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

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus

Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.

Oxytocin vs. placebo effects on intrusive memory consolidation using a trauma film paradigm: a randomized, controlled experimental study in healthy women

Oxytocin administration during a trauma analogue has been shown to increase intrusive memories, which are a core symptom of post-traumatic stress disorder (PTSD). However, it is unknown whether oxytocin influences the acquisition or the consolidation of the trauma. The current study investigates the effect of the activation of the oxytocin system during the consolidation of an analogue trauma on the formation of intrusive memories over four consecutive days and whether this effect is influenced by individual neurobiological, genetic, or psychological factors. We conducted a randomized double-blind placebo-controlled study in 217 healthy women. They received either a single dose of intranasal oxytocin (24 IU) or placebo after exposure to a trauma film paradigm, which reliably induces intrusive memories. We used a general random forest to examine a potential heterogeneous treatment effect of oxytocin on the consolidation of intrusive memories. Furthermore, we used a poisson regression to examine whether salivary alpha amylase activity (sAA) as a marker of noradrenergic activity and cortisol response to the film, polygenic risk score (PRS) for psychiatric disorders, and psychological factors influence the number of intrusive memories. We found no significant effect of oxytocin on the formation of intrusive memories (F(2, 543.16) = 0.75, p = 0.51, ηp2 = 0.00) and identified no heterogeneous treatment effect. We replicated previous associations of the PRS for PTSD, sAA and the cortisol response on intrusive memories. We further found a positive association between high trait anxiety and intrusive memories, and a negative association between the emotion regulation strategy reappraisal and intrusive memories. Data of the present study suggest that the consolidation of intrusive memories in women is modulated by genetic, neurobiological and psychological factors, but is not influenced by oxytocin. Trial registration: NCT03875391.

Weighted kernels improve multi-environment genomic prediction

Crucial to variety improvement programs is the reliable and accurate prediction of genotype's performance across environments. However, due to the impactful presence of genotype by environment (G×E) interaction that dictates how changes in expression and function of genes influence target traits in different environments, prediction performance of genomic selection (GS) using single-environment models often falls short. Furthermore, despite the successes of genome-wide association studies (GWAS), the genetic insights derived from genome-to-phenome mapping have not yet been incorporated in predictive analytics, making GS models that use Gaussian kernel primarily an estimator of genomic similarity, instead of the underlying genetics characteristics of the populations. Here, we developed a GS framework that, in addition to capturing the overall genomic relationship, can capitalize on the signal of genetic associations of the phenotypic variation as well as the genetic characteristics of the populations. The capacity of predicting the performance of populations across environments was demonstrated by an overall gain in predictability up to 31% for the winter wheat DH population. Compared to Gaussian kernels, we showed that our multi-environment weighted kernels could better leverage the significance of genetic associations and yielded a marked improvement of 4-33% in prediction accuracy for half-sib families. Furthermore, the flexibility incorporated in our Bayesian implementation provides the generalizable capacity required for predicting multiple highly genetic heterogeneous populations across environments, allowing reliable GS for genetic improvement programs that have no access to genetically uniform material.

Regional transcriptional vulnerability to basal forebrain functional dysconnectivity in mild cognitive impairment patients

Nucleus basalis of Meynert (NbM), one of the earliest targets of Alzheimer's disease (AD), may act as a seed for pathological spreading to its connected regions. However, the underlying basis of regional vulnerability to NbM dysconnectivity remains unclear. NbM functional dysconnectivity was assessed using resting-state fMRI data of health controls and mild cognitive impairment (MCI) patients from the Alzheimer's disease Neuroimaging Initiative (ADNI2/GO phase). Transcriptional correlates of NbM dysconnectivity was explored by leveraging public intrinsic and differential post-mortem brain-wide gene expression datasets from Allen Human Brain Atlas (AHBA) and Mount Sinai Brain Bank (MSBB). By constructing an individual-level tissue-specific gene set risk score (TGRS), we evaluated the contribution of NbM dysconnectivity-correlated gene sets to change rate of cerebral spinal fluid (CSF) biomarkers during preclinical stage of AD, as well as to MCI onset age. An independent cohort of health controls and MCI patients from ADNI3 was used to validate our main findings. Between-group comparison revealed significant connectivity reduction between the right NbM and right middle temporal gyrus in MCI. This regional vulnerability to NbM dysconnectivity correlated with intrinsic expression of genes enriched in protein and immune functions, as well as with differential expression of genes enriched in cholinergic receptors, immune, vascular and energy metabolism functions. TGRS of these NbM dysconnectivity-correlated gene sets are associated with longitudinal amyloid-beta change at preclinical stages of AD, and contributed to MCI onset age independent of traditional AD risks. Our findings revealed the transcriptional vulnerability to NbM dysconnectivity and their crucial role in explaining preclinical amyloid-beta change and MCI onset age, which offer new insights into the early AD pathology and encourage more investigation and clinical trials targeting NbM.

Major locus on ECA18 influences effectiveness of GonaCon vaccine in feral horses

Contraceptive vaccines are used to reduce birth rates in wild and feral animal populations. While the immunocontraceptive GonaCon-Equine has proven effective in reducing fertility among female feral horses, there is individual variation in the duration of infertility following treatment. To identify genetic factors influencing the effectiveness of GonaCon-Equine, we conducted a genome-wide association study of 88 mares from a feral population genotyped using the Illumina GGP Equine 70k SNP array. Contraceptive treatment schedules and long-term foaling rates have been recorded for each individual. We used mixed linear models to control for relatedness among mares. We found a significant association (p < 5 ×10^-8) with a locus on equine chromosome 18. The most likely candidate genes in this region are STAT1 and STAT4, which are both involved in immune system function. Variation in STAT function could affect the immune response to the vaccine, leading to variation in contraceptive efficacy. Additional SNPs reaching a less stringent threshold of significance (p < 5 ×10^-6) were located on other chromosomes near known immune system genes, supporting the hypothesis that variation in immunocontraceptive efficacy can be attributed to genetic variation in immune response rather than fertility genes.

A genetically informed Registered Report on adverse childhood experiences and mental health

Children who experience adversities have an elevated risk of mental health problems. However, the extent to which adverse childhood experiences (ACEs) cause mental health problems remains unclear, as previous associations may partly reflect genetic confounding. In this Registered Report, we used DNA from 11,407 children from the United Kingdom and the United States to investigate gene-environment correlations and genetic confounding of the associations between ACEs and mental health. Regarding gene-environment correlations, children with higher polygenic scores for mental health problems had a small increase in odds of ACEs. Regarding genetic confounding, elevated risk of mental health problems in children exposed to ACEs was at least partially due to pre-existing genetic risk. However, some ACEs (such as childhood maltreatment and parental mental illness) remained associated with mental health problems independent of genetic confounding. These findings suggest that interventions addressing heritable psychiatric vulnerabilities in children exposed to ACEs may help reduce their risk of mental health problems.

LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.

Design and characterization of a high-resolution multiple-SNP capture array by target sequencing for sheep

The efficiency of molecular breeding largely depends on inexpensive genotyping arrays. In this study, we aimed to develop an ovine high-resolution multiple-single-nucleotide polymorphism (SNP) capture array, based on genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology. All the markers were from 40K captured regions, including genes located within selective sweep regions, breed-specific regions, quantitative trait loci (QTL), and the potential functional SNPs on the sheep genome. The results showed that a total of 210K high-quality SNPs were identified in the 40K regions, indicating a high average capture ratio (99.7%) for the target genomic regions. Using genotyped data (n = 317) from liquid chip technology, we further performed genome-wide association studies (GWAS) to detect the genetic loci affecting sheep hair types and teat number. A single significant association signal for hair types was identified on 6.7-7.1 Mb of chromosome 25. The IRF2BP2 gene (chr25: 7,067,974-7,071,785), which is located within this genomic region, has been previously known to be involved in hair/wool traits in sheep. The results further showed a new candidate region around 26.4 Mb of chromosome 13, between the ARHGAP21 and KIAA1217 genes, that was significantly related to teat number in sheep. The haplotype patterns of this region also showed differences in animals with 2, 3, or 4 teats. Advances in using the high-accuracy and low-cost liquid chip are expected to accelerate sheep genomic and breeding studies in the coming years.

A cause-effect relationship between Graves' disease and the gut microbiome contributes to the thyroid-gut axis: A bidirectional two-sample Mendelian randomization study

Background

An association between Graves' disease (GD) and the gut microbiome has been identified, but the causal effect between them remains unclear.

Methods

Bidirectional two-sample Mendelian randomization (MR) analysis was used to detect the causal effect between GD and the gut microbiome. Gut microbiome data were derived from samples from a range of different ethnicities (18,340 samples) and data on GD were obtained from samples of Asian ethnicity (212,453 samples). Single nucleotide polymorphisms (SNPs) were selected as instrumental variables according to different criteria. They were used to evaluate the causal effect between exposures and outcomes through inverse-variance weighting (IVW), weighted median, weighted mode, MR-Egger, and simple mode methods. F-statistics and sensitivity analyses were performed to evaluate bias and reliability.

Results

In total, 1,560 instrumental variables were extracted from the gut microbiome data (p< 1 × 10⁵). The classes Deltaproteobacteria [odds ratio (OR) = 3.603] and Mollicutes, as well as the genera Ruminococcus torques group, Oxalobacter, and Ruminococcaceae UCG 011 were identified as risk factors for GD. The family Peptococcaceae and the genus Anaerostipes (OR = 0.489) were protective factors for GD. In addition, 13 instrumental variables were extracted from GD (p< 1 × 10^-8), causing one family and eight genera to be regulated. The genus Clostridium innocuum group (p = 0.024, OR = 0.918) and Anaerofilum (p = 0.049, OR = 1.584) had the greatest probability of being regulated. Significant bias, heterogeneity, and horizontal pleiotropy were not detected.

Conclusion

A causal effect relationship exists between GD and the gut microbiome, demonstrating regulatory activity and interactions, and thus providing evidence supporting the involvement of a thyroid-gut axis.

[Anhedonia in mood disorders and somatic diseases: results of exploratory Mendelian randomization analysis]

OBJECTIVE

To conduct an exploratory Mendelian randomization analysis of the causal relationships of anhedonia with a wide range of psychiatric and somatic phenotypes based on the genetic data of participants in a population study.

MATERIAL AND METHODS

This cross-sectional study included 4520 participants, of which 50.4% (n=2280) were female. The mean age was 36.8 (S.D.=9.8) years. Participants were pheno-nailed based on the DSM-5 criteria for anhedonia in the framework of depression. An episode of anhedonia exceeding 2 weeks during life was reported by 57.6% (n=2604) of participants. A genome-wide association study (GWAS) of the anhedonia phenotype was performed, as well as a Mendelian randomization analysis using summary statistics of large-scale GWASs on psychiatric and somatic phenotypes.

RESULTS

The GWAS on anhedonia did not reveal the variants with genome-wide significant association (p<10^-8). The most significant (p=9.71×10^-7) was the variant rs296009 (chr5:168513184) in an intron of the slit guidance ligand 3 (SLIT3) gene. Using Mendelian randomization, nominally significant (p<0.05) causal associations of anhedonia with 24 phenotypes were identified, which can be divided into 5 main groups: psychiatric/neurological diseases, inflammatory diseases of the digestive system, respiratory diseases, oncological diseases and metabolic disorders. The most significant causal effects of anhedonia were found for breast cancer (p=0.0004, OR=0.9986, 95% confidence interval (CI) (0.9978-0.999)), minimal depression phenotype (p=0.009, OR=1.004, 95% CI (1.001-1.007)), as well as for apolipoprotein A (p=0.01, OR=0.973, 95% CI (0.952-0.993)) and respiratory diseases (p=0.01, OR=0.9988, 95% CI (0.9980-0.9997)).

CONCLUSION

The polygenic nature of anhedonia may cause the risks of comorbidity of this phenotype with a wide range of somatic diseases, as well as may be associated with mood disorders.

Genetic Backgrounds Associated With Stent Thrombosis: A Pilot Study From a Percutaneous Coronary Intervention Registry

Background

Stent thrombosis (ST) is a rare, yet devastating, complication following percutaneous coronary intervention (PCI), with poorly understood pathophysiologic characteristics and genetic backgrounds.

Objectives

The authors performed a genome-wide association study to identify the common genetic loci associated with early stent thrombosis (EST) and late/very late ST (LST/VLST) in a contemporary Japanese multicenter PCI registry.

Methods

Among 8,642 PCI patients included in the registry, 42 who experienced stent thrombosis [EST (n = 15) and LST/VLST (n = 27)] were included (mean age, 67.6 ± 10.8 years; and 88.1% men). We conducted a genome-wide association study using the BioBank Japan patient population as the control (control #1: acute coronary syndrome [n = 29,542] and control #2: effort angina [n = 8,900]) to identify significant single nucleotide polymorphisms (SNPs) and evaluate the performance of polygenic risk scores (PRSs) for predicting these conditions.

Results

We compared patients with EST with controls #1 and #2 and identified SNPs (rs565401593 and rs561634568) in NSD1, and patients with LST/VLST with controls #1 and #2 and identified SNPs (rs532623294 and rs199546342) in GRIN2A. PRS for LST/VLST showed high predictive performance (area under the curve 0.83 [95% CI: 0.76-0.89] and 0.83 [95% CI: 0.77-0.89]), whereas PRS for EST showed modest predictive performance (area under the curve 0.71 [95% CI: 0.58-0.85] and 0.72 [95% CI: 0.58-0.85]).

Conclusions

We identified different genetic predispositions between EST and LST/VLST and demonstrated that the incorporation of PRS may aid in risk prediction of this highly fatal event.

A Guide to Genome-Wide Association Study Design for Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common microvascular complication related to diabetes. There is evidence that genetics play an important role in DR pathogenesis, but the complexity of the disease makes genetic studies a challenge. This chapter is a practical overview of the basic steps for genome-wide association studies with respect to DR and its associated traits. Also described are approaches that can be adopted in future DR studies. This is intended to serve as a guide for beginners and to provide a framework for further in-depth analysis.

Exploring the association and causal effect between white blood cells and psoriasis using large-scale population data

Introduction

Psoriasis is a chronic inflammatory disease of the skin. A few studies have shown that psoriasis is an immune-mediated disease in which multiple immune cells play crucial roles. However, the association between circulating immune cells and psoriasis remains elusive.

Methods

To explore the role of circulating immune cells in psoriasis, 361,322 individuals from the UK Biobank (UKB) and 3,971 patients with psoriasis from China were included to investigate the association between white blood cells and psoriasis via an observational study. Genome-wide association studies (GWAS) and Mendelian randomization (MR) were used to evaluate the causal relationship between circulating leukocytes and psoriasis.

Results

The risk of psoriasis increased with high levels of monocytes, neutrophils, and eosinophils (relative risks and 95% confidence intervals, respectively: 1.430 (1.291-1.584) for monocytes, 1.527 (1.379-1.692) for neutrophils, and 1.417 (1.294-1.551) for eosinophils). Upon further MR analysis, eosinophils showed a definite causal relationship with psoriasis (odds ratio of inverse-variance weighted: 1.386, 95% confidence intervals: 1.092-1.759) and a positive correlation with the psoriasis area and severity index (PASI) score (P = 6.6 × 10^-5). The roles of the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR) in psoriasis were also assessed. More than 20,000 genetic variations associated with NLR, PLR, and LMR were discovered in a GWAS analysis using the UKB data. Following adjustment for covariates in the observational study, NLR and PLR were shown to be risk factors for psoriasis, whereas LMR was a protective factor. MR results indicated that there was no causal relationship between these three indicators and psoriasis; however, NLR, PLR, and LMR correlated with the PASI score (NLR: rho = 0.244, P = 2.1 × 10^-21; PLR: rho = 0.113, P = 1.4 × 10^-5; LMR: rho = -0.242, P = 3.5×10^-21).

Discussion

Our findings revealed an important association between circulating leukocytes and psoriasis, which is instructive for the clinical practice of psoriasis treatment.

Analytical Approaches to Uncover Genetic Associations for Rare Outcomes: Lessons from West Nile Neuroinvasive Disease

West Nile viral infection causes severe neuroinvasive disease in less than 1% of infected humans. There are no targeted therapeutics for this serious and potentially fatal disease, and to date no vaccine has been approved for humans. With climate change expected to result in rising incidence of West Nile and other related vector-borne viral infections, there is an increasing need to identify those at risk for serious disease and potential leads for therapeutic and vaccine development. Genetic variation, particularly in genes whose products are either directly or indirectly connected to immune response to infections, is a critical avenue of investigation to identify those at higher risk of clinically apparent West Nile infection. Given the small percent of infections that progress to severe disease and the relatively low numbers of reported infections, it is challenging to conduct well-powered studies to identify genetic factors associated with more severe outcomes. In this chapter, we outline several approaches with the objective to take full advantage of all available data in order to identify genetic factors which lead to increased risk of severe West Nile neuroinvasive disease. These methods are generalizable to other conditions with limited cohort size and rare outcomes.

Polygenic Risk Score Impact on Susceptibility to Age-Related Macular Degeneration in Polish Patients

Age-related macular degeneration (AMD) is a common retina degenerative disease with a complex genetic and environmental background. This study aimed to determine the polygenic risk score (PRS) stratification between the AMD case and control patients. The PRS model was established on the targeted sequencing data of a cohort of 471 patients diagnosed with AMD and 167 healthy controls without symptoms of retinal degeneration. The highest predictive value to the target dataset was achieved for a 22-variant model with a p-value lower than threshold PT = 0.0123. The median PRS for cases was higher by 1.1 than for control samples (95% CI: (−1.19; −0.85)). The patients in the highest quantile had a significantly higher relative risk of developing AMD than those in the lowest reference quantile (OR = 35.13, 95% CI: (7.9; 156.1), p < 0.001). The diagnostic ability was investigated using ROC analysis with AUC = 0.76 (95% CI: (0.72; 0.80)). The polygenic susceptibility to AMD may be the starting point to expand AMD diagnostics based on rare highly penetrant variants and investigate associations with disease progression and treatment response in Polish patients in future studies.

The NBDC-DDBJ imputation server facilitates the use of controlled access reference panel datasets in Japan

Accurate genotype imputation requires large-scale reference panel datasets. When conducting genotype imputation on the Japanese population, researchers can use such datasets under collaborative studies or controlled access conditions in public databases. We developed the NBDC-DDBJ imputation server, which securely provides users with a web user interface to execute genotype imputation on the server. Our benchmarking analysis showed that the accuracy of genotype imputation was improved by leveraging controlled access datasets to increase the number of haplotypes available for analysis compared to using publicly available reference panels such as the 1000 Genomes Project. The NBDC-DDBJ imputation server facilitates the use of controlled access datasets for accurate genotype imputation.

Genome-wide association study of dermatophytosis in the UK Biobank cohort

BACKGROUND

Analyses of the hereditary propensity to dermatophytosis have revealed several proven genetic relationships. They include CARD9 deficiency, HLA-DR4 and HLA-DR8 type and genes encoding interleukin-22, defensin 2 and 4, and genetic defects in dectin-1, which increased the prevalence of dermatophytosis in families and were involved in the inheritance of susceptibility in their members.

METHODS

To further investigate the genetic basis of dermatophytosis, we performed a genome-wide association study (GWAS) of the UK Biobank cohort. To identify cases of dermatophytosis, we used ICD10 code B35, which covers Tinea barbae, Tinea capitis, Tinea unguium, Tinea manuum, Tinea pedis, Tinea corporis, Tinea imbricata, Tinea cruris, other dermatophytoses and dermatophytosis, unspecified. Data processing was performed on Minerva, a Linux mainframe with Centos 7.6, at the Icahn School of Medicine at Mount Sinai. We used PLINK, a whole-genome association analysis toolset, to analyse the UKB chromosome files and the UK Biobank Data Parser (ukbb parser), a python-based package that allows easy interfacing with the large UK Biobank dataset. We used LocusZoom for the Manhattan and q-q plots. Other statistical analyses were done with R and SPSS 25.

RESULTS

Genome-wide association study (GWAS) and meta-analysis association statistics highlighted one susceptibility locus, Tubulointerstitial Nephritis Antigen (TINAG), with genome-wide significance for dermatophytosis. The top SNP was rs16885197, a missense variant within TINAG, position chr6:54308557, alleles A > G, minor allele frequency (MAF) 0.014. Multivariate logistic regression indicated that the minor G allele increased odds ratio of dermatophytosis by 7.8. Carrying two G alleles raised dermatophytosis odds ratio by a factor of 14.

CONCLUSION

More research into genetic and other predisposing factors for dermatophytosis is critical because of the implications for prophylaxis and therapy. It might be possible to prevent infection and recurrence by identifying people who are vulnerable to chronic dermatophytosis. Identifying high-risk families would enable their members to be educated about the dangers of fungal diseases. New therapeutic techniques to target altered hormonal and immune response pathways might be created. TINAG is a prospective target that should be investigated, based on the findings of this article.

A Genome-Wide Association Study Reveals a BDNF-Centered Molecular Network Associated with Alcohol Dependence and Related Clinical Measures

At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (p-value = 1.33 × 10^-8 calculated with the Yates-corrected χ² test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWASs of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network, where BDNF is the most significant hub gene. This study indicates that several genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The study also reveals a central role of BDNF in the pathogenesis of AD.

H3AGWAS: a portable workflow for genome wide association studies

Background

Genome-wide association studies (GWAS) are a powerful method to detect associations between variants and phenotypes. A GWAS requires several complex computations with large data sets, and many steps may need to be repeated with varying parameters. Manual running of these analyses can be tedious, error-prone and hard to reproduce.

Results

The H3AGWAS workflow from the Pan-African Bioinformatics Network for H3Africa is a powerful, scalable and portable workflow implementing pre-association analysis, implementation of various association testing methods and post-association analysis of results.

Conclusions

The workflow is scalable—laptop to cluster to cloud (e.g., SLURM, AWS Batch, Azure). All required software is containerised and can run under Docker or Singularity.

Teaching computational genomics and bioinformatics on a high performance computing cluster-a primer

The burgeoning field of genomics as applied to personalized medicine, epidemiology, conservation, agriculture, forensics, drug development, and other fields comes with large computational and bioinformatics costs, which are often inaccessible to student trainees in classroom settings at universities. However, with increased availability of resources such as NSF XSEDE, Google Cloud, Amazon AWS, and other high-performance computing (HPC) clouds and clusters for educational purposes, a growing community of academicians are working on teaching the utility of HPC resources in genomics and big data analyses. Here, I describe the successful implementation of a semester-long (16 week) upper division undergraduate/graduate level course in Computational Genomics and Bioinformatics taught at San Diego State University in Spring 2022. Students were trained in the theory, algorithms and hands-on applications of genomic data quality control, assembly, annotation, multiple sequence alignment, variant calling, phylogenomic analyses, population genomics, genome-wide association studies, and differential gene expression analyses using RNAseq data on their own dedicated 6-CPU NSF XSEDE Jetstream virtual machines. All lesson plans, activities, examinations, tutorials, code, lectures, and notes are publicly available at https://github.com/arunsethuraman/biomi609spring2022.

Mendelian randomization and pathway analysis demonstrate shared genetic associations between lupus and coronary artery disease

Coronary artery disease (CAD) is a leading cause of death in patients with systemic lupus erythematosus (SLE). Despite clinical evidence supporting an association between SLE and CAD, pleiotropy-adjusted genetic association studies are limited and focus on only a few common risk loci. Here, we identify a net positive causal estimate of SLE-associated non-HLA SNPs on CAD by traditional Mendelian randomization (MR) approaches. Pathway analysis using SNP-to-gene mapping followed by unsupervised clustering based on protein-protein interactions (PPIs) identifies biological networks composed of positive and negative causal sets of genes. In addition, we confirm the casual effects of specific SNP-to-gene modules on CAD using only SNP mapping to each PPI-defined functional gene set as instrumental variables. This PPI-based MR approach elucidates various molecular pathways with causal implications between SLE and CAD and identifies biological pathways likely causative of both pathologies, revealing known and novel therapeutic interventions for managing CAD in SLE.

Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease

Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

The role of genetic predisposition in cardiovascular risk after cancer diagnosis: a matched cohort study of the UK Biobank

BACKGROUND

Evidence is scarce regarding the potential modifying role of disease susceptibility on the association between a prior cancer diagnosis and cardiovascular disease (CVD).

METHODS

We conducted a matched cohort study of UK Biobank including 78,860 individuals with a cancer diagnosis between January 1997 and January 2020, and 394,300 birth year and sex individually matched unexposed individuals. We used Cox model to assess the subsequent relative risk of CVD, which was further stratified by individual genetic predisposition.

RESULTS

During nearly 23 years of follow-up, an elevated risk of CVD was constantly observed among cancer patients, compared to their matched unexposed individuals. Such excess risk was most pronounced (hazard ratio [HR] = 5.28, 95% confidence interval [CI] 4.90-5.69) within 3 months after a cancer diagnosis, which then decreased rapidly and stabilised for >6 months (HR = 1.22, 95% CI 1.19-1.24). For all the studied time periods, stratification analyses by both levels of polygenic risk score for CVD and by family history of CVD revealed higher estimates among individuals with lower genetic risk predisposition.

CONCLUSIONS

Our findings suggest that patients with a recent cancer diagnosis were at an increased risk of multiple types of CVD and the excess CVD risk was higher among individuals with lower genetic susceptibility to CVD, highlighting a general need for enhanced psychological assistance and clinical surveillance of CVD among newly diagnosed cancer patients.

Dietary protein interacts with polygenic risk scores and modulates serum concentrations of C-reactive protein in overweight and obese Malaysian adults

Dietary intake may interact with gene variants and modulate inflammatory status. This study aimed to investigate the combined effect of fat mass and obesity-associated rs9930501, rs9930506, and rs9932754 and beta-2 adrenergic receptor rs1042713 on C-reactive protein (CRP) concentrations using polygenic risk scores (PRS), and modulatory effect of dietary nutrients on these associations. We hypothesized that higher protein intake is associated with lower inflammatory status in individuals genetically predisposed to obesity. PRS was computed as the weighted sum of the risk alleles possessed and stratified into first (0-0.64), second (0.65-3.59), and third (3.60-8.18) tertiles. A total of 128 overweight and obese Malaysian adults were dichotomized into groups of low and elevated inflammatory status (CRP concentrations ≤3 and >3 mg/L, respectively). One-half of the study participants (51%) were found to have elevated inflammatory status. Second- and third-tertile PRS were significantly associated with increased odds of elevated inflammatory status, 7.56 (95% confidence interval [CI], 1.98-28.80; adjusted P = .003) and 3.87 (95% CI, 1.10-13.60; adjusted P = .035), respectively. Individuals in the third-tertile PRS had significantly lower CRP concentrations (4.61 ± 1.3 mg/L vs 9.60 ± 2.6 mg/L, P = .019) when consuming ≥14% energy from protein (with an average of 18.0% ± 2.4%, 43.0% ± 7.7%, and 39.0% ± 8.0% energy from protein, carbohydrate, and fat per day). In conclusion, third-tertile PRS was significantly associated with increased odds of elevated CRP; higher protein intake may alleviate inflammatory status and reduce CRP concentrations systemically in those individuals.

Quality Control Procedures for Genome-Wide Association Studies

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

Generalization of cortical MOSTest genome-wide associations within and across samples

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

Conventional and Omics Approaches for Understanding the Abiotic Stress Response in Cereal Crops-An Updated Overview

Cereals have evolved various tolerance mechanisms to cope with abiotic stress. Understanding the abiotic stress response mechanism of cereal crops at the molecular level offers a path to high-yielding and stress-tolerant cultivars to sustain food and nutritional security. In this regard, enormous progress has been made in the omics field in the areas of genomics, transcriptomics, and proteomics. Omics approaches generate a massive amount of data, and adequate advancements in computational tools have been achieved for effective analysis. The combination of integrated omics and bioinformatics approaches has been recognized as vital to generating insights into genome-wide stress-regulation mechanisms. In this review, we have described the self-driven drought, heat, and salt stress-responsive mechanisms that are highlighted by the integration of stress-manipulating components, including transcription factors, co-expressed genes, proteins, etc. This review also provides a comprehensive catalog of available online omics resources for cereal crops and their effective utilization. Thus, the details provided in the review will enable us to choose the appropriate tools and techniques to reduce the negative impacts and limit the failures in the intensive crop improvement study.

Considering strategies for SNP selection in genetic and polygenic risk scores

Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors.

A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in terms of concordance to PGS estimated from whole-genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole-genome sequencing data in most cases. When optimal arrays are used, the correlations of PGS between two types of data are higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/ .

IsomiR-eQTL: A Cancer-Specific Expression Quantitative Trait Loci Database of miRNAs and Their Isoforms

The identification of expression quantitative trait loci (eQTL) is an important component in efforts to understand how genetic variants influence disease risk. MicroRNAs (miRNAs) are short noncoding RNA molecules capable of regulating the expression of several genes simultaneously. Recently, several novel isomers of miRNAs (isomiRs) that differ slightly in length and sequence composition compared to their canonical miRNAs have been reported. Here we present isomiR-eQTL, a user-friendly database designed to help researchers find single nucleotide polymorphisms (SNPs) that can impact miRNA (miR-eQTL) and isomiR expression (isomiR-eQTL) in 30 cancer types. The isomiR-eQTL includes a total of 152,671 miR-eQTLs and 2,390,805 isomiR-eQTLs at a false discovery rate (FDR) of 0.05. It also includes 65,733 miR-eQTLs overlapping known cancer-associated loci identified through genome-wide association studies (GWAS). To the best of our knowledge, this is the first study investigating the impact of SNPs on isomiR expression at the genome-wide level. This database may pave the way for researchers toward finding a model for personalised medicine in which miRNAs, isomiRs, and genotypes are utilised.

Identification of Candidate mRNA Isoforms for Prostate Cancer-Risk SNPs Utilizing Iso-eQTL and sQTL Methods

Single nucleotide polymorphisms (SNPs) impacting the alternative splicing (AS) process (sQTLs) or isoform expression (iso-eQTL) are implicated as important cancer regulatory elements. To find the sQTL and iso-eQTL, we retrieved prostate cancer (PrCa) tissue RNA-seq and genotype data originating from 385 PrCa European patients from The Cancer Genome Atlas. We conducted RNA-seq analysis with isoform-based and splice event-based approaches. The MatrixEQTL was used to identify PrCa-associated sQTLs and iso-eQTLs. The overlap between sQTL and iso-eQTL with GWAS loci and those that are differentially expressed between cancer and normal tissue were identified. The cis-acting associations (FDR < 0.05) for PrCa-risk SNPs identified 42, 123, and 90 PrCa-associated cassette exons, intron retention, and mRNA isoforms belonging to 25, 95, and 83 genes, respectively; while assessment of trans-acting association (FDR < 0.05) yielded 59, 65, and 196 PrCa-associated cassette exons, intron retention and mRNA isoforms belonging to 35, 55, and 181 genes, respectively. The results suggest that functional PrCa-associated SNPs can play a role in PrCa genesis by making an important contribution to the dysregulation of AS and, consequently, impacting the expression of the mRNA isoforms.

Genetic risk, incident colorectal cancer, and the benefits of adhering to a healthy lifestyle: A prospective study using data from UK Biobank and FinnGen

Background

Genetic factors increase the individual risk of colorectal cancer (CRC); however, the extent to which a healthy lifestyle can offset increased genetic risk is unknown. This study investigated whether a healthy lifestyle is associated with lower CRC risk, regardless of genetic risk.

Methods

We recruited 390,365 participants without cancer at baseline (2006-2010) from the UK Biobank. The primary outcome was CRC incidence. A healthy lifestyle score constructed using 16 factors of six dimensions (smoking, drinking, body mass index, diet, exercise, and sleep) was categorized into three risk categories: favorable, intermediate, and unfavorable. To calculate the polygenic risk scores (PRSs) of UK Biobank participants, we extracted 454,678 single nucleotide polymorphisms (SNPs) from the UK Biobank and FinnGen Biobank after quality control. Cox proportional hazards regression was performed to evaluate the associations and was expressed as hazard ratios (HRs) with 95% confidence intervals (CIs).

Results

During a median follow-up of 10.90 years, 4,090 new CRC cases were reported in the UK Biobank. The "best-fit" PRSs were constructed using 59 SNPs based on the UK Biobank cohort and FinnGen genome-wide association study summary data (R² = 0.23%) and were divided into low (lowest quintile), intermediate (including second-fourth quintile), and high (highest quintile) genetic risk categories. The multivariate-adjusted Cox model revealed that participants with favorable lifestyles had HRs of 0.66 (95% CI = 0.60-0.72) for developing CRC vs. those with unfavorable lifestyles; low genetic risk was associated with a decreased risk of CRC (HR = 0.67, 95% CI =0.61-0.74) compared with those with high genetic risk. The HRs for low genetic risk participants with favorable lifestyles were 0.44 (95% CI =0.36-0.55) vs. participants with high genetic risk and unfavorable lifestyles. Among the participants with low, intermediate, or high genetic risk, the HRs of favorable vs. unfavorable lifestyles were 0.74, 0.64, and 0.72 (all p< 0.05).

Conclusions

Low genetic risk and a favorable lifestyle were significantly associated with a decreased risk of CRC. A favorable lifestyle was associated with a lower CRC risk, regardless of genetic risk.

Robust association tests for quantitative traits on the X chromosome

The genome-wide association study is an elementary tool to assess the genetic contribution to complex human traits. However, such association tests are mainly proposed for autosomes, and less attention has been given to methods for identifying loci on the X chromosome due to their distinct biological features. In addition, the existing association tests for quantitative traits on the X chromosome either fail to incorporate the information of males or only detect variance heterogeneity. Therefore, we propose four novel methods, which are denoted as QXcat, QZmax, QMVXcat and QMVZmax. When using these methods, it is assumed that the risk alleles for females and males are the same and that the locus being studied satisfies the generalized genetic model for females. The first two methods are based on comparing the means of the trait value across different genotypes, while the latter two methods test for the difference of both means and variances. All four methods effectively incorporate the information of X chromosome inactivation. Simulation studies demonstrate that the proposed methods control the type I error rates well. Under the simulated scenarios, the proposed methods are generally more powerful than the existing methods. We also apply our proposed methods to data from the Minnesota Center for Twin and Family Research and find 10 single nucleotide polymorphisms that are statistically significantly associated with at least two traits at the significance level of 1 × 10-3.

COX7A2L genetic variants determine cardiorespiratory fitness in mice and human

Mitochondrial respiratory complexes form superassembled structures called supercomplexes. COX7A2L is a supercomplex-specific assembly factor in mammals, although its implication for supercomplex formation and cellular metabolism remains controversial. Here we identify a role for COX7A2L for mitochondrial supercomplex formation in humans. By using human cis-expression quantitative trait loci data, we highlight genetic variants in the COX7A2L gene that affect its skeletal muscle expression specifically. The most significant cis-expression quantitative trait locus is a 10-bp insertion in the COX7A2L 3' untranslated region that increases messenger RNA stability and expression. Human myotubes harboring this insertion have more supercomplexes and increased respiration. Notably, increased COX7A2L expression in the muscle is associated with lower body fat and improved cardiorespiratory fitness in humans. Accordingly, specific reconstitution of Cox7a2l expression in C57BL/6J mice leads to higher maximal oxygen consumption, increased lean mass and increased energy expenditure. Furthermore, Cox7a2l expression in mice is induced specifically in the muscle upon exercise. These findings elucidate the genetic basis of mitochondrial supercomplex formation and function in humans and show that COX7A2L plays an important role in cardiorespiratory fitness, which could have broad therapeutic implications in reducing cardiovascular mortality.

Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth

BACKGROUND

Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.

METHODS

Here, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.

RESULTS

We find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system.

CONCLUSIONS

By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings.

Association of Alzheimer’s disease polygenic risk scores with amyloid accumulation in cognitively intact older adults

Background

Early detection of individuals at risk for Alzheimer’s disease (AD) is highly important. Amyloid accumulation is an early pathological AD event, but the genetic association with known AD risk variants beyond the APOE4 effect is largely unknown. We investigated the association between different AD polygenic risk scores (PRS) and amyloid accumulation in the Flemish Prevent AD Cohort KU Leuven (F-PACK).

Methods

We calculated PRS with and without the APOE region in 90 cognitively healthy F-PACK participants (baseline age 67.8 (52–80) years, 41 APOE4 carriers), with baseline and follow-up amyloid-PET (time interval 6.1 (3.4–10.9) years). Individuals were genotyped using Illumina GSA and imputed. PRS were calculated using three p-value thresholds (pT) for variant inclusion: 5 × 10−8, 1 × 10−5, and 0.1, based on the stage 1 summary statistics from Kunkle et al. (Nat Genet 51:414–30, 2019). Linear regression models determined if these PRS predicted amyloid accumulation.

Results

A score based on PRS excluding the APOE region at pT = 5 × 10−8 plus the weighted sum of the two major APOE variants (rs429358 and rs7412) was significantly associated with amyloid accumulation (p = 0.0126). The two major APOE variants were also significantly associated with amyloid accumulation (p = 0.0496). The other PRS were not significant.

Conclusions

Specific PRS are associated with amyloid accumulation in the asymptomatic phase of AD.

Medication-Wide Association Study Plus (MWAS+): A Proof of Concept Study on Drug Repurposing

The high cost and time for developing a new drug or repositioning a partially-developed drug has fueled interest in "repurposing" drugs. Drug repurposing is particularly of interest for Alzheimer's disease (AD) or AD-related dementias (ADRD) because there are no unrestricted disease-modifying treatments for ADRD. We have designed and pilot tested a 3-Step Medication-Wide Association Study Plus (MWAS+) approach to rigorously accelerate the identification of drugs with a high potential to be repurposed for delaying and preventing AD/ADRD: Step 1 is a hypothesis-free exploration; Step 2 is mechanistic filtering; And Step 3 is hypothesis testing using observational data and prospective cohort design. Our results demonstrated the feasibility of the MWAS+ approach. The Step 1 analysis identified potential candidate drugs including atorvastatin and GLP1. The literature search in Step 2 found evidence supporting the mechanistic plausibility of the statin-ADRD association. Finally, Step 3 confirmed our hypothesis that statin may lower the risk of incident ADRD, which was statistically significant using a target trial design that emulated randomized controlled trials.

G-DIRT: a web server for identification and removal of duplicate germplasms based on identity-by-state analysis using single nucleotide polymorphism genotyping data

Maintaining duplicate germplasms in genebanks hampers effective conservation and utilization of genebank resources. The redundant germplasm adds to the cost of germplasm conservation by requiring a large proportion of the genebank financial resources towards conservation rather than enriching the diversity. Besides, genome-wide-association analysis using an association panel with over-represented germplasms can be biased resulting in spurious marker-trait associations. The conventional methods of germplasm duplicate removal using passport information suffer from incomplete or missing passport information and data handling errors at various stages of germplasm enrichment. This limitation is less likely in the case of genotypic data. Therefore, we developed a web-based tool, Germplasm Duplicate Identification and Removal Tool (G-DIRT), which allows germplasm duplicate identification based on identity-by-state analysis using single-nucleotide polymorphism genotyping information along with pre-processing of genotypic data. A homozygous genotypic difference threshold of 0.1% for germplasm duplicates has been determined using tetraploid wheat genotypic data with 94.97% of accuracy. Based on the genotypic difference, the tool also builds a dendrogram that can visually depict the relationship between genotypes. To overcome the constraint of high-dimensional genotypic data, an offline version of G-DIRT in the interface of R has also been developed. The G-DIRT is expected to help genebank curators, breeders and other researchers across the world in identifying germplasm duplicates from the global genebank collections by only using the easily sharable genotypic data instead of physically exchanging the seeds or propagating materials. The web server will complement the existing methods of germplasm duplicate identification based on passport or phenotypic information being freely accessible at http://webtools.nbpgr.ernet.in/gdirt/.

Human leukocyte antigen class II gene diversity tunes antibody repertoires to common pathogens

Allelic diversity of human leukocyte antigen (HLA) class II genes may help maintain humoral immunity against infectious diseases. In this study, we investigated germline genetic variation in classical HLA class II genes and employed a systematic, unbiased approach to explore the relative contribution of this genetic variation in the antibody repertoire to various common pathogens. We leveraged a well-defined cohort of 800 adults representing the general Arab population in which genetic material is shared because of the high frequency of consanguineous unions. By applying a high-throughput method for large-scale antibody profiling to this well-defined cohort, we were able to dissect the overall effect of zygosity for classical HLA class II genes, as well as the effects associated with specific HLA class II alleles, haplotypes and genotypes, on the antimicrobial antibody repertoire breadth and antibody specificity with unprecedented resolution. Our population genetic studies revealed that zygosity of the classical HLA class II genes is a strong predictor of antibody responses to common human pathogens, suggesting that classical HLA class II gene heterozygosity confers a selective advantage. Moreover, we demonstrated that multiple HLA class II alleles can have additive effects on the antibody repertoire to common pathogens. We also identified associations of HLA-DRB1 genotypes with specific antigens. Our findings suggest that HLA class II gene polymorphisms confer specific humoral immunity against common pathogens, which may have contributed to the genetic diversity of HLA class II loci during hominine evolution.

Genome-wide association study of brain arteriolosclerosis

Brain arteriolosclerosis (B-ASC) is characterized by pathologically altered brain parenchymal arterioles. B-ASC is associated with cognitive impairment and increased likelihood of clinical dementia. To date, no study has been conducted on genome-wide genetic risk of autopsy-proven B-ASC. We performed a genome-wide association study (GWAS) of the B-ASC phenotype using multiple independent aged neuropathologic cohorts. Included in the study were participants with B-ASC autopsy and genotype data available from the NACC, ROSMAP, ADNI, and ACT data sets. Initial Stage 1 GWAS (n = 3382) and Stage 2 mega-analysis (n = 4569) were performed using data from the two largest cohorts (NACC and ROSMAP). Replication of top variants and additional Stage 3 mega-analysis were performed incorporating two smaller cohorts (ADNI and ACT). Lead variants in the top two loci in the Stage 2 mega-analysis (rs7902929, p = 1.8 × 10 - 7 ; rs2603462, p = 4 × 10 - 7 ) were significant in the ADNI cohort (rs7902929, p = 0.012 ; rs2603462, p =0.012 ). The rs2603462 lead variant colocalized with ELOVL4 expression in the cerebellum (posterior probability = 90.1%). Suggestive associations were also found near SORCS1 and SORCS3. We thus identified putative loci associated with B-ASC risk, but additional replication is needed.

Inherited genetics of adult diffuse glioma and polygenic risk scores-a review

Knowledge about inherited and acquired genetics of adult diffuse glioma has expanded significantly over the past decade. Genomewide association studies (GWAS) stratified by histologic subtype identified six germline variants that were associated specifically with glioblastoma (GBM) and 12 that were associated with lower grade glioma. A GWAS performed using the 2016 WHO criteria, stratifying patients by IDH mutation and 1p/19q codeletion (as well as TERT promoter mutation), discovered that many of the known variants are associated with specific WHO glioma subtypes. In addition, the GWAS stratified by molecular group identified two additional novel regions: variants in D2HGDH that were associated with tumors that had an IDH mutation and a variant near FAM20C that was associated with tumors that had both IDH mutation and 1p/19q codeletion. The results of these germline associations have been used to calculate polygenic risk scores, from which to estimate relative and absolute risk of overall glioma and risk of specific glioma subtypes. We will review the concept of polygenic risk models and their potential clinical utility, as well as discuss the published adult diffuse glioma polygenic risk models. To date, these prior genetic studies have been done on European populations. Using the published glioma polygenic risk model, we show that the genetic associations published to date do not generalize across genetic ancestries, demonstrating that genetic studies need to be done on more diverse populations.

Canary: an automated tool for the conversion of MaCH imputed dosage files to PLINK files

Background

Previous studies have demonstrated the value of re-analysing publicly available genetics data with recent analytical approaches. Publicly available datasets, such as the Women’s Health Initiative (WHI) offered by the database of genotypes and phenotypes (dbGaP), provide a wealthy resource for researchers to perform multiple analyses, including Genome-Wide Association Studies. Often, the genetic information of individuals in these datasets are stored in imputed dosage files output by MaCH; mldose and mlinfo files. In order for researchers to perform GWAS studies with this data, they must first be converted to a file format compatible with their tool of choice e.g., PLINK. Currently, there is no published tool which easily converts the datasets provided in MACH dosage files into PLINK-ready files.

Results

Herein, we present Canary a singularity-based tool which converts MaCH dosage files into PLINK-compatible files with a single line of user input at the command line. Further, we provide a detailed tutorial on preparation of phenotype files. Moreover, Canary comes with preinstalled software often used during GWAS studies, to further increase the ease-of-use of HPC systems for researchers.

Conclusions

Until now, conversion of imputed data in the form of MaCH mldose and mlinfo files needed to be completed manually. Canary uses singularity container technology to allow users to automatically convert these MaCH files into PLINK compatible files. Additionally, Canary provides researchers with a platform to conduct GWAS analysis more easily as it contains essential software needed for conducting GWAS studies, such as PLINK and Bioconductor. We hope that this tool will greatly increase the ease at which researchers can perform GWAS with imputed data, particularly on HPC environments.

Systems Genetic Identification of Mitochondrion-Associated Alzheimer's Disease Genes and Implications for Disease Risk Prediction

Cumulative evidence has revealed the association between mitochondrial dysfunction and Alzheimer’s disease (AD). Because the number of mitochondrial genes is very limited, the mitochondrial pathogenesis of AD must involve certain nuclear genes. In this study, we employed systems genetic methods to identify mitochondrion-associated nuclear genes that may participate in the pathogenesis of AD. First, we performed a mitochondrial genome-wide association study (MiWAS, n = 809) to identify mitochondrial single-nucleotide polymorphisms (MT-SNPs) associated with AD. Then, epistasis analysis was performed to examine interacting SNPs between the mitochondrial and nuclear genomes. Weighted co-expression network analysis (WGCNA) was applied to transcriptomic data from the same sample (n = 743) to identify AD-related gene modules, which were further enriched by mitochondrion-associated genes. Using hub genes derived from these modules, random forest models were constructed to predict AD risk in four independent datasets (n = 743, n = 542, n = 161, and n = 540). In total, 9 potentially significant MT-SNPs and 14,340 nominally significant MT-nuclear interactive SNPs were identified for AD, which were validated by functional analysis. A total of 6 mitochondrion-related modules involved in AD pathogenesis were found by WGCNA, from which 91 hub genes were screened and used to build AD risk prediction models. For the four independent datasets, these models perform better than those derived from AD genes identified by genome-wide association studies (GWASs) or differential expression analysis (DeLong’s test, p  <  0.05). Overall, through systems genetics analyses, mitochondrion-associated SNPs/genes with potential roles in AD pathogenesis were identified and preliminarily validated, illustrating the power of mitochondrial genetics in AD pathogenesis elucidation and risk prediction.

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

Your height affects your health: genetic determinants and health-related outcomes in Taiwan

BACKGROUND

Height is an important anthropometric measurement and is associated with many health-related outcomes. Genome-wide association studies (GWASs) have identified hundreds of genetic loci associated with height, mainly in individuals of European ancestry.

METHODS

We performed genome-wide association analyses and replicated previously reported GWAS-determined single nucleotide polymorphisms (SNPs) in the Taiwanese Han population (Taiwan Biobank; n = 67,452). A genetic instrument composed of 251 SNPs was selected from our GWAS, based on height and replication results as the best-fit polygenic risk score (PRS), in accordance with the clumping and p-value threshold method. We also examined the association between genetically determined height (PRS₂₅₁) and measured height (phenotype). We performed observational (phenotype) and genetic PRS₂₅₁ association analyses of height and health-related outcomes.

RESULTS

GWAS identified 6843 SNPs in 89 genomic regions with genome-wide significance, including 18 novel loci. These were the most strongly associated genetic loci (EFEMP1, DIS3L2, ZBTB38, LCORL, HMGA1, CS, and GDF5) previously reported to play a role in height. There was a positive association between PRS₂₅₁ and measured height (p < 0.001). Of the 14 traits and 49 diseases analyzed, we observed significant associations of measured and genetically determined height with only eight traits (p < 0.05/[14 + 49]). Height was positively associated with body weight, waist circumference, and hip circumference but negatively associated with body mass index, waist-hip ratio, body fat, total cholesterol, and low-density lipoprotein cholesterol (p < 0.05/[14 + 49]).

CONCLUSIONS

This study contributes to the understanding of the genetic features of height and health-related outcomes in individuals of Han Chinese ancestry in Taiwan.

Associations between depression-relevant genetic risk and youth stress exposure: Evidence of gene-environment correlations

Familial risk for depression is associated with youth exposure to self-generated dependent stressful life events and independent events that are out of youth's control. Familial risk includes both genetic and environmental influences, raising the question of whether genetic influences, specifically, are associated with youth exposure to both dependent and independent stressful life events. To address this question, this study examined the relation between a genome-wide association study (GWAS)-derived depression-based polygenic risk score (DEP-PRS) and youth experiences of dependent and independent stress. Participants were 180 youth (ages 8 to 14, 52.2% female) of European ancestry and their biological mothers recruited based on the presence versus absence of a history of major depressive disorder (MDD) in the mothers. Youth and mothers were interviewed every 6 months for 2 years regarding the occurrence of stressful life events, which were coded as independent or dependent (self-generated). Results indicated that youth's DEP-PRS and maternal history of MDD were uniquely associated with increased exposure to both dependent and independent events. Similar results were observed when examining major versus minor events separately, with the additional finding of a DEP-PRS × mother MDD interaction for major dependent events such that levels of moderate to severe dependent life stressors were highest among youth with high DEP-PRSs who also had mothers with MDD. These results not only support the presence of depression-relevant gene-environment correlations (rGEs), but also highlight the possibility that rather than only capturing depression-specific genetic liability, GWAS-derived polygenic risk scores may also capture genetic variance contributing to stress exposure. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies

Purpose of review

Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics. This review evaluates the application of machine learning to this new wealth of data for studies of the effects of neighborhood environments on health.

Recent findings

Prominent machine learning applications in this field include automated image analysis of archived imagery such as Google Street View images, variable selection methods to identify neighborhood environment factors that predict health outcomes from large pools of exposure variables, and spatial interpolation methods to estimate neighborhood conditions across large geographic areas.

Summary

In each domain, we highlight successes and cautions in the application of machine learning, particularly highlighting legal issues in applying machine learning approaches to Google’s geo-spatial data.

Pharmacogenetics of Praziquantel Metabolism: Evaluating the Cytochrome P450 Genes of Zimbabwean Patients During a Schistosomiasis Treatment

Schistosomiasis is a parasitic disease infecting over 236 million people annually, with the majority affected residing on the African continent. Control of this disease is reliant on the drug praziquantel (PZQ), with treatment success dependent on an individual reaching PZQ concentrations lethal to schistosomes. Despite the complete reliance on PZQ to treat schistosomiasis in Africa, the characterization of the pharmacogenetics associated with PZQ metabolism in African populations has been sparse. We aimed to characterize genetic variation in the drug-metabolising cytochrome P450 enzymes (CYPs) and determine the association between each variant and the efficacy of PZQ treatment in Zimbabwean patients exposed to Schistosoma haematobium infection. Genomic DNA from blood samples of 114 case-control Zimbabweans infected with schistosomes were sequenced using the CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5 genes as targets. Bioinformatic tools were used to identify and predict functional effects of detected single nucleotide polymorphisms (SNPs). A random forest (RF) model was then used to assess SNPs most predictive of PZQ efficacy, with a misclassification rate of 29%. SNPs were detected across all six genes, with 70 SNPs identified and multiple functional changes to the CYP enzymes predicted. Only four SNPs were significantly associated with PZQ efficacy using χ² tests, with rs951840747 (OR: 3.61, p = 0.01) in the CYP1A2 gene having the highest odds of an individual possessing this SNP clearing infection, and rs6976017 (OR: 2.19, p = 0.045) of CYP3A5 determined to be the most predictive of PZQ efficacy via the RF. Only the rs28371702 (CC) genotype (OR: 2.36, p = 0.024) of CYP2D6 was significantly associated with an unsuccessful PZQ treatment. This study adds to the genomic characterization of the diverse populations in Africa and identifies variants relevant to other pharmacogenetic studies crucial for the development and usage of drugs in these populations.