Replicating genotype–phenotype associations

Combined Genome-Wide Association Study and Haplotype Analysis Identifies Candidate Genes Affecting Growth Traits of Inner Mongolian Cashmere Goats

In this study, genome-wide association analysis was performed on the growth traits (body height, body length, chest circumference, chest depth, chest width, tube circumference, and body weight) of Inner Mongolian cashmere goats (Erlangshan type) based on resequencing data. The population genetic parameters were estimated, haplotypes were constructed for the significant sites, and association analysis was conducted between the haplotypes and phenotypes. A total of two hundred and eighty-four SNPs and eight candidate genes were identified by genome-wide association analysis, gene annotation, and enrichment analysis. The phenotypes of 16 haplotype combinations were significantly different by haplotype analysis. Combined with the above results, the TGFB2, BAG3, ZEB2, KCNJ12, MIF, MAP2K3, HACD3, and MEGF11 functional candidate genes and the haplotype combinations A2A2, C2C2, E2E2, F2F2, I2I2, J2J2, K2K2, N2N2, O2O2, P2P2, R1R1, T1T1, W1W1, X1X1, Y1Y1, and Z1Z1 affected the growth traits of the cashmere goats and could be used as molecular markers to improve the accuracy of early selection and the economic benefits of breeding.

Genome-Wide Association Study of Cuticle and Lipid Droplet Properties of Cucumber (Cucumis sativus L.) Fruit

The fruit surface is a critical first line of defense against environmental stress. Overlaying the fruit epidermis is the cuticle, comprising a matrix of cutin monomers and waxes that provides protection and mechanical support throughout development. The epidermal layer of the cucumber (Cucumis sativus L.) fruit also contains prominent lipid droplets, which have recently been recognized as dynamic organelles involved in lipid storage and metabolism, stress response, and the accumulation of specialized metabolites. Our objective was to genetically characterize natural variations for traits associated with the cuticle and lipid droplets in cucumber fruit. Phenotypic characterization and genome-wide association studies (GWAS) were performed using a resequenced cucumber core collection accounting for >96% of the allelic diversity present in the U.S. National Plant Germplasm System collection. The collection was grown in the field, and fruit were harvested at 16-20 days post-anthesis, an age when the cuticle thickness and the number and size of lipid droplets have stabilized. Fresh fruit tissue sections were prepared to measure cuticle thickness and lipid droplet size and number. The collection showed extensive variation for the measured traits. GWAS identified several QTLs corresponding with genes previously implicated in cuticle or lipid biosynthesis, including the transcription factor SHINE1/WIN1, as well as suggesting new candidate genes, including a potential lipid-transfer domain containing protein found in association with isolated lipid droplets.

Artificial replication cohort: Leveraging AI-fabricated data for genetic studies

Recent advancements in artificial intelligence (AI) present both opportunities and challenges within the scientific community. This study explores the capability of AI to replicate findings from genetic research, focusing on findings from prior work. Using an AI model without exposing any raw data, we created a dataset that closely mirrors the results of our original study, illustrating the ease of fabricating datasets with authenticity. This approach highlights the risks associated with AI misuse in scientific research. The study emphasizes the critical importance of maintaining the integrity of scientific inquiry in an era increasingly influenced by advanced AI technologies.

The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources

Although large-scale genetic association studies have proven opportunistic for the delineation of neurodegenerative disease processes, we still lack a full understanding of the pathological mechanisms of these diseases, resulting in few appropriate treatment options and diagnostic challenges. To mitigate these gaps, the Neurodegenerative Disease Knowledge Portal (NDKP) was created as an open-science initiative with the aim to aggregate, enable analysis, and display all available genomic datasets of neurodegenerative disease, while protecting the integrity and confidentiality of the underlying datasets. The portal contains 218 genomic datasets, including genotyping and sequencing studies, of individuals across ten different phenotypic groups, including neurological conditions such as Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease. In addition to securely hosting large genomic datasets, the NDKP provides accessible workflows and tools to effectively utilize the datasets and assist in the facilitation of customized genomic analyses. Here, we summarize the genomic datasets currently included within the portal, the bioinformatics processing of the datasets, and the variety of phenotypes captured. We also present example use-cases of the various user interfaces and integrated analytic tools to demonstrate their extensive utility in enabling the extraction of high-quality results at the source, for both genomics experts and those in other disciplines. Overall, the NDKP promotes open-science and collaboration, maximizing the potential for discovery from the large-scale datasets researchers and consortia are expending immense resources to produce and resulting in reproducible conclusions to improve diagnostic and therapeutic care for neurodegenerative disease patients.

A powerful approach to identify replicable variants in genome-wide association studies

Replicability is the cornerstone of modern scientific research. Reliable identifications of genotype-phenotype associations that are significant in multiple genome-wide association studies (GWASs) provide stronger evidence for the findings. Current replicability analysis relies on the independence assumption among single-nucleotide polymorphisms (SNPs) and ignores the linkage disequilibrium (LD) structure. We show that such a strategy may produce either overly liberal or overly conservative results in practice. We develop an efficient method, ReAD, to detect replicable SNPs associated with the phenotype from two GWASs accounting for the LD structure. The local dependence structure of SNPs across two heterogeneous studies is captured by a four-state hidden Markov model (HMM) built on two sequences of p values. By incorporating information from adjacent locations via the HMM, our approach provides more accurate SNP significance rankings. ReAD is scalable, platform independent, and more powerful than existing replicability analysis methods with effective false discovery rate control. Through analysis of datasets from two asthma GWASs and two ulcerative colitis GWASs, we show that ReAD can identify replicable genetic loci that existing methods might otherwise miss.

Novel functional insights into ischemic stroke biology provided by the first genome-wide association study of stroke in indigenous Africans

BACKGROUND

African ancestry populations have the highest burden of stroke worldwide, yet the genetic basis of stroke in these populations is obscure. The Stroke Investigative Research and Educational Network (SIREN) is a multicenter study involving 16 sites in West Africa. We conducted the first-ever genome-wide association study (GWAS) of stroke in indigenous Africans.

METHODS

Cases were consecutively recruited consenting adults (aged > 18 years) with neuroimaging-confirmed ischemic stroke. Stroke-free controls were ascertained using a locally validated Questionnaire for Verifying Stroke-Free Status. DNA genotyping with the H3Africa array was performed, and following initial quality control, GWAS datasets were imputed into the NIH Trans-Omics for Precision Medicine (TOPMed) release2 from BioData Catalyst. Furthermore, we performed fine-mapping, trans-ethnic meta-analysis, and in silico functional characterization to identify likely causal variants with a functional interpretation.

RESULTS

We observed genome-wide significant (P-value < 5.0E-8) SNPs associations near AADACL2 and miRNA (MIR5186) genes in chromosome 3 after adjusting for hypertension, diabetes, dyslipidemia, and cardiac status in the base model as covariates. SNPs near the miRNA (MIR4458) gene in chromosome 5 were also associated with stroke (P-value < 1.0E-6). The putative genes near AADACL2, MIR5186, and MIR4458 genes were protective and novel. SNPs associations with stroke in chromosome 2 were more than 77 kb from the closest gene LINC01854 and SNPs in chromosome 7 were more than 116 kb to the closest gene LINC01446 (P-value < 1.0E-6). In addition, we observed SNPs in genes STXBP5-AS1 (chromosome 6), GALTN9 (chromosome 12), FANCA (chromosome 16), and DLGAP1 (chromosome 18) (P-value < 1.0E-6). Both genomic regions near genes AADACL2 and MIR4458 remained significant following fine mapping.

CONCLUSIONS

Our findings identify potential roles of regulatory miRNA, intergenic non-coding DNA, and intronic non-coding RNA in the biology of ischemic stroke. These findings reveal new molecular targets that promise to help close the current gaps in accurate African ancestry-based genetic stroke's risk prediction and development of new targeted interventions to prevent or treat stroke.

False discovery rate-controlled multiple testing for union null hypotheses: a knockoff-based approach

False discovery rate (FDR) controlling procedures provide important statistical guarantees for replicability in signal identification based on multiple hypotheses testing. In many fields of study, FDR controling procedures are used in high-dimensional (HD) analyses to discover features that are truly associated with the outcome. In some recent applications, data on the same set of candidate features are independently collected in multiple different studies. For example, gene expression data are collected at different facilities and with different cohorts, to identify the genetic biomarkers of multiple types of cancers. These studies provide us with opportunities to identify signals by considering information from different sources (with potential heterogeneity) jointly. This paper is about how to provide FDR control guarantees for the tests of union null hypotheses of conditional independence. We present a knockoff-based variable selection method (Simultaneous knockoffs) to identify mutual signals from multiple independent datasets, providing exact FDR control guarantees under finite sample settings. This method can work with very general model settings and test statistics. We demonstrate the performance of this method with extensive numerical studies and two real-data examples.

Identification of QTL associated with resistance to Phytophthora fruit rot in cucumber (Cucumis sativus L.)

Phytophthora fruit rot (PFR) caused by the soilborne oomycete pathogen, Phytophthora capsici, can cause severe yield loss in cucumber. With no resistant variety available, genetic resources are needed to develop resistant varieties. The goal of this work was to identify quantitative trait loci (QTL) associated with resistance to PFR using multiple genomic approaches and populations. Two types of resistances have been identified: age-related resistance (ARR) and young fruit resistance. ARR occurs at 12-16 days post pollination (dpp), coinciding with the end of exponential fruit growth. A major QTL for ARR was discovered on chromosome 3 and a candidate gene identified based on comparative transcriptomic analysis. Young fruit resistance, which is observed during the state of rapid fruit growth prior to commercial harvest, is a quantitative trait for which multiple QTL were identified. The largest effect QTL, qPFR5.1, located on chromosome 5 was fine mapped to a 1-Mb region. Genome-wide association studies (GWAS) and extreme-phenotype genome-wide association study (XP-GWAS) for young fruit resistance were also performed on a cucumber core collection representing > 96% of the genetic diversity of the USDA cucumber germplasm. Several SNPs overlapped with the QTL identified from QTL-seq analysis on biparental populations. In addition, novel SNPs associated with the resistance were identified from the germplasm. The resistant alleles were found mostly in accessions from India and South Asia, the center of diversity for cucumber. The results from this work can be applied to future disease resistance studies and marker-assisted selection in breeding programs.

IGF1 Genetic Polymorphism and the Association between Vitamin D Status and BMI Percentiles in Children

Both the IGF1 axis and hypovitaminosis D play a role in childhood obesity, either as a cause or a causality. While some studies suggest an interrelation between vitamin D status, IGF1, and obesity, this mechanism remains obscure. The aim of this study, therefore, was to explore associations between four genetic polymorphisms in the IGF1 axis in hypovitaminosis D-related obesity. The study included 116 pre-pubertal Israeli Arab children (52 girls), mean age 9.4 ± 2.6. Serum 25(OH)D was measured and anthropometric measures were obtained. Genomic DNA was extracted from peripheral EDTA-treated anti-coagulated blood using a standard protocol. Genotypes were determined using the Taqman allelic discrimination assay. The IGF genetic score was computed according to the additive genetic score model. A moderate-to-high negative correlation (r = 0.580, p < 0.05) was seen between the vitamin D status and body mass index (BMI) percentile of participants with high GS. Yet, no correlations were seen between vitamin D status and BMI percentile for participants with a low-to-moderate genetic score (GS) (GS ≤ 2). These results suggest that IGF1 genetic scores associated with elevated circulating IGF1 may indicate a tendency toward developing hypovitaminosis D-associated obesity.

The phenotype-genotype reference map: Improving biobank data science through replication

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.

Published registry-based pharmacoepidemiologic associations show limited concordance with agnostic medication-wide analyses

OBJECTIVES

To assess how the results of published national registry-based pharmacoepidemiology studies (where select associations are of interest) compare with an agnostic medication-wide approach (where all possible drug associations are tested).

STUDY DESIGN AND SETTING

We systematically searched for publications that reported drug associations with any, breast, colon/colorectal, or prostate cancer in the Swedish Prescribed Drug Registry. Results were compared against a previously performed agnostic medication-wide study on the same registry.

PROTOCOL

https://osf.io/kqj8n.

RESULTS

Most published studies (25/32) investigated previously reported associations. 421/913 (46%) associations had statistically significant results. 134 of the 162 unique drug-cancer associations could be paired with 70 associations in the agnostic study (corresponding drug categories and cancer types). Published studies reported smaller effect sizes and absolute effect sizes than the agnostic study, and generally used more adjustments. Agnostic analyses were less likely to report statistically significant protective associations (based on a multiplicity-corrected threshold) than their paired associations in published studies (McNemar odds ratio 0.13, P = 0.0022). Among 162 published associations, 36 (22%) showed increased risk signal and 25 (15%) protective signal at P < 0.05, while for agnostic associations, 237 (11%) showed increased risk signal and 108 (5%) protective signal at a multiplicity-corrected threshold. Associations belonging to drug categories targeted by individual published studies vs. nontargeted had smaller average effect sizes; smaller P values; and more frequent risk signals.

CONCLUSION

Published pharmacoepidemiology studies using a national registry addressed mostly previously proposed associations, were mostly "negative", and showed only modest concordance with their respective agnostic analyses in the same registry.

A Polynesian-specific missense CETP variant alters the lipid profile

Identifying population-specific genetic variants associated with disease and disease-predisposing traits is important to provide insights into the genetic determinants of health and disease between populations, as well as furthering genomic justice. Various common pan-population polymorphisms at CETP associate with serum lipid profiles and cardiovascular disease. Here, sequencing of CETP identified a missense variant rs1597000001 (p.Pro177Leu) specific to Māori and Pacific people that associates with higher HDL-C and lower LDL-C levels. Each copy of the minor allele associated with higher HDL-C by 0.236 mmol/L and lower LDL-C by 0.133 mmol/L. The rs1597000001 effect on HDL-C is comparable with CETP Mendelian loss-of-function mutations that result in CETP deficiency, consistent with our data, which shows that rs1597000001 lowers CETP activity by 27.9%. This study highlights the potential of population-specific genetic analyses for improving equity in genomics and health outcomes for population groups underrepresented in genomic studies.

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

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

Analysis of convergence of linkage and association studies in autism spectrum disorders

Autism spectrum disorder (ASD) is a clinically and genetically heterogeneous group of pervasive neurodevelopmental disorders with a strong hereditary component. Although genome-wide linkage studies (GWLS) and [genome-wide association studies (GWAS)] have previously identified hundreds of ASD risk gene loci, the results remain inconclusive. In this study, a genomic convergence approach of GWAS and GWLS for ASD was implemented for the first time in order to identify genomic loci supported by both methods. A database with 32 GWLS and five GWAS for ASD was created. Convergence was quantified as the proportion of significant GWAS markers located within linked regions. Convergence was not found to be significantly higher than expected by chance (z-test = 1,177, P = 0,239). Although convergence is supportive of genuine effects, the lack of agreement between GWLS and GWAS is also indicative that these studies are designed to answer different questions and are not equally well suited for deciphering the genetics of complex traits.

Novel Genetic Variants Associated with Chronic Kidney Disease Progression

SIGNIFICANCE STATEMENT

eGFR slope has been used as a surrogate outcome for progression of CKD. However, genetic markers associated with eGFR slope among patients with CKD were unknown. We aimed to identify genetic susceptibility loci associated with eGFR slope. A two-phase genome-wide association study identified single nucleotide polymorphisms (SNPs) in TPPP and FAT1-LINC02374 , and 22 of them were used to derive polygenic risk scores that mark the decline of eGFR by disrupting binding of nearby transcription factors. This work is the first to identify the impact of TPPP and FAT1-LINC02374 on CKD progression, providing predictive markers for the decline of eGFR in patients with CKD.

BACKGROUND

The incidence of CKD is associated with genetic factors. However, genetic markers associated with the progression of CKD have not been fully elucidated.

METHODS

We conducted a genome-wide association study among 1738 patients with CKD, mainly from the KoreaN cohort study for Outcomes in patients With CKD. The outcome was eGFR slope. We performed a replication study for discovered single nucleotide polymorphisms (SNPs) with P <10 -6 in 2498 patients with CKD from the Chronic Renal Insufficiency Cohort study. Several expression quantitative trait loci (eQTL) studies, pathway enrichment analyses, exploration of epigenetic architecture, and predicting disruption of transcription factor (TF) binding sites explored potential biological implications of the loci. We developed and evaluated the effect of polygenic risk scores (PRS) on incident CKD outcomes.

RESULTS

SNPs in two novel loci, TPPP and FAT1-LINC02374 , were replicated (rs59402340 in TPPP , Pdiscovery =7.11×10 -7 , PCRIC =8.13×10 -4 , Pmeta =7.23×10 -8 ; rs28629773 in FAT1-LINC02374 , Pdiscovery =6.08×10 -7 , PCRIC =4.33×10 -2 , Pmeta =1.87×10 -7 ). The eQTL studies revealed that the replicated SNPs regulated the expression level of nearby genes associated with kidney function. Furthermore, these SNPs were near gene enhancer regions and predicted to disrupt the binding of TFs. PRS based on the independently significant top 22 SNPs were significantly associated with CKD outcomes.

CONCLUSIONS

This study demonstrates that SNP markers in the TPPP and FAT1-LINC02374 loci could be predictive markers for the decline of eGFR in patients with CKD.

Current challenges and future implications of exploiting the omics data into nutrigenetics and nutrigenomics for personalized diagnosis and nutrition-based care

Nutrigenetics and nutrigenomics, combined with the omics technologies, are a demanding and an increasingly important field in personalizing nutrition-based care to understand an individual's response to nutrition-guided therapy. Omics is defined as the analysis of the large data sets of the biological system featuring transcriptomics, proteomics, and metabolomics and providing new insights into cell regulation. The effect of combining nutrigenetics and nutrigenomics with omics will give insight into molecular analysis, as human nutrition requirements vary per individual. Omics measures modest intraindividual variability and is critical to exploit these data for use in the development of precision nutrition. Omics, combined with nutrigenetics and nutrigenomics, is instrumental in the creation of goals for improving the accuracy of nutrition evaluations. Although dietary-based therapies are provided for various clinical conditions such as inborn errors of metabolism, limited advancement has been done to expand the omics data for a more mechanistic understanding of cellular networks dependent on nutrition-based expression and overall regulation of genes. The greatest challenge remains in the clinical sector to integrate the current data available, overcome the well-established limits of self-reported methods in research, and provide omics data, combined with nutrigenetics and nutrigenomics research, for each individual. Hence, the future seems promising if a design for personalized, nutrition-based diagnosis and care can be implemented practically in the health care sector.

Democratizing clinical-genomic data: How federated platforms can promote benefits sharing in genomics

Since the first sequencing of the human genome, associated sequencing costs have dramatically lowered, leading to an explosion of genomic data. This valuable data should in theory be of huge benefit to the global community, although unfortunately the benefits of these advances have not been widely distributed. Much of today's clinical-genomic data is siloed and inaccessible in adherence with strict governance and privacy policies, with more than 97% of hospital data going unused, according to one reference. Despite these challenges, there are promising efforts to make clinical-genomic data accessible and useful without compromising security. Specifically, federated data platforms are emerging as key resources to facilitate secure data sharing without having to physically move the data from outside of its organizational or jurisdictional boundaries. In this perspective, we summarize the overarching progress in establishing federated data platforms, and highlight critical considerations on how they should be managed to ensure patient and public trust. These platforms are enabling global collaboration and improving representation of underrepresented groups, since sequencing efforts have not prioritized diverse population representation until recently. Federated data platforms, when combined with advances in no-code technology, can be accessible to the diverse end-users that make up the genomics workforce, and we discuss potential strategies to develop sustainable business models so that the platforms can continue to enable research long term. Although these platforms must be carefully managed to ensure appropriate and ethical use, they are democratizing access and insights to clinical-genomic data that will progress research and enable impactful therapeutic findings.

Quality Control Procedures for Genome-Wide Association Studies

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of many complex diseases. Regardless of the context, the practical utility of this information ultimately depends upon the quality of the data used for statistical analyses. Quality control (QC) procedures for GWAS are constantly evolving. Here, we enumerate some of the challenges in QC of genotyped GWAS data and describe the approaches involving genotype imputation of a sample dataset along with post-imputation quality assurance, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of the GWAS data (genotyped and imputed), including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We provide detailed guidelines along with a sample dataset to suggest current best practices and discuss areas of ongoing and future research. © 2022 Wiley Periodicals LLC.

A possible genomic footprint of polygenic adaptation on population divergence in seed beetles?

Efforts to unravel the genomic basis of incipient speciation are hampered by a mismatch between our toolkit and our understanding of the ecology and genetics of adaptation. While the former is focused on detecting selective sweeps involving few independently acting or linked speciation genes, the latter states that divergence typically occurs in polygenic traits under stabilizing selection. Here, we ask whether a role of stabilizing selection on polygenic traits in population divergence may be unveiled by using a phenotypically informed integrative approach, based on genome‐wide variation segregating in divergent populations. We compare three divergent populations of seed beetles (Callosobruchus maculatus) where previous work has demonstrated a prominent role for stabilizing selection on, and population divergence in, key life history traits that reflect rate‐dependent metabolic processes. We derive and assess predictions regarding the expected pattern of covariation between genetic variation segregating within populations and genetic differentiation between populations. Population differentiation was considerable (mean F_ST = 0.23–0.26) and was primarily built by genes showing high selective constraints and an imbalance in inferred selection in different populations (positive Tajima's D_NS in one and negative in one), and this set of genes was enriched with genes with a metabolic function. Repeatability of relative population differentiation was low at the level of individual genes but higher at the level of broad functional classes, again spotlighting metabolic genes. Absolute differentiation (d_XY) showed a very different general pattern at this scale of divergence, more consistent with an important role for genetic drift. Although our exploration is consistent with stabilizing selection on polygenic metabolic phenotypes as an important engine of genome‐wide relative population divergence and incipient speciation in our study system, we note that it is exceedingly difficult to firmly exclude other scenarios.

Anterior cruciate ligament injury and its postoperative outcomes are not associated with polymorphism in COL1A1 rs1107946 (G/T): a case-control study in the Middle East elite athletes

BACKGROUND

It is unclear what role COL1A1 polymorphisms play in anterior cruciate ligament (ACL) injury pathophysiology. The present study investigated the relationship between COL1A1-1997 guanine (G)/thymine (T) (rs1107946) polymorphism and ACL injury. Moreover, the possible effect of this polymorphism on the postoperative outcomes of ACL reconstruction surgery was evaluated.

METHODS

This prospective case-control study was performed on 200 young professional men with an ACL tear who underwent arthroscopic ACL reconstruction surgery. Moreover, 200 healthy athletes without a history of tendon or ligament injury who were matched with the case group were selected as the control group. DNA was extracted from the leukocytes of participants, and the desired allele was genotyped. Clinical outcomes were collected for the case group before and one year after surgery.

RESULTS

The genotype distribution was in accordance with the Hardy-Weinberg principle. In the ACL injury group, the G allele frequency was non-significantly higher than the healthy controls, with an odds ratio [95% CI] of 1.08 [0.79-1.47] (P = 64). We did not find a significant difference between the genotype of individuals-GG, GT, and TT-in the case and control groups (P > 0.05). Clinical outcomes of the ACL tear group were significantly improved in terms of preoperative values. However, none of them were significantly different between the three genotypes (GG, GT, and TT).

CONCLUSION

According to the findings of the present investigation, single-nucleotide polymorphism (SNP) at COL1A1 rs1107946 (G/T) was not a predisposing genetic factor for ACL injury in a young professional male athlete population in the Middle East. Furthermore, patients' responses to treatment were not different between distinct genotypes.

LEVEL OF EVIDENCE III

Oxytocin-a social peptide? Deconstructing the evidence

In this paper, we analyse the claim that oxytocin is a 'social neuropeptide'. This claim originated from evidence that oxytocin was instrumental in the initiation of maternal behaviour and it was extended to become the claim that oxytocin has a key role in promoting social interactions between individuals. We begin by considering the structure of the scientific literature on this topic, identifying closely interconnected clusters of papers on particular themes. We then analyse this claim by considering evidence of four types as generated by these clusters: (i) mechanistic studies in animal models, designed to understand the pathways involved in the behavioural effects of centrally administered oxytocin; (ii) evidence from observational studies indicating an association between oxytocin signalling pathways and social behaviour; (iii) evidence from intervention studies, mainly involving intranasal oxytocin administration; and (iv) evidence from translational studies of patients with disorders of social behaviour. We then critically analyse the most highly cited papers in each segment of the evidence; we conclude that, if these represent the best evidence, then the evidence for the claim is weak. This article is part of the theme issue 'Interplays between oxytocin and other neuromodulators in shaping complex social behaviours'.

Advancing artificial intelligence-assisted pre-screening for fragile X syndrome

BACKGROUND

Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism, is significantly underdiagnosed in the general population. Diagnosing FXS is challenging due to the heterogeneity of the condition, subtle physical characteristics at the time of birth and similarity of phenotypes to other conditions. The medical complexity of FXS underscores an urgent need to develop more efficient and effective screening methods to identify individuals with FXS. In this study, we evaluate the effectiveness of using artificial intelligence (AI) and electronic health records (EHRs) to accelerate FXS diagnosis.

METHODS

The EHRs of 2.1 million patients served by the University of Wisconsin Health System (UW Health) were the main data source for this retrospective study. UW Health includes patients from south central Wisconsin, with approximately 33 years (1988-2021) of digitized health data. We identified all participants who received a code for FXS in the form of International Classification of Diseases (ICD), Ninth or Tenth Revision (ICD9 = 759.83, ICD10 = Q99.2). Only individuals who received the FXS code on at least two occasions ("Rule of 2") were classified as clinically diagnosed cases. To ensure the availability of sufficient data prior to clinical diagnosis to test the model, only individuals who were diagnosed after age 10 were included in the analysis. A supervised random forest classifier was used to create an AI-assisted pre-screening tool to identify cases with FXS, 5 years earlier than the time of clinical diagnosis based on their medical records. The area under receiver operating characteristic curve (AUROC) was reported. The AUROC shows the level of success in identification of cases and controls (AUROC = 1 represents perfect classification).

RESULTS

52 individuals were identified as target cases and matched with 5200 controls. AI-assisted pre-screening tool successfully identified cases with FXS, 5 years earlier than the time of clinical diagnosis with an AUROC of 0.717. A separate model trained and tested on UW Health cases achieved the AUROC of 0.798.

CONCLUSIONS

This result shows the potential utility of our tool in accelerating FXS diagnosis in real clinical settings. Earlier diagnosis can lead to more timely intervention and access to services with the goal of improving patients' health outcomes.

Analysis of Genetic Association Between ABCA7 Polymorphism and Alzheimer’s Disease Risk in the Southern Chinese Population

Objective

The study aimed to clarify the association of the 21 single nucleotide polymorphisms (SNPs) with Alzheimer’s disease (AD) in the population of southern China.

Methods

A case-control study was conducted with a total sample size of 490 subjects (246 patients with AD and 244 age- and gender-matched healthy controls) enrolled in this study. Twenty-one selected SNPs were detected using SNaPshot assay and polymerase chain reaction (PCR) technique. Then, we assessed how these SNPs correlated with AD susceptibility.

Results

The results showed that rs3764650 of ABCA7 was closely correlated with risen AD morbidity in the allele [P = 0.010, odds ratio (OR) = 1.43, 95% confidence interval (CI) 1.09–1.89], dominant (P = 0.004, OR = 1.71, 95% CI 1.19–2.46), and additive (P = 0.012, OR = 1.42, 95% CI 1.08–1.86) models. However, rs4147929 of ABCA7 was related to higher AD risk in the allele (P = 0.006, OR = 1.45, 95% CI 1.11–1.89), dominant (P = 0.012, OR = 1.59, 95% CI 1.11–2.27), and additive (P = 0.010, OR = 1.40, 95% CI 1.08–1.81) models. In addition, the frequencies of the G-allele at rs3764650 (P = 0.030) and the A-allele at rs4147929 (P = 0.001) in AD were statistically higher in APOE ε4 carriers in comparison to non-carriers.

Conclusion

This study demonstrated that the G-allele at rs3764650 and the A-allele at rs4147929 appeared at higher risk for developing AD, particularly in APOE ε4 carriers. Moreover, it was observed that rs3764650 and rs4147929 of ABCA7 were linked to AD. More in-depth research with a relatively large sample is needed to make the results more convincing.

Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization

Isolating the causal genes from numerous genetic association signals in genome-wide association studies (GWASs) of complex phenotypes remains an open and challenging question. In the present study, we proposed a statistical approach, the effective-median-based Mendelian randomization (MR) framework, for inferring the causal genes of complex phenotypes with the GWAS summary statistics (named EMIC). The effective-median method solved the high false-positive issue in the existing MR methods due to either correlation among instrumental variables or noises in approximated linkage disequilibrium (LD). EMIC can further perform a pleiotropy fine-mapping analysis to remove possible false-positive estimates. With the usage of multiple cis-expression quantitative trait loci (eQTLs), EMIC was also more powerful than the alternative methods for the causal gene inference in the simulated datasets. Furthermore, EMIC rediscovered many known causal genes of complex phenotypes (schizophrenia, bipolar disorder, and total cholesterol) and reported many new and promising candidate causal genes. In sum, this study provided an efficient solution to discriminate the candidate causal genes from vast amounts of GWAS signals with eQTLs. EMIC has been implemented in our integrative software platform KGGSEE.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Identification of Secreted Protein Gene-Based SNP Markers Associated with Virulence Phenotypes of Puccinia striiformis f. sp. tritici, the Wheat Stripe Rust Pathogen

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a destructive disease that occurs throughout the major wheat-growing regions of the world. This pathogen is highly variable due to the capacity of virulent races to undergo rapid changes in order to circumvent resistance in wheat cultivars and genotypes and to adapt to different environments. Intensive efforts have been made to study the genetics of wheat resistance to this disease; however, no known avirulence genes have been molecularly identified in Pst so far. To identify molecular markers for avirulence genes, a Pst panel of 157 selected isolates representing 126 races with diverse virulence spectra was genotyped using 209 secreted protein gene-based single nucleotide polymorphism (SP-SNP) markers via association analysis. Nineteen SP-SNP markers were identified for significant associations with 12 avirulence genes: AvYr1, AvYr6, AvYr7, AvYr9, AvYr10, AvYr24, AvYr27, AvYr32, AvYr43, AvYr44, AvYrSP, and AvYr76. Some SP-SNPs were associated with two or more avirulence genes. These results further confirmed that association analysis in combination with SP-SNP markers is a powerful tool for identifying markers for avirulence genes. This study provides genomic resources for further studies on the cloning of avirulence genes, understanding the mechanisms of host–pathogen interactions, and developing functional markers for tagging specific virulence genes and race groups.

Genome-Wide Association of New-Onset Hypertension According to Renin Concentration: The Korean Genome and Epidemiology Cohort Study

The renin-angiotensin system (RAS) is a crucial regulator of vascular resistance and blood volume in the body. This study aimed to examine the genetic predisposition of the plasma renin concentration influencing future hypertension incidence. Based on the Korean Genome and Epidemiology Cohort dataset, 5211 normotensive individuals at enrollment were observed over 12 years, categorized into the low-renin and high-renin groups. We conducted genome-wide association studies for the total, low-renin, and high-renin groups. Among the significant SNPs, the lead SNPs of each locus were focused on for further interpretation. The effect of genotypes was determined by logistic regression analysis between controls and new-onset hypertension, after adjusting for potential confounding variables. During a mean follow-up period of 7.6 years, 1704 participants (32.7%) developed hypertension. The low-renin group showed more incidence rates of new-onset hypertension (35.3%) than the high-renin group (26.5%). Among 153 SNPs in renin-related gene regions, two SNPs (rs11726091 and rs8137145) showed an association in the high-renin group, four SNPs (rs17038966, rs145286444, rs2118663, and rs12336898) in the low-renin group, and three SNPs (rs1938859, rs7968218, and rs117246401) in the total population. Most significantly, the low-renin SNP rs12336898 in the SPTAN1 gene, closely related to vascular wall remodeling, was associated with the development of hypertension (p-value = 1.3 × 10−6). We found the candidate genetic polymorphisms according to blood renin concentration. Our results might be a valuable indicator for hypertension risk prediction and preventive measure, considering renin concentration with genetic susceptibility.

Differential Relations of Parental Behavior to Children's Early Executive Function as a Function of Child Genotype: A Systematic Review

Child genotype is an important biologically based indicator of sensitivity to the effects of parental behavior on children's executive function (EF) in early childhood, birth to age 5. While evidence for gene × parental behavior interactions on children's early EF is growing, researchers have called the quality of evidence provided by gene × environment interaction studies into question. For this reason, this review comprehensively examined the literature and evaluated the evidence for gene × parental behavior interactions on children's early EF abilities. Psychology and psychiatry databases were searched for published peer-reviewed studies. A total of 18 studies met inclusion criteria. Twenty-nine of 89 (33%) examined interactions were significant. However, a p-curve analysis did not find the significant interactions to be of evidential value. A high rate of false positives, due to the continued use of candidate gene and haplotype measures of child genotype and small sample sizes, likely contributed to the high rate of significant interactions and low evidential value. The use of contemporary molecular genetic measures and larger sample sizes are necessary to advance our understanding of child genotype as a moderator of parental effects on children's EF during early childhood and the biopsychosocial mechanisms underlying children's EF development during this critical period. Without these changes, future research is likely to be stymied by the same limitations as current research.

Investigating Genetic and Other Determinants of First-Onset Myocardial Infarction in Malaysia: Protocol for the Malaysian Acute Vascular Events Risk Study

BACKGROUND

Although the burden of premature myocardial infarction (MI) is high in Malaysia, direct evidence on the determinants of MI in this multi-ethnic population remains sparse.

OBJECTIVE

The Malaysian Acute Vascular Events Risk (MAVERIK) study is a retrospective case-control study established to investigate the genomic, lipid-related, and other determinants of acute MI in Malaysia. In this paper, we report the study protocol and early results.

METHODS

By June 2019, we had enrolled approximately 2500 patients with their first MI and 2500 controls without cardiovascular disease, who were frequency-matched by age, sex, and ethnicity, from 17 hospitals in Malaysia. For each participant, serum and whole blood have been collected and stored. Clinical, demographic, and behavioral information has been obtained using a 200-item questionnaire.

RESULTS

Tobacco consumption, a history of diabetes, hypertension, markers of visceral adiposity, indicators of lower socioeconomic status, and a family history of coronary disease were more prevalent in cases than in controls. Adjusted (age and sex) logistic regression models for traditional risk factors indicated that current smoking (odds ratio [OR] 4.11, 95% CI 3.56-4.75; P<.001), previous smoking (OR 1.34, 95% CI 1.12-1.60; P=.001), a history of high blood pressure (OR 2.13, 95% CI 1.86-2.44; P<.001), a history of diabetes mellitus (OR 2.72, 95% CI 2.34-3.17; P<.001), a family history of coronary heart disease (OR 1.28, 95% CI 1.07-1.55; P=.009), and obesity (BMI >30 kg/m²; OR 1.19, 95% CI 1.05-1.34; P=.009) were associated with MI in age- and sex-adjusted models.

CONCLUSIONS

The MAVERIK study can serve as a useful platform to investigate genetic and other risk factors for MI in an understudied Southeast Asian population. It should help to hasten the discovery of disease-causing pathways and inform regionally appropriate strategies that optimize public health action.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/31885.

Metagenomics and chemotherapy-induced nausea: A roadmap for future research

Uncontrolled chemotherapy-induced nausea and vomiting can reduce patients' quality of life and may result in premature discontinuation of chemotherapy. Although nausea and vomiting are commonly grouped together, research has shown that antiemetics are clinically effective against chemotherapy-induced vomiting (CIV) but less so against chemotherapy-induced nausea (CIN). Nausea remains a problem for up to 68% of patients who are prescribed guideline-consistent antiemetics. Despite the high prevalence of CIN, relatively little is known regarding its etiology independent of CIV. This review summarizes a metagenomics approach to the study and treatment of CIN with the goal of encouraging future research. Metagenomics focuses on genetic risk factors and encompasses both human (ie, host) and gut microbial genetic variation. Little work to date has focused on metagenomics as a putative biological mechanism of CIN. Metagenomics has the potential to be a powerful tool in advancing scientific understanding of CIN by identifying new biological pathways and intervention targets. The investigation of metagenomics in the context of well-established demographic, clinical, and patient-reported risk factors may help to identify patients at risk and facilitate the prevention and management of CIN.

Turning the Curve Into Straight: Phenogenetics of the Spine Morphology and Coordinate Maintenance in the Zebrafish

The vertebral column, or spine, provides mechanical support and determines body axis posture and motion. The most common malformation altering spine morphology and function is adolescent idiopathic scoliosis (AIS), a three-dimensional spinal deformity that affects approximately 4% of the population worldwide. Due to AIS genetic heterogenicity and the lack of suitable animal models for its study, the etiology of this condition remains unclear, thus limiting treatment options. We here review current advances in zebrafish phenogenetics concerning AIS-like models and highlight the recently discovered biological processes leading to spine malformations. First, we focus on gene functions and phenotypes controlling critical aspects of postembryonic aspects that prime in spine architecture development and straightening. Second, we summarize how primary cilia assembly and biomechanical stimulus transduction, cerebrospinal fluid components and flow driven by motile cilia have been implicated in the pathogenesis of AIS-like phenotypes. Third, we highlight the inflammatory responses associated with scoliosis. We finally discuss recent innovations and methodologies for morphometrically characterize and analyze the zebrafish spine. Ongoing phenotyping projects are expected to identify novel and unprecedented postembryonic gene functions controlling spine morphology and mutant models of AIS. Importantly, imaging and gene editing technologies are allowing deep phenotyping studies in the zebrafish, opening new experimental paradigms in the morphometric and three-dimensional assessment of spinal malformations. In the future, fully elucidating the phenogenetic underpinnings of AIS etiology in zebrafish and humans will undoubtedly lead to innovative pharmacological treatments against spinal deformities.

COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia

Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.

Finding genetically-supported drug targets for Parkinson's disease using Mendelian randomization of the druggable genome

Parkinson's disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson's disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson's disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson's disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson's disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson's disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson's disease drug development.

Improving rigor and reproducibility in nonhuman primate research

Nonhuman primates (NHPs) are a critical component of translational/preclinical biomedical research due to the strong similarities between NHP and human physiology and disease pathology. In some cases, NHPs represent the most appropriate, or even the only, animal model for complex metabolic, neurological, and infectious diseases. The increased demand for and limited availability of these valuable research subjects requires that rigor and reproducibility be a prime consideration to ensure the maximal utility of this scarce resource. Here, we discuss a number of approaches that collectively can contribute to enhanced rigor and reproducibility in NHP research.

Genetic variants in histone modification regions are associated with the prognosis of lung adenocarcinoma

We investigated the association between genetic variants in the histone modification regions and the prognosis of lung adenocarcinoma after curative surgery. Potentially functional SNPs were selected using integrated analysis of ChIP-seq and RNA-seq. The SNPs were analyzed in a discovery set (n = 166) and a validation set (n = 238). The associations of the SNPs with overall survival (OS) and disease-free survival (DFS) were analyzed. A total of 279 SNPs were selected for genotyping. Among these, CAPN1 rs17583C>T was significantly associated with better OS and DFS (P = 0.001 and P = 0.007, respectively), and LINC00959 rs4751162A>G was significantly associated with worse DFS (P = 0.008). Luciferase assays showed a significantly lower promoter activity of CAPN1 in the rs17583 T allele than C allele (P = 0.008), and consistently the CT + TT genotypes had significantly lower CAPN1 expression than CC genotype (P = 0.01) in clinical samples. The rs4751162 G allele had higher promoter activity of GLRX3 than A allele (P = 0.05). The motif analyses and ChIP-qPCR confirmed that the variants are located in the active promoter/enhancer regions where transcription factor binding occurs. This study showed that genetic variants in the histone modification regions could predict the prognosis of lung adenocarcinoma after surgery.

Little Evidence of Modified Genetic Effect of rs16969968 on Heavy Smoking Based on Age of Onset of Smoking

INTRODUCTION

Tobacco smoking is the leading cause of preventable death globally. Smoking quantity, measured in cigarettes per day, is influenced both by the age of onset of regular smoking (AOS) and by genetic factors, including a strong effect of the nonsynonymous single-nucleotide polymorphism rs16969968. A previous study by Hartz et al. reported an interaction between these two factors, whereby rs16969968 risk allele carriers who started smoking earlier showed increased risk for heavy smoking compared with those who started later. This finding has yet to be replicated in a large, independent sample.

METHODS

We performed a preregistered, direct replication attempt of the rs16969968 × AOS interaction on smoking quantity in 128 383 unrelated individuals from the UK Biobank, meta-analyzed across ancestry groups. We fit statistical association models mirroring the original publication as well as formal interaction tests on multiple phenotypic and analytical scales.

RESULTS

We replicated the main effects of rs16969968 and AOS on cigarettes per day but failed to replicate the interaction using previous methods. Nominal significance of the rs16969968 × AOS interaction term depended strongly on the scale of analysis and the particular phenotype, as did associations stratified by early/late AOS. No interaction tests passed genome-wide correction (α = 5e-8), and all estimated interaction effect sizes were much smaller in magnitude than previous estimates.

CONCLUSIONS

We failed to replicate the strong rs16969968 × AOS interaction effect previously reported. If such gene-moderator interactions influence complex traits, they likely depend on scale of measurement, and current biobanks lack the power to detect significant genome-wide associations given the minute effect sizes expected.

IMPLICATIONS

We failed to replicate the strong rs16969968 × AOS interaction effect on smoking quantity previously reported. If such gene-moderator interactions influence complex traits, current biobanks lack the power to detect significant genome-wide associations given the minute effect sizes expected. Furthermore, many potential interaction effects are likely to depend on the scale of measurement employed.

COL5A1 RS12722 Is Associated with Temporomandibular Joint Anterior Disc Displacement without Reduction in Polish Caucasians

Numerous reports describe the association between the single-nucleotide polymorphism (SNP) rs12722 and rs13946 in the COL5A1 gene and injuries, such as Achilles tendon pathology, anterior cruciate ligament (ACL) injuries, and tennis elbow. Hence, there were no studies investigating COL5A1 and temporomandibular joint (TMJ) pathology. The aim of this study is to evaluate the relationship between COL5A1 rs12722 and rs13946 SNPs and TMJ articular disc displacement without reduction (ADDwoR). In this case-control study, the study group consisted of 124 Caucasian patients of both sexes. Each patient had a history of ADDwoR no more than 3 months prior. The control group comprised 126 patients with no signs of TMD according to DC/TMD. Genotyping of the selected SNPs was performed by real-time PCR using TaqMan probes. The significance of the differences in the distribution of genotypes was analyzed using Pearson’s chi-square test. Logistic regression modeling was performed to analyze the influence of the 164 investigated SNPs on ADDwoR. The COL5A1 marker rs12722 turned out to be statistically significant (p-value = 0.0119), implying that there is a difference in the frequencies of TMJ ADDwoR. The distribution of rs12722 SNPs in the study group TT(66), CC(27), CT(31) vs. control group TT(45), CC(26), CT(51) indicates that patients with CT had an almost 2.4 times higher likelihood of ADDwoR (OR = 2.41) than those with reference TT (OR = 1), while rs13946 genotypes were shown to be insignificant, with a p-value of 0.1713. The COL5A1 rs12722 polymorphism is a risk factor for ADDwoR in the Polish Caucasian population.

Insulin-like Growth Factor Axis Genetic Score and Sports Excellence

ABSTRACT

Ben-Zaken, S, Meckel, Y, Nemet, D, and Eliakim, A. Insulin-like growth factor axis genetic score and sports excellence. J Strength Cond Res 35(9): 2421-2426, 2021-It has been suggested that IGF1 polymorphisms associated with circulating IGF1 levels may be linked to elite short-distance running performance. This study assessed genetic score based on 6 polymorphisms related to the Insulin-like growth factor axis (rs7136446, rs35767, rs6220, rs680, rs2854744, and rs1805086) among elite Israeli runners and swimmers. One hundred sixty-one track and field athletes (123 men and 38 women, age 17-50 years) and 94 swimmers (61 men and 33 women, age 16-49 years) participated in the study. Athletes were divided into short-distance runners (SDRs, major event: 100-200-m sprints and jumps, n = 63) and long-distance runners (LDRs, major event: 5,000 m and marathon, n = 98). Swimmers were divided into short-distance swimmers (SDSs, major event: 50-100 m, n = 44) and long-distance swimmers (LDSs, major event: 400-1,500 m, n = 50). Groups were subdivided into top-level and national-level athletes. We calculated the IGF genetic score (IGF-GS) of all the subjects on a 0-100 scale. Top-level SDRs' mean IGF-GS (30.8 ± 11.7) was significantly higher (p < 0.006) compared with national-level SDRs' (20.5 ± 11.3) and top-level SDSs' (19.9 ± 8.5). Subjects with IGF-GS >25 had an increased odds ratio (OR) of being elite-level SDRs (OR: 4.2; 95% confidence interval: 0.68-26.09; p < 0.001). In summary, a combined assessment of 6 single-nucleotide polymorphisms, all known to modulate circulation IGF1 levels, was associated with a higher genetic score among SDRs, emphasizing the importance of the IGF system to land speed sports events but not to swimming events. Whether the IGF-GS may be used for selection of elite-level sprinters in early stages of their athletic career needs to be further investigated.

Serotonergic receptor gene polymorphism and response to selective serotonin reuptake inhibitors in ethnic Malay patients with first episode of major depressive disorder

The polymorphisms of the 5HTR1A and 5HTR2A receptor genes (rs6295C/G and rs6311G/A) have been evaluated for association with SSRI treatment outcome in various populations with different results. The present study was carried out to determine the association between genotypes of HTR1A-rs6295 and HTR2A-rs6311 with SSRI treatment outcome among the ethnic Malay patients diagnosed with first-episode major depressive disorder (MDD). The patients were recruited from four tertiary hospitals in the Klang Valley region of Malaysia. Predefined efficacy phenotypes based on 25% (partial early response) and 50% (clinical efficacy response) reduction in Montgomery Asberg Depression Rating Scale-self Rated score (MADRS-S) were adopted for assessment of treatment efficacy in this study. Self-reporting for adverse effects (AE) was documented using the Patient Rated Inventory of Side Effect (PRISE) after treatment with SSRI for up to 6 weeks. Adjusted binary logistic regression between genotypes of the polymorphism obtained using sequencing technique with the treatment outcome phenotypes was performed. The 142 patients recruited were made up of 96 females (67.6%) and 46 males (32.4%). Clinical efficacy and Partial early response phenotypes were not significantly associated with genotypes of HTR1A and HTR2A polymorphism. The GG genotype of HTR2A polymorphism has decreased odds for dizziness (CNS) and increased odds for poor concentration. The GA genotype increases the odd for excessive sweating, diarrhoea, constipation and blurred vision. The CC genotype of HTR1A-rs6295 decreases the odd for nausea/vomiting and increases the odd for anxiety. Thus, some genotypes of HTR1A and HTR2A polymorphism were associated with SSRI treatment outcomes in ethnic Malay MDD patients.

Gene Variants Associated With Venous Thrombosis: A Replication Study in a Brazilian Multicentre Study

Single nucleotide polymorphisms (SNP) associated with Venous Thromboembolism (VTE) risk have been identified in European and American populations. Replicate SNPs associated with VTE in a Brazilian multicenter case-control study of the Southeast region. Patients with previous VTE assisted at the Outpatient Clinics of 3 centers of the Southeast Brazilian region were compared to normal controls of the same geographic region. We evaluated 29 SNPs associated with VTE risk in other populations, and 90 SNPs for stratification analysis of the population. Due to high admixture of Brazilian population and lack of previous studies, the calculation of the sample power was performed after genotyping. Sample size, allelic frequency and Hardy-Weinberg equilibrium were estimated. The association and odds ratio analyses were estimated by logistic regression and the results were adjusted for multiple tests using Bonferroni correction. The evaluation of the genetic structure similarity in the cases and controls was performed by AMOVA. 436 cases and 430 controls were included. It was demonstrated that this sample has a statistical power to detect a genetic association of 79.4%. AMOVA showed that the genetic variability between groups was 0.0% and 100% within each group. None of the SNPs showed association with VTE in our population. A Brazilian multicenter case-control study with adequate sample power, high genetic variability though no stratification between groups, showed no replication of SNPs associated with VTE. The high admixture of Brazilian population may be responsible for these results, emphasizing the influence of the population genetic structure in association studies.

Do monogenic inborn errors of immunity cause susceptibility to severe COVID-19?

The SARS-CoV-2 virus, which causes COVID-19, has been associated globally with substantial morbidity and mortality. Numerous reports over the past year have described the clinical and immunological profiles of COVID-19 patients, and while some trends have emerged for risk stratification, they do not provide a complete picture. Therefore, efforts are ongoing to identify genetic susceptibility factors of severe disease. In this issue of the JCI, Povysil et al. performed a large, multiple-country study, sequencing genomes from patients with mild and severe COVID-19, along with population controls. Contrary to previous reports, the authors observed no enrichment of predicted loss-of-function variants in genes in the type I interferon pathway, which might predispose to severe disease. These studies suggest that more evidence is needed to substantiate the hypothesis for a genetic immune predisposition to severe COVID-19, and highlights the importance of considering experimental design when implicating a monogenic basis for severe disease.

Planning and Conducting a Pharmacogenetics Association Study

Pharmacogenetics (PGx) association studies are used to discover, replicate, and validate the association between an inherited genotype and a treatment outcome. The objective of this tutorial is to provide trainees and novice PGx researchers with an overview of the major decisions that need to be made when designing and conducting a PGx association study. The first critical decision is to determine whether the objective of the study is discovery, replication, or validation. Next, the researcher must identify a patient cohort that has all of the data necessary to conduct the intended analysis. Then, the investigator must select and define the treatment outcome, or phenotype, that will be analyzed. Next, the investigator must determine what genotyping approach and genetic data will be included in the analysis. Finally, the association between the genotype and phenotype is tested using some statistical analysis methodology. This tutorial is divided into five sections; each section describes commonly used approaches and provides suggestions and resources for designing and conducting a PGx association study. Successful PGx association studies are necessary to discover and validate associations between inherited genetic variation and treatment outcomes, which enable clinical translation to improve efficacy and reduce toxicity of treatment.

Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations

Genome-wide association studies (GWASs) have successfully identified a large amount of single-nucleotide polymorphisms associated with many complex phenotypes in diverse populations. However, a comprehensive understanding of the genetic correlation of associated loci of phenotypes across populations remains lacking and the extent to which associations discovered in one population can be generalized to other populations or can be utilized for trans-ethnic genetic prediction is also unclear. By leveraging summary statistics, we proposed MAGIC to evaluate the trans-ethnic marginal genetic correlation (r_m) of per-allele effect sizes for associated SNPs (P < 5E-8) under the framework of measurement error models. We confirmed the methodological advantage of MAGIC over general approaches through simulations and demonstrated its utility by analyzing 34 GWAS summary statistics of phenotypes from the East Asian (N_max = 254,373) and European (N_max = 1,220,901) populations. Among these phenotypes, r_m was estimated to range from 0.584 (se = 0.140) for breast cancer to 0.949 (se = 0.035) for age of menarche, with an average of 0.835 (se = 0.045). We also uncovered that the trans-ethnic genetic prediction accuracy for phenotypes in the target population would substantially become low when using associated SNPs identified in non-target populations, indicating that associations discovered in the one population cannot be simply generalized to another population and that the accuracy of trans-ethnic phenotype prediction is generally dissatisfactory. Overall, our study provides in-depth insight into trans-ethnic genetic correlation and prediction for complex phenotypes across diverse populations.

MultiGWAS: An integrative tool for Genome Wide Association Studies in tetraploid organisms

The genome-wide association studies (GWASs) are essential to determine the genetic bases of either ecological or economic phenotypic variation across individuals within populations of the model and nonmodel organisms. For this research question, the GWAS replication testing different parameters and models to validate the results' reproducibility is common. However, straightforward methodologies that manage both replication and tetraploid data are still missing. To solve this problem, we designed the MultiGWAS, a tool that does GWAS for diploid and tetraploid organisms by executing in parallel four software packages, two designed for polyploid data (GWASpoly and SHEsis) and two designed for diploid data (GAPIT and TASSEL). MultiGWAS has several advantages. It runs either in the command line or in a graphical interface; it manages different genotype formats, including VCF. Moreover, it allows control for population structure, relatedness, and several quality control checks on genotype data. Besides, MultiGWAS can test for additive and dominant gene action models, and, through a proprietary scoring function, select the best model to report its associations. Finally, it generates several reports that facilitate identifying false associations from both the significant and the best-ranked association Single Nucleotide Polymorphisms (SNPs) among the four software packages. We tested MultiGWAS with public tetraploid potato data for tuber shape and several simulated data under both additive and dominant models. These tests demonstrated that MultiGWAS is better at detecting reliable associations than using each of the four software packages individually. Moreover, the parallel analysis of polyploid and diploid software that only offers MultiGWAS demonstrates its utility in understanding the best genetic model behind the SNP association in tetraploid organisms. Therefore, MultiGWAS probed to be an excellent alternative for wrapping GWAS replication in diploid and tetraploid organisms in a single analysis environment.

SINGLE NUCLEOTIDE POLYMORPHISMS IN RETINAL DETACHMENT PATIENTS WITH AND WITHOUT PROLIFERATIVE VITREORETINOPATHY

PURPOSE

To investigate differences in genotype distributions of single nucleotide polymorphisms within genes, encoding inflammatory mediators, among patients with rhegmatogenous retinal detachment (RRD) and patients with proliferative vitreoretinopathy (PVR).

METHODS

A genetic association study was performed on 191 Slovenian patients, divided into 2 groups: 113 RRD patients with PVR and 78 RRD patients without PVR. Genotype distributions were investigated within the following 13 single nucleotide polymorphisms: rs3760396 (CCL2), rs9990554 (FGF2), rs17561 (IL1A), rs2069763 (IL2), rs1800795 (IL6), rs1800871 (IL10), rs3008 (JAK3), rs2229094 (LTA), rs1042522 (TP53), rs7656613 (PDGFRA), rs7226855 (SMAD7), rs1800471 (TGFB1), and rs1800629 (TNF).

RESULTS

Differences in genotype distributions between patients with RRD with or without PVR were detected in rs1800795 (IL6) (P = 0.04), rs1800871 (in the vicinity of the IL10) (P = 0.034), and rs1800471 (TGFB1) (P = 0.032). After adjustment none of the 13 analyzed single nucleotide polymorphisms showed statistically significant associations in single nucleotide polymorphism genotype distributions between patients with RRD with and without PVR.

CONCLUSION

Further research is needed, particularly expanded multicentric population-based studies, to clarify the issue of genetic contribution to PVR from different genetic, clinical, and population-based aspects.

Systems Exercise Genetics Research Design Standards

It is clear, based on a deep scientific literature base, that genetic and genomic factors play significant roles in determining a wide range of sport and exercise characteristics including exercise endurance capacity, strength, daily physical activity levels, and trainability of both endurance and strength. Although the research field of exercise systems genetics has rapidly expanded over the past two decades, many researchers publishing in this field are not extensively trained in molecular biology or genomics techniques, sometimes creating gaps in generating high-quality and cutting-edge research for publication. As current or former Associate Editors for Medicine and Science in Sports and Exercise that have handled the majority of exercise genetics articles for Medicine and Science in Sports and Exercise in the past 15 yr, we have observed a large number of scientific manuscripts submitted for publication review that have exhibited significant flaws preventing their publication; flaws that often directly stem from a lack of knowledge regarding the "state-of-the-art" methods and accepted literature base that is rapidly changing as the field evolves. The purpose of this commentary is to provide researchers-especially those coming from a nongenetics background attempting to publish in the exercise system genetics area-with recommendations regarding best-practice research standards and data analysis in the field of exercise systems genetics, to strengthen the overall literature in this important and evolving field of research.

Predicting gene phenotype by multi-label multi-class model based on essential functional features

Phenotype is one of the most significant concepts in genetics, which is used to describe all the characteristics of a research object that can be observed. Considering that phenotype reflects the integrated features of genotype and environment factors, it is hard to define phenotype characteristics, even difficult to predict unknown phenotypes. Restricted by current biological techniques, it is still quite expensive and time-consuming to obtain sufficient structural information of large-scale phenotype-associated genes/proteins. Various bioinformatics methods have been presented to solve such problem, and researchers have confirmed the efficacy and prediction accuracy of functional network-based prediction. But general functional descriptions have highly complicated inner structures for phenotype prediction. To further address this issue and improve the efficacy of phenotype prediction on more than ten kinds of phenotypes, we first extract functional enrichment features from GO and KEGG, and then use node2vec to learn functional embedding features of genes from a gene-gene network. All these features are analyzed by some feature selection methods (Boruta, minimum redundancy maximum relevance) to generate a feature list. Such list is fed into the incremental feature selection, incorporating some multi-label classifiers built by RAkEL and some classic base classifiers, to build an optimum multi-label multi-class classification model for phenotype prediction. According to recent researches, our method has indeed identified many literature-supported genes/proteins and their associated phenotypes, and even some candidate genes with re-assigned new phenotypes, which provide a new computational tool for the accurate and effective phenotypic prediction.

The Association of Aging-Related Polymorphisms with Susceptibility to Lung Cancer: A Case-Control Study in a Japanese Population

BACKGROUND

Telomere length is associated with cancer as well as aging.  Telomerase reverse transcriptase (TERT), telomere RNA component (TERC) and oligonucleotide/oligosaccharide-binding fold containing 1 (OBFC1) are known to be involved in telomere length regulation. The tumor suppressor p53 (TP53), which has been shown to interact with tumor protein p53-binding protein 1 (TP53BP1), is implicated in the response to telomere shortening and aging.  Polymorphisms in the TP53 and TP53BP1 genes are associated with various types of cancer. The aim of this study is to evaluate the impact of aging-related polymorphisms on lung cancer risk.

MATERIALS AND METHODS

This case-control study consists of 462 lung cancer cases and 379 controls from Japan.  We examined the effect of TERT rs2736100, TERC rs1881984, OBFC1 rs11191865, TP53 rs1042522 and TP53BP1 rs560191 on the risk of lung cancer using a Taq-Man real-time PCR assay. Unconditional logistic regression was used to assess the adjusted odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

None of the main effects of any of the telomere-related polymorphisms were related to the risk of lung cancer.  Similarly, none of the interactive effects of any of the telomere-related polymorphisms with smoking were associated with lung cancer risk. The significant multiplicative interaction between TERT rs2736100 and TP53BP1 rs560191 was statistically significant (OR for interaction = 0.34, 95% CI = 0.14-0.84). The multiplicative interaction between OBFC1 rs11191865 and TP53BP1 rs560191 was also statistically significant (OR for interaction = 2.44, 95% CI = 1.02-5.87) but the OR for interaction was in the opposite direction.

CONCLUSIONS

Our findings indicate that TP53BP1 rs560191 may predispose to lung cancer risk depending on the genotypes of telomere-related polymorphisms. Additional studies are warranted to confirm the findings suggested in the present study.<br />.

Exploring allele specific methylation in drug dependence susceptibility

Drug dependence is a neuropsychiatric condition that involves genetic, epigenetic and environmental factors. Allele-specific methylation (ASM) is a common and stable epigenetic mechanism that involves genetic variants correlating with differential levels of methylation at CpG sites. We selected 182 single-nucleotide polymorphisms (SNPs) described to influence cis ASM in human brain regions to evaluate their possible contribution to drug dependence susceptibility. We performed a case-control association study in a discovery sample of 578 drug-dependent patients (including 428 cocaine-dependent subjects) and 656 controls from Spain, and then, we followed-up the significant associations in an independent sample of 1119 cases (including 589 cocaine-dependent subjects) and 1092 controls. In the discovery sample, we identified five nominal associations, one of them replicated in the follow-up sample (rs6020251). The pooled analysis revealed an association between drug dependence and rs6020251 but also rs11585570, both overcoming the Bonferroni correction for multiple testing. We performed the same analysis considering only cocaine-dependent patients and obtained similar results. The rs6020251 variant correlates with differential methylation levels of cg17974185 and lies in the first intron of the CTNNBL1 gene, in a genomic region with multiple histone marks related to enhancer and promoter regions in brain. Rs11585570 is an eQTL in brain and blood for the SCP2 and ECHDC2 genes and correlates with differential methylation of cg27535305 and cg13461509, located in the promoter regions of both genes. To conclude, using an approach that combines genetic and epigenetic data, we highlighted the CTNNBL1, SCP2 and ECHDC2 genes as potential contributors to drug dependence susceptibility.