Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study

The exposome collectively refers to all exposures, beginning in utero and continuing throughout life, and comprises not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The exposome interacts with individual genetic and epigenetic characteristics to affect human health and disease, but large-scale studies that characterize the exposome and its relationships with human disease are limited. To address this gap, we used extensive questionnaire data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS, n = 9, 429) to evaluate exposure associations in relation to common diseases. We performed an exposome-wide association study (ExWAS) to examine single exposure models and their associations with 11 common complex diseases, namely allergic rhinitis, asthma, bone loss, fibroids, high cholesterol, hypertension, iron-deficient anemia, ovarian cysts, lower GI polyps, migraines, and type 2 diabetes. Across diseases, we found associations with lifestyle factors and socioeconomic status as well as asbestos, various dust types, biohazardous material, and textile-related exposures. We also found disease-specific associations such as fishing with lead weights and migraines. To differentiate between a replicated result and a novel finding, we used an AI-based literature search and database tool that allowed us to examine the current literature. We found both replicated findings, especially for lifestyle factors such as sleep and smoking across diseases, and novel findings, especially for occupational exposures and multiple diseases.

Influence of autozygosity on common disease risk across the phenotypic spectrum

Autozygosity is associated with rare Mendelian disorders and clinically relevant quantitative traits. We investigated associations between the fraction of the genome in runs of homozygosity (FROH) and common diseases in Genes & Health (n = 23,978 British South Asians), UK Biobank (n = 397,184), and 23andMe. We show that restricting analysis to offspring of first cousins is an effective way of reducing confounding due to social/environmental correlates of FROH. Within this group in G&H+UK Biobank, we found experiment-wide significant associations between FROH and twelve common diseases. We replicated associations with type 2 diabetes (T2D) and post-traumatic stress disorder via within-sibling analysis in 23andMe (median n = 480,282). We estimated that autozygosity due to consanguinity accounts for 5%-18% of T2D cases among British Pakistanis. Our work highlights the possibility of widespread non-additive genetic effects on common diseases and has important implications for global populations with high rates of consanguinity.

Comparison of Rare and Common Diseases in the Setting of Healthcare Priorities: Evidence of Social Preferences Based on a Systematic Review

Background

In light of the limited availability of healthcare resources, providing universal access to healthcare is a challenging task. As a result, prioritizing healthcare services has emerged as a crucial issue. This study aims to explore the preferences of the public regarding healthcare prioritization for rare and common diseases. By examining public attitudes, this study seeks to inform government decisions concerning resource allocation and distribution within healthcare.

Methods

"Social preference" and "rare disease" were searched as MeSH terms in the electronic databases of Ovid Medline, Web of Science, Embase, and Econlit for articles published since their establishment, and the information on the characteristics of the articles and the results of social preferences for rare diseases were analyzed and summarized.

Results

The public held predominantly neutral views on the setting of healthcare priorities for rare and common diseases. The results of the included studies showed that with all else being equal, no social preference for rarity was found, but when the public considered the proportional advantage of rare diseases or when the respondents were young, a social preference for rarity existed. In addition, the public weighed attributes such as the health benefits of treatments, the effectiveness of treatment options, the safety of treatment, equity, unmet needs, and disease severity in the process of setting of treatment priorities for rare diseases. Furthermore, in consideration of equity, the public showed a willingness to pay for rare diseases in spite of the high medical costs.

Conclusion

International studies on social preferences provide some evidence for the setting of healthcare priorities for rare diseases, and health policymakers should consider social preferences in an integrated manner in order to set healthcare priorities appropriately.

Association of sickle cell trait with adverse pregnancy outcomes in a population-based cohort

INTRODUCTION

Sickle cell trait (SCT) is common in African descendants. Its association with several adverse pregnancy outcomes (APOs) has been reported but remains inconsistent. The objectives of this study are to test associations of SCT with APOs in non-Hispanic Black women, including (1) validate the associations of SCT with previously reported APOs, (2) test novel associations of SCT with broad spectrum of APOs, and (3) estimate the attributable risk of SCT for implicated APOs.

MATERIAL AND METHODS

This is a retrospective analysis of a prospectively designed population-based cohort. Subjects were self-reported non-Hispanic Black women from the UK Biobank (UKB). SCT status was determined based on heterozygous Glu6Val in the HBB gene. Several APOs were studied, including four previously reported SCT-associated APOs (preeclampsia, bacteriuria, pregnancy loss, and preterm delivery), and broad conditions related to pregnancy, childbirth, and the puerperium. APOs were curated by experts' peer review and consensus processes. Associations of SCT with APOs were tested by estimating its relative risk and 95% confidence interval (95% CI), adjusting for number of live births and age at first birth. Attributable risk proportion (ARP) and population attributable risk proportion (PARP) of SCT to APOs were estimated.

RESULTS

Among the 4057 self-reported non-Hispanic Black women with pregnancy records in the UKB, 581 (14.32%) were SCT carriers. For four previously reported SCT-associated APOs, two were confirmed at a nominal P < 0.05; relative risk (RR) was 2.39 (95% CI 1.09-5.23) for preeclampsia, and 4.85 (95% CI 1.77-13.27) for bacteriuria. SCT contributed substantially to these two APOs among SCT carriers, with attributable risk proportion estimated at 61.00% and 68.96% for preeclampsia and bacteriuria, respectively. SCT also contributed substantially to these two APOs in the population (self-reported Black UK women), with population attributable risk proportion estimated at 18.30% and 24.14% for preeclampsia and bacteriuria, respectively. In addition, novel associations were found for seven other APOs (nominal P < 0.05).

CONCLUSIONS

SCT is significantly associated with APOs in this study and contributes substantially to APOs among self-reported Black women in the UK. Confirmation of these findings in independent study populations is required.

A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants

Quantifying an individual's risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multiple risk alleles of candidate diseases, has emerged as a standard approach for identifying high-risk individuals. Although several studies have been performed to benchmark the PRS calculation tools and assess their potential to guide future clinical applications, some issues remain to be further investigated, such as lacking (i) various simulated data with different genetic effects; (ii) evaluation of machine learning models and (iii) evaluation on multiple ancestries studies. In this study, we systematically validated and compared 13 statistical methods, 5 machine learning models and 2 ensemble models using simulated data with additive and genetic interaction models, 22 common diseases with internal training sets, 4 common diseases with external summary statistics and 3 common diseases for trans-ancestry studies in UK Biobank. The statistical methods were better in simulated data from additive models and machine learning models have edges for data that include genetic interactions. Ensemble models are generally the best choice by integrating various statistical methods. LDpred2 outperformed the other standalone tools, whereas PRS-CS, lassosum and DBSLMM showed comparable performance. We also identified that disease heritability strongly affected the predictive performance of all methods. Both the number and effect sizes of risk SNPs are important; and sample size strongly influences the performance of all methods. For the trans-ancestry studies, we found that the performance of most methods became worse when training and testing sets were from different populations.

Impact of common diseases and habits on daytime sleepiness in adults

We aimed to investigate the impact of common diseases and habits on daytime sleepiness in adults. We retrospectively collected the clinical and overnight polysomnographic data of 2829 adults. The impact of common diseases and habits on the Epworth Sleepiness Scale (ESS) score was analyzed by univariate and multivariate linear regression analyses. The mean ESS score was 6.2 (standard deviation = 4.3; range = 0-24) for all adults. Multivariate linear regression analysis showed that dyslipidemia, acute myocardial infarction (AMI), liver cirrhosis, alcohol drinking, and tea consumption had a significantly positive association with ESS score for all adults after adjusting for age, sex, body mass index, apnea-hypopnea index, sleep efficiency, percentage of sleep N3 stage, and depression. Subgroup analysis by sex showed that AMI, liver cirrhosis, alcohol drinking, and tea consumption had significantly positive association with ESS scores in males, whereas only dyslipidemia had significantly positive association with ESS scores in females. Subgroup analysis by age showed that alcohol drinking had a significantly positive association with ESS scores in young adults. AMI had a significantly positive association with ESS scores, but chronic kidney disease had a significantly negative association with ESS scores in middle-aged adults. Furthermore, dyslipidemia, chronic kidney disease, and cancers had a significantly positive association with ESS scores in older adults. Dyslipidemia, AMI, liver cirrhosis, alcohol drinking, and tea consumption had a significantly positive association with daytime sleepiness in adults but differed by sex and age.

Reasons for medical students selecting a rural prefecture in Japan for initial clinical training: a single-center-based cross-sectional study

Objective: Securing a sufficient number of medical residents to work in rural areas is an urgent issue. This study sought to clarify the factors that cause medical students at a rural university in Japan to select a particular place for their initial clinical training. Materials and Methods: A questionnaire was administered to all medical students at Saga University between February and March 2021. Participants were divided into two groups based on their training location choice: those who chose Saga Prefecture (Saga group) and those who selected other prefectures (non-Saga group). Then, logistic regression analysis was performed. Results: The questionnaire was answered by 300 students (46.3% response rate), of whom 291 agreed to participate in the study; 122 (41.9%) and 169 (58.1%) students were allocated to the Saga and non-Saga groups. Within the Saga group, the following factors were statistically significant: being admitted to Saga University's medical school through the system of special allotment of admission to applicants pledging to work in Saga Prefecture following graduation (or regional quota programs for admission) (odds ratio [OR], 19.18; 95% confidence interval [CI], 6.99-52.60); and being from Saga Prefecture (OR, 6.05; 95% CI, 2.24-16.35). With the non-Saga group, the desire to work in an urban area (OR, 0.03; 95% CI, 0.00-0.37) was statistically significant. Conclusion: To encourage medical residents to choose this prefecture for their initial clinical training, the focus should be on medical students who are from Saga Prefecture or admitted through the regional quota program.

A current guide to candidate gene association studies

Important factors contribute to a gained momentum in candidate gene association studies (CGASs), including the generalized use of next-generation sequencing (NGS), growing opportunities for hospital-based research, and the availability of open-source databases and bioinformatics tools. This article summarizes the general principles and analytical methods as a guide to CGASs in today's favorable context.

Perspectives on Drug Repurposing

Complex common diseases are a significant burden for our societies and demand not only preventive measures but also more effective, safer, and more affordable treatments. The whole process of the current model of drug discovery and development implies a high investment by the pharmaceutical industry, which ultimately impact in high drug prices. In this sense, drug repurposing would help meet the needs of patients to access useful and novel treatments. Unlike the traditional approach, drug repurposing enters both the preclinical evaluation and clinical trials of the compound of interest faster, budgeting research and development costs, and limiting potential biosafety risks. The participation of government, society, and private investors is needed to secure the funds for experimental design and clinical development of repurposing candidates to have affordable, effective, and safe repurposed drugs. Moreover, extensive advertising of repurposing as a concept in the health community, could reduce prescribing bias when enough clinical evidence exists, which will support the employment of cheaper and more accessible repurposed compounds for common conditions.

[Involvement of Variants in the Genes Encoding BRCA1-Associated Genome Surveillance Complex (BASC) in the Development of Human Common Diseases]

The "Mendelian code" hypothesis postulates a relationship between Mendelian (monogenic) and common pathologies. In this hypothesis, polymorphisms in the genes of Mendelian diseases may have a significant contribution to predisposition to common diseases in which the same biochemical pathways may be involved. In this review a group of genes encoding various proteins participating in the DNA repair, with a particular focus on the BRCA1-associated genome surveillance complex (BASC), is presented through the prism of the "Mendelian code" hypothesis. Here we discuss (1) their main functions in the repair of DNA double-strand breaks (ATM, MRE11, NBN, RAD50, BRCA1, and BLM) and mismatch repair (MSH2, MSH6, MLH1, PMS2, RF-C, and PCNA); (2) the mitochondrial involvement of these proteins; (3) the involvement of BASC proteins in the development of an adaptive immune response. For 13 out of 16 BASC protein encoding genes, mutations leading to monogenic diseases have already been described; for 11, there are associations with common diseases or individual biological processes. Patients with mutations in the genes of the BASC complex and patients with severe combined immunodeficiency share similar symptoms. Polymorphisms within DNA repair genes may play a role in the development of common diseases through the involvement of the immune response. The pleiotropic effects of these genes suggest their participation in the development of various conditions, both in health and pathology.

Polymorphic Inversions Underlie the Shared Genetic Susceptibility of Obesity-Related Diseases

The burden of several common diseases including obesity, diabetes, hypertension, asthma, and depression is increasing in most world populations. However, the mechanisms underlying the numerous epidemiological and genetic correlations among these disorders remain largely unknown. We investigated whether common polymorphic inversions underlie the shared genetic influence of these disorders. We performed an inversion association analysis including 21 inversions and 25 obesity-related traits on a total of 408,898 Europeans and validated the results in 67,299 independent individuals. Seven inversions were associated with multiple diseases while inversions at 8p23.1, 16p11.2, and 11q13.2 were strongly associated with the co-occurrence of obesity with other common diseases. Transcriptome analysis across numerous tissues revealed strong candidate genes for obesity-related traits. Analyses in human pancreatic islets indicated the potential mechanism of inversions in the susceptibility of diabetes by disrupting the cis-regulatory effect of SNPs from their target genes. Our data underscore the role of inversions as major genetic contributors to the joint susceptibility to common complex diseases.

Regulation of nuclear epigenome by mitochondrial DNA heteroplasmy

Diseases associated with mitochondrial DNA (mtDNA) mutations are highly variable in phenotype, in large part because of differences in the percentage of normal and mutant mtDNAs (heteroplasmy) present within the cell. For example, increasing heteroplasmy levels of the mtDNA tRNA^Leu(UUR) nucleotide (nt) 3243A > G mutation result successively in diabetes, neuromuscular degenerative disease, and perinatal lethality. These phenotypes are associated with differences in mitochondrial function and nuclear DNA (nDNA) gene expression, which are recapitulated in cybrid cell lines with different percentages of m.3243G mutant mtDNAs. Using metabolic tracing, histone mass spectrometry, and NADH fluorescence lifetime imaging microscopy in these cells, we now show that increasing levels of this single mtDNA mutation cause profound changes in the nuclear epigenome. At high heteroplasmy, mitochondrially derived acetyl-CoA levels decrease causing decreased histone H4 acetylation, with glutamine-derived acetyl-CoA compensating when glucose-derived acetyl-CoA is limiting. In contrast, α-ketoglutarate levels increase at midlevel heteroplasmy and are inversely correlated with histone H3 methylation. Inhibition of mitochondrial protein synthesis induces acetylation and methylation changes, and restoration of mitochondrial function reverses these effects. mtDNA heteroplasmy also affects mitochondrial NAD⁺/NADH ratio, which correlates with nuclear histone acetylation, whereas nuclear NAD⁺/NADH ratio correlates with changes in nDNA and mtDNA transcription. Thus, mutations in the mtDNA cause distinct metabolic and epigenomic changes at different heteroplasmy levels, potentially explaining transcriptional and phenotypic variability of mitochondrial disease.

DNA Sequence Analysis in Clinical Medicine, Proceeding Cautiously

Delineation of underlying genomic and genetic factors in a specific disease may be valuable in establishing a definitive diagnosis and may guide patient management and counseling. In addition, genetic information may be useful in identification of at risk family members. Gene mapping and initial genome sequencing data enabled the development of microarrays to analyze genomic variants. The goal of this review is to consider different generations of sequencing techniques and their application to exome sequencing and whole genome sequencing and their clinical applications. In recent decades, exome sequencing has primarily been used in patient studies. Discussed in some detail, are important measures that have been developed to standardize variant calling and to assess pathogenicity of variants. Examples of cases where exome sequencing has facilitated diagnosis and led to improved medical management are presented. Whole genome sequencing and its clinical relevance are presented particularly in the context of analysis of nucleotide and structural genomic variants in large population studies and in certain patient cohorts. Applications involving analysis of cell free DNA in maternal blood for prenatal diagnosis of specific autosomal trisomies are reviewed. Applications of DNA sequencing to diagnosis and therapeutics of cancer are presented. Also discussed are important recent diagnostic applications of DNA sequencing in cancer, including analysis of tumor derived cell free DNA and exosomes that are present in body fluids. Insights gained into underlying pathogenetic mechanisms of certain complex common diseases, including schizophrenia, macular degeneration, neurodegenerative disease are presented. The relevance of different types of variants, rare, uncommon, and common to disease pathogenesis, and the continuum of causality, are addressed. Pharmogenetic variants detected by DNA sequence analysis are gaining in importance and are particularly relevant to personalized and precision medicine.

Circulating long noncoding RNAs as novel biomarkers of human diseases

Long noncoding RNAs (lncRNAs) are a kind of noncoding RNAs which are longer than ˜200 nucleotides, lacking of protein-encoding capacity and are implicated in the pathogenesis of various diseases. Recently, it was demonstrated that lncRNAs could be released into the circulation and be stable in blood. Circulating lncRNAs have been reported to have potential in distinguishing patients from healthy individuals. Therefore, the detection of circulating lncRNAs may be valuable for improving the diagnosis and prognosis of various diseases. This review summarized the current understanding of circulating lncRNAs as novel biomarkers of various human diseases, such as cancer, cardiovascular diseases, nervous system diseases and other diseases, which highlighted the significance of circulating lncRNAs as novel diagnostic and prognostic biomarkers of human diseases.

Progress and promise in understanding the genetic basis of common diseases

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that--even at large sample sizes--these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.

Family health history communication networks of older adults: importance of social relationships and disease perceptions

Older individuals play a critical role in disseminating family health history (FHH) information that can facilitate disease prevention among younger family members. This study evaluated the characteristics of older adults and their familial networks associated with two types of communication (have shared and intend to share new FHH information with family members) to inform public health efforts to facilitate FHH dissemination. Information on 970 social network members enumerated by 99 seniors (aged 57 years and older) at 3 senior centers in Memphis, Tennessee, through face-to-face interviews was analyzed. Participants shared FHH information with 27.5% of the network members; 54.7% of children and 24.4% of siblings. Two-level logistic regression models showed that participants had shared FHH with those to whom they provided emotional support (odds ratio [OR] = 1.836) and felt close to (OR = 1.757). Network-members were more likely to have received FHH from participants with a cancer diagnosis (OR = 2.617) and higher familiarity with (OR = 1.380) and importance of sharing FHH with family (OR = 1.474). Participants intended to share new FHH with those who provide tangible support to (OR = 1.804) and were very close to them (OR = 2.112). Members with whom participants intend to share new FHH were more likely to belong to the network of participants with higher perceived severity if family members encountered heart disease (OR = 1.329). Many first-degree relatives were not informed of FHH. Perceptions about FHH and disease risk as well as quality of social relationships may play roles in whether seniors communicate FHH with their families. Future studies may consider influencing these perceptions and relationships.

Public interest and expectations concerning commercial genotyping and genetic risk assessment

In contrast to the modest progress made in the interpretation and clinical application of genomic data, genotyping technologies have experienced great progress. Genotyping costs are progressively decreasing making individual genotyping more commonly available. Financial availability of individual genome analysis and the strong desire of many people to know about their individual genomic characteristics, promotes the marketing of genetic tests of variable predictive value directly to the public. A survey of 2000 Russian respondents revealed very positive attitudes and beliefs towards these genetic developments: 85% of surveyed individuals would like to have their genetic risk for avoidable diseases estimated, and 89% responded stating that they would try to change their lifestyle by giving up bad habits, following a recommended diet or taking medications if a high risk of disease was identified. It is believed that with time, validated genetic information will find its rightful place in medicine, by supplementing phenotypic clinical data with validated genetic interpretations.

Clinical phenome scanning

Large population-based cohorts are ideal for the study of common, complex disorders because they allow characterization of gene-gene and gene-environment interactions. We propose a clinical phenome scanning approach to genotype-phenotype association studies, as this approach acknowledges the heterogeneous nature of common diseases and takes advantage of the unprecedented density of phenotypic data available in population-based DNA biobanks. By analogy to genome-wide scanning, the construction of a clinical phenome scan includes a complete scan of all clinically available information (housed in electronic medical records). This is done on a subject-by-subject basis and the resulting phenomes can subsequently be interrogated for association with a single allele for any given gene. By prioritizing phenotype (rather than genotype), this approach allows investigators to ask the question "Which disease is associated with a given gene?" rather than "Which gene is associated with a given disease?".