Genetic Predisposition To Disease Research Articles

A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease.

Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types like macrophages. We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo. We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus. Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.

Prevalence of kidney health genetic variants in adults with sickle cell nephropathy.

The pathophysiology and genetic risk for sickle cell disease (SCD)-related chronic kidney disease (CKD) are not well understood. In 70 adults with SCD-related CKD and without APOL1 inherited in a high-risk pattern, 24 (34%) had pathogenic variants in candidate genes using KidneySeq™. A moderate impact INF2 variant was observed in 20 (29%) patients and those with 3 versus 0-2 pathogenic or moderate impact glomerular genetic variants had higher albuminuria and lower estimated glomerular filtration rate (adjusted p ≤ 0.015). Using a panel of preselected genes implicated in kidney health, we observed several variants in people with sickle cell nephropathy.

Association of Rest-Activity Rhythm and Risk of Developing Dementia or Mild Cognitive Impairment in the Middle-Aged and Older Population: Prospective Cohort Study.

The relationship between 24-hour rest-activity rhythms (RARs) and risk for dementia or mild cognitive impairment (MCI) remains an area of growing interest. Previous studies were often limited by small sample sizes, short follow-ups, and older participants. More studies are required to fully explore the link between disrupted RARs and dementia or MCI in middle-aged and older adults. We leveraged the UK Biobank data to examine how RAR disturbances correlate with the risk of developing dementia and MCI in middle-aged and older adults. We analyzed the data of 91,517 UK Biobank participants aged between 43 and 79 years. Wrist actigraphy recordings were used to derive nonparametric RAR metrics, including the activity level of the most active 10-hour period (M10) and its midpoint, the activity level of the least active 5-hour period (L5) and its midpoint, relative amplitude (RA) of the 24-hour cycle [RA=(M10-L5)/(M10+L5)], interdaily stability, and intradaily variability, as well as the amplitude and acrophase of 24-hour rhythms (cosinor analysis). We used Cox proportional hazards models to examine the associations between baseline RAR and subsequent incidence of dementia or MCI, adjusting for demographic characteristics, comorbidities, lifestyle factors, shiftwork status, and genetic risk for Alzheimer's disease. During the follow-up of up to 7.5 years, 555 participants developed MCI or dementia. The dementia or MCI risk increased for those with lower M10 activity (hazard ratio [HR] 1.28, 95% CI 1.14-1.44, per 1-SD decrease), higher L5 activity (HR 1.15, 95% CI 1.10-1.21, per 1-SD increase), lower RA (HR 1.23, 95% CI 1.16-1.29, per 1-SD decrease), lower amplitude (HR 1.32, 95% CI 1.17-1.49, per 1-SD decrease), and higher intradaily variability (HR 1.14, 95% CI 1.05-1.24, per 1-SD increase) as well as advanced L5 midpoint (HR 0.92, 95% CI 0.85-0.99, per 1-SD advance). These associations were similar in people aged <70 and >70 years, and in non-shift workers, and they were independent of genetic and cardiovascular risk factors. No significant associations were observed for M10 midpoint, interdaily stability, or acrophase. Based on findings from a large sample of middle-to-older adults with objective RAR assessment and almost 8-years of follow-up, we suggest that suppressed and fragmented daily activity rhythms precede the onset of dementia or MCI and may serve as risk biomarkers for preclinical dementia in middle-aged and older adults.

Abstract PO2-08-03: Developing a polygenic risk score for precision prevention of estrogen receptor-positive (ER+)/HER2- breast cancer in Brazilian women

Abstract Polygenic risk scores (PRS) quantify the genetic risk of diseases based on risk alleles in genomic loci identified in genome-wide association studies (GWAS). As individuals of European ancestry are overrepresented in GWAS, PRS performance may vary in individuals of different ancestries. Brazil is the largest and most populated country in Latin America, with around 43% of admixed individuals, and data on breast cancer PRS performance for this population is lacking. We aimed to evaluate the performance of a breast cancer PRS with 313 SNPs generated with Europeans in Brazilian population. In this case-control study, we expect to include 1000 women age &amp;gt;= 18 years old with current or previous diagnosis of ER+/HER2- breast cancer. Germline DNA samples are genotyped with Illumina Infinium Global Screening Array-24 v3.0 chip. Controls are healthy woman previously genotyped with Global Screening Array-24 v1.0 chip. We selected the 313 SNPs PRS for ER+/HER2- breast cancer from PGS Catalog for evaluation. Quality control, imputation, PRS calculation and statistical analysis were performed in PLINK v2.0, Michigan server, PGS Catalog and R, respectively. We present the preliminary results for 1347 women (504 cases and 843 controls) included until March of 2023. After quality control and imputation, we calculated PRS for 1212 subjects (475 cases and 737 controls). Out of 313 SNPs, 211 were validated in our samples. For ER+/HER2- breast cancer, the odds ratio per SD of the 211-variant PRS (PRS211) was 1.363 (95% confidence interval [CI]: 1.211 to 1.537). Compared with women with average risk (40th-60th PRS percentile), women in the top 10% of PRS211 had a 1.623-fold increased risk (95% CI: 1.047 to 2.522). The area under the receiver operating characteristic curve of 0.581. Our preliminary results indicate that a PRS derived from European-ancestry population correlates with ER+/HER2- breast cancer in admixed Brazilian population, although with attenuated performance. Further work is needed to better characterize PRS risk stratification in admixed populations. Citation Format: Marina Candido Visontai Cormedi, Álison Luís Eburnêo Pereira, Fernanda Borchers Coeli-Lacchini, Sabrina Baroni, Lívia Mara Torres Bueno, Evelyn Ayumi Onga, Victor Girelli Piloto De Oliveira, Fabiana De Oliveira Buono, Aline Fares, Tatiana Strava Correa, Iasmin Alves Chirichela, Fernanda Peria, Romulado Barroso-Sousa, Rodrigo Santa Cruz Guindalini, Taisa Manuela Bonfim Machado Lopes, Rosimeire Aparecida Roela, Maria Aparecida Azevedo Koike Folgueira, Daniel Guimarães Tiezzi, Leandro Machado Colli, Liane Rapatoni. Developing a polygenic risk score for precision prevention of estrogen receptor-positive (ER+)/HER2- breast cancer in Brazilian women [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-08-03.

All of Us Participant Perspectives on the Return of Value in Research

To understand participant preferences for receiving specific types of research information, whether information preferences vary across sociodemographic groups, and the types of health providers participants could access to understand returned information. All of Us Research Program participants completed a value of returning research information survey. Stratified sampling was implemented to enhance participant diversity and avoid non-coverage. We used weighted multivariable logistic regression to evaluate associations between the most valuable information types, access to providers, and sociodemographic variables. Participants (N=20,405) were diverse in their race/ethnicity (e.g., 52% were White, 18% were Hispanic/Latino or Spanish, 3% were Asian, and 20% were Black or African American). Most participants (78.6%) valued information about their risk of serious genetic diseases with available treatment. Primary care physicians, specialists, and genetic counselors were the top providers that participants could access for help understanding returned information. Information preferences and provider access varied across sociodemographic groups. For example, as income levels increased, the odds of placing value on genetic results indicating risk of serious disease with available treatment increased when compared to the lowest income levels (p-value<0.001). Although genetic information was most valuable to participants, preferences about specific information types varied across sociodemographic groups.

In vivo validation of late-onset Alzheimer's disease genetic risk factors.

Genome-wide association studies have identified over 70 genetic loci associated with late-onset Alzheimer's disease (LOAD), but few candidate polymorphisms have been functionally assessed for disease relevance and mechanism of action. Candidate genetic risk variants were informatically prioritized and individually engineered into a LOAD-sensitized mouse model that carries the AD risk variants APOE ε4/ε4 and Trem2*R47H. The potential disease relevance of each model was assessed by comparing brain transcriptomes measured with the Nanostring Mouse AD Panel at 4 and 12 months of age with human study cohorts. We created new models for 11 coding and loss-of-function risk variants. Transcriptomic effects from multiple genetic variants recapitulated a variety of human gene expression patterns observed in LOAD study cohorts. Specific models matched to emerging molecular LOAD subtypes. These results provide an initial functionalization of 11 candidate risk variants and identify potential preclinical models for testing targeted therapeutics. A novel approach to validate genetic risk factors for late-onset AD (LOAD) is presented. LOAD risk variants were knocked in to conserved mouse loci. Variant effects were assayed by transcriptional analysis. Risk variants in Abca7, Mthfr, Plcg2, and Sorl1 loci modeled molecular signatures of clinical disease. This approach should generate more translationally relevant animal models.

A State-of-the-art Review on Artificial Intelligence and Machine Learning Applications

Background: The integration of artificial intelligence and machine learning holds great promise for enhancing healthcare institutions and providing fresh perspectives on the origins and advancement of long-term illnesses. In the healthcare sector, artificial intelligence and machine learning are used to address supply and demand concerns, genomic applications, and new advancements in drug development, cancer, and heart disease. Objective: The article explores the ways that machine learning, AI, precision medicine, and genomics are changing healthcare. The essay also discusses how AI's examination of various patient data could enhance healthcare institutions, provide fresh insights into chronic conditions, and advance precision medicine. The potential uses of machine learning for genome analysis are also examined in the paper, particularly about genetic biomarker-based disease risk and symptom prediction. Discussion: The challenges posed by the phenotype-genotype relationship are examined, as well as the significance of comprehending disease pathways in order to create tailored treatments. Moreover, it offers a streamlined and modularized method that predicts how genotypes affect cell properties using machine-learning models, enabling the development of personalized drugs. The collective feedback highlights the rapid interdisciplinary growth of medical genomics following the completion of the Human Genome Project. It also emphasizes how important genomic data is for improving healthcare outcomes and facilitating personalized medicine. Conclusion: The study's conclusions point to a revolutionary shift in healthcare: the application of AI/ML to illness control. Even though these innovations have a lot of potential benefits, problems like algorithm interpretability and ethical issues need to be worked out before they can be successfully incorporated into routine medical practice. Using machine learning in medicine has enormous potential benefits for the biotech industry. Further research, ongoing regulatory frameworks, and collaboration between medical professionals and data analysts are necessary to fully utilize machine learning as well as artificial intelligence in disease management.

Alzheimer's disease genetic risk score and neuroimaging in the FINGER lifestyle trial.

We assessed a genetic risk score for Alzheimer's disease (AD-GRS) and apolipoprotein E (APOE4) in an exploratory neuroimaging substudy of the FINGER trial. 1260 at-risk older individuals without dementia were randomized to multidomain lifestyle intervention or health advice. N=126 participants underwent magnetic resonance imaging (MRI), and N=47 positron emission tomography (PET) scans (Pittsburgh Compund B [PiB], Fluorodeoxyglucose) at baseline; N=107 and N=38 had repeated 2-year scans. The APOE4 allele, but not AD-GRS, was associated with baseline lower hippocampus volume (β=-0.27, p=0.001), greater amyloid deposition (β=0.48, p=0.001), 2-year decline in hippocampus (β=-0.27, p=0.01), total gray matter volume (β=-0.25, p=0.01), and cortical thickness (β=-0.28, p=0.003). In analyses stratified by AD-GRS (below vs above median), the PiB composite score increased less in intervention versus control in the higher AD-GRS group (β=-0.60, p=0.03). AD-GRS and APOE4 may have different impacts on potential intervention effects on amyloid, that is, less accumulation in the higher-risk group (AD-GRS) versus lower-risk group (APOE). First study of neuroimaging and AD genetics in a multidomain lifestyle intervention. Possible intervention effect on brain amyloid deposition may rely on genetic risk. AD-GRS and APOE4 allele may have different impacts on amyloid during intervention.

Sleep and circadian biomarkers of postoperative delirium (SLEEP-POD): protocol for a prospective and observational cohort study

IntroductionSurgical patients over 70 experience postoperative delirium (POD) complications in up to 50% of procedures. Sleep/circadian disruption has emerged as a potential risk factor for POD in epidemiological studies. This...

Impact of Short-Term Computerized Cognitive Training on Cognition in Older Adults With and Without Genetic Risk of Alzheimer's Disease: Outcomes From the START Randomized Controlled Trial

ObjectivesTo establish the impact of a 3-minute computerized cognitive training program (START) on cognition in older adults with and without genetic risk of Alzheimer's disease. DesignTwo-arm randomized controlled trial of the START program. Setting and ParticipantsRemote online trial in adults older than 50 taking part from home. MethodsThe trial compared the START program with placebo in 6544 people older than 50. Primary outcome was executive function measured through Trailmaking B, with other secondary cognitive measures. Genetic risk profile and ApoE4 status were determined by Illumina Array. ResultsSTART conferred benefit to executive function, attention, memory, and a composite measure, including in people with the ApoE4 genotype. Conclusions and ImplicationsThe 3-minute START task offers a means of supporting cognitive health in older adults and could be used at scale and within a precision medicine approach to reduce risk of cognitive decline in a targeted way.

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

Coronary Artery Disease Risk Variant Dampens the Expression of CALCRL by Reducing HSF Binding to Shear Stress Responsive Enhancer in Endothelial Cells In Vitro.

CALCRL (calcitonin receptor-like) protein is an important mediator of the endothelial fluid shear stress response, which is associated with the genetic risk of coronary artery disease. In this study, we functionally characterized the noncoding regulatory elements carrying coronary artery disease that risks single-nucleotide polymorphisms and studied their role in the regulation of CALCRL expression in endothelial cells. To functionally characterize the coronary artery disease single-nucleotide polymorphisms harbored around the gene CALCRL, we applied an integrative approach encompassing statistical, transcriptional (RNA-seq), and epigenetic (ATAC-seq, chromatin immunoprecipitation assay-quantitative polymerase chain reaction, and electromobility shift assay) analyses, alongside luciferase reporter assays, and targeted gene and enhancer perturbations (siRNA and clustered regularly interspaced short palindromic repeats/clustered regularly interspaced short palindromic repeat-associated 9) in human aortic endothelial cells. We demonstrate that the regulatory element harboring rs880890 exhibits high enhancer activity and shows significant allelic bias. The A allele was favored over the G allele, particularly under shear stress conditions, mediated through alterations in the HSF1 (heat shock factor 1) motif and binding. CRISPR deletion of rs880890 enhancer resulted in downregulation of CALCRL expression, whereas HSF1 knockdown resulted in a significant decrease in rs880890-enhancer activity and CALCRL expression. A significant decrease in HSF1 binding to the enhancer region in endothelial cells was observed under disturbed flow compared with unidirectional flow. CALCRL knockdown and variant perturbation experiments indicated the role of CALCRL in mediating eNOS (endothelial NO synthase), APLN (apelin), angiopoietin, prostaglandins, and EDN1 (endothelin-1) signaling pathways leading to a decrease in cell proliferation, tube formation, and NO production. Overall, our results demonstrate the existence of an endothelial-specific HSF (heat shock factor)-regulated transcriptional enhancer that mediates CALCRL expression. A better understanding of CALCRL gene regulation and the role of single-nucleotide polymorphisms in the modulation of CALCRL expression could provide important steps toward understanding the genetic regulation of shear stress signaling responses.

Bayesian approach to assessing population differences in genetic risk of disease with application to prostate cancer.

Population differences in risk of disease are common, but the potential genetic basis for these differences is not well understood. A standard approach is to compare genetic risk across populations by testing for mean differences in polygenic scores, but existing studies that use this approach do not account for statistical noise in effect estimates (i.e., the GWAS betas) that arise due to the finite sample size of GWAS training data. Here, we show using Bayesian polygenic score methods that the level of uncertainty in estimates of genetic risk differences across populations is highly dependent on the GWAS training sample size, the polygenicity (number of causal variants), and genetic distance (FST) between the populations considered. We derive a Wald test for formally assessing the difference in genetic risk across populations, which we show to have calibrated type 1 error rates under a simplified assumption that all SNPs are independent, which we achieve in practise using linkage disequilibrium (LD) pruning. We further provide closed-form expressions for assessing the uncertainty in estimates of relative genetic risk across populations under the special case of an infinitesimal genetic architecture. We suggest that for many complex traits and diseases, particularly those with more polygenic architectures, current GWAS sample sizes are insufficient to detect moderate differences in genetic risk across populations, though more substantial differences in relative genetic risk (relative risk > 1.5) can be detected. We show that conventional approaches that do not account for sampling error from the training sample, such as using a simple t-test, have very high type 1 error rates. When applying our approach to prostate cancer, we demonstrate a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry.

Lifestyle and genetic risk for chronic liver disease in metabolically healthy and unhealthy individuals from a general population

Background & AimsIt is unclear to what extent lifestyle and genetic factors affect incidence of chronic liver disease (CLD) in a general population and if lifestyle affects CLD independently of underlying cardiometabolic perturbations and genetic predisposition. MethodsWe examined 27,991 men and women aged 44-73 years from the Malmö Diet and Cancer Study recruited between 1991-1996 and followed until end of 2020 using registry-linkage (median follow-up time 25.1 years; 382 incident first-time CLD events). Associations between cardiometabolic factors, polygenic risk scores (PRSs), and lifestyle factors in relation to CLD were examined using multivariable Cox proportional hazards regression models. ResultsIncidence of CLD increased with number of cardiometabolic risk factors (the hazard ratio (HR) per each additional cardiometabolic risk factor was 1.33 (95% CI: 1.21-1.45; p= 5.1 x 10-10). Two novel polygenic risk scores (PRS) for metabolic dysfunction-associated steatotic liver disease (MASLD) and a PRS for liver cirrhosis associated with higher risk of CLD but provided marginal predictive utility on top of other risk factors and compared to the PNPLA3 rs738409 genetic variant. An unhealthy lifestyle (high alcohol intake, current smoking, physical inactivity and unhealthy diet) markedly increased risk of CLD (HR=3.97, 95% CI: 2.59-6.10). Observed associations between examined lifestyle factors and CLD were largely independent of cardiometabolic perturbations and polygenic risk. ConclusionsWe confirmed the importance of cardiometabolic dysfunction in relation to risk of CLD in a general population. Lifestyle risk factors were shown to independently associate with CLD and added predictive information on top of cardiometabolic risk factors. Information on the polygenic risk of liver disease does not currently add to prediction of CLD in a general population. Lay summaryIn this large longitudinal study of a general population, we found that cardiometabolic, genetic and lifestyle risk factors all contribute to the development of chronic liver disease (CLD). Our results suggest that an unhealthy lifestyle, including alcohol consumption, smoking, physical inactivity, and an unhealthy diet, substantially increase risk of CLD independently of underlying genetic risk or cardiometabolic health. This suggest that a large proportion of CLD cases are potentially preventable through adoption of healthy lifestyle changes. Impact and implicationsThis large population-based prospective study suggests largely independent roles of cardiometabolic, lifestyle, and genetic risk factors in the development of chronic liver disease. Findings strengthen the evidence base for a beneficial effect of modification of high-risk lifestyle behaviors in the primary prevention of chronic liver disease in the general population.

Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk

BACKGROUNDOptical coherence tomography (OCT) and electroretinography (ERG) studies have revealed structural and functional retinal alterations in individuals with schizophrenia spectrum disorders (SSD). However, it remains unclear which specific retinal layers are affected, how the retina, brain, and clinical symptomatology are connected, and how alterations of the visual system are related to genetic disease risk. METHODSOCT, ERG, and brain magnetic resonance imaging (MRI) were applied to comprehensively investigate the visual system in a cohort of 103 patients with SSD and 130 healthy control individuals. The sparse partial least squares (SPLS) algorithm was used to identify multivariate associations between clinical disease phenotype and biological alterations of the visual system. The association of the revealed patterns with the individual polygenetic disease risk for schizophrenia was explored in a post hoc analysis. In addition, covariate-adjusted case-control comparisons were performed for each individual OCT and ERG parameter. RESULTSThe SPLS analysis yielded a phenotype-eye-brain signature of SSD in which greater disease severity, longer duration of illness, and impaired cognition were associated with electrophysiological alterations and microstructural thinning of most retinal layers. Higher individual loading onto this disease-relevant signature of the visual system was significantly associated with elevated polygenic risk for schizophrenia. In case-control comparisons, patients with SSD had lower macular thickness, thinner retinal nerve fiber and inner plexiform layers, less negative a-wave amplitude, and lower b-wave amplitude. CONCLUSIONSThis study demonstrates multimodal microstructural and electrophysiological retinal alterations in individuals with SSD that are associated with disease severity and individual polygenetic burden.

Fluctuating salience in those living with genetic risk of motor neuron disease: A qualitative interview study.

Motor neuron disease (MND) (also known as amyotrophic lateral sclerosis) is a life-limiting neurodegenerative condition. In up to 20% of people with MND, a pathogenic variant associated with autosomal dominant inheritance can be identified. Children of people carrying a pathogenic variant have a 50% chance of inheriting this and a higher, although harder to predict, chance of developing the disease compared to the general adult population. This paper explores the experience of living with the genetic risk of MND. We undertook a UK-based interview study with 35 individuals, including: 7 people living with genetically-mediated forms of MND; 24 asymptomatic relatives, the majority of whom had an increased risk of developing the disease; and 4 unrelated partners. We explore how individuals make sense of genetic risk, unpacking the interplay between genetic knowledge, personal perception, experiences of the disease in the family, age and life stageand the implications that living with risk has for different aspects of their lives. We balance an emphasis on the emotional and psychological impact described by participants, with a recognition that the salience of risk fluctuates over time. Furthermore, we highlight the diverse strategies and approaches people employ to live well in the face of uncertaintyand the complex ways they engage with the possibility of developing symptoms in the future. Finally, we outline the need for open-ended, tailored support and information provision. Drawing on wider literature on genetic risk, we foreground how knowledge of MND risk can disrupt individuals' taken-for-granted assumptions on life and perceptions of the future, but also its contextuality, whereby its relevance becomes more prominent at critical junctures. This research has been used in the development of a public-facing resource on the healthtalk.org website. People with experience of living with genetic risk were involved throughout the design and conduct of the study and advised on aspects including the topic guide, sampling and recruitmentand the developing analysis. Two patient and public involvementcontributors joined a formal advisory panel.

Genetics Visit Uptake Among Individuals Receiving Clinically Actionable Genomic Screening Results

Screening unselected populations for clinically actionable genetic disease risk can improve ascertainment and facilitate risk management. Genetics visits may encourage at-risk individuals to perform recommended management, but little has been reported on genetics visit completion or factors associated with completion in genomic screening programs. To identify factors associated with postdisclosure genetics visits in a genomic screening cohort. This was a cohort study of biobank data in a health care system in central Pennsylvania. Participants' exome sequence data were reviewed for pathogenic or likely pathogenic (P/LP) results in all genes on the American College of Medical Genetics and Genomics Secondary Findings list. Clinically confirmed results were disclosed by phone and letter. Participants included adult MyCode biobank participants who received P/LP results between July 2015 and November 2019. Data were analyzed from May 2021 to March 2022. Clinically confirmed P/LP result disclosed by phone or letter. Completion of genetics visit in which the result was discussed and variables associated with completion were assessed by electronic health record (EHR) review. Among a total of 1160 participants (703 [60.6%] female; median [IQR] age, 57.0 [42.1-68.5] years), fewer than half of participants (551 of 1160 [47.5%]) completed a genetics visit. Younger age (odds ratio [OR] for age 18-40 years, 2.98; 95% CI, 1.40-6.53; OR for age 41-65 years, 2.36; 95% CI, 1.22-4.74; OR for age 66-80 years, 2.60; 95% CI, 1.41-4.98 vs age ≥81 years); female sex (OR, 1.49; 95% CI, 1.14-1.96); being married (OR, 1.74; 95% CI, 1.23-2.47) or divorced (OR, 1.80; 95% CI, 1.11-2.91); lower Charlson comorbidity index (OR for score of 0-2, 1.76; 95% CI, 1.16-2.68; OR for score of 3-4, 1.73; 95% CI, 1.18-2.54 vs score of ≥5); EHR patient portal use (OR, 1.42; 95% CI, 1.06-1.89); living closer to a genetics clinic (OR, 1.64; 95% CI, 1.14-2.36 for <8.9 miles vs >20.1 miles); successful results disclosure (OR for disclosure by genetic counselor, 16.32; 95% CI, 8.16-37.45; OR for disclosure by research assistant, 20.30; 95% CI, 10.25-46.31 vs unsuccessful phone disclosure); and having a hereditary cancer result (OR, 2.13; 95% CI, 1.28-3.58 vs other disease risk) were significantly associated with higher rates of genetics visit completion. Preference to follow up with primary care was the most common reported reason for declining a genetics visit (68 of 152 patients [44.7%]). This cohort study of a biobank-based population genomic screening program suggests that targeted patient engagement, improving multidisciplinary coordination, and reducing barriers to follow-up care may be necessary for enhancing genetics visit uptake.

Diet quality in relation to kidney function and its potential interaction with genetic risk of kidney disease among Dutch post-myocardial infarction patients.

We examined the relation between diet quality, its components and kidney function decline in post-myocardial infarction (MI) patients, and we explored differences by genetic risk of chronic kidney disease (CKD). We analysed 2169 patients from the Alpha Omega Cohort (aged 60-80 years, 81% male). Dietary intake was assessed at baseline (2002-2006) using a validated food-frequency questionnaire and diet quality was defined using the Dutch Healthy Diet Cardiovascular Disease (DHD-CVD) index. We calculated 40-months change in estimated glomerular filtration rate (eGFR, mL/min per 1.73m2). We constructed a weighted genetic risk score (GRS) for CKD using 88 single nucleotide polymorphisms previously linked to CKD. Betas with 95%-confidence intervals (CIs) were obtained using multivariable linear regression models for the association between DHD-CVD index and its components and eGFR change, by GRS. The average DHD-CVD index was 79 (SD 15) points and annual eGFR decline was 1.71 (SD 3.86) mL/min per 1.73 m2. The DHD-CVD index was not associated with annual eGFR change (per 1-SD increment in adherence score: -0.09 [95% CI -0.26,0.08]). Results for adherence to guidelines for red meat showed less annual eGFR decline (per 1-SD: 0.21 [0.04,0.38]), whereas more annual eGFR decline was found for legumes and dairy (per 1-SD: -0.20legumes [-0.37,-0.04] and - 0.18dairy [-0.34,-0.01]). Generally similar results were obtained in strata of GRS. The DHD-CVD index for overall adherence to Dutch dietary guidelines for CVD patients was not associated with kidney function decline after MI, irrespective of genetic CKD risk. The preferred dietary pattern for CKD prevention in CVD patients warrants further research.

Identification of Genetic Risk Factors Based on Disease Progression Derived From Longitudinal Brain Imaging Phenotypes.

Neurodegenerative disorders usually happen stage-by-stage rather than overnight. Thus, cross-sectional brain imaging genetic methods could be insufficient to identify genetic risk factors. Repeatedly collecting imaging data over time appears to solve the problem. But most existing imaging genetic methods only use longitudinal imaging phenotypes straightforwardly, ignoring the disease progression trajectory which might be a more stable disease signature. In this paper, we propose a novel sparse multi-task mixed-effects longitudinal imaging genetic method (SMMLING). In our model, disease progression fitting and genetic risk factors identification are conducted jointly. Specifically, SMMLING models the disease progression using longitudinal imaging phenotypes, and then associates fitted disease progression with genetic variations. The baseline status and changing rate, i.e., the intercept and slope, of the progression trajectory thus shoulder the responsibility to discover loci of interest, which would have superior and stable performance. To facilitate the interpretation and stability, we employ l2,1 -norm and the fused group lasso (FGL) penalty to identify loci at both the individual level and group level. SMMLING can be solved by an efficient optimization algorithm which is guaranteed to converge to the global optimum. We evaluate SMMLING on synthetic data and real longitudinal neuroimaging genetic data. Both results show that, compared to existing longitudinal methods, SMMLING can not only decrease the modeling error but also identify more accurate and relevant genetic factors. Most risk loci reported by SMMLING are missed by comparison methods, implicating its superiority in genetic risk factors identification. Consequently, SMMLING could be a promising computational method for longitudinal imaging genetics.

Common genetic risk for Parkinson's disease and dysfunction of the endo-lysosomal system.

Parkinson's disease is a progressive neurological disorder, characterized by prominent movement dysfunction. The past two decades have seen a rapid expansion of our understanding of the genetic basis of Parkinson's, initially through the identification of monogenic forms and, more recently, through genome-wide association studies identifying common risk variants. Intriguingly, a number of cellular pathways have emerged from these analysis as playing central roles in the aetiopathogenesis of Parkinson's. In this review, the impact of data deriving from genome-wide analyses for Parkinson's upon our functional understanding of the disease will be examined, with a particular focus on examples of endo-lysosomal and mitochondrial dysfunction. The challenges of moving from a genetic to a functional understanding of common risk variants for Parkinson's will be discussed, with a final consideration of the current state of the genetic architecture of the disorder. This article is part of a discussion meeting issue 'Understanding the endo-lysosomal network in neurodegeneration'.