we characterize the expression pattern of the C-C chemokine receptor 4 (CCR4) and its ligands CCL17 and CCL22 in MDD and its clinical relevance in predicting disease severity.

METHODS

Expression of CCR4 on peripheral blood lymphocytes and serum CCL17/CCL22 levels were measured using multiparameter flow cytometry and multiplex assays in 33 depressed inpatients at baseline (T0) and after 6-week multimodal treatment (T1) compared with 21 healthy controls (HC). Using stratified and correlation analysis, we examined the associations of CCR4-CCL17/CCL22 expression with depression severity and symptoms according to standard clinical rating scales and questionnaires. Additionally, we assessed whether polygenic risk score (PRS) for psychiatric disorders and chronotype are associated with disease status or CCR4-CCL17/CCL22 expression. Regression analysis was performed to assess the capacity of CCR4 and PRS in predicting disease severity.

RESULTS

Compared with HC, MDD patients showed significantly decreased CCR4 expression on T cells (T0 and T1), whereas CCL17/CCL22 serum levels were increased. Stratified and correlation analysis revealed an association of CCR4 expression on CD4⁺ T cells with depression severity as well as Beck Depression Inventory-II items including loss of pleasure, agitation and cognitive deficits. CCR4 expression levels on CD4⁺ T cells together with cross-disorder and chronotype PRS significantly predicted disease severity.

LIMITATIONS

This exploratory study with small sample size warrants future studies.

CONCLUSIONS

This newly identified CCR4-CCL17/CCL22 signature and its predictive capacity for MDD severity suggest its potential functional involvement in the pathophysiology of MDD.

Molecular Genetic Screening in Patients With ACE Inhibitor/Angiotensin Receptor Blocker-Induced Angioedema to Explore the Role of Hereditary Angioedema Genes

Angioedema is a relatively rare but potentially life-threatening adverse reaction to angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARBs). As with hereditary forms of angioedema (HAE), this adverse reaction is mediated by bradykinin. Research suggests that ACEi/ARB-induced angioedema has a multifactorial etiology. In addition, recent case reports suggest that some ACEi/ARB-induced angioedema patients may carry pathogenic HAE variants. The aim of the present study was to investigate the possible association between ACEi/ARB-induced angioedema and HAE genes via systematic molecular genetic screening in a large cohort of ACEi/ARB-induced angioedema cases. Targeted re-sequencing of five HAE-associated genes (SERPING1, F12, PLG, ANGPT1, and KNG1) was performed in 212 ACEi/ARB-induced angioedema patients recruited in Germany/Austria, Sweden, and Denmark, and in 352 controls from a German cohort. Among patients, none of the identified variants represented a known pathogenic variant for HAE. Moreover, no significant association with ACEi/ARB-induced angioedema was found for any of the identified common [minor allele frequency (MAF) >5%] or rare (MAF < 5%) variants. However, several non-significant trends suggestive of possible protective effects were observed. The lowest p-value for an individual variant was found in PLG (rs4252129, p.R523W, p = 0.057, p.adjust > 0.999, Fisher’s exact test). Variant p.R523W was found exclusively in controls and has previously been associated with decreased levels of plasminogen, a precursor of plasmin which is part of a pathway directly involved in bradykinin production. In addition, rare, potentially functional variants (MAF < 5%, Phred-scaled combined annotation dependent depletion score >10) showed a nominally significant enrichment in controls both: 1) across all five genes; and 2) in the F12 gene alone. However, these results did not withstand correction for multiple testing. In conclusion, our results suggest that HAE-associated mutations are, at best, a rare cause of ACEi/ARB-induced angioedema. Furthermore, we were unable to identify a significant association between ACEi/ARB-induced angioedema and other variants in the investigated genes. Further studies with larger sample sizes are warranted to draw more definite conclusions concerning variants with limited effect sizes, including protective variants.

Detailed stratified GWAS analysis for severe COVID-19 in four European populations

Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended genome-wide association meta-analysis of a well-characterized cohort of 3255 COVID-19 patients with respiratory failure and 12 488 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a ~0.9-Mb inversion polymorphism that creates two highly differentiated haplotypes and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative including non-Caucasian individuals, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.

Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages

Importance

Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures.

Objective

To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages.

Design, Setting, and Participants

A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022.

Main Outcomes and Measures

A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample.

Results

There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample.

Conclusions and Relevance

The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.

GenRisk: A tool for comprehensive genetic risk modeling

Summary

The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here, we present GenRisk, a python package for the computation and the integration of gene scores based on the burden of rare deleterious variants and common-variants-based polygenic risk scores. The derived scores can be analyzed within GenRisk to perform association tests or to derive phenotype prediction models by testing multiple classification and regression approaches. GenRisk is compatible with VCF input file formats.

Availability and implementation

GenRisk is an open source publicly available python package that can be downloaded or installed from Github (https://github.com/AldisiRana/GenRisk).

Supplementary information

Supplementary data are available at Bioinformatics online.

Breast and prostate cancer risk: The interplay of polygenic risk, rare pathogenic germline variants, and family history

PURPOSE

We aimed to investigate to what extent polygenic risk scores (PRS), rare pathogenic germline variants (PVs), and family history jointly influence breast cancer and prostate cancer risk.

METHODS

A total of 200,643 individuals from the UK Biobank were categorized as follows: (1) heterozygotes or nonheterozygotes for PVs in moderate to high-risk cancer genes, (2) PRS strata, and (3) with or without a family history of cancer. Multivariable logistic regression and Cox proportional hazards models were used to compute the odds ratio across groups and the cumulative incidence through life.

RESULTS

Cumulative incidence by age 70 years among the nonheterozygotes across PRS strata ranged from 9% to 32% and from 9% to 35% for breast cancer and prostate cancer, respectively. Among the PV heterozygotes it ranged from 20% to 48% in moderate-risk genes and from 51% to 74% in high-risk genes for breast cancer, and it ranged from 30% to 59% in prostate cancer risk genes. Family history was always associated with an increased cancer odds ratio.

CONCLUSION

PRS alone provides a meaningful risk gradient leading to a cancer risk stratification comparable to PVs in moderate risk genes, whereas acts as a risk modifier when considering high-risk genes. Including family history along with PV and PRS further improves cancer risk stratification.

Dissecting the Polygenic Basis of Primary Hypertension: Identification of Key Pathway-Specific Components

Introduction and Objectives

Genome-wide association studies have identified a high number of genetic loci associated with hypertension suggesting the presence of an underlying polygenic architecture. In this study, we aimed to dissect the polygenic component of primary hypertension searching also for pathway-specific components.

Methods

The polygenic risk score (PRS) models, based on the UK biobank genetic signals for hypertension status, were obtained on a target Italian case/control cohort including 561 cases and 731 hyper-normal controls from HYPERGENES, and were then applied to an independent validation cohort composed by multi-countries European-based samples including 1,284 cases and 960 hyper-normal controls.

Results

The resulting genome-wide PRS was capable of stratifying the individuals for hypertension risk by comparing between individuals in the last PRS decile and the median decile: we observed an odds ratio (OR) of 3.62, CI = [2.01, 6.32] (P = 9.01E-07) and 3.22, 95% CI = [2.06, 5.10] (P = 6.47E-08) in the target and validation cohorts, respectively. The relatively high case/control ORs across PRS quantiles corroborates the presence of strong polygenic components which could be driven by an enrichment of risk alleles within the cases but also by potential enrichment of protective alleles in the old normotensive controls. Moreover, novel pathway-specific PRS revealed an enrichment of the polygenic signal attributable to specific biological pathways. Among those the most significantly associated with hypertension status was the calcium signaling pathway together with other mainly related such as the phosphatidylinositol/inositol phosphate pathways.

Conclusions

The development of pathway-specific PRS could prioritize biological mechanisms, according to their contribution to the genetic susceptibility, whose regulations might be a potential pharmacological preventive target.

Genome-wide association study in patients with posterior urethral valves

Congenital lower urinary tract obstructions (LUTO) are most often caused by posterior urethral valves (PUV), a male limited anatomical obstruction of the urethra affecting 1 in 4,000 male live births. Little is known about the genetic background of PUV. Here, we report the largest genome-wide association study (GWAS) for PUV in 4 cohorts of patients and controls. The final meta-analysis included 756 patients and 4,823 ethnicity matched controls and comprised 5,754,208 variants that were genotyped or imputed and passed quality control in all 4 cohorts. No genome-wide significant locus was identified, but 33 variants showed suggestive significance (P < 1 × 10^-5). When considering only loci with multiple variants residing within < 10 kB of each other showing suggestive significance and with the same effect direction in all 4 cohorts, 3 loci comprising a total of 9 variants remained. These loci resided on chromosomes 13, 16, and 20. The present GWAS and meta-analysis is the largest genetic study on PUV performed to date. The fact that no genome-wide significant locus was identified, can be explained by lack of power or may indicate that common variants do not play a major role in the etiology of PUV. Nevertheless, future studies are warranted to replicate and validate the 3 loci that yielded suggestive associations.

Systematic investigation of a potential epidemiological and genetic association between male androgenetic alopecia and COVID-19

Background

Male androgenetic alopecia (AGA) has been implicated as a putative risk factor in severe COVID‐19 based on high incidences of advanced AGA in male hospitalized COVID‐19 patients. Research further suggests that androgen signalling, which plays a central role in AGA aetiology, promotes SARS‐CoV‐2 infection and is associated with severe COVID‐19 symptoms in men.

Objectives

We aimed to systematically investigate a potential association between AGA and COVID‐19 both on an epidemiological and a genetic level in a large single‐population cohort.

Methods

We performed regression, genetic correlation and polygenic risk score (PRS) analyses using data from the UK Biobank and published GWAS data on AGA and COVID‐19.

Results

Our analyses did not reveal any significant epidemiological or genome‐wide genetic association between AGA and severe COVID‐19. Pathway‐based PRS analyses however revealed a significant association in specific pathways, namely vitamin metabolism, natural killer cell‐mediated cytotoxicity, WNT signalling and aryl hydrocarbon receptor signalling.

Limitations

We restricted our analyses to the white British population and used self‐reported AGA status. Sample size may be a limitation in our regression and PRS analyses.

Conclusions

Our data yield no evidence for an epidemiological association between AGA and COVID‐19 but suggest that a shared genetic basis for both traits exists in specific pathways.

TBK1 and TNFRSF13B mutations and an autoinflammatory disease in a child with lethal COVID-19

Among children, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are typically mild. Here, we describe the case of a 3.5-year-old girl with an unusually severe presentation of coronavirus disease (COVID-19). The child had an autoinflammatory disorder of unknown etiology, which had been treated using prednisolone and methotrexate, and her parents were half cousins of Turkish descent. After 5 days of nonspecific viral infection symptoms, tonic-clonic seizures occurred followed by acute cardiac insufficiency, multi-organ insufficiency, and ultimate death. Trio exome sequencing identified a homozygous splice-variant in the gene TBK1, and a homozygous missense variant in the gene TNFRSF13B. Heterozygous deleterious variants in the TBK1 gene have been associated with severe COVID-19, and the variant in the TNFRSF13B gene has been associated with common variable immunodeficiency (CVID). We suggest that the identified variants, the autoinflammatory disorder and its treatment, or a combination of these factors probably predisposed to lethal COVID-19 in the present case.

Extending the allelic spectrum at noncoding risk loci of orofacial clefting

Genome-wide association studies (GWAS) have generated unprecedented insights into the genetic etiology of orofacial clefting (OFC). The moderate effect sizes of associated noncoding risk variants and limited access to disease-relevant tissue represent considerable challenges for biological interpretation of genetic findings. As rare variants with stronger effect sizes are likely to also contribute to OFC, an alternative approach to delineate pathogenic mechanisms is to identify private mutations and/or an increased burden of rare variants in associated regions. This report describes a framework for targeted resequencing at selected noncoding risk loci contributing to nonsyndromic cleft lip with/without cleft palate (nsCL/P), the most frequent OFC subtype. Based on GWAS data, we selected three risk loci and identified candidate regulatory regions (CRRs) through the integration of credible SNP information, epigenetic data from relevant cells/tissues, and conservation scores. The CRRs (total 57 kb) were resequenced in a multiethnic study population (1061 patients; 1591 controls), using single-molecule molecular inversion probe technology. Combining evidence from in silico variant annotation, pedigree- and burden analyses, we identified 16 likely deleterious rare variants that represent new candidates for functional studies in nsCL/P. Our framework is scalable and represents a promising approach to the investigation of additional congenital malformations with multifactorial etiology.

Intermediate lengths of the C9ORF72 hexanucleotide repeat expansion may synergistically contribute to attention deficit hyperactivity disorder in child and his father: case report

We have summarized the abstract section as follows: "We report a son and his father affected by Attention Deficit Hyperactivity Disorder (ADHD). They belonged to a larger cohort (116 ADHD children, 20 related parents, 77 controls) wholly genotyped forC9ORF72 expansion. Ten ADHD susceptibility genes were further investigated in the family. We revealed that son and father shared an intermediateC9ORF72 expansion and common variants inCDH23, ITGAE and MTRR. Bioinformatics highlighted aC9ORF72-MTRR interaction. This case-report underlines that in relatives with ADHD, carrying variants in ADHD susceptibility genes, the intermediateC9ORF72 repeats might have a potentially pathogenetic synergistic effect, supporting the multifactorial polygenic aetiopathogenetic profile of disease".

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

IMPORTANCE

Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear.

OBJECTIVES

To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system.

DESIGN, SETTING, AND PARTICIPANTS

This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020.

MAIN OUTCOMES AND MEASURES

Accuracy and generalizability of prognostic systems.

RESULTS

A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results.

CONCLUSIONS AND RELEVANCE

These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.

Sex-Specific Genetic Associations for Barrett's Esophagus and Esophageal Adenocarcinoma

BACKGROUND & AIMS

Esophageal adenocarcinoma (EA) and its premalignant lesion, Barrett's esophagus (BE), are characterized by a strong and yet unexplained male predominance (with a male-to-female ratio in EA incidence of up to 6:1). Genome-wide association studies (GWAS) have identified more than 20 susceptibility loci for these conditions. However, potential sex differences in genetic associations with BE/EA remain largely unexplored.

METHODS

Given strong genetic overlap, BE and EA cases were combined into a single case group for analysis. These were compared with population-based controls. We performed sex-specific GWAS of BE/EA in 3 separate studies and then used fixed-effects meta-analysis to provide summary estimates for >9 million variants for male and female individuals. A series of downstream analyses were conducted separately in male and female individuals to identify genes associated with BE/EA and the genetic correlations between BE/EA and other traits.

RESULTS

We included 6758 male BE/EA cases, 7489 male controls, 1670 female BE/EA cases, and 6174 female controls. After Bonferroni correction, our meta-analysis of sex-specific GWAS identified 1 variant at chromosome 6q11.1 (rs112894788, KHDRBS2-MTRNR2L9, PBONF = .039) that was statistically significantly associated with BE/EA risk in male individuals only, and 1 variant at chromosome 8p23.1 (rs13259457, PRSS55-RP1L1, PBONF = 0.057) associated, at borderline significance, with BE/EA risk in female individuals only. We also observed strong genetic correlations of BE/EA with gastroesophageal reflux disease in male individuals and obesity in female individuals.

CONCLUSIONS

The identified novel sex-specific variants associated with BE/EA could improve the understanding of the genetic architecture of the disease and the reasons for the male predominance.

Long-term effects of stress early in life on microRNA-30a and its network: Preventive effects of lurasidone and potential implications for depression vulnerability

Exposure to early life stress can interfere with neurodevelopmental trajectories to increase the vulnerability for psychiatric disorders later in life. With this respect, epigenetic mechanisms play a key role for the long-lasting changes in brain functions that may elicit and sustain psychopathologic outcomes.

Here, we investigated DNA methylation changes as possible epigenetic mechanism mediating the effect of prenatal stress (PNS), an experimental paradigm associated with behavioral and molecular alterations relevant for psychiatric disorders. We identified 138 genes as being differentially methylated in the prefrontal cortex (PFC) and in the hippocampus (HIP) of male and female adult rats exposed to PNS. Among these genes, miR-30a and Neurod1 emerged as potential players for the negative outcomes associated with PNS exposure. Indeed, in addition to showing consistent methylation differences in both brain regions and in both sexes, and interacting with each other, they are both involved in Axon guidance and Neurotrophin signaling, which are important to neurodevelopmental disorders. We also found a significant reduction in the expression of a panel of genes (CAMK2A, c-JUN, LIMK1, MAP2K1, MAP2K2, PIK3CA and PLCG1) that belong to these two biological pathways and are also validated targets of miR-30a, pointing to a down-regulation of these pathways as a consequence of PNS exposure. Interestingly, we also found that miR-30a levels were significantly upregulated in depressed patients exposed to childhood trauma, as compared to control individuals. Importantly, we also found that a sub-chronic treatment with the atypical antipsychotic drug, lurasidone, during adolescence was able to prevent the up-regulation of miR-30a and normalized the expression of its target genes in response to PNS exposure.

Our results demonstrate that miR-30a undergoes epigenetic changes following early life stress exposure and suggest that this miRNA could play a key role in producing broad and long-lasting alterations in neuroplasticity-related pathways, contributing to the etiology of psychiatric disorders.

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MiR-30a and Neurod1 undergo epigenetic changes following PNS exposure.

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MiR-30 and Neurod1 are involved in Axon guidance and Neurotrophin signaling, two important pathways for neurodevelopment.

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We found lower expression levels of a panel of genes targeted by miR-30a.

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MiR-30a was significantly up-regulated in depressed patients exposed to childhood trauma.

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A chronic treatment with lurasidone during adolescence prevented the up-regulation of miR-30a following PNS exposure.

Behavioral and Psychological Symptoms of Dementia (BPSD): Clinical Characterization and Genetic Correlates in an Italian Alzheimer's Disease Cohort

BACKGROUND

The occurrence of Behavioral and Psychological Symptoms of Dementia (BPSD) in Alzheimer's Disease (AD) patients hampers the clinical management and exacerbates the burden for caregivers. The definition of the clinical distribution of BPSD symptoms, and the extent to which symptoms are genetically determined, are still open to debate. Moreover, genetic factors that underline BPSD symptoms still need to be identified.

PURPOSE

To characterize our Italian AD cohort according to specific BPSD symptoms as well as to endophenotypes. To evaluate the associations between the considered BPSD traits and COMT, MTHFR, and APOE genetic variants.

METHODS

AD patients (n = 362) underwent neuropsychological examination and genotyping. BPSD were assessed with the Neuropsychiatric Inventory scale.

RESULTS

APOE and MTHFR variants were significantly associated with specific single BPSD symptoms. Furthermore, "Psychosis" and "Hyperactivity" resulted in the most severe endophenotypes, with APOE and MTHFR implicated as both single risk factors and "genexgene" interactions.

CONCLUSIONS

We strongly suggest the combined use of both BPSD single symptoms/endophenotypes and the "genexgene" interactions as valid strategies for expanding the knowledge about the BPSD aetiopathogenetic mechanisms.

Correlations between immune and metabolic serum markers and schizophrenia/bipolar disorder polygenic risk score in first-episode psychosis

AIMS

There is a strong interest in identifying the biological mechanisms involved in the genetic risk for psychotic disorders. In this study, we evaluated the correlation between serum concentrations of specific molecular markers and the genetic component for schizophrenia and bipolar disorder.

METHODS

We analysed the association between the polygenic risk score (PRS) and the serum levels of different inflammatory/metabolic markers in a sample of 81 first-episode psychosis patients (FEP) with a diagnosis of schizophrenia or bipolar disorder and 33 controls.

RESULTS

A positive correlation of schizophrenia and bipolar disorder PRS with the inflammatory marker C-C Motif Chemokine Ligand 4 serum concentration (ρ = 0.42, P = 1.56 × 10^-04 and ρ = 0.40, P = 1.65 × 10^-03 , respectively) and a negative correlation with the serum ghrelin content (ρ = - 0.35, P = 4.27 × 10^-03 and ρ = - 0.45, P = 6.05 × 10^-04 , respectively) were observed.

CONCLUSION

These findings provide new insight into the biological underpinnings of the PRS component, thus supporting a role of the genetic liability on the inflammatory and metabolic alterations that characterize psychosis onset.

The Long-Term Effects of Early Life Stress on the Modulation of miR-19 Levels

MicroRNAs (miRNAs), one of the major small non-coding RNA classes, have been proposed as regulatory molecules in neurodevelopment and stress response. Although alterations in miRNAs profiles have been implicated in several psychiatric and neurodevelopmental disorders, the contribution of individual miRNAs in brain development and function is still unknown. Recent studies have identified miR-19 as a key regulator of brain trajectories, since it drives the differentiation of neural stem cells into mature neurons. However, no findings are available on how vulnerability factors for these disorders, such as early life stress (ELS), can modulate the expression of miR-19 and its target genes. To reach our aim, we investigated miR-19 modulation in human hippocampal progenitor stem cells (HPCs) treated with cortisol during 3 days of proliferation and harvested immediately after the end of the treatment or after 20 days of differentiation into mature neurons. We also analyzed the long-term expression changes of miR-19 and of its validated target genes, involved in neurodevelopment and inflammation, in the hippocampus of adult rats exposed or not to prenatal stress (PNS). Interestingly, we observed a significant downregulation of miR-19 levels both in proliferating (FC = −1.59, p-value = 0.022 for miR-19a; FC = −1.79, p-value = 0.016 for miR-19b) as well as differentiated HPCs (FC = −1.28, p-value = 0.065 for miR-19a; FC = −1.75, p-value = 0.047 for miR-19b) treated with cortisol. Similarly, we found a long-term decrease of miR-19 levels in the hippocampus of adult PNS rats (FC = −1.35, p-value = 0.025 for miR-19a; FC = −1.43, p-value = 0.032 for miR-19b). Among all the validated target genes, we observed a significant increase of NRCAM (FC = 1.20, p-value = 0.027), IL4R (FC = 1.26, p-value = 0.046), and RAPGEF2 (FC = 1.23, p-value = 0.020).We suggest that ELS can cause a long-term downregulation of miR-19 levels, which may be responsible of alterations in neurodevelopmental pathways and in immune/inflammatory processes, leading to an enhanced risk for mental disorders later in life. Intervention strategies targeting miR-19 may prevent alterations in these pathways, reducing the ELS-related effects.

Next Generation Sequencing Analysis in Early Onset Dementia Patients

Background:

Early onset dementias (EOD) are rare neurodegenerative dementias that present before 65 years. Genetic factors have a substantially higher pathogenetic contribution in EOD patients than in late onset dementia.

Objective:

To identify known and/or novel rare variants in major candidate genes associated to EOD by high-throughput sequencing. Common-risk variants of apolipoprotein E (APOE) and prion protein (PRNP) genes were also assessed.

Methods:

We studied 22 EOD patients recruited in Memory Clinics, in the context of studies investigating genetic forms of dementia. Two methodological approaches were applied for the target-Next Generation Sequencing (NGS) analysis of these patients. In addition, we performed progranulin plasma dosage, C9Orf72 hexanucleotide repeat expansion analysis, and APOE genotyping.

Results:

We detected three rare known pathogenic mutations in the GRN and PSEN2 genes and eleven unknown-impact mutations in the GRN, VCP, MAPT, FUS, TREM2, and NOTCH3 genes. Six patients were carriers of only common risk variants (APOE and PRNP), and one did not show any risk mutation/variant. Overall, 69% (n = 9) of our early onset Alzheimer’s disease (EAOD) patients, compared with 34% (n = 13) of sporadic late onset Alzheimer’s disease (LOAD) patients and 27% (n = 73) of non-affected controls (ADNI, whole genome data), were carriers of at least two rare/common risk variants in the analyzed candidate genes panel, excluding the full penetrant mutations.

Conclusion:

This study suggests that EOD patients without full penetrant mutations are characterized by higher probability to carry polygenic risk alleles that patients with LOAD forms. This finding is in line with recently reported evidence, thus suggesting that the genetic risk factors identified in LOAD might modulate the risk also in EOAD.

Corrigendum: The Long-Term Effects of Early Life Stress on the Modulation of miR-19 Levels

[This corrects the article DOI: 10.3389/fpsyt.2020.00389.].