Osteoarthritis, osteoarthritis treatment and risk of incident dementia: a prospective cohort study based on UK Biobank.

BACKGROUND: We aimed to investigate the association between OA and treatment with dementia risk and structural brain abnormalities. METHODS: We recruited a total of 466,460 individuals from the UK Biobank to investigate the impact of OA on the incidence of dementia. Among the total population, there were 63,081 participants diagnosed with OA. We subsequently categorised the OA patients into medication and surgery groups based on treatment routes. Cox regression models explored the associations between OA/OA treatment and dementia risk, with the results represented as hazard ratios (HRs) and 95% confidence intervals (95% CI). Linear regression models assessed the associations of OA/OA therapy with alterations in cortical structure. RESULTS: During an average of 11.90 (± 1.01) years of follow-up, 5,627 individuals were diagnosed with all-cause dementia (ACD), including 2,438 AD (Alzheimer's disease), and 1,312 VaD (vascular dementia) cases. Results revealed that OA was associated with the elevated risk of ACD (HR: 1.116; 95% CI: 1.039-1.199) and AD (HR: 1.127; 95% CI: 1.013-1.254). OA therapy lowered the risk of dementia in both medication group (HR: 0.746; 95% CI: 0.652-0.854) and surgery group (HR: 0.841; 95% CI: 0.736-0.960). OA was negatively associated with cortical area, especially precentral, postcentral and temporal regions. CONCLUSIONS: Osteoarthritis increased the likelihood of developing dementia, and had an association with regional brain atrophy. OA treatment lowered the dementia risk. OA is a promising modifiable risk factor for dementia.

Whole exome sequencing analyses identified novel genes for Alzheimer's disease and related dementia.

INTRODUCTION: The heritability of Alzheimer's disease (AD) is estimated to be 58%-79%. However, known genes can only partially explain the heritability. METHODS: Here, we conducted gene-based exome-wide association study (ExWAS) of rare variants and single-variant ExWAS of common variants, utilizing data of 54,569 clinically diagnosed/proxy AD and related dementia (ADRD) and 295,421 controls from the UK Biobank. RESULTS: Gene-based ExWAS identified 11 genes predicting a higher ADRD risk, including five novel ones, namely FRMD8, DDX1, DNMT3L, MORC1, and TGM2, along with six previously reported ones, SORL1, GRN, PSEN1, ABCA7, GBA, and ADAM10. Single-variant ExWAS identified two ADRD-associated novel genes, SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-ß process pathways, microglia, and brain regions like hippocampus. The druggability evidence suggests that DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets. DISCUSSION: Our study contributes to the current body of evidence on the genetic etiology of ADRD. HIGHLIGHTS: Gene-based analyses of rare variants identified five novel genes for Alzheimer's disease and related dementia (ADRD), including FRMD8, DDX1, DNMT3L, MORC1, and TGM2. Single-variant analyses of common variants identified two novel genes for ADRD, including SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-ß process pathways, microglia, and brain regions like hippocampus. DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.

Large-scale whole-exome sequencing analyses identified protein-coding variants associated with immune-mediated diseases in 350,770 adults.

The genetic contribution of protein-coding variants to immune-mediated diseases (IMDs) remains underexplored. Through whole exome sequencing of 40 IMDs in 350,770 UK Biobank participants, we identified 162 unique genes in 35 IMDs, among which 124 were novel genes. Several genes, including FLG which is associated with atopic dermatitis and asthma, showed converging evidence from both rare and common variants. 91 genes exerted significant effects on longitudinal outcomes (interquartile range of Hazard Ratio: 1.12-5.89). Mendelian randomization identified five causal genes, of which four were approved drug targets (CDSN, DDR1, LTA, and IL18BP). Proteomic analysis indicated that mutations associated with specific IMDs might also affect protein expression in other IMDs. For example, DXO (celiac disease-related gene) and PSMB9 (alopecia areata-related gene) could modulate CDSN (autoimmune hypothyroidism-, psoriasis-, asthma-, and Graves' disease-related gene) expression. Identified genes predominantly impact immune and biochemical processes, and can be clustered into pathways of immune-related, urate metabolism, and antigen processing. Our findings identified protein-coding variants which are the key to IMDs pathogenesis and provided new insights into tailored innovative therapies.

Genome-wide meta-analysis identifies ancestry-specific loci for Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) is a devastating neurological disease with complex genetic etiology. Yet most known loci have only identified from the late-onset type AD in populations of European ancestry. METHODS: We performed a two-stage genome-wide association study (GWAS) of AD totaling 6878 Chinese and 63,926 European individuals. RESULTS: In addition to the apolipoprotein E (APOE) locus, our GWAS of two independent Chinese samples uncovered three novel AD susceptibility loci (KIAA2013, SLC52A3, and TCN2) and a novel ancestry-specific variant within EGFR (rs1815157). More replicated variants were observed in the Chinese (31%) than in the European samples (15%). In combining genome-wide associations and functional annotations, EGFR and TCN2 were prioritized as two of the most biologically significant genes. Phenome-wide Mendelian randomization suggests that high mean corpuscular hemoglobin concentration might protect against AD. DISCUSSION: The current study reveals novel AD susceptibility loci, emphasizes the importance of diverse populations in AD genetic research, and advances our understanding of disease etiology. HIGHLIGHTS: Loci KIAA2013, SLC52A3, and TCN2 were associated with Alzheimer's disease (AD) in Chinese populations. rs1815157 within the EGFR locus was associated with AD in Chinese populations. The genetic architecture of AD varied between Chinese and European populations. EGFR and TCN2 were prioritized as two of the most biologically significant genes. High mean corpuscular hemoglobin concentrations might have protective effects against AD.

Vascular cognitive impairment: Advances in clinical research and management.

ABSTRACT: Vascular cognitive impairment (VCI) encompasses a wide spectrum of cognitive disorders, ranging from mild cognitive impairment to vascular dementia. Its diagnosis relies on thorough clinical evaluations and neuroimaging. VCI predominately arises from vascular risk factors (VRFs) and cerebrovascular disease, either independently or in conjunction with neurodegeneration. Growing evidence underscores the prevalence of VRFs, highlighting their potential for early prediction of cognitive impairment and dementia in later life. The precise mechanisms linking vascular pathologies to cognitive deficits remain elusive. Chronic cerebrovascular pathology is the most common neuropathological feature of VCI, often interacting synergistically with neurodegenerative processes. Current research efforts are focused on developing and validating reliable biomarkers to unravel the etiology of vascular brain changes in VCI. The collaborative integration of these biomarkers into clinical practice, alongside routine incorporation into neuropathological assessments, presents a promising strategy for predicting and stratifying VCI. The cornerstone of VCI prevention remains the control of VRFs, which includes multi-domain lifestyle modifications. Identifying appropriate pharmacological approaches is also of paramount importance. In this review, we synthesize recent advancements in the field of VCI, including its definition, determinants of vascular risk, pathophysiology, neuroimaging and fluid-correlated biomarkers, predictive methodologies, and current intervention strategies. Increasingly evident is the notion that more rigorous research for VCI, which arises from a complex interplay of physiological events, is still needed to pave the way for better clinical outcomes and enhanced quality of life for affected individuals.

Novel diagnostic and prognostic approach for rapidly progressive dementias: Indicators based on amyloid/tau/neurodegeneration (ATN) framework.

AIMS: Apply established cerebrospinal fluid (CSF) and serum biomarkers and novel combined indicators based on the amyloid/tau/neurodegeneration (ATN) framework to improve diagnostic and prognostic power in patients with rapidly progressive dementias (RPDs). METHODS: CSF and serum biomarkers of Alzheimer's disease (AD) common neuropathology including Aß42, Aß40, p-Tau, and t-Tau were measured in cognitively normal (CN) controls (n = 33) and three RPD groups with rapidly progressive AD (rpAD, n = 23), autoimmune encephalitis (AE, n = 25), and Creutzfeldt-Jakob disease (CJD, n = 28). Logistic regression and multiple linear regression were used for producing combined indicators and prognostic assessment, respectively, including A&T, A&N, T&N, A&T&N, etc. RESULTS: Combined diagnostic indicator with A&T&N had the potential for differentiating AE from other types of RPDs, identifying 62.51% and 75% of AE subjects based on CSF and serum samples, respectively, compared to 39.13% and 37.5% when using autoantibodies. CSF t-Tau was associated with survival in the CJD group (adjusted R-Square = 0.16, p = 0.02), and its prognosis value improved when using combined predictors based on the ATN framework (adjusted R-Square = 0.273, p = 0.014). CONCLUSION: Combined indicators based on the ATN framework provide a novel perspective for establishing biomarkers for early recognition of RPDs due to treatment-responsive causes.

Expanding the targeting scope of CRISPR/Cas9-mediated genome editing by Cas9 variants in Brassica.

CRISPR/Cas9, presently the most widely used genome editing technology, has provided great potential for functional studies and plant breeding. However, the strict requirement for a protospacer adjacent motif (PAM) has hindered the application of the CRISPR/Cas9 system because the number of targetable genomic sites is limited. Recently, the engineered variants Cas9-NG, SpG, and SpRY, which recognize non-canonical PAMs, have been successfully tested in plants (mainly in rice, a monocot). In this study, we evaluated the targeted mutagenesis capabilities of these Cas9 variants in two important Brassica vegetables, Chinese cabbage (Brassica rapa spp. pekinensis) and cabbage (Brassica oleracea var. capitata). Both Cas9-NG and SpG induced efficient mutagenesis at NGN PAMs, while SpG outperformed Cas9-NG at NGC and NGT PAMs. SpRY achieved efficient editing at almost all PAMs (NRN > NYN), albeit with some self-targeting activity at transfer (T)-DNA sequences. And SpRY-induced mutants were detected in cabbage plants in a PAM-less fashion. Moreover, an adenine base editor was developed using SpRY and TadA8e deaminase that induced A-to-G conversions within target sites using non-canonical PAMs. Together, the toolboxes developed here induced successful genome editing in Chinese cabbage and cabbage. Our work further expands the targeting scope of genome editing and paves the way for future basic research and genetic improvement in Brassica. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-024-00155-7.

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures.

BACKGROUND AND OBJECTIVES: Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables. METHODS: Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB. RESULTS: A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD. DISCUSSION: Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

Exome sequencing identifies novel genetic variants associated with varicose veins.

BACKGROUND: Varicose veins (VV) are one of the common human diseases, but the role of genetics in its development is not fully understood. METHODS: We conducted an exome-wide association study of VV using whole-exome sequencing data from the UK Biobank, and focused on common and rare variants using single-variant association analysis and gene-level collapsing analysis. FINDINGS: A total of 13,823,269 autosomal genetic variants were obtained after quality control. We identified 36 VV-related independent common variants mapping to 34 genes by single-variant analysis and three rare variant genes (PIEZO1, ECE1, FBLN7) by collapsing analysis, and most associations between genes and VV were replicated in FinnGen. PIEZO1 was the closest gene associated with VV (P = 5.05 × 10-31), and it was found to reach exome-wide significance in both single-variant and collapsing analyses. Two novel rare variant genes (ECE1 and METTL21A) associated with VV were identified, of which METTL21A was associated only with females. The pleiotropic effects of VV-related genes suggested that body size, inflammation, and pulmonary function are strongly associated with the development of VV. CONCLUSIONS: Our findings highlight the importance of causal genes for VV and provide new directions for treatment.

Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer's disease.

Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.

Whole exome sequencing analysis identifies genes for alcohol consumption.

Alcohol consumption is a heritable behavior seriously endangers human health. However, genetic studies on alcohol consumption primarily focuses on common variants, while insights from rare coding variants are lacking. Here we leverage whole exome sequencing data across 304,119 white British individuals from UK Biobank to identify protein-coding variants associated with alcohol consumption. Twenty-five variants are associated with alcohol consumption through single variant analysis and thirteen genes through gene-based analysis, ten of which have not been reported previously. Notably, the two unreported alcohol consumption-related genes GIGYF1 and ANKRD12 show enrichment in brain function-related pathways including glial cell differentiation and are strongly expressed in the cerebellum. Phenome-wide association analyses reveal that alcohol consumption-related genes are associated with brain white matter integrity and risk of digestive and neuropsychiatric diseases. In summary, this study enhances the comprehension of the genetic architecture of alcohol consumption and implies biological mechanisms underlying alcohol-related adverse outcomes.

Whole-exome sequencing identifies protein-coding variants associated with brain iron in 29,828 individuals.

Iron plays a fundamental role in multiple brain disorders. However, the genetic underpinnings of brain iron and its implications for these disorders are still lacking. Here, we conduct an exome-wide association analysis of brain iron, measured by quantitative susceptibility mapping technique, across 26 brain regions among 26,789 UK Biobank participants. We find 36 genes linked to brain iron, with 29 not being previously reported, and 16 of them can be replicated in an independent dataset with 3,039 subjects. Many of these genes are involved in iron transport and homeostasis, such as FTH1 and MLX. Several genes, while not previously connected to brain iron, are associated with iron-related brain disorders like Parkinson's (STAB1, KCNA10), Alzheimer's (SHANK1), and depression (GFAP). Mendelian randomization analysis reveals six causal relationships from regional brain iron to brain disorders, such as from the hippocampus to depression and from the substantia nigra to Parkinson's. These insights advance our understanding of the genetic architecture of brain iron and offer potential therapeutic targets for brain disorders.

Shift work effects on incident neuropsychiatric disorders and shift work tolerance.

BACKGROUND: Shift work is associated with susceptibility to several neuropsychiatric disorders. This study aims to investigate the effect of shift work on the incidence of neuropsychiatric disorders, and highlighting how individual variability may influence the association. METHODS: UK Biobank participants with employment information were included. Cox survival was conducted in main and subgroup analyses. Correlation analyses explored the impact of shift work on brain structures, and mediation analyses were performed to elucidate the shared underlying mechanisms. Shift work tolerance was evaluated through survival analyses contrasting the risks associated with five neuropsychiatric disorders in shift versus non-shift workers across different demographic or occupational strata. RESULTS: The analysis encompassed 254,646 participants. Shift work was associated with higher risk of dementia (HR 1.29, 95 % CI 1.10-1.52), anxiety (1.08, 1.01-1.15), depression (1.29, 1.22-1.36), and sleep disorders (1.18, 1.09-1.28), but not stroke (p = 0.20). Shift work was correlated with decreasing volume of various brain regions, particularly in thalamus, lateral orbitofrontal, and middle temporal. Mediation analysis revealed that increased immune response and glucose levels are common pathways linking shift work to these disorders. We observed diversity in shift work tolerance across different individual characteristics, among which socioeconomic status and length of working hours were the most essential. LIMITATIONS: Self-reported employment information may cause misclassification and recall bias. And since we focused on the middle-aged population, the conclusions may not be representative of younger or older populations. CONCLUSIONS: Our findings indicated the need to monitor shift worker health and provide personalized management to help adapt to shift work.

Identifying behaviour-related and physiological risk factors for suicide attempts in the UK Biobank.

Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case-control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.

The Etiology of Rapidly Progressive Dementia: A 3-Year Retrospective Study in a Tertiary Hospital in China.

Background: Rapidly progressive dementia (RPD), characterized by a rapid cognitive decline leading to dementia, comprises a diverse range of disorders. Despite advancements in diagnosis and treatment, research on RPD primarily focuses on Western populations. Objective: This study aims to explore the etiology and demographics of RPD in Chinese patients. Methods: We retrospectively analyzed 323 RPD inpatients at Huashan Hospital from May 2019 to March 2023. Data on sociodemographic factors, epidemiology, clinical presentation, and etiology were collected and analyzed. Results: The median onset age of RPD patients was 60.7 years. Two-thirds received a diagnosis within 6 months of symptom onset. Memory impairment was the most common initial symptom, followed by behavioral changes. Neurodegenerative diseases accounted for 47.4% of cases, with central nervous system inflammatory diseases at 30.96%. Autoimmune encephalitis was the leading cause (16.7%), followed by Alzheimer's disease (16.1%), neurosyphilis (11.8%), and Creutzfeldt-Jakob disease (9.0%). Alzheimer's disease, Creutzfeldt-Jakob disease, and frontotemporal dementia were the primary neurodegenerative causes, while autoimmune encephalitis, neurosyphilis, and vascular cognitive impairment were the main non-neurodegenerative causes. Conclusions: The etiology of RPD in Chinese patients is complex, with neurodegenerative and non-neurodegenerative diseases equally prevalent. Recognizing treatable conditions like autoimmune encephalitis and neurosyphilis requires careful consideration and differentiation.

DDAH-1 maintains endoplasmic reticulum-mitochondria contacts and protects dopaminergic neurons in Parkinson's disease.

The loss of dopaminergic neurons in the substantia nigra is a hallmark of pathology in Parkinson's disease (PD). Dimethylarginine dimethylaminohydrolase-1 (DDAH-1) is the critical enzyme responsible for the degradation of asymmetric dimethylarginine (ADMA) which inhibits nitric oxide (NO) synthase and has been implicated in neurodegeneration. Mitochondrial dysfunction, particularly in the mitochondria-associated endoplasmic reticulum membrane (MAM), plays a critical role in this process, although the specific molecular target has not yet been determined. This study aims to examine the involvement of DDAH-1 in the nigrostriatal dopaminergic pathway and PD pathogenesis. The distribution of DDAH-1 in the brain and its colocalization with dopaminergic neurons were observed. The loss of dopaminergic neurons and aggravated locomotor disability after rotenone (ROT) injection were showed in the DDAH-1 knockout rat. L-arginine (ARG) and NO donors were employed to elucidate the role of NO respectively. In vitro, we investigated the effects of DDAH-1 knockdown or overexpression on cell viability and mitochondrial functions, as well as modulation of ADMA/NO levels using ADMA or ARG. MAM formation was assessed by the Mitofusin2 oligomerization and the mitochondrial ubiquitin ligase (MITOL) phosphorylation. We found that DDAH-1 downregulation resulted in enhanced cell death and mitochondrial dysfunctions, accompanied by elevated ADMA and reduced NO levels. However, the recovered NO level after the ARG supplement failed to exhibit a protective effect on mitochondrial functions and partially restored cell viability. DDAH-1 overexpression prevented ROT toxicity, while ADMA treatment attenuated these protective effects. The declines of MAM formation in ROT-treated cells were exacerbated by DDAH-1 downregulation via reduced MITOL phosphorylation, which was reversed by DDAH-1 overexpression. Together, the abundant expression of DDAH-1 in nigral dopaminergic neurons may exert neuroprotective effects by maintaining MAM formation and mitochondrial function probably via ADMA, indicating the therapeutic potential of targeting DDAH-1 for PD.

Identifying modifiable factors and their joint effect on brain health: an exposome-wide association study.

Considerable uncertainty remains regarding the associations of multiple factors with brain health. We aimed to conduct an exposome-wide association study on neurodegenerative disease and neuropsychiatry disorders using data of participants from the UK Biobank. Multivariable Cox regression models with the least absolute shrinkage and selection operator technique as well as principal component analyses were used to evaluate the exposures in relation to common disorders of central nervous system (CNS). Restricted cubic splines were conducted to explore potential nonlinear correlations. Then, weighted standardized scores were generated based on the coefficients to calculate the joint effects of risk factors. We also estimated the potential impact of eliminating the unfavorable profiles of risk domains on CNS disorders using population attributable fraction (PAF). Finally, sensitivity analyses were performed to reduce the risk of reverse causality. The current study discovered the significantly associated exposures fell into six primary exposome categories. The joint effects of identified risk factors demonstrated higher risks for common disorders of CNS (HR = 1.278 ~ 3.743, p < 2e-16). The PAF varied by exposome categories, with lifestyle and medical history contributing to majority of disease cases. In total, we estimated that up to 3.7 ~ 64.1% of disease cases could be prevented.This study yielded modifiable variables of different categories and assessed their joint effects on common disorders of CNS. Targeting the identified exposures might help formulate effective strategies for maintaining brain health.

Whole exome sequencing analyses reveal novel genes in telomere length and their biomedical implications.

Telomere length is a putative biomarker of aging and is associated with multiple age-related diseases. There are limited data on the landscape of rare genetic variations in telomere length. Here, we systematically characterize the rare variant associations with leukocyte telomere length (LTL) through exome-wide association study (ExWAS) among 390,231 individuals in the UK Biobank. We identified 18 robust rare-variant genes for LTL, most of which estimated effects on LTL were significant (> 0.2 standard deviation per allele). The biological functions of the rare-variant genes were associated with telomere maintenance and capping and several genes were specifically expressed in the testis. Three novel genes (ASXL1, CFAP58, and TET2) associated with LTL were identified. Phenotypic association analyses indicated significant associations of ASXL1 and TET2 with cancers, age-related diseases, blood assays, and cardiovascular traits. Survival analyses suggested that carriers of ASXL1 or TET2 variants were at increased risk for cancers; diseases of the circulatory, respiratory, and genitourinary systems; and all-cause and cause-specific deaths. The CFAP58 carriers were at elevated risk of deaths due to cancers. Collectively, the present whole exome sequencing study provides novel insights into the genetic landscape of LTL, identifying novel genes associated with LTL and their implications on human health and facilitating a better understanding of aging, thus pinpointing the genetic relevance of LTL with clonal hematopoiesis, biomedical traits, and health-related outcomes.

Genetic architecture of brain morphology and overlap with neuropsychiatric traits.

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.