Gene-based association tests in family samples using GWAS summary statistics

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

Genetic predisposition to subjective well-being, depression, and suicide in relation to COVID-19 susceptibility and severity

BACKGROUND

Epidemiological studies have reported associations between subjective well-being (SWB), depression, and suicide with COVID-19 illness, but the causality has not been established. We performed a two-sample Mendelian randomization (MR) analysis to investigate the causal link between SWB, depression, suicide and COVID-19 susceptibility and severity.

METHODS

Summary statistics for SWB (298,420 cases), depression (113,769 cases) and suicide (52,208 cases) were obtained from three large-scale GWAS. Data on the associations between the Single Nucleotide Polymorphisms (SNPs) and COVID-19 (159,840 cases), hospitalized COVID-19 (44,986 cases), and severe COVID-19 (18,152 cases) were collected from the COVID-19 host genetics initiative. The causal estimate was calculated by the Inverse Variance Weighted, MR Egger and Weighted Median methods. Sensitivity tests were used to evaluate the validity of the causal relationship.

RESULTS

Our results showed that genetically predicted SWB (OR = 0.98, 95 % CI: 0.86-1.10, P = 0.69), depression (OR = 0.76, 95 % CI: 0.54-1.06, P = 0.11), and suicide (OR = 0.99, 95 % CI: 0.96-1.02, P = 0.56) were not causally related to COVID-19 susceptibility. Similarly, we did not find a potential causal relationship between SWB, depression, suicide and COVID-19 severity.

CONCLUSIONS

This indicated that positive or negative emotions would not make COVID-19 better or worse, and strategies that attempted to use positive emotions to improve COVID-19 symptoms may be useless. Improving knowledge about the SARS-CoV-2 and timely medical intervention to reduce panic during a pandemic is one of the effective measures to deal with the current decrease in well-being and increase in depression and suicide rates.

Robust multivariable Mendelian randomization based on constrained maximum likelihood

Mendelian randomization (MR) is a powerful tool for causal inference with observational genome-wide association study (GWAS) summary data. Compared to the more commonly used univariable MR (UVMR), multivariable MR (MVMR) not only is more robust to the notorious problem of genetic (horizontal) pleiotropy but also estimates the direct effect of each exposure on the outcome after accounting for possible mediating effects of other exposures. Despite promising applications, there is a lack of studies on MVMR's theoretical properties and robustness in applications. In this work, we propose an efficient and robust MVMR method based on constrained maximum likelihood (cML), called MVMR-cML, with strong theoretical support. Extensive simulations demonstrate that MVMR-cML performs better than other existing MVMR methods while possessing the above two advantages over its univariable counterpart. An application to several large-scale GWAS summary datasets to infer causal relationships between eight cardiometabolic risk factors and coronary artery disease (CAD) highlights the usefulness and some advantages of the proposed method. For example, after accounting for possible pleiotropic and mediating effects, triglyceride (TG), low-density lipoprotein cholesterol (LDL), and systolic blood pressure (SBP) had direct effects on CAD; in contrast, the effects of high-density lipoprotein cholesterol (HDL), diastolic blood pressure (DBP), and body height diminished after accounting for other risk factors.

Genetic predisposition to female infertility in relation to epithelial ovarian and endometrial cancers

BACKGROUND

The associations between female infertility and epithelial ovarian cancer (EOC) or endometrial cancer (EC) have been reported in observational studies, but its causal relationship remains unknown. We intended to assess the causal effect of female infertility on EOCs and ECs using a two-sample Mendelian Randomization (MR) approach.

METHODS

Large pooled genome-wide association study (GWAS) datasets for female infertility (6481 cases and 68 969 controls), EOC (25 509 cases and 40 941 controls), and EC (12 906 cases and 108 979 controls) were derived from public GWAS databases and published studies. The Inverse Variance Weighted method, Weighted Median method, MR-Egger regression, and MR-Pleiotropy Residual Sum and Outlier test were adopted for MR analyses.

RESULTS

Our results suggested that genetically predicted infertility was positively associated with the risk of EOC (OR = 1.117, 95% CI = 1.003-1.245, P = .045), but did not find a causal relationship between infertility and EC (OR = 1.081, 95% CI = 0.954-1.224, P = .223). As to the reverse direction, our study did not obtain evidence from genetics that EOCs (OR = 0.974, 95% CI = 0.825-1.150, P = .755) and ECs (OR = 1.039, 95% CI = 0.917-1.177, P = .548) were associated with an increased risk of infertility.

CONCLUSIONS

This large MR analysis supported a causal association between female infertility and increased risk of EOCs, but did not find a causal relationship between infertility and ECs.

Genetic predisposition to blood cell indices in relation to severe COVID-19

Despite considerable variation in disease manifestations observed among coronavirus disease 2019 (COVID-19) patients infected with severe acute respiratory syndrome coronavirus 2, the risk factors predicting disease severity remain elusive. Recent studies suggest that peripheral blood cells play a pivotal role in COVID-19 pathogenesis. Here, we applied two-sample Mendelian randomization (MR) analyses to evaluate the potential causal contributions of blood cell indices variation to COVID-19 severity, using single-nucleotide polymorphisms (SNPs) as instrumental variables for 17 indices from the UK Biobank and INTERVAL genome-wide association studies (N = 173 480). Data on the associations between the SNPs and very severe respiratory confirmed COVID-19 were obtained from the COVID-19 host genetics initiative (N = 8779/1 001 875). We observed significant negative association between hematocrit (HCT; odds ratio, OR = 0.775, 95% confidence interval, CI = 0.635-0.915, p = 3.48E-04) or red blood cell count (OR = 0.830, 95% CI = 0.728-0.932, p = 2.19E-03) and very severe respiratory confirmed COVID-19, as well as nominal negative association of hemoglobin concentration (OR = 0.808, 95% CI = 0.673-0.943, p = 3.95E-03) with very severe respiratory confirmed COVID-19 (no effect survived multiple correction). In conclusion, the MR study supports a protective effect of high HCT and red blood cell count from very severe respiratory confirmed COVID-19, suggesting potential strategies to ameliorate/treat clinical conditions in very severe respiratory confirmed COVID-19.

Identification of Parkinson's Disease-Causing Genes via Omics Data

Parkinson's disease (PD) is the second most frequent neurogenic disease after Alzheimer's disease. The clinical manifestations include mostly motor disorders, such as bradykinesia, myotonia, and static tremors. Since the cause of this pathological features remain unclear, there is currently no radical treatment for PD. Environmental and genetic factors are thought to contribute to the pathology of PD. To identify the genetic factors, some studies employed the Genome-Wide Association Studies (GWAS) method and detected certain genes closely related to PD. However, the functions of these gene mutants in the development of PD are unknown. Combining GWAS and expression Quantitative Trait Loci (eQTL) analysis, the biological meaning of mutation could be explained to some extent. Therefore, the present investigation used Summary data-based Mendelian Randomization (SMR) analysis to integrate of two PD GWAS datasets and four eQTL datasets with the objective of identifying casual genes. Using this strategy, we found six Single Nucleotide Polymorphism (SNP) loci which could cause the development of PD through altering the susceptibility gene expression, and three risk genes: Synuclein Alpha (SNCA), Mitochondrial Poly(A) Polymerase (MTPAP), and RP11-305E6.4. We proved the accuracy of results through case studies and inferred the functions of these genes in PD. Overall, this study provides insights into the genetic mechanism behind PD, which is crucial for the study of the development of this disease and its diagnosis and treatment.

Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank

Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type 1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type 1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.