What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations

From Genes to Therapy: A Comprehensive Exploration of Congenital Heart Disease Through the Lens of Genetics and Emerging Technologies

Congenital heart disease (CHD) affects approximately 1% of live births worldwide, making it the most common congenital anomaly in newborns. Recent advancements in genetics and genomics have significantly deepened our understanding of the genetics of CHDs. While the majority of CHD etiology remains unclear, evidence consistently indicates that genetics play a significant role in its development. CHD etiology holds promise for enhancing diagnosis and developing novel therapies to improve patient outcomes. In this review, we explore the contributions of both monogenic and polygenic factors of CHDs and highlight the transformative impact of emerging technologies on these fields. We also summarized the state-of-the-art techniques, including targeted next-generation sequencing (NGS), whole genome and whole exome sequencing (WGS, WES), single-cell RNA sequencing (scRNA-seq), human induced pluripotent stem cells (hiPSCs) and others, that have revolutionized our understanding of cardiovascular disease genetics both from diagnosis perspective and from disease mechanism perspective in children and young adults. These molecular diagnostic techniques have identified new genes and chromosomal regions involved in syndromic and non-syndromic CHD, enabling a more defined explanation of the underlying pathogenetic mechanisms. As our knowledge and technologies continue to evolve, they promise to enhance clinical outcomes and reduce the CHD burden worldwide.

A genome-wide association study of neutrophil count in individuals associated to an African continental ancestry group facilitates studies of malaria pathogenesis

BACKGROUND

'Benign ethnic neutropenia' (BEN) is a heritable condition characterized by lower neutrophil counts, predominantly observed in individuals of African ancestry, and the genetic basis of BEN remains a subject of extensive research. In this study, we aimed to dissect the genetic architecture underlying neutrophil count variation through a linear-mixed model genome-wide association study (GWAS) in a population of African ancestry (N = 5976). Malaria caused by P. falciparum imposes a tremendous public health burden on people living in sub-Saharan Africa. Individuals living in malaria endemic regions often have a reduced circulating neutrophil count due to BEN, raising the possibility that reduced neutrophil counts modulate severity of malaria in susceptible populations. As a follow-up, we tested this hypothesis by conducting a Mendelian randomization (MR) analysis of neutrophil counts on severe malaria (MalariaGEN, N = 17,056).

RESULTS

We carried out a GWAS of neutrophil count in individuals associated to an African continental ancestry group within UK Biobank, identifying 73 loci (r2 = 0.1) and 10 index SNPs (GCTA-COJO loci) associated with neutrophil count, including previously unknown rare loci regulating neutrophil count in a non-European population. BOLT-LMM was reliable when conducted in a non-European population, and additional covariates added to the model did not largely alter the results of the top loci or index SNPs. The two-sample bi-directional MR analysis between neutrophil count and severe malaria showed the greatest evidence for an effect between neutrophil count and severe anaemia, although the confidence intervals crossed the null.

CONCLUSION

Our GWAS of neutrophil count revealed unique loci present in individuals of African ancestry. We note that a small sample-size reduced our power to identify variants with low allele frequencies and/or low effect sizes in our GWAS. Our work highlights the need for conducting large-scale biobank studies in Africa and for further exploring the link between neutrophils and severe malaria.

Multi-ancestry meta-analysis of host genetic susceptibility to tuberculosis identifies shared genetic architecture

The heritability of susceptibility to tuberculosis (TB) disease has been well recognized. Over 100 genes have been studied as candidates for TB susceptibility, and several variants were identified by genome-wide association studies (GWAS), but few replicate. We established the International Tuberculosis Host Genetics Consortium to perform a multi-ancestry meta-analysis of GWAS, including 14,153 cases and 19,536 controls of African, Asian, and European ancestry. Our analyses demonstrate a substantial degree of heritability (pooled polygenic h2 = 26.3%, 95% CI 23.7-29.0%) for susceptibility to TB that is shared across ancestries, highlighting an important host genetic influence on disease. We identified one global host genetic correlate for TB at genome-wide significance (p<5 × 10-8) in the human leukocyte antigen (HLA)-II region (rs28383206, p-value=5.2 × 10-9) but failed to replicate variants previously associated with TB susceptibility. These data demonstrate the complex shared genetic architecture of susceptibility to TB and the importance of large-scale GWAS analysis across multiple ancestries experiencing different levels of infection pressure.

Dark chocolate intake and cardiovascular diseases: a Mendelian randomization study

Previous intervention studies have shown some benefits of dark chocolate for the cardiovascular system, but it has not been established whether dark chocolate intake is associated with the risk of cardiovascular diseases (CVDs). To investigate the causality between dark chocolate intake and the risk of CVDs, a Mendelian randomization (MR) study was conducted. We obtained summary-level data on dark chocolate intake and CVDs from publicly available genome-wide association studies. In this MR study, the main approach was to use a fixed-effect model with inverse variance weighted (IVW) and evaluate the robustness of the results via sensitivity analysis. We found that dark chocolate intake was significantly associated with the reduction of the risk of essential hypertension (EH) (OR = 0.73; 95% CI 0.60-0.88; p = 1.06 × 10-3), as well as with the suggestive association to the reduced risk of venous thromboembolism (OR = 0.69; 95% CI 0.50-0.96; p = 2.81 × 10-2). However, no association was found between dark chocolate intake and the other ten CVDs. Our study provides evidence for a causality between dark chocolate intake and a reduced risk of EH, which has important implications for the prevention of EH in the population.

Genome-wide Analysis Identified SEMA4D, Novel Candidate Gene for Temperature Sensitivity in Patients With Non-Small Cell Lung Cancer

BACKGROUND

In the era of precision medicine, individual temperature sensitivity has been highlighted. This trait has traditionally been used for cold-heat pattern identification to understand the inherent physical characteristics, which are influenced by genetic factors, of an individual. However, genome-wide association studies (GWASs) on this trait are limited.

METHODS

Using genotype data from 90 patients with advanced non-small cell lung cancer (NSCLC) and epidermal growth factor receptor mutations, we performed a GWAS to assess the association between single nucleotide polymorphisms (SNPs) and temperature sensitivity, such as cold and heat scores. The score of each participant was evaluated using self-administered questionnaires on common symptoms and a 15-item symptom-based cold-heat pattern identification questionnaire.

RESULTS

The GWAS was adjusted for confounding factors, including age and sex, and significant associations were identified for cold and heat scores: SNP rs145814326, located on the intron of SORCS2 at chromosome 4p16.1, had a P-value of 1.86 × 10-7; and SNP rs79297667, located upstream from SEMA4D at chromosome 9q22.2, had a P-value of 8.97 × 10-8. We also found that the genetic variant regulates the expression level of SEMA4D in the main tissues, including the lungs and white blood cells, in NSCLC.

CONCLUSIONS

SEMA4D was found to be significantly associated with temperature sensitivity in patients with NSCLC, suggesting an increased expression of SEMA4D in patients with higher heat scores. The potential role of temperature sensitivity as a prognostic or predictive marker of immune response in NSCLC should be further studied.

The Role of Genetics in Preterm Birth

Preterm birth (PTB), defined as the birth of a child before 37 completed weeks gestation, affects approximately 11% of live births and is the leading cause of death in children under 5 years. PTB is a complex disease with multiple risk factors including genetic variation. Much research has aimed to establish the biological mechanisms underlying PTB often through identification of genetic markers for PTB risk. The objective of this review is to present a comprehensive and updated summary of the published data relating to the field of PTB genetics. A literature search in PubMed was conducted and English studies related to PTB genetics were included. Genetic studies have identified genes within inflammatory, immunological, tissue remodeling, endocrine, metabolic, and vascular pathways that may be involved in PTB. However, a substantial proportion of published data have been largely inconclusive and multiple studies had limited power to detect associations. On the contrary, a few large hypothesis-free approaches have identified and replicated multiple novel variants associated with PTB in different cohorts. Overall, attempts to predict PTB using single "-omics" datasets including genomic, transcriptomic, and epigenomic biomarkers have been mostly unsuccessful and have failed to translate to the clinical setting. Integration of data from multiple "-omics" datasets has yielded the most promising results.

Genome-wide determinants of cellular immune responses to mumps vaccine

BACKGROUND

We have previously described genetic polymorphisms in candidate genes that are associated with inter-individual variations in antibody responses to mumps vaccination. To expand upon our previous work, we performed a genome-wide association study (GWAS) to discover host genetic variants associated with mumps vaccine-induced cellular immune responses.

METHODS

We performed a GWAS of mumps-specific immune response outcomes (11 secreted cytokines/chemokines) in a cohort of 1,406 subjects.

RESULTS

Among the 11 cytokine/chemokines we studied, four (IFN-γ, IL-2, IL-1β, and TNFα) demonstrated GWAS signals reaching genome-wide significance (p < 5 × 10-8). A genomic region (encoding Sialic acid-binding immunoglobulin-type lectins/SIGLEC) located on chromosome 19q13 (p < 5 × 10-8) was associated with both IL-1β and TNFα responses. The SIGLEC5/SIGLEC14 region contained 11 statistically significant single nucleotide polymorphisms (SNPs), including the intronic SIGLEC5 rs872629 (p = 1.3E-11) and rs1106476 (p = 1.32E-11) whose alternate alleles were significantly associated with decreased levels of mumps-specific IL-1β (rs872629, p = 1.77E-09; rs1106476, p = 1.78E-09) and TNFα (rs872629, p = 1.3E-11; rs1106476, p = 1.32E-11) production.

CONCLUSIONS

Our results suggest that SNPs in the SIGLEC5/SIGLEC14 genes play a role in cellular and inflammatory immune responses to mumps vaccination. These findings motivate further research into the functional roles of SIGLEC genes in the regulation of mumps vaccine-induced immunity.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Genome-Wide Determinants of Cellular Immune Responses to Mumps Vaccine

Background

We have previously described genetic polymorphisms in candidate genes that are associated with inter-individual variations in antibody responses to mumps vaccination. To expand upon our previous work, we performed a genome-wide association study (GWAS) to discover host genetic variants associated with mumps vaccine-induced cellular immune responses.

Methods

We performed a GWAS of mumps-specific immune response outcomes (11 secreted cytokines/chemokines) in a cohort of 1,406 subjects.

Results

Among the 11 cytokine/chemokines we studied, four (IFN-γ, IL-2, IL-1β, and TNFα) demonstrated GWAS signals reaching genome-wide significance (p<5 x 10 -8 ). A genomic region (encoding Sialic acid-binding immunoglobulin-type lectins/SIGLEC) located on chromosome 19q13 (p<5×10 -8 ) was associated with both IL-1β and TNFα responses. The SIGLEC5/SIGLEC14 region contained 11 statistically significant single nucleotide polymorphisms (SNPs), including the intronic SIGLEC5 rs872629 (p=1.3E-11) and rs1106476 (p=1.32E-11) whose alternate alleles were significantly associated with decreased levels of mumps-specific IL-1β (rs872629, p=1.77E-09; rs1106476, p=1.78E-09) and TNFα (rs872629, p=1.3E-11; rs1106476, p=1.32E-11) production.

Conclusions

Our results suggest that SNPs in the SIGLEC5/SIGLEC14 genes play a role in cellular and inflammatory immune responses to mumps vaccination. These findings motivate further research into the functional roles of SIGLEC genes in the regulation of mumps vaccine-induced immunity.

Late-onset neonatal sepsis: genetic differences by sex and involvement of the NOTCH pathway

BACKGROUND

Late-Onset Neonatal Sepsis (LOS) is a rare condition, involving widespread infection, immune disruption, organ dysfunction, and often death. Because exposure to pathogens is not completely preventable, identifying susceptibility factors is critical to characterizing the pathophysiology and developing interventions. Prior studies demonstrated both genetics and infant sex influence susceptibility. Our study was designed to identify LOS associated genetic variants.

METHODS

We performed an exploratory genome wide association study (GWAS) with 224 LOS cases and 273 controls from six European countries. LOS was defined as sepsis presenting from 3 to 90 days of age; diagnosis was established by clinical criteria consensus guidelines. We tested for association with both autosomal and X-chromosome variants in the total sample and in sex-stratified analyses.

RESULTS

In total, 71 SNPs associated with neonatal sepsis at p < 1 × 10^-4 in at least one analysis. Most importantly, sex-stratified analyses revealed associations with multiple SNPs (28 in males and 16 in females), but no variants from single-sex analyses associated with sepsis in the other sex. Pathway analyses showed NOTCH signaling is over-represented among genes linked to these SNPS.

CONCLUSION

Our results indicate genetic susceptibility to LOS is sexually dimorphic and corroborate that NOTCH signaling plays a role in determining risk.

IMPACT

Genes associate with late onset neonatal sepsis. Notch pathway genes are overrepresented in associations with sepsis. Genes associating with sepsis do not overlap between males and females. Sexual dimorphism can lead to sex specific treatment of sepsis.

Genetic architecture of the white matter connectome of the human brain

White matter tracts form the structural basis of large-scale brain networks. We applied brain-wide tractography to diffusion images from 30,810 adults (U.K. Biobank) and found significant heritability for 90 node-level and 851 edge-level network connectivity measures. Multivariate genome-wide association analyses identified 325 genetic loci, of which 80% had not been previously associated with brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia, and neurons. The multivariate association profiles implicated 31 loci in connectivity between core regions of the left-hemisphere language network. Polygenic scores for psychiatric, neurological, and behavioral traits also showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to variation in the structural connectome of the human brain.

Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects

Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.

Significant association between HLA-B*35:01 and onset of drug-induced liver injury caused by Kampo medicines in Japanese patients

AIM

Drug-induced liver injury (DILI) is a severe and life-threatening immune-mediated adverse effect, occurring rarely among treated patients. We examined genomic biomarkers in the Japanese population that predict the onset of DILI after using a certain class of drugs, such as Kampo products (Japanese traditional medicines).

METHODS

A total of 287 patients diagnosed as DILI by hepatology specialists were recruited after written informed consent was obtained. A genome-wide association analysis and human leukocyte antigen (HLA) typing in four digits were performed.

RESULTS

We found a significant association (p = 9.41 × 10^-10 ) of rs146644517 (G > A) with Kampo product-related DILI. As this polymorphism is located in the HLA region, we evaluated the association of HLA types and found that 12 (63.2%) of 19 Kampo-DILI patients contained HLA-B*35:01, whereas only 15.2% were positive for this HLA among healthy volunteers. The odds ratio was 9.56 (95% confidence interval 3.75-24.46; p = 2.98 × 10^-6 , corrected p = 4.17 × 10^-5 ), and it increased to 13.55 compared with the DILI patients not exposed to Kampo products. The individual crude drug components in the Kampo products, including Scutellaria root (ougon in Japanese), rhubarb (daiou), Gardenia fruit (sanshishi), and Glycyrrhiza (kanzou), were significantly associated with HLA-B*35:01.

CONCLUSIONS

HLA-B*35:01 is a genetic risk factor and a potential predictive biomarker for Kampo-induced DILI in the Japanese population.

A comprehensive in silico analysis of multiple sclerosis related non-synonymous SNPs and their potential effects on protein structure and function

BACKGROUND

Multiple Sclerosis (MS) is an autoimmune and central nervous system disease characterized by an inflammatory demyelinating process in the brain. Although the exact cause of MS is still unclear, environmental, and genetic factors are known to play a role in the development of disease. New molecular markers must be identified to understand the mechanism of disease formation and progression. We investigated the effects of MS-related non-synonymous single-nucleotide polymorphisms (nsSNPs) on the structure and function of identified proteins in this study.

METHODS

Missense variations associated with MS were extracted from the NHGRI-EBI GWAS database. Functional and structural analysis of nsSNPs on mapped genes was performed using g:Profiler, Wikipathway, KEGG, Reactome and Gene ontology programs (p < 0.05 was accepted statistically significant). Amino acid sequence-based analysis was performed to identify deleterious variants by using PROVEAN and PredictSNP tools. Finally, protein structure analyzes were performed on deleterious protein variants by DynaMut, Mutabind2 and Missense3D servers to identify changes in protein stability and flexibility.

RESULTS

10 target nsSNPs were identified. Among these rs34536443, rs10936599, rs2293152, rs11808092, rs1129183 were found deleterious according to amino acid sequence-based analysis. Furthermore, structure-based analyses show that TYK2 (P1104A), MYNN (H6Q), EVI5 (Q612H), and LZTFL1 (D246N) substitutions increase protein stability and decrease structure flexibility, whereas STAT3 (R426G) substitution decreases protein stability and increases structure flexibility.

CONCLUSION

We revealed that identified nsSNPs have potential effects on stability and flexibility of the target proteins. The prominent target genes are thought to have significant impacts on the pathogenesis of MS. Further in vitro and in vivo studies are required to validate our in silico results.

Retinol and Retinol Binding Protein 4 Levels and Cardiometabolic Disease Risk

BACKGROUND

Despite mechanistic studies linking retinol and RBP4 (retinol binding protein 4) to the pathogenesis of cardiovascular diseases (CVD) and type 2 diabetes (T2D), epidemiological evidence is still conflicting. We investigated whether conflicting results of previous studies may be explained by differences in the association of retinol and RBP4 with cardiometabolic risk across subgroups with distinct sex, hypertension state, liver, or kidney function.

METHODS

We used case-cohorts nested in the EPIC (European Prospective Investigation Into Cancer and Nutrition)-Potsdam cohort (N=27 548) comprising a random sample of participants (n=2500) and all physician-verified cases of incident CVD (n=508, median follow-up time 8.2 years) and T2D (n=820, median follow-up time 6.3 years). We estimated nonlinear and linear multivariable-adjusted associations between the biomarkers and cardiometabolic diseases by restricted cubic splines and Cox regression, respectively, testing potential interactions with hypertension, liver, and kidney function. Additionally, we performed 2-sample Mendelian Randomization analyses in publicly available data.

RESULTS

The association of retinol with cardiometabolic risk was modified by hypertension state (P interaction CVD<0.001; P interaction T2D<0.001). Retinol was associated with lower cardiometabolic risk in participants with treated hypertension (hazard ratio_{per SD} [95% CI]: CVD, 0.71 [0.56-0.90]; T2D, 0.81 [0.70-0.94]) but with higher cardiometabolic risk in normotensive participants (CVD, 1.32 [1.06-1.64]; T2D, 1.15 [0.98-1.36]). Our analyses also indicated a significant interaction between RBP4 and hypertension on CVD risk (P interaction=0.04). Regarding T2D risk, we observed a u-shaped association with RBP4 in women (P nonlinearity=0.01, P effect=0.02) and no statistically significant association in men. The biomarkers' interactions with liver or kidney function were not statistically significant. Hypertension state-specific associations for retinol concentrations with cardiovascular mortality risk were replicated in National Health and Nutrition Examination Survey III.

CONCLUSIONS

Our findings suggest a hypertension-dependent relationship between plasma retinol and cardiometabolic risk and complex interactions of RBP4 with sex and hypertension on cardiometabolic risk.

The Genetic Architecture of the Etiology of Lower Extremity Peripheral Artery Disease: Current Knowledge and Future Challenges in the Era of Genomic Medicine

Lower extremity artery disease (LEAD), caused by atherosclerotic obstruction of the arteries of the lower limb extremities, has exhibited an increase in mortality and morbidity worldwide. The phenotypic variability of LEAD is correlated with its complex, multifactorial etiology. In addition to traditional risk factors, it has been shown that the interaction between genetic factors (epistasis) or between genes and the environment potentially have an independent role in the development and progression of LEAD. In recent years, progress has been made in identifying genetic variants associated with LEAD, by Genome-Wide Association Studies (GWAS), Whole Exome Sequencing (WES) studies, and epigenetic profiling. The aim of this review is to present the current knowledge about the genetic factors involved in the etiopathogenic mechanisms of LEAD, as well as possible directions for future research. We analyzed data from the literature, starting with candidate gene-based association studies, and then continuing with extensive association studies, such as GWAS and WES. The results of these studies showed that the genetic architecture of LEAD is extremely heterogeneous. In the future, the identification of new genetic factors will allow for the development of targeted molecular therapies, and the use of polygenic risk scores (PRS) to identify individuals at an increased risk of LEAD will allow for early prophylactic measures and personalized therapy to improve their prognosis.

Identification of Two Genetic Loci Associated with Leukopenia after Chemotherapy in Patients with Breast Cancer

PURPOSE

To identify molecular predictors of grade 3/4 neutropenic or leukopenic events (NLE) after chemotherapy using a genome-wide association study (GWAS).

EXPERIMENTAL DESIGN

A GWAS was performed on patients in the phase III chemotherapy study SUCCESS-A (n = 3,322). Genotyping was done using the Illumina HumanOmniExpress-12v1 array. Findings were functionally validated with cell culture models and the genotypes and gene expression of possible causative genes were correlated with clinical treatment response and prognostic outcomes.

RESULTS

One locus on chromosome 16 (rs4784750; NLRC5; P = 1.56E-8) and another locus on chromosome 13 (rs16972207; TNFSF13B; P = 3.42E-8) were identified at a genome-wide significance level. Functional validation revealed that expression of these two genes is altered by genotype-dependent and chemotherapy-dependent activity of two transcription factors. Genotypes also showed an association with disease-free survival in patients with an NLE.

CONCLUSIONS

Two loci in NLRC5 and TNFSF13B are associated with NLEs. The involvement of the MHC I regulator NLRC5 implies the possible involvement of immuno-oncological pathways.

Combinational Drug Repurposing from Genetic Networks Applied to Alzheimer’s Disease

Background: Human diseases are multi-factorial biological phenomena resulting from perturbations of numerous functional networks. The complex nature of human diseases explains frequently observed marginal or transitory efficacy of mono-therapeutic interventions. For this reason, combination therapy is being increasingly evaluated as a biologically plausible strategy for reversing disease state, fostering the development of dedicated methodological and experimental approaches. In parallel, genome-wide association studies (GWAS) provide a prominent opportunity for disclosing human-specific therapeutic targets and rational drug repurposing. Objective: In this context, our objective was to elaborate an integrated computational platform to accelerate discovery and experimental validation of synergistic combinations of repurposed drugs for treatment of common human diseases. Methods: The proposed approach combines adapted statistical analysis of GWAS data, pathway-based functional annotation of genetic findings using gene set enrichment technique, computational reconstruction of signaling networks enriched in disease-associated genes, selection of candidate repurposed drugs and proof-of-concept combinational experimental screening. Results: It enables robust identification of signaling pathways enriched in disease susceptibility loci. Therapeutic targeting of the disease-associated signaling networks provides a reliable way for rational drug repurposing and rapid development of synergistic drug combinations for common human diseases. Conclusion: Here we demonstrate the feasibility and efficacy of the proposed approach with an experiment application to Alzheimer’s disease.

A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction

Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data. However, creating an accurate prediction model based on genotype data remains challenging due to the so-called “curse of dimensionality” (i.e., extensively larger number of features compared to the number of samples). Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most “informative” features and remove noisy “non-informative,” irrelevant and redundant features. In this article, we provide a general overview of the different feature selection methods, their advantages, disadvantages, and use cases, focusing on the detection of relevant features (i.e., SNPs) for disease risk prediction.

Human genetic diversity regulating the TLR10/TLR1/TLR6 locus confers increased cytokines in response to Chlamydia trachomatis

Human genetic diversity can have profound effects on health outcomes upon exposure to infectious agents. For infections with Chlamydia trachomatis (C. trachomatis), the wide range of genital and ocular disease manifestations are likely influenced by human genetic differences that regulate interactions between C. trachomatis and host cells. We leveraged this diversity in cellular responses to demonstrate the importance of variation at the Toll-like receptor 1 (TLR1), TLR6, and TLR10 locus to cytokine production in response to C. trachomatis. We determined that a single-nucleotide polymorphism (SNP) (rs1057807), located in a region that forms a loop with the TLR6 promoter, is associated with increased expression of TLR1, TLR6, and TLR10 and secreted levels of ten C. trachomatis-induced cytokines. Production of these C. trachomatis-induced cytokines is primarily dependent on MyD88 and TLR6 based on experiments using inhibitors, blocking antibodies, RNAi, and protein overexpression. Population genetic analyses further demonstrated that the mean IL-6 response of cells from two European populations were higher than the mean response of cells from three African populations and that this difference was partially attributable to variation in rs1057807 allele frequency. In contrast, a SNP associated with a different pro-inflammatory cytokine (rs2869462 associated with the chemokine CXCL10) exhibited an opposite response, underscoring the complexity of how different genetic variants contribute to an individual's immune response. This multidisciplinary study has identified a long-range chromatin interaction and genetic variation that regulates TLR6 to broaden our understanding of how human genetic variation affects the C. trachomatis-induced immune response.

Genome-wide association study and functional validation implicates JADE1 in tauopathy

Primary age-related tauopathy (PART) is a neurodegenerative pathology with features distinct from but also overlapping with Alzheimer disease (AD). While both exhibit Alzheimer-type temporal lobe neurofibrillary degeneration alongside amnestic cognitive impairment, PART develops independently of amyloid-β (Aβ) plaques. The pathogenesis of PART is not known, but evidence suggests an association with genes that promote tau pathology and others that protect from Aβ toxicity. Here, we performed a genetic association study in an autopsy cohort of individuals with PART (n = 647) using Braak neurofibrillary tangle stage as a quantitative trait. We found some significant associations with candidate loci associated with AD (SLC24A4, MS4A6A, HS3ST1) and progressive supranuclear palsy (MAPT and EIF2AK3). Genome-wide association analysis revealed a novel significant association with a single nucleotide polymorphism on chromosome 4 (rs56405341) in a locus containing three genes, including JADE1 which was significantly upregulated in tangle-bearing neurons by single-soma RNA-seq. Immunohistochemical studies using antisera targeting JADE1 protein revealed localization within tau aggregates in autopsy brains with four microtubule-binding domain repeats (4R) isoforms and mixed 3R/4R, but not with 3R exclusively. Co-immunoprecipitation in post-mortem human PART brain tissue revealed a specific binding of JADE1 protein to four repeat tau lacking N-terminal inserts (0N4R). Finally, knockdown of the Drosophila JADE1 homolog rhinoceros (rno) enhanced tau-induced toxicity and apoptosis in vivo in a humanized 0N4R mutant tau knock-in model, as quantified by rough eye phenotype and terminal deoxynucleotidyl transferase dUTP nick end-labeling (TUNEL) in the fly brain. Together, these findings indicate that PART has a genetic architecture that partially overlaps with AD and other tauopathies and suggests a novel role for JADE1 as a modifier of neurofibrillary degeneration.

Deciphering Genetic Susceptibility to Tuberculous Meningitis

Tuberculous meningitis (TBM) is the most severe form of extrapulmonary tuberculosis (TB) that arises when a caseating meningeal granuloma discharges its contents into the subarachnoid space. It accounts for ~1% of all disease caused by Mycobacterium tuberculosis and the age of peak incidence is from 2-4 years. The exact pathogenesis of TBM is still not fully understood and the mechanism(s) by which the bacilli initially invade the blood-brain-barrier are still to be elucidated. This study investigated the involvement of the host genome in TBM susceptibility, by considering common variants (minor allele frequency (MAF) >5%) using microarray genotyping and rare variants (MAF <1%) via exome sequencing. A total of 123 TBM cases, 400 pulmonary TB (pTB) cases and 477 healthy controls were genotyped on the MEGA array. A genome-wide association study (GWAS) comparing 114 TBM cases to 395 healthy controls showed no association with TBM susceptibility. A second analysis comparing 114 TBM cases to 382 pTB cases was conducted to investigate variants associated with different TB phenotypes. No significant associations were found with progression from pTB to TBM. Ten TBM cases and 10 healthy controls were exome sequenced. Gene set association tests SKAT-O and SKAT Common Rare were used to assess the association of rare SNPs and the cumulative effect of both common and rare SNPs with susceptibility to TBM, respectively. Ingenuity Pathway Analysis (IPA) of the top-hits of the SKAT-O analysis showed that NOD2 and CYP4F2 are both important in TBM pathogenesis and highlighted these as targets for future study. For the SKAT Common Rare analysis Centriolar Coiled-Coil Protein 110 (CCP110) was nominally associated (p = 5.89x10^-6) with TBM susceptibility. In addition, several top-hit genes ascribed to the development of the central nervous system (CNS) and innate immune system regulation were identified. Exome sequencing and GWAS of our TBM cohort has identified a single previously undescribed association of CCP110 with TBM susceptibility. These results advance our understanding of TBM in terms of both variants and genes that influence susceptibility. In addition, several candidate genes involved in innate immunity have been identified for further genotypic and functional investigation.

A systematic review of GWAS identified SNPs associated with outcomes of medications for opioid use disorder

BACKGROUND

Patients with opioid use disorder (OUD) display an interindividual variability in their response to medications for opioid use disorder (MOUD). A genetic basis may explain the variability in this response. However, no consensus has been reached regarding which genetic variants significantly contribute to MOUD outcomes.

OBJECTIVES

This systematic review aims to summarize genome-wide significant findings on MOUD outcomes and critically appraise the quality of the studies involved.

METHODS

Databases searched from inception until August 21st, 2020 include: MEDLINE, Web of Science, EMBASE, CINAHL and Pre-CINAHL, GWAS Catalog and GWAS Central. The included studies had to be GWASs that assessed MOUD in an OUD population. All studies were screened in duplicate. The quality of the included studies was scored and assessed using the Q-Genie tool. Quantitative analysis, as planned in the protocol, was not feasible, so the studies were analyzed qualitatively.

RESULTS

Our search identified 7292 studies. Five studies meeting the eligibility criteria were included. However, only three studies reported results that met our significance threshold of p ≤ 1.0 × 10-7. In total, 43 genetic variants were identified. Variants corresponding to CNIH3 were reported to be associated with daily heroin injection in Europeans, OPRM1, TRIB2, and ZNF146 with methadone dose in African Americans, EYS with methadone dose in Europeans, and SPON1 and intergenic regions in chromosomes 9 and 3 with plasma concentrations of S-methadone, R-methadone, and R-EDDP, respectively, in Han Chinese.

LIMITATIONS

The limitations of this study include not being able to synthesize the data in a quantitative way and a conservative eligibility and data collection model.

CONCLUSION

The results from this systematic review will aid in highlighting significant genetic variants that can be replicated in future OUD pharmacogenetics research to ascertain their role in patient-specific MOUD outcomes. Systematic review registration number CRD42020169121.

Interpretable machine learning for genomics

High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines.

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

Whole-genome re-sequencing association study on yearling wool traits in Chinese fine-wool sheep

To investigate single nucleotide polymorphism (SNP) loci associated with yearling wool traits of fine-wool sheep for optimizing marker-assisted selection and dissection of the genetic architecture of wool traits, we conducted a genome-wide association study (GWAS) based on the fixed and random model circulating probability unification (FarmCPU) for yearling staple length (YSL), yearling mean fiber diameter (YFD), yearling greasy fleece weight (YGFW), and yearling clean fleece rate (YCFR) by using the whole-genome re-sequenced data (totaling 577 sheep) from the following four fine-wool sheep breeds in China: Alpine Merino sheep (AMS), Chinese Merino sheep (CMS), Qinghai fine-wool sheep (QHS), and Aohan fine-wool sheep (AHS). A total of 16 SNPs were detected above the genome-wise significant threshold (P = 5.45E-09), and 79 SNPs were located above the suggestive significance threshold (P = 5.00E-07) from the GWAS results. For YFD and YGFW traits, 7 and 9 SNPs reached the genome-wise significance thresholds, whereas 10 and 12 SNPs reached the suggestive significance threshold, respectively. For YSL and YCFR traits, none of the SNPs reached the genome-wise significance thresholds, whereas 57 SNPs exceeded the suggestive significance threshold. We recorded 14 genes located at the region of ±50-kb near the genome-wise significant SNPs and 59 genes located at the region of ±50-kb near the suggestive significant SNPs. Meanwhile, we used the Average Information Restricted Maximum likelihood algorithm (AI-REML) in the “HIBLUP” package to estimate the heritability and variance components of the four desired yearling wool traits. The estimated heritability values (h²) of YSL, YFD, YGFW, and YCFR were 0.6208, 0.7460, 0.6758, and 0.5559, respectively. We noted that the genetic parameters in this study can be used for fine-wool sheep breeding. The newly detected significant SNPs and the newly identified candidate genes in this study would enhance our understanding of yearling wool formation, and significant SNPs can be applied to genome selection in fine-wool sheep breeding.

Identifying causal variants by fine mapping across multiple studies

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of "fine mapping" methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).

Multiethnic Genome-Wide Association Study of Subclinical Atherosclerosis in Individuals With Type 2 Diabetes

BACKGROUND

Coronary artery calcification (CAC) and carotid artery intima-media thickness (cIMT) are measures of subclinical atherosclerosis in asymptomatic individuals and strong risk factors for cardiovascular disease. Type 2 diabetes (T2D) is an independent cardiovascular disease risk factor that accelerates atherosclerosis.

METHODS

We performed meta-analyses of genome-wide association studies in up to 2500 T2D individuals of European ancestry (EA) and 1590 T2D individuals of African ancestry with or without exclusion of prevalent cardiovascular disease, for CAC measured by cardiac computed tomography, and 3608 individuals of EA and 838 individuals of African ancestry with T2D for cIMT measured by ultrasonography within the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium.

RESULTS

We replicated 2 loci (rs9369640 and rs9349379 near PHACTR1 and rs10757278 near CDKN2B) for CAC and one locus for cIMT (rs7412 and rs445925 near APOE-APOC1) that were previously reported in the general EA populations. We identified one novel CAC locus (rs8000449 near CSNK1A1L/LINC00547/POSTN at 13q13.3) at P=2.0×10-8 in EA. No additional loci were identified with the meta-analyses of EA and African ancestry. The expression quantitative trait loci analysis with nearby expressed genes derived from arterial wall and metabolic tissues from the Genotype-Tissue Expression project pinpoints POSTN, encoding a matricellular protein involved in bone formation and bone matrix organization, as the potential candidate gene at this locus. In addition, we found significant associations (P<3.1×10-4) for 3 previously reported coronary artery disease loci for these subclinical atherosclerotic phenotypes (rs2891168 near CDKN2B-AS1 and rs11170820 near FLJ12825 for CAC, and rs7412 near APOE for cIMT).

CONCLUSIONS

Our results provide potential biological mechanisms that could link CAC and cIMT to increased cardiovascular disease risk in individuals with T2D.

Large-Scale Phenomic and Genomic Analysis of Brain Asymmetrical Skew

The human cerebral hemispheres show a left-right asymmetrical torque pattern, which has been claimed to be absent in chimpanzees. The functional significance and developmental mechanisms are unknown. Here, we carried out the largest-ever analysis of global brain shape asymmetry in magnetic resonance imaging data. Three population datasets were used, UK Biobank (N = 39 678), Human Connectome Project (N = 1113), and BIL&GIN (N = 453). At the population level, there was an anterior and dorsal skew of the right hemisphere, relative to the left. Both skews were associated independently with handedness, and various regional gray and white matter metrics oppositely in the two hemispheres, as well as other variables related to cognitive functions, sociodemographic factors, and physical and mental health. The two skews showed single nucleotide polymorphisms-based heritabilities of 4-13%, but also substantial polygenicity in causal mixture model analysis, and no individually significant loci were found in genome-wide association studies for either skew. There was evidence for a significant genetic correlation between horizontal brain skew and autism, which requires future replication. These results provide the first large-scale description of population-average brain skews and their inter-individual variations, their replicable associations with handedness, and insights into biological and other factors which associate with human brain asymmetry.

Genetic variation in recombination rate in the pig

Background

Meiotic recombination results in the exchange of genetic material between homologous chromosomes. Recombination rate varies between different parts of the genome, between individuals, and is influenced by genetics. In this paper, we assessed the genetic variation in recombination rate along the genome and between individuals in the pig using multilocus iterative peeling on 150,000 individuals across nine genotyped pedigrees. We used these data to estimate the heritability of recombination and perform a genome-wide association study of recombination in the pig.

Results

Our results confirmed known features of the recombination landscape of the pig genome, including differences in genetic length of chromosomes and marked sex differences. The recombination landscape was repeatable between lines, but at the same time, there were differences in average autosome-wide recombination rate between lines. The heritability of autosome-wide recombination rate was low but not zero (on average 0.07 for females and 0.05 for males). We found six genomic regions that are associated with recombination rate, among which five harbour known candidate genes involved in recombination: RNF212, SHOC1, SYCP2, MSH4 and HFM1.

Conclusions

Our results on the variation in recombination rate in the pig genome agree with those reported for other vertebrates, with a low but nonzero heritability, and the identification of a major quantitative trait locus for recombination rate that is homologous to that detected in several other species. This work also highlights the utility of using large-scale livestock data to understand biological processes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00643-0.

Planning and Conducting a Pharmacogenetics Association Study

Pharmacogenetics (PGx) association studies are used to discover, replicate, and validate the association between an inherited genotype and a treatment outcome. The objective of this tutorial is to provide trainees and novice PGx researchers with an overview of the major decisions that need to be made when designing and conducting a PGx association study. The first critical decision is to determine whether the objective of the study is discovery, replication, or validation. Next, the researcher must identify a patient cohort that has all of the data necessary to conduct the intended analysis. Then, the investigator must select and define the treatment outcome, or phenotype, that will be analyzed. Next, the investigator must determine what genotyping approach and genetic data will be included in the analysis. Finally, the association between the genotype and phenotype is tested using some statistical analysis methodology. This tutorial is divided into five sections; each section describes commonly used approaches and provides suggestions and resources for designing and conducting a PGx association study. Successful PGx association studies are necessary to discover and validate associations between inherited genetic variation and treatment outcomes, which enable clinical translation to improve efficacy and reduce toxicity of treatment.

From Stem Cells to Populations-Using hiPSC, Next-Generation Sequencing, and GWAS to Explore the Genetic and Molecular Mechanisms of Congenital Heart Defects

Congenital heart defects (CHD) are developmental malformations affecting the heart and the great vessels. Early heart development requires temporally regulated crosstalk between multiple cell types, signaling pathways, and mechanical forces of early blood flow. While both genetic and environmental factors have been recognized to be involved, identifying causal genes in non-syndromic CHD has been difficult. While variants following Mendelian inheritance have been identified by linkage analysis in a few families with multiple affected members, the inheritance pattern in most familial cases is complex, with reduced penetrance and variable expressivity. Furthermore, most non-syndromic CHD are sporadic. Improved sequencing technologies and large biobank collections have enabled genome-wide association studies (GWAS) in non-syndromic CHD. The ability to generate human to create human induced pluripotent stem cells (hiPSC) and further differentiate them to organotypic cells enables further exploration of genotype–phenotype correlations in patient-derived cells. Here we review how these technologies can be used in unraveling the genetics and molecular mechanisms of heart development.

Are there monogenic hereditary forms of bladder cancer or only genetic susceptibilities?

Bladder cancer (BC) is the most common cancer involving the urinary system and the ninth most common cancer worldwide. Tobacco smoking is the most important environmental risk factor of BC. Several single nucleotide polymorphisms have been validated by genome-wide association studies as genetic risk factors for BC. However, the identification of DNA mismatch-repair genes, including MSH2 in Lynch syndrome and MUTYH in MUTYH-associated polyposis, raises the possibility of monogenic hereditary forms of BC. Moreover, other genetic mutations may play a key role in familial and hereditary transmissions of BC. Therefore, the aim of this review is to focus on the major hereditary syndromes involved in the development of BC and to report BC genetic susceptibilities established with genome-wide significance level.

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer's disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .

GWAS Links New Variant in Long Non-Coding RNA LINC02006 with Colorectal Cancer Susceptibility

Simple Summary

Identifying risk factors for cancer development can allow for appropriate stratification and surveillance of individuals at risk, increasing their chances of benefiting from early disease detection; however, most of the genetic factors contributing to the risk of colorectal cancer (CRC) remain undetermined. Here, we adopted a new approach for selecting index polymorphism for further validation in combination with a genome-wide association study of pooled DNA samples for CRC susceptibility variants in the Polish population. This study, including 2013 patients and controls, uncovered five susceptibility loci not previously reported for CRC. Four of identified variants were located within genes likely involved in tumor invasiveness and metastasis, suggesting that they could be markers of poor prognosis in CRC patients. Our results provide evidence that conducting association studies on small but homogenous populations can help us discover new common risk variants specific to the studied population.

Abstract

Despite great efforts, most of the genetic factors contributing to the risk of colorectal cancer (CRC) remain undetermined. Including small but homogenous populations in genome-wide association studies (GWAS) can help us discover new common risk variants specific to the studied population. In this study, including 465 CRC patients and 1548 controls, a pooled DNA samples-based GWAS was conducted in search of genetic variants associated with CRC in a Polish population. Combined with a new method of selecting single-nucleotide polymorphisms (SNPs) for verification in individual DNA samples, this approach allowed the detection of five new susceptibility loci not previously reported for CRC. The discovered loci were found to explain 10% of the overall risk of developing CRC. The strongest association was observed for rs10935945 in long non-coding RNA LINC02006 (3q25.2). Three other SNPs were also located within genes (rs17575184 in NEGR1, rs11060839 in PIWIL1, rs12935896 in BCAS3), while one was intergenic (rs9927668 at 16p13.2). An expression quantitative trait locus (eQTL) bioinformatic analysis suggested that these polymorphisms may affect transcription factor binding sites. In conclusion, four of the identified variants were located within genes likely involved in tumor invasiveness and metastasis. Therefore, they could possibly be markers of poor prognosis in CRC patients.

Identification of KIF4A as a pan-cancer diagnostic and prognostic biomarker via bioinformatics analysis and validation in osteosarcoma cell lines

Background

Cancer is a disease of abnormal cell proliferation caused by abnormal expression of cancer-related genes. However, it is still difficult to distinguish benign and malignant lesions in many cases. KIF4A has been reported to be associated with a variety of cancer lesions. We aimed to explore whether KIF4A could be used as a biomarker of pan-cancer diagnostic.

Methods

We identified twenty-eight cell cycle-related genes that were overexpressed in no less than ten types of cancer. We determined KIF4A mRNA and protein expression in osteosarcoma (OS) cells. Furthermore, to determine the effect of KIF4A in OS, we silenced KIF4A in OS cells and detected cell viability, colony formation, invasion, migration, apoptosis and cell cycle parameters.

Results

KIF4A exhibited upregulated expression in eleven types of cancer. Cell cycle-related genes are extensively overexpressed in various types of cancers. KIF4A overexpression can serve as a diagnostic and prognostic marker in various cancers. Silencing KIF4A inhibited the viability, colony formation, invasion and migration and induced apoptosis and cell cycle arrest of OS cells. Our findings revealed that high expression of KIF4A could serve as a diagnostic and prognostic marker in OS cancers.

Conclusion

KIF4A could serve as a pan-cancer diagnostic and prognostic marker. KIF4A could be used as a novel therapeutic target for OS.

Host Genome-Wide Association Study of Infant Susceptibility to Shigella-Associated Diarrhea

Shigella is a leading cause of moderate-to-severe diarrhea globally and the causative agent of shigellosis and bacillary dysentery. Associated with 80 to 165 million cases of diarrhea and >13% of diarrheal deaths, in many regions, Shigella exposure is ubiquitous while infection is heterogenous. To characterize host-genetic susceptibility to Shigella-associated diarrhea, we performed two independent genome-wide association studies (GWAS) including Bangladeshi infants from the PROVIDE and CBC birth cohorts in Dhaka, Bangladesh. Cases were infants with Shigella-associated diarrhea (n = 143) and controls were infants with no Shigella-associated diarrhea in the first 13 months of life (n = 446). Shigella-associated diarrhea was identified via quantitative PCR (qPCR) threshold cycle (C_T ) distributions for the ipaH gene, carried by all four Shigella species and enteroinvasive Escherichia coli Host GWAS were performed under an additive genetic model. A joint analysis identified protective loci on chromosomes 11 (rs582240, within the KRT18P59 pseudogene; P = 6.40 × 10^-8; odds ratio [OR], 0.43) and 8 (rs12550437, within the lincRNA RP11-115J16.1; P = 1.49 × 10^-7; OR, 0.48). Conditional analyses identified two previously suggestive loci, a protective locus on chromosome 7 (rs10266841, within the 3' untranslated region [UTR] of CYTH3; P _conditional = 1.48 × 10^-7; OR, 0.44) and a risk-associated locus on chromosome 10 (rs2801847, an intronic variant within MPP7; P _conditional = 8.37 × 10^-8; OR, 5.51). These loci have all been indirectly linked to bacterial type 3 secretion system (T3SS) activity, its components, and bacterial effectors delivered into host cells. Host genetic factors that may affect bacterial T3SS activity and are associated with the host response to Shigella-associated diarrhea may provide insight into vaccine and drug development efforts for Shigella-associated diarrheal disease.

Risk Stratification and Clinical Utility of Polygenic Risk Scores in Ophthalmology

Translational Relevance

Common genetic variants can be used to effectively stratify the risk of disease development and progression and may be used to guide screening, triaging, monitoring, or treatment thresholds.

Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis

The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.

Semi-parametric empirical Bayes factor for genome-wide association studies

Bayes factor analysis has the attractive property of accommodating the risks of both false negatives and false positives when identifying susceptibility gene variants in genome-wide association studies (GWASs). For a particular SNP, the critical aspect of this analysis is that it incorporates the probability of obtaining the observed value of a statistic on disease association under the alternative hypotheses of non-null association. An approximate Bayes factor (ABF) was proposed by Wakefield (Genetic Epidemiology 2009;33:79-86) based on a normal prior for the underlying effect-size distribution. However, misspecification of the prior can lead to failure in incorporating the probability under the alternative hypothesis. In this paper, we propose a semi-parametric, empirical Bayes factor (SP-EBF) based on a nonparametric effect-size distribution estimated from the data. Analysis of several GWAS datasets revealed the presence of substantial numbers of SNPs with small effect sizes, and the SP-EBF attributed much greater significance to such SNPs than the ABF. Overall, the SP-EBF incorporates an effect-size distribution that is estimated from the data, and it has the potential to improve the accuracy of Bayes factor analysis in GWASs.

3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies

The analysis of contemporary genomic data typically operates on one-dimensional phenotypic measurements (e.g. standing height). Here we report on a data-driven, family-informed strategy to facial phenotyping that searches for biologically relevant traits and reduces multivariate 3D facial shape variability into amendable univariate measurements, while preserving its structurally complex nature. We performed a biometric identification of siblings in a sample of 424 children, defining 1,048 sib-shared facial traits. Subsequent quantification and analyses in an independent European cohort (n = 8,246) demonstrated significant heritability for a subset of traits (0.17-0.53) and highlighted 218 genome-wide significant loci (38 also study-wide) associated with facial variation shared by siblings. These loci showed preferential enrichment for active chromatin marks in cranial neural crest cells and embryonic craniofacial tissues and several regions harbor putative craniofacial genes, thereby enhancing our knowledge on the genetic architecture of normal-range facial variation.

Common genetic associations between age-related diseases

Age is a common risk factor in many diseases, but the molecular basis for this relationship is elusive. In this study we identified 4 disease clusters from 116 diseases in the UK Biobank data, defined by their age-of-onset profiles, and found that diseases with the same onset profile are genetically more similar, suggesting a common etiology. This similarity was not explained by disease categories, co-occurrences or disease cause-effect relationships. Two of the four disease clusters had an increased risk of occurrence from age 20 and 40 years respectively. They both showed an association with known aging-related genes, yet differed in functional enrichment and evolutionary profiles. Moreover, they both had age-related expression and methylation changes. We also tested mutation accumulation and antagonistic pleiotropy theories of aging and found support for both.

Heterogeneity of a dwarf phenotype in Dutch traditional chicken breeds revealed by genomic analyses

The growth of animals is a complex trait, in chicken resulting in a diverse variety of forms, caused by a heterogeneous genetic basis. Bantam chicken, known as an exquisite form of dwarfism, has been used for crossbreeding to create corresponding dwarf counterparts for native fowls in the Dutch populations. Here, we demonstrate the heterogeneity of the bantam trait in Dutch chickens and reveal the underlying genetic causes, using whole-genome sequence data from matching pairs of bantam and normal-sized breeds. During the bantam-oriented crossbreeding, various bantam origins were used to introduce the bantam phenotype, and three major bantam sources were identified and clustered. The genome-wide association studies revealed multiple genetic variants and genes associated with bantam phenotype, including HMGA2 and PRDM16, genes involved in body growth and stature. The comparison of associated variants among studies illustrated differences related to divergent bantam origins, suggesting a clear heterogeneity among bantam breeds. We show that in neo-bantam breeds, the bantam-related regions underwent a strong haplotype introgression from the bantam source, outcompeting haplotypes from the normal-sized counterpart. The bantam heterogeneity is further confirmed by the presence of multiple haplotypes comprising associated alleles, which suggests the selection of the bantam phenotype is likely subject to a convergent direction across populations. Our study demonstrates that the diverse history of human-mediated crossbreeding has contributed to the complexity and heterogeneity of the bantam phenotype.

A network algorithm for the X chromosomal exact test for Hardy-Weinberg equilibrium with multiple alleles

Statistical methodology for testing the Hardy–Weinberg equilibrium at X chromosomal variants has recently experienced considerable development. Up to a few years ago, testing X chromosomal variants for equilibrium was basically done by applying autosomal test procedures to females only. At present, male alleles can be taken into account in asymptotic and exact test procedures for both the bi‐ and multiallelic case. However, current X chromosomal exact procedures for multiple alleles rely on a classical full enumeration algorithm and are computationally expensive, and in practice not feasible for more than three alleles. In this article, we extend the autosomal network algorithm for exact Hardy–Weinberg testing with multiple alleles to the X chromosome, achieving considerable reduction in computation times for multiallelic variants with up to five alleles. The performance of the X chromosomal network algorithm is assessed in a simulation study. Beyond four alleles, a permutation test is, in general, the more feasible approach. A detailed description of the algorithm is given, and examples of X chromosomal indels and microsatellites are discussed.

Identification of susceptibility loci for cardiovascular disease in adults with hypertension, diabetes, and dyslipidemia

BACKGROUND

Hypertension (HTN), diabetes mellitus (DM), and dyslipidemia (DL) are well-known risk factors of cardiovascular disease (CVD), but not all patients develop CVDs. Studies have been limited investigating genetic risk of CVDs specific to individuals with metabolic diseases. This study aimed to identify disease-specific and/or common genetic loci associated with CVD susceptibility in chronic metabolic disease patients.

METHODS

We conducted a genome-wide association study (GWAS) of a multiple case-control design with data from the City Cohort within Health EXAminees subcohort of the Korean Genome and Epidemiology Study (KoGES_HEXA). KoGES_HEXA is a population-based prospective cohort of 173,357 urban Korean adults that had health examinations at medical centers. 42,393 participants (16,309 HTN; 5,314 DM; 20,770 DL) were analyzed, and each metabolic disease group was divided into three CVD case-controls: coronary artery disease (CAD), ischemic stroke (IS), and cardio-cerebrovascular disease (CCD). GWASs were conducted for each case-control group with 7,975,321 imputed single nucleotide polymorphisms using the Phase 3 Asian panel from 1000 Genomes Project, by logistic regression and controlled for confounding variables. Genome-wide significant levels were implemented to identify important susceptibility loci.

RESULTS

Totaling 42,393 individuals, this study included 16,309 HTN (mean age [SD], 57.28 [7.45]; 816 CAD, 398 IS, and 1,185 CCD cases), 5,314 DM (57.79 [7.39]; 361 CAD, 153 IS, and 497 CCD cases), and 20,770 DL patients (55.34 [7.63]; 768 CAD, 295 IS, and 1,039 CCD cases). Six genome-wide significant CVD risk loci were identified, with relatively large effect sizes: 1 locus in HTN (HTN-CAD: 17q25.3/CBX8-CBX4 [OR, 2.607; P = 6.37 × 10^-9]), 2 in DM (DM-IS: 4q32.3/MARCH1-LINC01207 [OR, 5.587; P = 1.34 × 10^-8], and DM-CCD: 17q25.3/RPTOR [OR, 3.511; P = 1.99 × 10^-8]), and 3 in DL (DL-CAD: 9q22.2/UNQ6494-LOC101927847 [OR, 2.282; P = 7.78 × 10^-9], DL-IS: 3p22.1/ULK4 [OR, 2.162; P = 2.97 × 10^-8], and DL-CCD: 2p22.2/CYP1B1-CYP1B1-AS1 [OR, 2.027; P = 4.24 × 10^-8]).

CONCLUSIONS

This study identified 6 susceptibility loci and positional candidate genes for CVDs in HTN, DM, and DL patients using an unprecedented study design. 1 locus (17q25.3) was commonly associated with CAD. These associations warrant validation in additional studies for potential therapeutic applications.

Can public online databases serve as a source of phenotypic information for Cannabis genetic association studies?

The use of Cannabis is gaining greater social acceptance for its beneficial medicinal and recreational uses. With this acceptance has come new opportunities for crop management, selective breeding, and the potential for targeted genetic manipulation. However, as an agricultural product Cannabis lags far behind other domesticated plants in knowledge of the genes and genetic variation that influence plant traits of interest such as growth form and chemical composition. Despite this lack of information, there are substantial publicly available resources that document phenotypic traits believed to be associated with particular Cannabis varieties. Such databases could be a valuable resource for developing a greater understanding of genes underlying phenotypic variation if combined with appropriate genetic information. To test this potential, we collated phenotypic data from information available through multiple online databases. We then produced a Cannabis SNP database from 845 strains to examine genome wide associations in conjunction with our assembled phenotypic traits. Our goal was not to locate Cannabis-specific genetic variation that correlates with phenotypic variation as such, but rather to examine the potential utility of these databases more broadly for future, explicit genome wide association studies (GWAS), either in stand-alone analyses or to complement other types of data. For this reason, we examined a very broad array of phenotypic traits. In total, we performed 201 distinct association tests using web-derived phenotype data appended to 290 uniquely named Cannabis strains. Our results indicated that chemical phenotypes, such as tetrahydrocannabinol (THC) and cannabidiol (CBD) content, may have sufficiently high-quality information available through web-based sources to allow for genetic association inferences. In many cases, variation in chemical traits correlated with genetic variation in or near biologically reasonable candidate genes, including several not previously implicated in Cannabis chemical variation. As with chemical phenotypes, we found that publicly available data on growth traits such as height, area of growth, and floral yield may be precise enough for use in future association studies. In contrast, phenotypic information for subjective traits such as taste, physiological affect, neurological affect, and medicinal use appeared less reliable. These results are consistent with the high degree of subjectivity for such trait data found on internet databases, and suggest that future work on these important but less easily quantifiable characteristics of Cannabis may require dedicated, controlled phenotyping.

Revisiting the genome-wide significance threshold for common variant GWAS

Over the last decade, GWAS meta-analyses have used a strict P-value threshold of 5 × 10-8 to classify associations as significant. Here, we use our current understanding of frequently studied traits including lipid levels, height, and BMI to revisit this genome-wide significance threshold. We compare the performance of studies using the P = 5 × 10-8 threshold in terms of true and false positive rate to other multiple testing strategies: (1) less stringent P-value thresholds, (2) controlling the FDR with the Benjamini-Hochberg and Benjamini-Yekutieli procedure, and (3) controlling the Bayesian FDR with posterior probabilities. We applied these procedures to re-analyze results from the Global Lipids and GIANT GWAS meta-analysis consortia and supported them with extensive simulation that mimics the empirical data. We observe in simulated studies with sample sizes ∼20,000 and >120,000 that relaxing the P-value threshold to 5 × 10-7 increased discovery at the cost of 18% and 8% of additional loci being false positive results, respectively. FDR and Bayesian FDR are well controlled for both sample sizes with a few exceptions that disappear under a less stringent definition of true positives and the two approaches yield similar results. Our work quantifies the value of using a relaxed P-value threshold in large studies to increase their true positive discovery but also show the excess false positive rates due to such actions in modest-sized studies. These results may guide investigators considering different thresholds in replication studies and downstream work such as gene-set enrichment or pathway analysis. Finally, we demonstrate the viability of FDR-controlling procedures in GWAS.

The genetic architecture of structural left-right asymmetry of the human brain

Left-right hemispheric asymmetry is an important aspect of healthy brain organization for many functions including language, and it can be altered in cognitive and psychiatric disorders. No mechanism has yet been identified for establishing the human brain's left-right axis. We performed multivariate genome-wide association scanning of cortical regional surface area and thickness asymmetries, and subcortical volume asymmetries, using data from 32,256 participants from the UK Biobank. There were 21 significant loci associated with different aspects of brain asymmetry, with functional enrichment involving microtubule-related genes and embryonic brain expression. These findings are consistent with a known role of the cytoskeleton in left-right axis determination in other organs of invertebrates and frogs. Genetic variants associated with brain asymmetry overlapped with those associated with autism, educational attainment and schizophrenia. Comparably large datasets will likely be required in future studies, to replicate and further clarify the associations of microtubule-related genes with variation in brain asymmetry, behavioural and psychiatric traits.