A tutorial on conducting genome-wide association studies: Quality control and statistical analysis

Network medicine-based epistasis detection in complex diseases: ready for quantum computing

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

Genetic association with autoimmune diseases identifies molecular mechanisms of coronary artery disease

Autoimmune patients have a significantly increased risk of developing coronary artery disease (CAD) compared to the general population. However, autoimmune patients often lack traditional risk factors for CAD and there is increasing recognition of inflammation in CAD development. In this study, we leveraged genome-wide association study (GWAS) data to understand whether there is a genetic relationship between CAD and autoimmunity. Statistical genetic comparison methods were used to identify correlated and causal SNPs between various autoimmune diseases and CAD. Pleiotropic SNPs were identified by cross-phenotype association analysis (CPASSOC) and overlap between GWAS. Causal SNPs were identified using Mendelian Randomization (MR) and Colocalization (COLOC). Using SNP-to-gene mapping, we additionally identified pleiotropic and causal genes and pathways associated between autoimmunity and CAD, which were contextualized by documentation of enrichment in individual cell types identified from coronary atherosclerotic plaques by single-cell RNA sequencing. These results provide insight into potential inflammatory therapeutic targets for CAD.

Networks of human milk microbiota are associated with host genomics, childhood asthma, and allergic sensitization

The human milk microbiota (HMM) is thought to influence the long-term health of offspring. However, its role in asthma and atopy and the impact of host genomics on HMM composition remain unclear. Through the CHILD Cohort Study, we followed 885 pregnant mothers and their offspring from birth to 5 years and determined that HMM was associated with maternal genomics and prevalence of childhood asthma and allergic sensitization (atopy) among human milk-fed infants. Network analysis identified modules of correlated microbes in human milk that were associated with subsequent asthma and atopy in preschool-aged children. Moreover, reduced alpha-diversity and increased Lawsonella abundance in HMM were associated with increased prevalence of childhood atopy. Genome-wide association studies (GWASs) identified maternal genetic loci (e.g., ADAMTS8, NPR1, and COTL1) associated with HMM implicated with asthma and atopy, notably Lawsonella and alpha-diversity. Thus, our study elucidates the role of host genomics on the HMM and its potential impact on childhood asthma and atopy.

Neural EGFL like 1 as a novel gene for Trabecular Bone Score in older adults: The Bushehr Elderly Health (BEH) program

Neural EGFL like 1 (NELL-1), is a secreted glycoprotein and stimulates osteogenic cell differentiation and bone mineralization. This study aimed to explore the relationship between NELL-1 and Trabecular Bone Score (TBS) as a novel tool for the evaluation of osteoporosis in an elderly population-based cohort study in Iran. A single-locus analysis was performed on TBS using data from 2,071 participants in the Bushehr Elderly Health (BEH) Program. The study investigated 376 independent single nucleotide polymorphisms (SNPs) within the NELL-1 on chromosome 11p15.1. The association between SNPs and the mean TBS L1 to L4 was analyzed through an additive model. Significant variants in the additive model (PFDR<0.05) were further examined within dominant, recessive, over-dominant, and co-dominant models. Multiple linear regression was employed to assess the relationship between the genetic risk score (GRS) derived from significant SNPs and TBS. Three SNPs within the NELL-1 showed a statistically significant association with TBS after adjusting for age and sex. The associations for rs1901945 (β = 0.013, PFDR = 0.0007), rs1584851 (β = -0.011, PFDR = 0.0003), and rs58028601 (β = 0.011, PFDR = 0.0003) were significant in the additive model. Additionally, significant results were observed for rs1901945 and rs58028601 in the dominant model (P<0.05). The GRS showed a statistically significant relationship with TBS, considering adjustments for age, sex, Body Mass Index, type 2 diabetes, and smoking (β = 0.077, P = 1.7×10-5). This study highlights the association of NELL-1 with TBS, underscoring its potential as a candidate for further research and personalized medicine concerning the impact of this gene on bone quality.

Genome-wide association studies on periodontitis: A systematic review

OBJECTIVES

This study aims to systematically review the existing literature and critically appraise the evidence of genome-wide association studies (GWAS) on periodontitis. This study also aims to synthesise the findings of genetic risk variants of periodontitis from included GWAS.

METHODS

A systematic search was conducted on PubMed, GWAS Catalog, MEDLINE, GLOBAL HEALTH and EMBASE via Ovid for GWAS on periodontitis. Only studies exploring single-nucleotide polymorphisms(SNPs) associated with periodontitis were eligible for inclusion. The quality of the GWAS was assessed using the Q-genie tool. Information such as study population, ethnicity, genomic data source, phenotypic characteristics(definition of periodontitis), and GWAS methods(quality control, analysis stages) were extracted. SNPs that reached conventional or suggestive GWAS significance level(5e-8 or 5e-06) were extracted and synthesized.

RESULTS

A total of 15 good-quality GWAS on periodontitis were included (Q-genie scores ranged from 38-50). There were huge heterogeneities among studies. There were 11 identified risk SNPs (rs242016, rs242014, rs10491972, rs242002, rs2978951, rs2738058, rs4284742, rs729876, rs149133391, rs1537415, rs12461706) at conventional GWAS significant level (p<5x10-8), and 41 at suggestive level (p<5x10-6), but no common SNPs were found between studies. Three SNPs (rs4284742 [G], rs11084095 [A], rs12461706 [T]) from three large studies were from the same gene region-SIGLEC5.

CONCLUSION

GWAS of periodontitis showed high heterogeneity of methodology used and provided limited SNPs statistics, making identifying reliable risk SNPs challenging. A clear guidance in dental research with requirement of expectation to make GWAS statistics available to other investigators are needed.

Evidence of symptom specificity for depression in multiple sclerosis: A two sample Mendelian randomization study

BACKGROUND

Depression is common and phenotypically heterogenous in multiple sclerosis (MS). MS may increase risk of some but not all affective symptoms or certain symptoms may predispose individuals to higher MS risk.

OBJECTIVE

To assess the existence and direction of causality between distinct depressive symptoms and MS using two-sample Mendelian randomization (MR).

METHODS

Using summary data from genome-wide association studies, we selected genetic instrument variables (IV) for MS (n = 115,776) and IVs for depressive symptoms (average n = 117,713): anhedonia, altered appetite, concentration, depressed mood, fatigue, inadequacy, psychomotor changes, sleeping problems and suicidality. We performed two-sample MR in either direction using inverse-variance models. Sensitivity analyses included weighted-median and MR-Egger regression. Obesity is a known risk factor for MS and depression; we adjusted for body mass index in multivariable-MR.

RESULTS

Genetic liability to MS was associated with anhedonia (IVW estimate per 102: 0.69; 95 % CI: 0.24-1.13; p = 0.002), concentration difficulty (0.66; 0.19-1.13; p = 0.006) and psychomotor changes (0.37; 0.08-0.65; p = 0.01). Results were similar in sensitivity analyses. In the opposite direction, we found no evidence of a causal relationship for any affective symptom on MS risk.

CONCLUSIONS

Genetic susceptibility to MS was associated with anhedonia, concentration, and psychomotor-related symptoms, suggesting a specific phenotype of depression in MS.

Human milk oligosaccharides are associated with maternal genetics and respiratory health of human milk-fed children

Breastfeeding provides many health benefits, but its impact on respiratory health remains unclear. This study addresses the complex and dynamic nature of the mother-milk-infant triad by investigating maternal genomic factors regulating human milk oligosaccharides (HMOs), and their associations with respiratory health among human milk-fed infants. Nineteen HMOs are quantified from 980 mothers of the CHILD Cohort Study. Genome-wide association studies identify HMO-associated loci on chromosome 19p13.3 and 19q13.33 (lowest P = 2.4e-118), spanning several fucosyltransferase (FUT) genes. We identify novel associations on chromosome 3q27.3 for 6'-sialyllactose (P = 2.2e-9) in the sialyltransferase (ST6GAL1) gene. These, plus additional associations on chromosomes 7q21.32, 7q31.32 and 13q33.3, are replicated in the independent INSPIRE Cohort. Moreover, gene-environment interaction analyses suggest that fucosylated HMOs may modulate overall risk of recurrent wheeze among preschoolers with variable genetic risk scores (P < 0.01). Thus, we report novel genetic factors associated with HMOs, some of which may protect the respiratory health of children.

Predictive value of polygenic risk score for prostate cancer incidence and prognosis in the Han Chinese

Although prostate cancer is a common occurrence among males, the relationship between existing risk prediction models remains unclear. The objective of this hospital-based retrospective study is to investigate the impact of polygenic risk scores (PRSs) on the incidence and prognosis of prostate cancer in the Han Chinese population. A total of 24,778 male participants including 903 patients with prostate cancer at Taichung Veterans General Hospital were enrolled in the study. PRS was calculated using 269 single nucleotide polymorphisms and their corresponding effect sizes from the polygenic score catalog. The association between PRS and the risk prostate cancer was evaluated using Cox proportional hazards regression model. Among the 24,778 participants, 903 were diagnosed with prostate cancer. The risk of prostate cancer was significantly higher in the highest quartile of PRS distribution compared to the lowest (hazard ratio = 4.770, 95% CI = 3.999-5.689, p < 0.0001), with statistical significance across all age groups. Patients in the highest quartile were diagnosed with prostate cancer at a younger age (66.8 ± 8.3 vs. 69.5 ± 8.8, p = 0.002). Subgroup analysis of patients with localized or stage 4 prostate cancer showed no significant differences in biochemical failure or overall survival. This hospital-based cohort study observed that a higher PRS was associated with increased susceptibility to prostate cancer and younger age of diagnosis. However, PRS was not found to be a significant predictor of disease stage and prognosis. These findings suggest that PRS could serve as a useful tool in prostate cancer risk assessment.

Pharmacogenetic biomarkers for secukinumab response in psoriasis patients in real-life clinical practice

BACKGROUND

Prediction of the response to a biological treatment in psoriasis patients would allow efficient treatment allocation.

OBJECTIVE

To identify polymorphisms associated with secukinumab response in psoriasis patients in a daily practice setting.

METHODS

We studied 180 SNPs in patients with moderate-to-severe plaque psoriasis recruited from 15 Spanish hospitals. Treatment effectiveness was evaluated by absolute PASI ≤3 and ≤1 at 6 and 12 months. Individuals were genotyped using a custom Taqman array. Multiple logistic regression models were generated. Sensitivity, specificity and area under the curve (AUC) were analysed.

RESULTS

A total of 173 patients were studied at 6 months, (67% achieved absolute PASI ≤ 3 and 65% PASI ≤ 1) and 162 at 12 months (75% achieved absolute PASI ≤ 3 and 64% PASI ≤ 1). Multivariable analysis showed the association of different sets of SNPs with the response to secukinumab. The model of absolute PASI≤3 at 6 months showed best values of sensitivity and specificity. Four SNPs were associated with the capability of achieving absolute PASI ≤ 3 at 6 months. rs1801274 (FCGR2A), rs2431697 (miR-146a) and rs10484554 (HLCw6) were identified as risk factors for failure to achieve absolute PASI≤3, while rs1051738 (PDE4A) was protective. AUC including these genotypes, weight of patients and history of biological therapy was 0.88 (95% CI 0.83-0.94), with a sensitivity of 48.6% and specificity of 95.7% to discriminate between both phenotypes.

CONCLUSION

We have identified a series of polymorphisms associated with the response to secukinumab capable of predicting the potential response/non-response to this drug in patients with plaque psoriasis.

Evaluation of Genetic Associations with Clinical Phenotypes of Kidney Stone Disease

Background and objective

Previous studies have reported a strong genetic contribution to kidney stone risk. This study aims to identify genetic associations of kidney stone disease within a large-scale electronic health record system.

Methods

We performed genome-wide association studies (GWASs) for nephrolithiasis from genotyped samples of 5571 cases and 83 692 controls. This analysis included a primary GWAS focused on nephrolithiasis and subsequent subgroup GWASs stratified by stone composition types. For significant risk variants, we performed association analyses with stone composition and first-time 24-h urine parameters. To assess disease severity, we investigated the associations with age at first stone diagnosis, age at first stone-related procedure, and time between first and second stone-related procedures.

Key findings and limitations

The primary GWAS analysis identified ten significant loci, all located on chromosome 16 within coding regions of the UMOD gene. The strongest signal was rs28544423 (odds ratio 1.17, 95% confidence interval 1.11-1.23, p = 2.7 × 10-9). In subgroup GWASs stratified by six kidney stone composition subtypes, 19 significant loci were identified including two loci in coding regions (brushite; NXPH1, rs79970906 and rs4725104). The UMOD single nucleotide polymorphism rs28544423 was associated with differences in 24-h excretion of urinary analytes, and the minor allele was positively associated with calcium oxalate dihydrate stone composition (p < 0.05). No associations were found between UMOD variants and disease severity. Limitations include an omitted variable bias and a misclassification bias.

Conclusions and clinical implications

We replicated germline variants associated with kidney stone disease risk at UMOD and reported novel variants associated with stone composition. Genetic variants of UMOD are associated with differences in 24-h urine parameters and stone composition, but not disease severity.

Patient summary

We identify genetic variants linked to kidney stone disease within an electronic health record (EHR) system. These findings suggest a role for the EHR to enable a precision-medicine approach for stone disease.

The genetic association between bipolar disorder and dementia: a qualitative review

Bipolar disorder is a chronic disorder characterized by fluctuations in mood state and energy and recurrent episodes of mania/hypomania and depression. Bipolar disorder may be regarded as a neuro-progressive disorder in which repeated mood episodes may lead to cognitive decline and dementia development. In the current review, we employed genome-wide association studies to comprehensively investigate the genetic variants associated with bipolar disorder and dementia. Thirty-nine published manuscripts were identified: 20 on bipolar disorder and 19 on dementia. The results showed that the genes CACNA1C, GABBR2, SCN2A, CTSH, MSRA, and SH3PXD2A were overlapping between patients with bipolar disorder and dementia. In conclusion, the genes CACNA1C, GABBR2, SCN2A, CTSH, MSRA, and SH3PXD2A may be associated with the neuro-progression of bipolar disorder to dementia. Further genetic studies are needed to comprehensively clarify the role of genes in cognitive decline and the development of dementia in patients with bipolar disorder.

Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults

Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.

Human genetic variation determines 24-hour rhythmic gene expression and disease risk

24-hour biological rhythms are essential to maintain physiological homeostasis. Disruption of these rhythms increases the risks of multiple diseases. The biological rhythms are known to have a genetic basis formed by core clock genes, but how individual genetic variation shapes the oscillating transcriptome and contributes to human chronophysiology and disease risk is largely unknown. Here, we mapped interactions between temporal gene expression and genotype to identify quantitative trait loci (QTLs) contributing to rhythmic gene expression. These newly identified QTLs were termed as rhythmic QTLs (rhyQTLs), which determine previously unappreciated rhythmic genes in human subpopulations with specific genotypes. Functionally, rhyQTLs and their associated rhythmic genes contribute extensively to essential chronophysiological processes, including bile acid and lipid metabolism. The identification of rhyQTLs sheds light on the genetic mechanisms of gene rhythmicity, offers mechanistic insights into variations in human disease risk, and enables precision chronotherapeutic approaches for patients.

GRIEVOUS: your command-line general for resolving cross-dataset genotype inconsistencies

SUMMARY

Harmonizing variant indexing and allele assignments across datasets is crucial for data integrity in cross-dataset studies such as multi-cohort genome-wide association studies, meta-analyses, and the development, validation, and application of polygenic risk scores. Ensuring this indexing and allele consistency is a laborious, time-consuming, and error-prone process requiring a certain degree of computational proficiency. Here, we introduce GRIEVOUS, a command-line tool for cross-dataset variant homogenization. By means of an internal database and a custom indexing methodology, GRIEVOUS identifies, formats, and aligns all biallelic single nucleotide polymorphisms (SNPs) across all summary statistic and genotype files of interest. Upon completion of dataset harmonization, GRIEVOUS can also be used to extract the maximal set of biallelic SNPs common to all datasets.

AVAILABILITY AND IMPLEMENTATION

GRIEVOUS and all supporting documentation and tutorials can be found at https://github.com/jvtalwar/GRIEVOUS. It is freely and publicly available under the MIT license and can be installed via pip.

Integrating genomic variants and developmental milestones to predict cognitive and adaptive outcomes in autistic children

Although the first signs of autism are often observed as early as 18-36 months of age, there is a broad uncertainty regarding future development, and clinicians lack predictive tools to identify those who will later be diagnosed with co-occurring intellectual disability (ID). Here, we developed predictive models of ID in autistic children (n=5,633 from three cohorts), integrating different classes of genetic variants alongside developmental milestones. The integrated model yielded an AUC ROC=0.65, with this predictive performance cross-validated and generalised across cohorts. Positive predictive values reached up to 55%, accurately identifying 10% of ID cases. The ability to stratify the probabilities of ID using genetic variants was up to twofold greater in individuals with delayed milestones compared to those with typical development. These findings underscore the potential of models in neurodevelopmental medicine that integrate genomics and clinical observations to predict outcomes and target interventions.

The association between cytomegalovirus infection and neurodegenerative diseases: a prospective cohort using UK Biobank data

Background

Certain viral infections have been linked to the development of neurodegenerative diseases. This study aimed to investigate the association between cytomegalovirus (CMV) infection and five neurodegenerative diseases, spinal muscular atrophy (SMA) and related syndromes, Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis (MS), and disorders of the autonomic nervous system (DANS).

Methods

This prospective cohort included white British individuals who underwent CMV testing in the UK Biobank from January 1, 2006 to December 31, 2021. A Cox proportional hazard model was utilized to estimate the future risk of developing five neurodegenerative diseases in individuals with or without CMV infection, adjusted for batch effect, age, sex, and Townsend deprivation index in Model 1, and additionally for type 2 diabetes, cancer, osteoporosis, vitamin D, monocyte count and leukocyte count in Model 2. Bidirectional Mendelian randomization was employed to validate the potential causal relationship between CMV infection and PD.

Findings

A total of 8346 individuals, consisting of 4620 females (55.4%) and 3726 males (44.6%) who were white British at an average age of 56.74 (8.11), were included in this study. The results showed that CMV infection did not affect the risk of developing AD (model 1: HR [95% CI] = 1.01 [0.57, 1.81], P = 0.965; model 2: HR = 1.00 [0.56, 1.79], P = 0.999), SMA and related syndromes (model 1: HR = 3.57 [0.64, 19.80], P = 0.146; model 2: HR = 3.52 [0.63, 19.61], P = 0.152), MS (model 1: HR = 1.16 [0.45, 2.97], P = 0.756; model 2: HR = 1.16 [0.45, 2.97], P = 0.761) and DANS (model 1: HR = 0.65 [0.16, 2.66], P = 0.552; model 2: HR = 0.65 [0.16, 2.64], P = 0.543). Interestingly, it was found that participants who were CMV seronegative had a higher risk of developing PD compared to those who were seropositive (model 1: HR = 2.37 [1.25, 4.51], P = 0.009; model 2: HR = 2.39 [1.25, 4.54], P = 0.008) after excluding deceased individuals. This association was notably stronger in males (model 1: HR = 3.16 [1.42, 7.07], P = 0.005; model 2: HR = 3.41 [1.50, 7.71], P = 0.003), but no significant difference was observed in the female subgroup (model 1: HR = 1.28 [0.40, 4.07], P = 0.679; model 2: HR = 1.27 [0.40, 4.06], P = 0.684). However, a bidirectional Mendelian randomization analysis did not find a genetic association between CMV infection and PD.

Interpretation

The study found that males who did not have a CMV infection were at a higher risk of developing PD. The findings provided a new viewpoint on the risk factors for PD and may potentially influence public health approaches for the disease.

Funding

National Natural Science Foundation of China (81873776), Natural Science Foundation of Guangdong Province, China (2021A1515011681, 2023A1515010495).

Genetics in neovascular age-related macular degeneration susceptibility and treatment response to anti-VEGF intravitreal injection: A case series study

BACKGROUND

To identify genotypes associated with neovascular age-related macular degeneration (nAMD) and investigate the associations between genotype variations and anti-vascular endothelial growth factor (VEGF) treatment response.

METHODS

This observational, retrospective, case series study enrolled patients diagnosed with nAMD who received anti-VEGF treatment in National Taiwan University Hospital with at least one-year follow-up between 2012 and 2020. A genome-wide association study (GWAS) was conducted on enrolled patients and controls. Correlations between the genotypes identified from GWAS and the treatment response of functional/anatomical biomarkers, including visual acuity (VA), presence of intraretinal or subretinal fluid (SRF), serous or fibrovascular pigmented epithelium detachment (PED), and disruption of the ellipsoid zone (EZ), were analysed.

RESULTS

In total, 182 patients with nAMD and 1748 controls were enrolled. GWAS revealed 16 single nucleotide polymorphisms (SNPs) as risk loci for nAMD, including seven loci in CFH and ARMS2/HTRA1 and nine novel loci, including rs117517872 and rs79835234(COPB2-DT), rs7525578(RAP1A), rs2123738(LOC105376755), rs1374879(CNTN3), rs3812692(SAR1A), rs117501587(PRKCA), rs9965945(CNDP1), and rs189769231(MATK). Our study revealed rs800292(CFH), rs11200638(HTRA1), and rs2123738(LOC105376755) correlated with poor treatment response in VA (P = 0.005), SRF (P = 0.044), and fibrovascular PED (P = 0.007), respectively. Rs9965945(CNDP1) was correlated with poor response in disruption of EZ (P = 0.046) and serous PED (P = 0.049).

CONCLUSIONS

Among the 16 SNPs found in the GWAS, four loci-CFH, ARMS2/HTRA1, and two novel loci-were correlated with the susceptibility of nAMD and anatomical/functional responses after anti-VEGF treatment.

Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes

Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.

Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation

Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.

gwid: an R package and Shiny application for Genome-Wide analysis of IBD data

Summary

Genome-wide identity by descent (gwid) is an R package developed for the analysis of identity-by-descent (IBD) data pertaining to dichotomous traits. This package offers a set of tools to assess differential IBD levels for the two states of a binary trait, yielding informative and meaningful results. Furthermore, it provides convenient functions to visualize the outcomes of these analyses, enhancing the interpretability and accessibility of the results. To assess the performance of the package, we conducted an evaluation using real genotype data derived from the SNPs to investigate rheumatoid arthritis susceptibility from the Marshfield Clinic Personalized Medicine Research Project.

Availability and implementation

gwid is available as an open-source R package. Release versions can be accessed on CRAN (https://cran.r-project.org/package=gwid) for all major operating systems. The development version is maintained on GitHub (https://github.com/soroushmdg/gwid) and full documentation with examples and workflow templates is provided via the package website (http://tinyurl.com/gwid-tutorial). An interactive R Shiny dashboard is also developed (https://tinyurl.com/gwid-shiny).

Genome-Wide Association Study as an Efficacious Approach to Discover Candidate Genes Associated with Body Linear Type Traits in Dairy Cattle

Body shape traits are very important and play a crucial role in the economic development of dairy farming. By improving the accuracy of selection for body size traits, we can enhance economic returns across the dairy industry and on farms, contributing to the future profitability of the dairy sector. Registered body conformation traits are reliable and cost-effective tools for use in national cattle breeding selection programs. These traits are significantly related to the production, longevity, mobility, health, fertility, and environmental adaptation of dairy cows. Therefore, they can be considered indirect indicators of economically important traits in dairy cows. Utilizing efficacious genetic methods, such as genome-wide association studies (GWASs), allows for a deeper understanding of the genetic architecture of complex traits through the identification and application of genetic markers. In the current review, we summarize information on candidate genes and genomic regions associated with body conformation traits in dairy cattle worldwide. The manuscript also reviews the importance of body conformation, the relationship between body conformation traits and other traits, heritability, influencing factors, and the genetics of body conformation traits. The information on candidate genes related to body conformation traits provided in this review may be helpful in selecting potential genetic markers for the genetic improvement of body conformation traits in dairy cattle.

GWAS-based polygenic risk scoring for predicting cerebral artery dissection in the Chinese population

OBJECTIVE

Cerebral artery dissection (CeAD) is a rare but serious disease. Genetic risk assessment for CeAD is lacking in Chinese population. We performed genome-wide association study (GWAS) and computed polygenic risk score (PRS) to explore genetic susceptibility factors and prediction model of CeAD based on patients in Huashan Hospital.

METHODS

A total of 210 CeAD patients and 280 controls were enrolled from June 2017 to September 2022 in Department of Neurology, Huashan Hospital, Fudan University. We performed GWAS to identify genetic variants associated with CeAD in 140 CeAD patients and 210 control individuals according to a case and control 1:1.5 design rule in the training dataset, while the other 70 patients with CeAD and 70 controls were used as validation. Then Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were utilized to identify the significant pathways. We constructed a PRS by capturing all independent GWAS SNPs in the analysis and explored the predictivity of PRS, age, and sex for CeAD.

RESULTS

Through GWAS analysis of the 140 cases and 210 controls in the training dataset, we identified 13 leading SNPs associated with CeAD at a genome-wide significance level of P < 5 × 10- 8. Among them, 10 SNPs were annotated in or near (in the upstream and downstream regions of ± 500Kb) 10 functional genes. rs34508376 (OR2L13) played a suggestive role in CeAD pathophysiology which was in line with previous observations in aortic aneurysms. The other nine genes were first-time associations in CeAD cases. GO enrichment analyses showed that these 10 genes have known roles in 20 important GO terms clustered into two groups: (1) cellular biological processes (BP); (2) molecular function (MF). We used genome-wide association data to compute PRS including 32 independent SNPs and constructed predictive model for CeAD by using age, sex and PRS as predictors both in training and validation test. The area under curve (AUC) of PRS predictive model for CeAD reached 99% and 95% in the training test and validation test respectively, which were significantly larger than the age and sex models of 83% and 86%.

CONCLUSIONS

Our study showed that ten risk loci were associated with CeAD susceptibility, and annotated functional genes had roles in 20 important GO terms clustered into biological process and molecular function. The PRS derived from risk variants was associated with CeAD incidence after adjusting for age and sex both in training test and validation.

Association of whole-person eigen-polygenic risk scores with Alzheimer's disease

Late-Onset Alzheimer's Disease (LOAD) is a heterogeneous neurodegenerative disorder with complex etiology and high heritability. Its multifactorial risk profile and large portions of unexplained heritability suggest the involvement of yet unidentified genetic risk factors. Here we describe the "whole person" genetic risk landscape of polygenic risk scores for 2218 traits in 2044 elderly individuals and test if novel eigen-PRSs derived from clustered subnetworks of single-trait PRSs can improve the prediction of LOAD diagnosis, rates of cognitive decline, and canonical LOAD neuropathology. Network analyses revealed distinct clusters of PRSs with clinical and biological interpretability. Novel eigen-PRSs (ePRS) from these clusters significantly improved LOAD-related phenotypes prediction over current state-of-the-art LOAD PRS models. Notably, an ePRS representing clusters of traits related to cholesterol levels was able to improve variance explained in a model of the brain-wide beta-amyloid burden by 1.7% (likelihood ratio test P = 9.02 × 10-7). All associations of ePRS with LOAD phenotypes were eliminated by the removal of APOE-proximal loci. However, our association analysis identified modules characterized by PRSs of high cholesterol and LOAD. We believe this is due to the influence of the APOE region from both PRSs. We found significantly higher mean SNP effects for LOAD in the intersecting APOE region SNPs. Combining genetic risk factors for vascular traits and dementia could improve current single-trait PRS models of LOAD, enhancing the use of PRS in risk stratification. Our results are catalogued for the scientific community, to aid in generating new hypotheses based on our maps of clustered PRSs and associations with LOAD-related phenotypes.

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.

Genetic Associations of Anhedonia: Insights into Overlap of Mental and Somatic Disorders

BACKGROUND

Anhedonia is characterized by a reduced ability to anticipate, experience, and/or learn about pleasure. This phenomenon has a transdiagnostic nature and is one of the key symptoms of mood disorders, schizophrenia, addictions, and somatic conditions.

AIM

To evaluate the genetic architecture of anhedonia and its overlap with other mental disorders and somatic conditions.

METHODS

We performed a genome-wide association study of anhedonia on a sample of 4,520 individuals from a Russian non-clinical population. Using the available summary statistics, we calculated polygenic risk scores (PRS) to investigate the genetic relationship between anhedonia and other psychiatric or somatic phenotypes.

RESULTS

No variants with a genome-wide significant association were identified. PRS for major depression, bipolar disorder, and schizophrenia were significantly associated with anhedonia. Conversely, no significant associations were found between PRS for anxiety and anhedonia, which aligns well with existing clinical evidence. None of the PRS for somatic phenotypes attained a significance level after correction for multiple comparisons. A nominal significance for the anhedonia association was determined for omega-3 fatty acids, type 2 diabetes mellitus, and Crohn's disease.

CONCLUSION

Anhedonia has a complex polygenic architecture, and its presence in somatic diseases or normal conditions may be due to a genetic predisposition to mood disorders or schizophrenia.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Analysis-ready VCF at Biobank scale using Zarr

Background

Variant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasises efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. Biobank scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed.

Results

We present the VCF Zarr specification, an encoding of the VCF data model using Zarr which makes retrieving subsets of the data much more efficient. Zarr is a cloud-native format for storing multi-dimensional data, widely used in scientific computing. We show how this format is far more efficient than standard VCF based approaches, and competitive with specialised methods for storing genotype data in terms of compression ratios and calculation performance. We demonstrate the VCF Zarr format (and the vcf2zarr conversion utility) on a subset of the Genomics England aggV2 dataset comprising 78,195 samples and 59,880,903 variants, with a 5X reduction in storage and greater than 300X reduction in CPU usage in some representative benchmarks.

Conclusions

Large row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely-used, open-source technologies has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores.

Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement

BACKGROUND

Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets.

METHODS

We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups.

RESULTS

Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations.

CONCLUSIONS

This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.

Leveraging sex-genetic interactions to understand brain disorders: recent advances and current gaps

It is established that there are sex differences in terms of prevalence, age of onset, clinical manifestations, and response to treatment for a variety of brain disorders, including neurodevelopmental, psychiatric, and neurodegenerative disorders. Cohorts of increasing sample sizes with diverse data types collected, including genetic, transcriptomic and/or phenotypic data, are providing the building blocks to permit analytical designs to test for sex-biased genetic variant-trait associations, and for sex-biased transcriptional regulation. Such molecular assessments can contribute to our understanding of the manifested phenotypic differences between the sexes for brain disorders, offering the future possibility of delivering personalized therapy for females and males. With the intention of raising the profile of this field as a research priority, this review aims to shed light on the importance of investigating sex-genetic interactions for brain disorders, focusing on two areas: (i) variant-trait associations and (ii) transcriptomics (i.e. gene expression, transcript usage and regulation). We specifically discuss recent advances in the field, current gaps and provide considerations for future studies.

Computing linkage disequilibrium aware genome embeddings using autoencoders

MOTIVATION

The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction.

RESULTS

We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data.

AVAILABILITY AND IMPLEMENTATION

Data are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders.

Techniques, procedures, and applications in host genetic analysis

This chapter overviews genetic techniques' fundamentals and methodological features, including different approaches, analyses, and applications that have contributed to advancing health and disease. The aim is to describe laboratory methodologies and analyses employed to understand the genetic landscape of different biological contexts, from conventional techniques to cutting-edge technologies. Besides describing detailed aspects of the polymerase chain reaction (PCR) and derived types as one of the principles for many novel techniques, we also discuss microarray analysis, next-generation sequencing, and genome editing technologies such as transcription activator-like effector nucleases (TALENs) and the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) systems. These techniques study several phenotypes, ranging from autoimmune disorders to viral diseases. The significance of integrating diverse genetic methodologies and tools to understand host genetics comprehensively and addressing the ethical, legal, and social implications (ELSI) associated with using genetic information is highlighted. Overall, the methods, procedures, and applications in host genetic analysis provided in this chapter furnish researchers and practitioners with a roadmap for navigating the dynamic landscape of host-genome interactions.

A Genotype-Phenotype Model for Predicting Resistance Training Effects on Leg Press Performance

This study develops a comprehensive genotype-phenotype model for predicting the effects of resistance training on leg press performance. A cohort of physically inactive adults (N=193) underwent 12 weeks of resistance training, and measurements of maximum isokinetic leg press peak force, muscle mass, and thickness were taken before and after the intervention. Whole-genome genotyping was performed, and genome-wide association analysis identified 85 novel SNPs significantly associated with changes in leg press strength after training. A prediction model was constructed using stepwise linear regression, incorporating seven lead SNPs that explained 40.4% of the training effect variance. The polygenic score showed a significant positive correlation with changes in leg press strength. By integrating genomic markers and phenotypic indicators, the comprehensive prediction model explained 75.4% of the variance in the training effect. Additionally, five SNPs were found to potentially impact muscle contraction, metabolism, growth, and development through their association with REACTOME pathways. Individual responses to resistance training varied, with changes in leg press strength ranging from -55.83% to 151.20%. The study highlights the importance of genetic factors in predicting training outcomes and provides insights into the potential biological functions underlying resistance training effects. The comprehensive model offers valuable guidance for personalized fitness programs based on individual genetic profiles and phenotypic characteristics.

Analysis of somatic mutations in whole blood from 200,618 individuals identifies pervasive positive selection and novel drivers of clonal hematopoiesis

Human aging is marked by the emergence of a tapestry of clonal expansions in dividing tissues, particularly evident in blood as clonal hematopoiesis (CH). CH, linked to cancer risk and aging-related phenotypes, often stems from somatic mutations in a set of established genes. However, the majority of clones lack known drivers. Here we infer gene-level positive selection in whole blood exomes from 200,618 individuals in UK Biobank. We identify 17 additional genes, ZBTB33, ZNF318, ZNF234, SPRED2, SH2B3, SRCAP, SIK3, SRSF1, CHEK2, CCDC115, CCL22, BAX, YLPM1, MYD88, MTA2, MAGEC3 and IGLL5, under positive selection at a population level, and validate this selection pattern in 10,837 whole genomes from single-cell-derived hematopoietic colonies. Clones with mutations in these genes grow in frequency and size with age, comparable to classical CH drivers. They correlate with heightened risk of infection, death and hematological malignancy, highlighting the significance of these additional genes in the aging process.

Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.

Overweight as a Causal Factor Contributing to Better Survival at the Oldest Old Ages: A Mendelian Randomization Study

Overweight, defined by a body mass index (BMI) between 25 and 30, has been associated with enhanced survival among older adults in some studies. However, whether being overweight is causally linked to longevity remains unclear. To investigate this, we conducted a Mendelian randomization (MR) study of lifespan 85+ years, using overweight as an exposure variable and data from the Health and Retirement Study and the Long Life Family Study. An essential aspect of MR involves selecting appropriate single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs). This is challenging due to the limited number of SNP candidates within biologically relevant genes that can satisfy all necessary assumptions and criteria. To address this challenge, we employed a novel strategy of creating additional IVs by pairing SNPs between candidate genes. This strategy allowed us to expand the pool of IV candidates with new 'composite' SNPs derived from eight candidate obesity genes. Our study found that being overweight between ages 75 and 85, compared to having a normal weight (BMI 18.5-24.9), significantly contributes to improved survival beyond age 85. Results of this MR study thus support a causal relationship between overweight and longevity in older adults.

A Polygenic Risk Analysis for Identifying Ulcerative Colitis Patients with European Ancestry

The incidence of ulcerative colitis (UC) has increased globally. As a complex disease, the genetic predisposition for UC could be estimated by the polygenic risk score (PRS), which aggregates the effects of a large number of genetic variants in a single quantity and shows promise in identifying individuals at higher lifetime risk of UC. Here, based on a cohort of 2869 UC cases and 2900 controls with genotype array datasets, we used PRSice-2 to calculate PRS, and systematically analyzed factors that could affect the power of PRS, including GWAS summary statistics, population stratification, and impact of variants. After leveraging a stepwise condition analysis, we eventually established the best PRS model, achieving an AUC of 0.713. Meanwhile, samples in the top 20% of the PRS distribution had a risk of UC more than ten times higher than samples in the lowest 20% (OR = 10.435, 95% CI 8.571-12.703). Our analyses demonstrated that including population-enriched, more disease-associated SNPs and using GWAS summary statistics from similar ethnic background can improve the power of PRS. Strictly following the principle of focusing on one population in all aspects of generating PRS can be a cost-effective way to apply genotype-array-derived PRS to practical risk estimation.

A novel locus in CSMD1 gene is associated with increased susceptibility to severe malaria in Malian children

Background

Plasmodium falciparum malaria is still a leading cause of child mortality in sub-Saharan Africa. The clinical manifestations of malaria range from asymptomatic infection to severe disease. The variation in clinical presentation is partly attributed to host genetic factors with estimated narrow-sense heritability of 23%. Here, we investigate the associations between candidate gene polymorphisms and the likelihood of severe malaria (SM) in a cohort of Malian children.

Methods

Based on our previous genome-wide association studies (GWAS) analysis, candidate genes were selected for in-depth analysis using several criteria including gene-level GWAS scores, functional overlap with malaria pathogenesis, and evidence of association with protection or susceptibility to other infectious or inflammatory diseases. Single Nucleotide Polymorphisms (SNPs) residing within these genes were selected mainly based on p-values from previous severe malaria susceptibility GWAS studies and minor allele frequency (MAF) in West African populations.

Results

Of 182 candidate genes reported in our previous study, 11 genes and 22 SNPs residing in these genes were selected. The selected SNPs were genotyped using KASP technology in 477 DNA samples (87 SM and 390 controls). Logistic regression analysis revealed that a common intron variant, rs13340578 in CUB and Sushi Multi Domain (CSMD1) gene, is associated with increased odds of SM in recessive mode of inheritance (MAF = 0.42, OR = 1.8, 95% CI = [1.78, 1.84], p = 0.029). The SNP is in linkage disequilibrium (LD) with multiple variants with regulatory features.

Conclusion

Taken together, the current study showed that an intron variant rs13340578, residing in CSMD1 gene, is associated with increased susceptibility to malaria. This finding suggests that modified regulation of complement may contribute to malaria disease severity. Further studies are needed to identify the causal variants and the underlying molecular mechanisms.

Integrating human endogenous retroviruses into transcriptome-wide association studies highlights novel risk factors for major psychiatric conditions

Human endogenous retroviruses (HERVs) are repetitive elements previously implicated in major psychiatric conditions, but their role in aetiology remains unclear. Here, we perform specialised transcriptome-wide association studies that consider HERV expression quantified to precise genomic locations, using RNA sequencing and genetic data from 792 post-mortem brain samples. In Europeans, we identify 1238 HERVs with expression regulated in cis, of which 26 represent expression signals associated with psychiatric disorders, with ten being conditionally independent from neighbouring expression signals. Of these, five are additionally significant in fine-mapping analyses and thus are considered high confidence risk HERVs. These include two HERV expression signatures specific to schizophrenia risk, one shared between schizophrenia and bipolar disorder, and one specific to major depressive disorder. No robust signatures are identified for autism spectrum conditions or attention deficit hyperactivity disorder in Europeans, or for any psychiatric trait in other ancestries, although this is likely a result of relatively limited statistical power. Ultimately, our study highlights extensive HERV expression and regulation in the adult cortex, including in association with psychiatric disorder risk, therefore providing a rationale for exploring neurological HERV expression in complex neuropsychiatric traits.

Polygenic risk score predicting susceptibility and outcome of benign prostatic hyperplasia in the Han Chinese

BACKGROUND

Given the high prevalence of BPH among elderly men, pinpointing those at elevated risk can aid in early intervention and effective management. This study aimed to explore that polygenic risk score (PRS) is effective in predicting benign prostatic hyperplasia (BPH) incidence, prognosis and risk of operation in Han Chinese.

METHODS

A retrospective cohort study included 12,474 male participants (6,237 with BPH and 6,237 non-BPH controls) from the Taiwan Precision Medicine Initiative (TPMI). Genotyping was performed using the Affymetrix Genome-Wide TWB 2.0 SNP Array. PRS was calculated using PGS001865, comprising 1,712 single nucleotide polymorphisms. Logistic regression models assessed the association between PRS and BPH incidence, adjusting for age and prostate-specific antigen (PSA) levels. The study also examined the relationship between PSA, prostate volume, and response to 5-α-reductase inhibitor (5ARI) treatment, as well as the association between PRS and the risk of TURP.

RESULTS

Individuals in the highest PRS quartile (Q4) had a significantly higher risk of BPH compared to the lowest quartile (Q1) (OR = 1.51, 95% CI = 1.274-1.783, p < 0.0001), after adjusting for PSA level. The Q4 group exhibited larger prostate volumes and a smaller volume reduction after 5ARI treatment. The Q1 group had a lower cumulative TURP probability at 3, 5, and 10 years compared to the Q4 group. PRS Q4 was an independent risk factor for TURP.

CONCLUSIONS

In this Han Chinese cohort, higher PRS was associated with an increased susceptibility to BPH, larger prostate volumes, poorer response to 5ARI treatment, and a higher risk of TURP. Larger prospective studies with longer follow-up are warranted to further validate these findings.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Genome-wide association study and fine-mapping on Korean biobank to discover renal trait-associated variants

BACKGROUND

Chronic kidney disease is a significant health burden worldwide, with increasing incidence. Although several genome- wide association studies (GWAS) have investigated single nucleotide polymorphisms (SNP) associated with kidney trait, most studies were focused on European ancestry.

METHODS

We utilized clinical and genetic information collected from the Korean Genome and Epidemiology Study (KoGES).

RESULTS

More than five million SNPs from 58,406 participants were analyzed. After meta-GWAS, 1,360 loci associated with estimated glomerular filtration rate (eGFR) at a genome-wide significant level (p = 5 × 10-8) were identified. Among them, 399 loci were validated with at least one other biomarker (blood urea nitrogen [BUN] or eGFRcysC) and 149 loci were validated using both markers. Among them, 18 SNPs (nine known ones and nine novel ones) with 20 putative genes were found. The aggregated effect of genes estimated by MAGMA gene analysis showed that these significant genes were enriched in kidney-associated pathways, with the kidney and liver being the most enriched tissues.

CONCLUSION

In this study, we conducted GWAS for more than 50,000 Korean individuals and identified several variants associated with kidney traits, including eGFR, BUN, and eGFRcysC. We also investigated functions of relevant genes using computational methods to define putative causal variants.

Examination of the shared genetic architecture between multiple sclerosis and systemic lupus erythematosus facilitates discovery of novel lupus risk loci

Systemic Lupus Erythematosus (SLE) is an autoimmune disease with heterogeneous manifestations, including neurological and psychiatric symptoms. Genetic association studies in SLE have been hampered by insufficient sample size and limited power compared to many other diseases. Multiple Sclerosis (MS) is a chronic relapsing autoimmune disease of the central nervous system (CNS) that also manifests neurological and immunological features. Here, we identify a method of leveraging large-scale genome wide association studies (GWAS) in MS to identify novel genetic risk loci in SLE. Statistical genetic comparison methods including linkage disequilibrium score regression (LDSC) and cross-phenotype association analysis (CPASSOC) to identify genetic overlap in disease pathophysiology, traditional 2-sample and novel PPI-based mendelian randomization to identify causal associations and Bayesian colocalization were applied to association studies conducted in MS to facilitate discovery in the smaller, more limited datasets available for SLE. Pathway analysis using SNP-to-gene mapping identified biological networks composed of molecular pathways with causal implications for CNS disease in SLE specifically, as well as pathways likely causal of both pathologies, providing key insights for therapeutic selection.

Genome-Wide Analyses of Vocabulary Size in Infancy and Toddlerhood: Associations With Attention-Deficit/Hyperactivity Disorder, Literacy, and Cognition-Related Traits

BACKGROUND

The number of words children produce (expressive vocabulary) and understand (receptive vocabulary) changes rapidly during early development, partially due to genetic factors. Here, we performed a meta-genome-wide association study of vocabulary acquisition and investigated polygenic overlap with literacy, cognition, developmental phenotypes, and neurodevelopmental conditions, including attention-deficit/hyperactivity disorder (ADHD).

METHODS

We studied 37,913 parent-reported vocabulary size measures (English, Dutch, Danish) for 17,298 children of European descent. Meta-analyses were performed for early-phase expressive (infancy, 15-18 months), late-phase expressive (toddlerhood, 24-38 months), and late-phase receptive (toddlerhood, 24-38 months) vocabulary. Subsequently, we estimated single nucleotide polymorphism-based heritability (SNP-h2) and genetic correlations (rg) and modeled underlying factor structures with multivariate models.

RESULTS

Early-life vocabulary size was modestly heritable (SNP-h2 = 0.08-0.24). Genetic overlap between infant expressive and toddler receptive vocabulary was negligible (rg = 0.07), although each measure was moderately related to toddler expressive vocabulary (rg = 0.69 and rg = 0.67, respectively), suggesting a multifactorial genetic architecture. Both infant and toddler expressive vocabulary were genetically linked to literacy (e.g., spelling: rg = 0.58 and rg = 0.79, respectively), underlining genetic similarity. However, a genetic association of early-life vocabulary with educational attainment and intelligence emerged only during toddlerhood (e.g., receptive vocabulary and intelligence: rg = 0.36). Increased ADHD risk was genetically associated with larger infant expressive vocabulary (rg = 0.23). Multivariate genetic models in the ALSPAC (Avon Longitudinal Study of Parents and Children) cohort confirmed this finding for ADHD symptoms (e.g., at age 13; rg = 0.54) but showed that the association effect reversed for toddler receptive vocabulary (rg = -0.74), highlighting developmental heterogeneity.

CONCLUSIONS

The genetic architecture of early-life vocabulary changes during development, shaping polygenic association patterns with later-life ADHD, literacy, and cognition-related traits.

VCF2PCACluster: a simple, fast and memory-efficient tool for principal component analysis of tens of millions of SNPs

Principal component analysis (PCA) is an important and widely used unsupervised learning method that determines population structure based on genetic variation. Genome sequencing of thousands of individuals usually generate tens of millions of SNPs, making it challenging for PCA analysis and interpretation. Here we present VCF2PCACluster, a simple, fast and memory-efficient tool for Kinship estimation, PCA and clustering analysis, and visualization based on VCF formatted SNPs. We implemented five Kinship estimation methods and three clustering methods for its users to choose from. Moreover, unlike other PCA tools, VCF2PCACluster possesses a clustering function based on PCA result, which enabling users to automatically and clearly know about population structure. We demonstrated the same accuracy but a higher performance of this tool in performing PCA analysis on tens of millions of SNPs compared to another popular PLINK2 software, especially in peak memory usage that is independent of the number of SNPs in VCF2PCACluster.

Are some children genetically predisposed to poor sleep? A polygenic risk study in the general population

BACKGROUND

Twin studies show moderate heritability of sleep traits: 40% for insomnia symptoms and 46% for sleep duration. Genome-wide association studies (GWAS) have identified genetic variants involved in insomnia and sleep duration in adults, but it is unknown whether these variants affect sleep during early development. We assessed whether polygenic risk scores for insomnia (PRS-I) and sleep duration (PRS-SD) affect sleep throughout early childhood to adolescence.

METHODS

We included 2,458 children of European ancestry (51% girls). Insomnia-related items of the Child Behavior Checklist were reported by mothers at child's age 1.5, 3, and 6 years. At 10-15 years, the Sleep Disturbance Scale for Children and actigraphy were assessed in a subsample (N = 975). Standardized PRS-I and PRS-SD (higher scores indicate genetic susceptibility for insomnia and longer sleep duration, respectively) were computed at multiple p-value thresholds based on largest GWAS to date.

RESULTS

Children with higher PRS-I had more insomnia-related sleep problems between 1.5 and 15 years (BPRS-I < 0.001 = .09, 95% CI: 0.05; 0.14). PRS-SD was not associated with mother-reported sleep problems. A higher PRS-SD was in turn associated with longer actigraphically estimated sleep duration (BPRS-SD < 5e08 = .05, 95% CI: 0.001; 0.09) and more wake after sleep onset (BPRS-SD < 0.005 = .25, 95% CI: 0.04; 0.47) at 10-15 years, but these associations did not survive multiple testing correction.

CONCLUSIONS

Children who are genetically predisposed to insomnia have more insomnia-like sleep problems, whereas those who are genetically predisposed to longer sleep have longer sleep duration, but are also more awake during the night in adolescence. This indicates that polygenic risk for sleep traits, based on GWAS in adults, affects sleep already in children.

Genetic insight into a polygenic trait using a novel genome-wide association approach in a wild amphibian population

Body size variation is central in the evolution of life-history traits in amphibians, but the underlying genetic architecture of this complex trait is still largely unknown. Herein, we studied the genetic basis of body size and fecundity of the alternative morphotypes in a wild population of the Greek smooth newt (Lissotriton graecus). By combining a genome-wide association approach with linkage disequilibrium network analysis, we were able to identify clusters of highly correlated loci thus maximizing sequence data for downstream analysis. The putatively associated variants explained 12.8% to 44.5% of the total phenotypic variation in body size and were mapped to genes with functional roles in the regulation of gene expression and cell cycle processes. Our study is the first to provide insights into the genetic basis of complex traits in newts and provides a useful tool to identify loci potentially involved in fitness-related traits in small data sets from natural populations in non-model species.

Copy-number variants and polygenic risk for intelligence confer risk for autism spectrum disorder irrespective of their effects on cognitive ability

Introduction

Rare copy number variants (CNVs) and polygenic risk for intelligence (PRS-IQ) both confer susceptibility for autism spectrum disorder (ASD) but have opposing effects on cognitive ability. The field has struggled to disentangle the effects of these two classes of genomic variants on cognitive ability from their effects on ASD susceptibility, in part because previous studies did not include controls with cognitive measures. We aim to investigate the impact of these genomic variants on ASD risk while adjusting for their known effects on cognitive ability.

Methods

In a cohort of 8,426 subjects with ASD and 169,804 controls with cognitive assessments, we found that rare coding CNVs and PRS-IQ increased ASD risk, even after adjusting for their effects on cognitive ability.

Results

Bottom decile PRS-IQ and CNVs both decreased cognitive ability but had opposing effects on ASD risk. Models combining both classes of variants showed that the effects of rare CNVs and PRS-IQ on ASD risk and cognitive ability were largely additive, further suggesting that susceptibility for ASD is conferred independently from its effects on cognitive ability. Despite imparting mostly additive effects on ASD risk, rare CNVs and PRS-IQ showed opposing effects on core and associated features and developmental history among subjects with ASD.

Discussion

Our findings suggest that cognitive ability itself may not be the factor driving the underlying liability for ASD conferred by these two classes of genomic variants. In other words, ASD risk and cognitive ability may be two distinct manifestations of CNVs and PRS-IQ. This study also highlights the challenge of understanding how genetic risk for ASD maps onto its dimensional traits.

Population structure and identification of genomic regions associated with productive traits in five Italian beef cattle breeds

Italy has a long history in beef production, with local breeds such as Marchigiana, Chianina, Romagnola, Maremmana, and Podolica which produce high-quality meat. Selection has improved meat production, precocity, growth ability and muscle development, but the genetic determinism of such traits is mostly unknown. Using 33K SNPs-data from young bulls (N = 4064) belonging to these five Italian breeds, we demonstrated that the Maremmana and Podolica rustic breeds are closely related, while the specialised Marchigiana, Chianina, and Romagnola breeds are more differentiated. A genome-wide association study for growth and muscle development traits (average daily gain during the performance test, weight at 1 year old, muscularity) was conducted in the five Italian breeds. Results indicated a region on chromosome 2, containing the myostatin gene (MSTN), which displayed significant genome-wide associations with muscularity in Marchigiana cattle, a breed in which the muscle hypertrophy phenotype is segregating. Moreover, a significant SNP on chromosome 14 was associated, in the Chianina breed, to muscularity. The identification of diverse genomic regions associated with conformation traits might increase our knowledge about the genomic basis of such traits in Italian beef cattle and, eventually, such information could be used to implement marker-assisted selection of young bulls tested in the performance test.

Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling

Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores' ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10-19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.

Population Neuroscience: Principles and Advances

In population neuroscience, three disciplines come together to advance our knowledge of factors that shape the human brain: neuroscience, genetics, and epidemiology (Paus, Human Brain Mapping 31:891-903, 2010). Here, I will come back to some of the background material reviewed in more detail in our previous book (Paus, Population Neuroscience, 2013), followed by a brief overview of current advances and challenges faced by this integrative approach.