LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information.

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

A genome-wide association study of susceptibility to upper urinary tract infections.

BACKGROUND: Our goal was to identify genetic and modifiable risk factors for upper urinary tract infections (UTIs). METHODS: We used data from UK Biobank, The Trøndelag Health Study (HUNT), and Michigan Genomics Initiative (MGI) to conduct genome-wide association studies (GWASs) and sex-stratified analyses on upper UTI. Mendelian randomization (MR) analyses were conducted to examine potential causal relationships between cardiometabolic risk factors and upper UTIs. RESULTS: One genome-wide significant (P ≤ 5E-08) locus was associated with the susceptibility to upper UTI, located near TSN in the female-only analysis. Additionally, we identified suggestive (P ≤ 5E-06) loci near DNAI3 for the females, SCAMP1-AS1 for the males, and near TSN, LINC00603, and HLA-DQA2 for both sexes. In MR analyses, higher genetically predicted lifetime smoking scores were associated with an increased risk of developing upper UTI for females and both sexes (OR of 4.84, P = 4.50E-06 and OR of 2.79, P = 3.02E-05, respectively). CONCLUSIONS: We found that genetic variants near TSN was associated with the risk of upper UTIs among females. In addition, we found several genetic loci with suggestive associations with the risk of upper UTIs. Finally, MR analyses found smoking to be a potential causal risk factor for upper UTIs.

Anticoagulation in Patients with Premature Peripheral Artery Disease Undergoing Lower Extremity Revascularization.

BACKGROUND: Premature peripheral artery disease (PAD), defined by lower extremity revascularization (LER) at age ≤ 50 years, is associated with poor major adverse limb events. The early onset of disease is thought to be influenced by genetic factors that regulate homeostasis of the vascular wall and coagulation. The aim of this study is to investigate the effect of anticoagulation as an adjunct to antiplatelet therapy on the outcomes of LER in patients with premature PAD. METHODS: There were 8,804 patients with premature PAD on preoperative and postoperative antiplatelet therapy only and 1,236 patients on preoperative and postoperative anticoagulation plus antiplatelet therapy in the Vascular Quality Initiative peripheral vascular intervention, infrainguinal, and suprainguinal files. Propensity score matching (2:1) was performed between patients with premature PAD who were on antiplatelet therapy and those on anticoagulation plus antiplatelet therapy. Perioperative and 1-year outcomes were analyzed including reintervention, major amputation, and mortality. RESULTS: Patients on anticoagulation were more likely to have coronary artery disease (48.7% vs. 41.2%, P < 0.001), congestive heart failure (20.2% vs. 13.1%, P < 0.001), and have undergone prior LER (73.9% vs. 49.2%, P < 0.001) compared to patients on antiplatelet therapy only. They were also less likely to be independently ambulatory (74.2% vs. 81.8%, P < 0.001) and be on a statin medication (66.8% vs. 74.3%, P < 0.001) compared to patients on antiplatelet therapy only. Patients on anticoagulation were also less likely to be treated for claudication (38.1% vs. 48.6%, P < 0.001), and less likely to be treated with an endovascular procedure (64.8% vs. 73.8%, P < 0.001). After matching for baseline characteristics, there were 1,256 patients on antiplatelet therapy only and 628 patients on anticoagulation. Patients on anticoagulation were more likely to require a return to the operating room (3.7% vs. 1.6%, P < 0.001) and had higher perioperative mortality (1.1% vs. 0.3%, P = 0.032), but major amputation was not significantly different (1.8% vs. 1.6%, P = 0.798) compared to patients on antiplatelet therapy alone. At 1 year, amputation-free survival was higher in patients on antiplatelets only compared to patients on anticoagulation and antiplatelet medications (87.5% vs. 80.9%, log-rank P = 0.001). CONCLUSIONS: Anticoagulation in addition to antiplatelet therapy in patients with premature PAD undergoing LER is associated with increased reintervention and mortality at 1 year.

A mediation analysis framework based on variance component to remove genetic confounding effect.

Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.

Systematic review and meta-analysis of the genetics of peripheral arterial disease.

Background: Peripheral artery disease (PAD) impacts more than 200 million people worldwide. The understanding of the genetics of the disease and its clinical implications continue to evolve. This systematic review provides a comprehensive summary of all DNA variants that have been studied in association with the diagnosis and progression of PAD, with a meta-analysis of the ones replicated in the literature. Methods: A systematic review of all studies examining DNA variants associated with the diagnosis and progression of PAD was performed. Candidate gene and genome-wide association studies (GWAS) were included. A meta-analysis of 13 variants derived from earlier smaller candidate gene studies of the diagnosis of PAD was performed. The literature on the progression of PAD was limited, and a meta-analysis was not feasible because of the heterogeneity in the criteria used to characterize it. Results: A total of 231 DNA variants in 112 papers were studied for the association with the diagnosis of PAD. There were significant variations in the definition of PAD and the selection of controls in the various studies. GWAS have established 19 variants associated with the diagnosis of PAD that were replicated in several large patient cohorts. Only variants in intercellular adhesion molecule-1 (rs5498), IL-6 (rs1800795), and hepatic lipase (rs2070895) showed significant association with the diagnosis of PAD. However, these variants were not noted in the published GWAS. Conclusions: Genetic research in the diagnosis of PAD has significant heterogeneity, but recent GWAS have demonstrated variants consistently associated with the disease. More research focusing on the progression of PAD is needed to identify patients at risk of adverse events and develop strategies that would improve their outcomes.

Variants in JAZF1 are associated with asthma, type 2 diabetes, and height in the United Kingdom biobank population.

Background: Asthma, type 2 diabetes (T2D), and anthropometric measures are correlated complex traits that all have a major genetic component. Objective: To investigate the overlap in genetic variants associated with these complex traits. Methods: Using United Kingdom Biobank data, we performed univariate association analysis, fine-mapping, and mediation analysis to identify and dissect shared genomic regions associated with asthma, T2D, height, weight, body mass index (BMI), and waist circumference (WC). Results: We found several genome-wide significant variants in and around the JAZF1 gene that are associated with asthma, T2D, or height with two of these variants shared by the three phenotypes. We also observed an association in this region with WC when adjusted for BMI. However, there was no association with WC when it was not adjusted for BMI or weight. Additionally, only suggestive associations between variants in this region and BMI were observed. Fine-mapping analyses suggested that within JAZF1 there are non-overlapping regions harboring causal susceptibility variants for asthma, T2D, and height. Mediation analyses supported the conclusion that these are independent associations. Conclusion: Our findings indicate that variants in the JAZF1 are associated with asthma, T2D, and height, but the associated causal variant(s) are different for each of the three phenotypes.

Comparison of multiple imputation and other methods for the analysis of imputed genotypes.

BACKGROUND: Analysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype is unknown and genotypes are inferred with uncertainty using statistical models. Here, we present a novel method for integrating imputation uncertainty into statistical association tests using a fully conditional multiple imputation (MI) approach which is implemented using the Substantive Model Compatible Fully Conditional Specification (SMCFCS). We compared the performance of this method to an unconditional MI and two additional approaches that have been shown to demonstrate excellent performance: regression with dosages and a mixture of regression models (MRM). RESULTS: Our simulations considered a range of allele frequencies and imputation qualities based on data from the UK Biobank. We found that the unconditional MI was computationally costly and overly conservative across a wide range of settings. Analyzing data with Dosage, MRM, or MI SMCFCS resulted in greater power, including for low frequency variants, compared to unconditional MI while effectively controlling type I error rates. MRM andl MI SMCFCS are both more computationally intensive then using Dosage. CONCLUSIONS: The unconditional MI approach for association testing is overly conservative and we do not recommend its use in the context of imputed genotypes. Given its performance, speed, and ease of implementation, we recommend using Dosage for imputed genotypes with MAF [Formula: see text] 0.001 and Rsq [Formula: see text] 0.3.

Systematically identifying genetic signatures including novel SNP-clusters, nonsense variants, frame-shift INDELs, and long STR expansions that potentially link to unknown phenotypes existing in dog breeds.

BACKGROUND: In light of previous studies that profiled breed-specific traits or used genome-wide association studies to refine loci associated with characteristic morphological features in dogs, the field has gained tremendous genetic insights for known dog traits observed among breeds. Here we aim to address the question from a reserve perspective: whether there are breed-specific genotypes that may underlie currently unknown phenotypes. This study provides a complete set of breed-specific genetic signatures (BSGS). Several novel BSGS with significant protein-altering effects were highlighted and validated. RESULTS: Using the next generation whole-genome sequencing technology coupled with unsupervised machine learning for pattern recognitions, we constructed and analyzed a high-resolution sequence map for 76 breeds of 412 dogs. Genomic structures including novel single nucleotide polymorphisms (SNPs), SNP clusters, insertions, deletions (INDELs) and short tandem repeats (STRs) were uncovered mutually exclusively among breeds. We also partially validated some novel nonsense variants by Sanger sequencing with additional dogs. Four novel nonsense BSGS were found in the Bernese Mountain Dog, Samoyed, Bull Terrier, and Basset Hound, respectively. Four INDELs resulting in either frame-shift or codon disruptions were found in the Norwich Terrier, Airedale Terrier, Chow Chow and Bernese Mountain Dog, respectively. A total of 15 genomic regions containing three types of BSGS (SNP-clusters, INDELs and STRs) were identified in the Akita, Alaskan Malamute, Chow Chow, Field Spaniel, Keeshond, Shetland Sheepdog and Sussex Spaniel, in which Keeshond and Sussex Spaniel each carried one amino-acid changing BSGS in such regions. CONCLUSION: Given the strong relationship between human and dog breed-specific traits, this study might be of considerable interest to researchers and all. Novel genetic signatures that can differentiate dog breeds were uncovered. Several functional genetic signatures might indicate potentially breed-specific unknown phenotypic traits or disease predispositions. These results open the door for further investigations. Importantly, the computational tools we developed can be applied to any dog breeds as well as other species. This study will stimulate new thinking, as the results of breed-specific genetic signatures may offer an overarching relevance of the animal models to human health and disease.

Paired Indoor and Outdoor Nitrogen Dioxide Associated With Childhood Asthma Outcomes in a Mixed Rural-Urban Setting: A Feasibility Study.

INTRODUCTION: Nitrogen dioxide (NO2) is known to be a trigger for asthma exacerbation. However, little is known about the role of seasonal variation in indoor and outdoor NO2 levels in childhood asthma in a mixed rural-urban setting of North America. METHODS: This prospective cohort study, as a feasibility study, included 62 families with children (5-17 years) that had diagnosed persistent asthma residing in Olmsted County, Minnesota. Indoor and outdoor NO2 concentrations were measured using passive air samples over 2 weeks in winter and 2 weeks in summer. We assessed seasonal variation in NO2 levels in urban and rural residential areas and the association with asthma control status collected from participants' asthma diaries during the study period. RESULTS: Outdoor NO2 levels were lower (median: 2.4 parts per billion (ppb) in summer, 3.9 ppb in winter) than the Environmental Protection Agency (EPA) annual standard (53 ppb). In winter, a higher level of outdoor NO2 was significantly associated with urban residential living area (P = .014) and lower socioeconomic status (SES) (P = .027). For both seasons, indoor NO2 was significantly higher (P < .05) in rural versus urban areas and in homes with gas versus electric stoves (P < .05). Asthma control status was not associated with level of indoor or outdoor NO2 in this cohort. CONCLUSIONS: NO2 levels were low in this mixed rural-urban community and not associated with asthma control status in this small feasibility study. Further research with a larger sample size is warranted for defining a lower threshold of NO2 concentration with health effect on asthma in mixed rural-urban settings.

Saddlepoint approximations to score test statistics in logistic regression for analyzing genome-wide association studies.

We investigate saddlepoint approximations of tail probabilities of the score test statistic in logistic regression for genome-wide association studies. The inaccuracy in the normal approximation of the score test statistic increases with increasing imbalance in the response and with decreasing minor allele counts. Applying saddlepoint approximation methods greatly improve the accuracy, even far out in the tails of the distribution. By using exact results for a simple logistic regression model, as well as simulations for models with nuisance parameters, we compare double saddlepoint methods for computing two-sided P $$ P $$ -values and mid- P $$ P $$ -values. These methods are also compared to a recent single saddlepoint procedure. We investigate the methods further on data from UK Biobank with skin and soft tissue infections as phenotype, using both common and rare variants.

Early-Onset Colorectal Cancer Somatic Gene Mutations by Population Subgroups.

SUMMARY: In this issue of Cancer Discovery, Holowatyj and colleagues uncover racial/ethnic and sex heterogeneity in somatic mutations among patients with early-onset colorectal cancer. The findings shed light on a deeper understanding of complex biological and genetic mechanisms for colorectal cancer in diverse populations. See related article by Holowatyj et al., p. 570 (6).

Dyslipidemia and Risk of Preeclampsia: A Multiancestry Mendelian Randomization Study.

BACKGROUND: Preeclampsia is a leading cause of maternal morbidity, and dyslipidemia has been associated with preeclampsia in observational studies. We use Mendelian randomization analyses to estimate the association between lipid levels, their pharmacological targets, and the risk of preeclampsia in 4 ancestry groups. METHODS: We extracted uncorrelated (R2<0.001) single-nucleotide polymorphisms strongly associated (P<5×10-8) with LDL-C (low-density lipoprotein cholesterol), HDL-C (high-density lipoprotein cholesterol), and triglycerides from genome-wide association studies of European, admixed African, Latino, and East Asian ancestry participants. Genetic associations with risk of preeclampsia were extracted from studies of the same ancestry groups. Inverse-variance weighted analyses were performed separately for each ancestry group before they were meta-analyzed. Sensitivity analyses were conducted to evaluate bias due to genetic pleiotropy, demography, and indirect genetic effects. RESULTS: The meta-analysis across 4 ancestry groups included 1.5 million subjects with lipid measurements, 7425 subjects with preeclampsia, and 239 290 without preeclampsia. Increasing HDL-C was associated with reduced risk of preeclampsia (odds ratio, 0.84 [95% CI, 0.74-0.94]; P=0.004; per SD increase in HDL-C), which was consistent across sensitivity analyses. We also observed cholesteryl ester transfer protein inhibition-a drug target that increases HDL-C-may have a protective effect. We observed no consistent effect of LDL-C or triglycerides on the risk of preeclampsia. CONCLUSIONS: We observed a protective effect of elevated HDL-C on risk of preeclampsia. Our findings align with the lack of effect in trials of LDL-C modifying drugs but suggest that HDL-C may be a new target for screening and intervention.

The genetic contribution of the X chromosome in age-related hearing loss.

Age-related (AR) hearing loss (HL) is the most common sensory impairment with heritability of 55%. The aim of this study was to identify genetic variants on chromosome X associated with ARHL through the analysis of data obtained from the UK Biobank. We performed association analysis between self-reported measures of HL and genotyped and imputed variants on chromosome X from ∼460,000 white Europeans. We identified three loci associated with ARHL with a genome-wide significance level (p < 5 × 10-8), ZNF185 (rs186256023, p = 4.9 × 10-10) and MAP7D2 (rs4370706, p = 2.3 × 10-8) in combined analysis of males and females, and LOC101928437 (rs138497700, p = 8.9 × 10-9) in the sex-stratified analysis of males. In-silico mRNA expression analysis showed MAP7D2 and ZNF185 are expressed in mice and adult human inner ear tissues, particularly in the inner hair cells. We estimated that only a small amount of variation of ARHL, 0.4%, is explained by variants on the X chromosome. This study suggests that although there are likely a few genes contributing to ARHL on the X chromosome, the role that the X chromosome plays in the etiology of ARHL may be limited.

Inferring feature importance with uncertainties with application to large genotype data.

Estimating feature importance, which is the contribution of a prediction or several predictions due to a feature, is an essential aspect of explaining data-based models. Besides explaining the model itself, an equally relevant question is which features are important in the underlying data generating process. We present a Shapley-value-based framework for inferring the importance of individual features, including uncertainty in the estimator. We build upon the recently published model-agnostic feature importance score of SAGE (Shapley additive global importance) and introduce Sub-SAGE. For tree-based models, it has the advantage that it can be estimated without computationally expensive resampling. We argue that for all model types the uncertainties in our Sub-SAGE estimator can be estimated using bootstrapping and demonstrate the approach for tree ensemble methods. The framework is exemplified on synthetic data as well as large genotype data for predicting feature importance with respect to obesity.

Rare-variant association analysis reveals known and new age-related hearing loss genes.

Age-related (AR) hearing loss (HL) is a prevalent sensory deficit in the elderly population. Several studies showed that common variants increase ARHL susceptibility. Here, we demonstrate that rare-variants play a crucial role in ARHL etiology. We analyzed exome and imputed data from white-European UK Biobank volunteers, performing both single-variant and rare-variant aggregate association analyses using self-reported ARHL phenotypes. We identified and replicated associations between ARHL and rare-variants in KLHDC7B, PDCD6, MYO6, SYNJ2, and TECTA. PUS7L and EYA4 also revealed rare-variant associations with ARHL. EYA4, MYO6, and TECTA are all known to underline Mendelian nonsyndromic HL. PDCD6, a new HL gene, plays an important role in apoptosis and has widespread inner ear expression, particularly in the inner hair cells. An unreplicated common variant association was previously observed for KHLDC7B, here we demonstrate that rare-variants in this gene also play a role in ARHL etiology. Additionally, the first replicated association between SYNJ2 and ARHL was detected. Analysis of common variants revealed several previously reported, i.e., ARHGEF28, and new, i.e., PIK3R3, ARHL associations, as well as ones we replicate here for the first time, i.e., BAIAP2L2, CRIP3, KLHDC7B, MAST2, and SLC22A7. It was also observed that the odds ratios for rare-variant ARHL associations, were higher than those for common variants. In conclusion, we demonstrate the vital role rare-variants, including those in Mendelian nonsyndromic HL genes, play in the etiology of ARHL.

Circulating miRNAs in the first trimester and pregnancy complications: a systematic review.

Most pregnancy complications originate with early placentation. MicroRNAs (miRNAs) may play an important role in placentation and function as biomarkers of future pregnancy complications. We summarized from the literature all first trimester circulating miRNAs associated with pregnancy complications of placental origin and further identified the miRNAs which have the most evidence as potential early biomarkers for pregnancy complications. We conducted a systematic review following PRISMA reporting guidelines (PROSPERO CRD42020183421). We identified all first trimester serum or plasma miRNAs associated with a pregnancy complication of placental origin (preeclampsia, intrauterine growth restriction (IUGR), gestational hypertension, preterm delivery) and the number of times those miRNAs were identified, as a measure of replication. Twenty-one studies examined 118 unique miRNAs, and 87 were associated with at least one pregnancy complication; preeclampsia was the most common. Seven miRNAs were significantly associated with a pregnancy complication in at least two studies: miR-125b, miR-518b, miR-628-3p, miR-365a-3p, miR-520h, miR-374a-5p, miR-191-5p. Few miRNAs were associated with more than one pregnancy complication: miR-518b and miR-520h with preeclampsia and gestational hypertension, miR-374a-5p and miR-191-5p with preterm birth and preeclampsia. Our systematic review suggests seven miRNAs as potential biomarkers of pregnancy complications. These complications are thought to originate with early placental defects and these miRNAs may also be biomarkers of placental pathology. First-trimester biomarkers of pregnancy complications can facilitate early detection and interventions.

Preimplantation genetic testing for aneuploidy: challenges in clinical practice.

Preimplantation genetic testing for aneuploidy (PGT-A) has been used widely during in vitro fertilization procedures in assisted reproductive centers throughout the world. Despite its wide use, concerns arise from the use of PGT-A technology in clinical decision-making. We address knowledge gaps in PGT-A, summarizing major challenges and current professional guidelines. First, PGT-A is a screening test and not a diagnostic test. Second, mosaicism is much higher in the blastocyst stage from PGT-A than had been recognized previously and a mosaic embryo may not accurately represent the genetic disease risk for future fetal disorders. Third, PGT-A was not validated clinically before use in patients; the best use of this technology for selected age-groups remains uncertain. Given these gaps, we believe that current professional policies relying on industry-self-regulation are insufficient. In the USA, the Food and Drug Administration may be the most appropriate agency to provide more definitive guidelines and regulations that are needed for better practice.