Effects of oral contraceptives and menopausal hormone therapy on the risk of rheumatoid arthritis: a prospective cohort study.

OBJECTIVES: Oral contraceptives (OC) and menopausal hormone therapy (MHT) contain exogenous sex hormones and are used by millions of women around the world. However, their effect on development of rheumatoid arthritis (RA) is still debated and the current literature suggests that they may exert opposite effects on the risk of RA. The present study aimed to estimate the effects of exogenous hormones on development of RA, both during the reproductive lifespan and later in life. METHODS: The association between OC and RA, as well as between MHT and late-onset RA (LORA), was investigated using time-dependent Cox regression modelling in white British women from the UK Biobank (N = 236 602 and N = 102 466, respectively) and replicated in women from all ethnic groups. RESULTS: OC use was associated with a decreased risk of RA in ever-users (hazard ratio [HR]=0.89; 95% CI = 0.82-0.96), as well as in current (HR = 0.81; 0.73-0.91) and former users (HR = 0.92; 0.84 -1.00), compared with never-users. In contrast, MHT use was associated with an increased risk of LORA in ever-users (HR = 1.16; 1.06-1.26) as well as in former users (HR = 1.13; 1.03-1.24) compared with never-users. CONCLUSION: OC use appears to protect against RA, while MHT may increase the risk of LORA. This study provides new insights into the possible inverse effect of exposure to different exogenous sex hormones on the risk of RA.

Oral Contraceptives, Hormone Replacement Therapy, and Stroke Risk.

BACKGROUND: Millions of women worldwide use exogenous hormones as oral contraceptives or hormone replacement therapy. Still, time-dependent and long-term consequences of exogenous hormones on stroke risk remains unclear. METHODS: We examined the association between self-reported oral contraceptive and hormone replacement therapy use and stroke risk in 257 194 women from the UK Biobank, born between 1939 and 1970. Outcomes included any type of stroke, ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage. Exposures were analyzed as time-varying variables in Cox regression models. RESULTS: During first year of oral contraceptive use, an increased event rate of any stroke was observed (hazard ratio [HR], 2.49 [95% CI, 1.44-4.30]), while the hazards were found to be comparable during remaining years of use (HR, 1.00 [95% CI, 0.86-1.14]), compared with nonusers. Similarly, first year of hormone replacement therapy use was associated with higher hazard rates of any stroke (HR, 2.12 [95% CI, 1.66-2.70]), as well as cause-specific stroke, including ischemic stroke (HR, 1.93 [95% CI, 1.05-3.57]) and subarachnoid hemorrhage (HR, 2.17 [95% CI, 1.25-3.78]), which remained increased for any stroke during remaining years of use (HR, 1.18 [95% CI, 1.05-1.31]), and after discontinuation (HR, 1.16 [95% CI, 1.02-1.32]). CONCLUSIONS: Oral contraceptive use and hormone replacement therapy were associated with an increased risk of stroke, especially during the first year of use, possibly due to immediate changes in hemostatic balance. This study provides new insights on the effects of hormone exposure on stroke risk and provide evidence of not only an overall risk but also a pronounced effects seen in the beginning of treatment.

Genome-wide association analysis of 350 000 Caucasians from the UK Biobank identifies novel loci for asthma, hay fever and eczema.

Even though heritability estimates suggest that the risk of asthma, hay fever and eczema is largely due to genetic factors, previous studies have not explained a large part of the genetics behind these diseases. In this genome-wide association study, we include 346 545 Caucasians from the UK Biobank to identify novel loci for asthma, hay fever and eczema and replicate novel loci in three independent cohorts. We further investigate if associated lead single nucleotide polymorphisms (SNPs) have a significantly larger effect for one disease compared to the other diseases, to highlight possible disease-specific effects. We identified 141 loci, of which 41 are novel, to be associated (P ≤ 3 × 10-8) with asthma, hay fever or eczema, analyzed separately or as disease phenotypes that includes the presence of different combinations of these diseases. The largest number of loci was associated with the combined phenotype (asthma/hay fever/eczema). However, as many as 20 loci had a significantly larger effect on hay fever/eczema only compared to their effects on asthma, while 26 loci exhibited larger effects on asthma compared with their effects on hay fever/eczema. At four of the novel loci, TNFRSF8, MYRF, TSPAN8, and BHMG1, the lead SNPs were in Linkage Disequilibrium (LD) (>0.8) with potentially casual missense variants. Our study shows that a large amount of the genetic contribution is shared between the diseases. Nonetheless, a number of SNPs have a significantly larger effect on one of the phenotypes, suggesting that part of the genetic contribution is more phenotype specific.

Characterization of the human ABO genotypes and their association to common inflammatory and cardiovascular diseases in the UK Biobank.

The ABO gene contains three major alleles that encodes different antigens; A, B, and O, which determine an individual's blood group. Previous studies have primarily focused on identifying associations between ABO blood groups and diseases risk. Here, we sought to test for association between ABO genotypes (OO, OA, AA; OB, BB, and AB) and a large set of common inflammatory and cardiovascular diseases in UK Biobank as well as disease-related protein biomarkers in NSPHS. We first tested for association by conducting a likelihood ratio test, testing whether ABO contributed significantly to the risk for 24 diseases, and 438 plasma proteins. For phenotypes with FDR < 0.05, we tested for pair-wise differences between genetically determined ABO genotypes using logistic or linear regression. Our study confirmed previous findings of a strong association between ABO and cardiovascular disease, identified associations for both type 1 and type 2 diabetes, and provide additional evidence of significant differences between heterozygous and homozygous allele carriers for pulmonary embolism, deep vein thrombosis, but also for von Willebrand factor levels. Furthermore, the results indicated an additive effect between genotypes, even between the two most common A subgroups, A1 and A2. Additionally, we found that ABO contributed significantly to 39 plasma proteins, of which 23 have never been linked to the ABO locus before. These results show the need of incorporating ABO genotype information in the consultation and management of patients at risk, rather than classifying patients into blood groups.

Genetic variants influencing phenotypic variance heterogeneity.

Most genetic studies identify genetic variants associated with disease risk or with the mean value of a quantitative trait. More rarely, genetic variants associated with variance heterogeneity are considered. In this study, we have identified such variance single-nucleotide polymorphisms (vSNPs) and examined if these represent biological gene × gene or gene × environment interactions or statistical artifacts caused by multiple linked genetic variants influencing the same phenotype. We have performed a genome-wide study, to identify vSNPs associated with variance heterogeneity in DNA methylation levels. Genotype data from over 10 million single-nucleotide polymorphisms (SNPs), and DNA methylation levels at over 430 000 CpG sites, were analyzed in 729 individuals. We identified vSNPs for 7195 CpG sites (P < 9.4 × 10-11). This is a relatively low number compared to 52 335 CpG sites for which SNPs were associated with mean DNA methylation levels. We further showed that variance heterogeneity between genotypes mainly represents additional, often rare, SNPs in linkage disequilibrium (LD) with the respective vSNP and for some vSNPs, multiple low frequency variants co-segregating with one of the vSNP alleles. Therefore, our results suggest that variance heterogeneity of DNA methylation mainly represents phenotypic effects by multiple SNPs, rather than biological interactions. Such effects may also be important for interpreting variance heterogeneity of more complex clinical phenotypes.

Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status.

Previous genome-wide association studies (GWAS) have identified hundreds of genetic loci to be associated with body mass index (BMI) and risk of obesity. Genetic effects can differ between individuals depending on lifestyle or environmental factors due to gene-environment interactions. In this study, we examine gene-environment interactions in 362,496 unrelated participants with Caucasian ancestry from the UK Biobank resource. A total of 94 BMI-associated SNPs, selected from a previous GWAS on BMI, were used to construct weighted genetic scores for BMI (GSBMI). Linear regression modeling was used to estimate the effect of gene-environment interactions on BMI for 131 lifestyle factors related to: dietary habits, smoking and alcohol consumption, physical activity, socioeconomic status, mental health, sleeping patterns, as well as female-specific factors such as menopause and childbirth. In total, 15 lifestyle factors were observed to interact with GSBMI, of which alcohol intake frequency, usual walking pace, and Townsend deprivation index, a measure of socioeconomic status, were all highly significant (p = 1.45*10-29, p = 3.83*10-26, p = 4.66*10-11, respectively). Interestingly, the frequency of alcohol consumption, rather than the total weekly amount resulted in a significant interaction. The FTO locus was the strongest single locus interacting with any of the lifestyle factors. However, 13 significant interactions were also observed after omitting the FTO locus from the genetic score. Our analyses indicate that many lifestyle factors modify the genetic effects on BMI with some groups of individuals having more than double the effect of the genetic score. However, the underlying causal mechanisms of gene-environmental interactions are difficult to deduce from cross-sectional data alone and controlled experiments are required to fully characterise the causal factors.

The relative contribution of DNA methylation and genetic variants on protein biomarkers for human diseases.

Associations between epigenetic alterations and disease status have been identified for many diseases. However, there is no strong evidence that epigenetic alterations are directly causal for disease pathogenesis. In this study, we combined SNP and DNA methylation data with measurements of protein biomarkers for cancer, inflammation or cardiovascular disease, to investigate the relative contribution of genetic and epigenetic variation on biomarker levels. A total of 121 protein biomarkers were measured and analyzed in relation to DNA methylation at 470,000 genomic positions and to over 10 million SNPs. We performed epigenome-wide association study (EWAS) and genome-wide association study (GWAS) analyses, and integrated biomarker, DNA methylation and SNP data using between 698 and 1033 samples depending on data availability for the different analyses. We identified 124 and 45 loci (Bonferroni adjusted P < 0.05) with effect sizes up to 0.22 standard units' change per 1% change in DNA methylation levels and up to four standard units' change per copy of the effective allele in the EWAS and GWAS respectively. Most GWAS loci were cis-regulatory whereas most EWAS loci were located in trans. Eleven EWAS loci were associated with multiple biomarkers, including one in NLRC5 associated with CXCL11, CXCL9, IL-12, and IL-18 levels. All EWAS signals that overlapped with a GWAS locus were driven by underlying genetic variants and three EWAS signals were confounded by smoking. While some cis-regulatory SNPs for biomarkers appeared to have an effect also on DNA methylation levels, cis-regulatory SNPs for DNA methylation were not observed to affect biomarker levels. We present associations between protein biomarker and DNA methylation levels at numerous loci in the genome. The associations are likely to reflect the underlying pattern of genetic variants, specific environmental exposures, or represent secondary effects to the pathogenesis of disease.

Tea and coffee consumption in relation to DNA methylation in four European cohorts.

Lifestyle factors, such as food choices and exposure to chemicals, can alter DNA methylation and lead to changes in gene activity. Two such exposures with pharmacologically active components are coffee and tea consumption. Both coffee and tea have been suggested to play an important role in modulating disease-risk in humans by suppressing tumour progression, decreasing inflammation and influencing estrogen metabolism. These mechanisms may be mediated by changes in DNA methylation. To investigate if DNA methylation in blood is associated with coffee and tea consumption, we performed a genome-wide DNA methylation study for coffee and tea consumption in four European cohorts (N = 3,096). DNA methylation was measured from whole blood at 421,695 CpG sites distributed throughout the genome and analysed in men and women both separately and together in each cohort. Meta-analyses of the results and additional regional-level analyses were performed. After adjusting for multiple testing, the meta-analysis revealed that two individual CpG-sites, mapping to DNAJC16 and TTC17, were differentially methylated in relation to tea consumption in women. No individual sites were associated with men or with the sex-combined analysis for tea or coffee. The regional analysis revealed that 28 regions were differentially methylated in relation to tea consumption in women. These regions contained genes known to interact with estradiol metabolism and cancer. No significant regions were found in the sex-combined and male-only analysis for either tea or coffee consumption.

Body Mass Index and the Risk of Rheumatic Disease: Linear and Nonlinear Mendelian Randomization Analyses.

OBJECTIVE: Although the association between obesity and risk of rheumatic disease is well established, the precise causal relation has not been conclusively proven. Here, we estimate the causal effect of body mass index (BMI) on the risk of developing 5 different rheumatic diseases. METHODS: Linear and nonlinear mendelian randomization (MR) were used to estimate the effect of BMI on risk of rheumatic disease, and sex-specific effects were identified. Analyses were performed in 361,952 participants from the UK Biobank cohort for 5 rheumatic diseases: rheumatoid arthritis (n = 8,381 cases), osteoarthritis (n = 87,430), psoriatic arthropathy (n = 933), gout (n = 13,638), and inflammatory spondylitis (n = 4,328). RESULTS: Using linear MR, we found that 1 SD increase in BMI increases the incidence rate for rheumatoid arthritis (incidence rate ratio [IRR] 1.52 [95% confidence interval (95% CI) 1.36-1.69]), osteoarthritis (IRR 1.49 [95% CI 1.43-1.55]), psoriatic arthropathy (IRR 1.80 [95% CI 1.31-2.48]), gout (IRR 1.73 [95% CI 1.56-1.92]), and inflammatory spondylitis (IRR 1.34 [95% CI 1.14-1.57]) in all individuals. BMI was found to be a stronger risk factor in women compared to men for psoriatic arthropathy (P for sex interaction = 3.3 × 10-4 ) and gout (P for sex interaction = 4.3 × 10-3 ), and the effect on osteoarthritis was stronger in premenopausal compared to postmenopausal women (P = 1.8 × 10-3 ). Nonlinear effects of BMI were identified for osteoarthritis and gout in men, and for gout in women. The nonlinearity for gout was also more extreme in men compared to women (P = 0.03). CONCLUSION: Higher BMI causes an increased risk for rheumatic disease, an effect that is more pronounced in women for both gout and psoriatic arthropathy. The novel sex- and BMI-specific causal effects identified here provide further insight into rheumatic disease etiology and mark an important step toward personalized medicine.

Adiposity and sex-specific cancer risk.

Obesity is associated with several types of cancer and fat distribution, which differs dramatically between sexes, has been suggested to be an independent risk factor. However, sex-specific effects on cancer risk have rarely been studied. Here we estimate the effects of fat accumulation and distribution on cancer risk in females and males. We performed a prospective study in 442,519 UK Biobank participants, for 19 cancer types and additional histological subtypes, with a mean follow-up time of 13.4 years. Cox proportional hazard models were used to estimate the effect of 14 different adiposity phenotypes on cancer rates, and a 5% false discovery rate was considered statistically significant. Adiposity-related traits are associated with all but three cancer types, and fat accumulation is associated with a larger number of cancers compared to fat distribution. In addition, fat accumulation or distribution exhibit differential effects between sexes on colorectal, esophageal, and liver cancer.

Gene-Based Variant Analysis of Whole-Exome Sequencing in Relation to Eosinophil Count.

Eosinophils play important roles in the release of cytokine mediators in response to inflammation. Many associations between common genetic variants and eosinophils have already been reported, using single nucleotide polymorphism (SNP) array data. Here, we have analyzed 200,000 whole-exome sequences (WES) from the UK Biobank cohort and performed gene-based analyses of eosinophil count. We defined five different variant weighting schemes to incorporate information on both deleteriousness and frequency. A total of 220 genes in 55 distinct (>10 Mb apart) genomic regions were found to be associated with eosinophil count, of which seven genes (ALOX15, CSF2RB, IL17RA, IL33, JAK2, S1PR4, and SH2B3) are driven by rare variants, independent of common variants identified in genome-wide association studies. Two additional genes, NPAT and RMI1, have not been associated with eosinophil count before and are considered novel eosinophil loci. These results increase our knowledge about the effect of rare variants on eosinophil count, which can be of great value for further identification of therapeutic targets.

Investigating the Effect of Estradiol Levels on the Risk of Breast, Endometrial, and Ovarian Cancer.

Background: High levels of estrogen are associated with increased risk of breast and endometrial cancer and have been suggested to also play a role in the development of ovarian cancer. Cancerogenic effects of estradiol, the most prominent form of estrogen, have been highlighted as a side effect of estrogen-only menopausal hormone therapy. However, whether high levels of endogenous estrogens, produced within the body, promote cancer development, has not been fully established. Objective: We aimed to examine causal effects of estradiol on breast, endometrial, and ovarian cancer. Methods: Here we performed a two-sample Mendelian randomization (MR) to estimate the effect of endogenous estradiol on the risk of developing breast, endometrial, and ovarian cancer, using the UK Biobank as well as 3 independent cancer cohorts. Results: Using 3 independent instrumental variables, we showed that higher estradiol levels significantly increase the risk for ovarian cancer (OR = 3.18 [95% CI, 1.47-6.87], P = 0.003). We also identified a nominally significant effect for ER-positive breast cancer (OR = 2.16 [95% CI, 1.09-4.26], P = 0.027). However, we could not establish a clear link to the risk of endometrial cancer (OR = 1.93 [95% CI, 0.77-4.80], P = 0.160). Conclusion: Our results suggest that high estradiol levels promote the development of ovarian and ER-positive breast cancer.

Contribution of rare whole-genome sequencing variants to plasma protein levels and the missing heritability.

Despite the success of genome-wide association studies, much of the genetic contribution to complex traits remains unexplained. Here, we analyse high coverage whole-genome sequencing data, to evaluate the contribution of rare genetic variants to 414 plasma proteins. The frequency distribution of genetic variants is skewed towards the rare spectrum, and damaging variants are more often rare. We estimate that less than 4.3% of the narrow-sense heritability is expected to be explained by rare variants in our cohort. Using a gene-based approach, we identify Cis-associations for 237 of the proteins, which is slightly more compared to a GWAS (N = 213), and we identify 34 associated loci in Trans. Several associations are driven by rare variants, which have larger effects, on average. We therefore conclude that rare variants could be of importance for precision medicine applications, but have a more limited contribution to the missing heritability of complex diseases.

Modification of Heritability for Educational Attainment and Fluid Intelligence by Socioeconomic Deprivation in the UK Biobank.

OBJECTIVE: Socioeconomic factors have been suggested to influence the effect of education- and intelligence-associated genetic variants. However, results from previous studies on the interaction between socioeconomic status and education or intelligence have been inconsistent. The authors sought to assess these interactions in the UK Biobank cohort of 500,000 participants. METHODS: The authors assessed the effect of socioeconomic deprivation on education- and intelligence-associated genetic variants by estimating the single-nucleotide polymorphism (SNP) heritability for fluid intelligence, educational attainment, and years of education in subsets of UK Biobank participants with different degrees of social deprivation, using linkage disequilibrium score regression. They also generated polygenic scores with LDpred and tested for interactions with social deprivation. RESULTS: SNP heritability increased with socioeconomic deprivation for fluid intelligence, educational attainment, and years of education. Polygenic scores were also found to interact with socioeconomic deprivation, where the effects of the scores increased with increasing deprivation for all traits. CONCLUSIONS: These results indicate that genetics have a larger influence on educational and cognitive outcomes in more socioeconomically deprived U.K. citizens, which has serious implications for equality of opportunity.

Time-Dependent Effects of Oral Contraceptive Use on Breast, Ovarian, and Endometrial Cancers.

Oral contraceptive use has been suggested to influence the risk of breast, ovarian, and endometrial cancer. The purpose of this study is to clarify the time-dependent effects between long-term oral contraceptive use and cancer risk. We performed an observational study in 256,661 women from UK Biobank, born between 1939 and 1970. Information on cancer diagnoses were collected from self-reported data and from national registers until March 2019. Cumulative risk of cancer over the timespan of the study, as measured by the OR, and instantaneous risk, as measured by the HR, were assessed using Logistic and Cox regression analyses, respectively. The odds were lower among ever users, compared with never users, for ovarian cancer [OR = 0.72; 95% confidence interval (CI), 0.65-0.81] and endometrial cancer (OR = 0.68; 95% CI, 0.62-0.75), an association that was stronger with longer use (P < 0.001). Increased odds were seen for breast cancer in women when limiting the follow-up to 55 years of age (OR = 1.10; 95% CI, 1.03-1.17), but not for the full timespan. We only found a higher HR for breast cancer in former users immediately (≤2 years) after discontinued oral contraceptive use (HR = 1.55; 95% CI, 1.06-2.28), whereas the protective association for ovarian and endometrial cancer remained significant up to 35 years after last use of oral contraceptives. Given the body of evidence presented in our study, we argue that oral contraceptives can dramatically reduce women's risk of ovarian and endometrial cancer, whereas their effect on lifetime risk of breast cancer is limited. SIGNIFICANCE: These results enable women and physicians to make more informed decisions considering oral contraceptive use, thus constituting an important step toward personalized medicine.

Causal effects of inflammatory protein biomarkers on inflammatory diseases.

Many circulating proteins are associated with the presence or severity of disease. However, whether these protein biomarkers are causal for disease development is usually unknown. We investigated the causal effect of 21 well-known or exploratory protein biomarkers of inflammation on 18 inflammatory diseases using two-sample Mendelian randomization. We identified six proteins to have causal effects on any of 11 inflammatory diseases (FDR < 0.05, corresponding to P < 1.4 × 10­3). IL-12B protects against psoriasis and psoriatic arthropathy, LAP-TGF-ß-1 protects against osteoarthritis, TWEAK protects against asthma, VEGF-A protects against ulcerative colitis, and LT-α protects against both type 1 diabetes and rheumatoid arthritis. In contrast, IL-18R1 increases the risk of developing allergy, hay fever, and eczema. Most proteins showed protective effects against development of disease rather than increasing disease risk, which indicates that many disease-related biomarkers are expressed to protect from tissue damage. These proteins represent potential intervention points for disease prevention and treatment.

Genome-wide Association Study of Estradiol Levels and the Causal Effect of Estradiol on Bone Mineral Density.

CONTEXT: Estradiol is the primary female sex hormone and plays an important role for skeletal health in both sexes. Several enzymes are involved in estradiol metabolism, but few genome-wide association studies (GWAS) have been performed to characterize the genetic contribution to variation in estrogen levels. OBJECTIVE: Identify genetic loci affecting estradiol levels and estimate causal effect of estradiol on bone mineral density (BMD). DESIGN: We performed GWAS for estradiol in males (n = 147 690) and females (n = 163 985) from UK Biobank. Estradiol was analyzed as a binary phenotype above/below detection limit (175 pmol/L). We further estimated the causal effect of estradiol on BMD using Mendelian randomization. RESULTS: We identified 14 independent loci associated (P < 5 × 10-8) with estradiol levels in males, of which 1 (CYP3A7) was genome-wide and 7 nominally (P < 0.05) significant in females. In addition, 1 female-specific locus was identified. Most loci contain functionally relevant genes that have not been discussed in relation to estradiol levels in previous GWAS (eg, SRD5A2, which encodes a steroid 5-alpha reductase that is involved in processing androgens, and UGT3A1 and UGT2B7, which encode enzymes likely to be involved in estradiol elimination). The allele that tags the O blood group at the ABO locus was associated with higher estradiol levels. We identified a causal effect of high estradiol levels on increased BMD in both males (P = 1.58 × 10-11) and females (P = 7.48 × 10-6). CONCLUSION: Our findings further support the importance of the body's own estrogen to maintain skeletal health in males and in females.

Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects.

Body mass and body fat composition are of clinical interest due to their links to cardiovascular- and metabolic diseases. Fat stored in the trunk has been suggested to be more pathogenic compared to fat stored in other compartments. In this study, we perform genome-wide association studies (GWAS) for the proportion of body fat distributed to the arms, legs and trunk estimated from segmental bio-electrical impedance analysis (sBIA) for 362,499 individuals from the UK Biobank. 98 independent associations with body fat distribution are identified, 29 that have not previously been associated with anthropometric traits. A high degree of sex-heterogeneity is observed and the effects of 37 associated variants are stronger in females compared to males. Our findings also implicate that body fat distribution in females involves mesenchyme derived tissues and cell types, female endocrine tissues as well as extracellular matrix maintenance and remodeling.

Improved power and precision with whole genome sequencing data in genome-wide association studies of inflammatory biomarkers.

Genome-wide association studies (GWAS) have identified associations between thousands of common genetic variants and human traits. However, common variants usually explain a limited fraction of the heritability of a trait. A powerful resource for identifying trait-associated variants is whole genome sequencing (WGS) data in cohorts comprised of families or individuals from a limited geographical area. To evaluate the power of WGS compared to imputations, we performed GWAS on WGS data for 72 inflammatory biomarkers, in a kinship-structured cohort. When using WGS data, we identified 18 novel associations that were not detected when analyzing the same biomarkers with genotyped or imputed SNPs. Five of the novel top variants were low frequency variants with a minor allele frequency (MAF) of <5%. Our results suggest that, even when applying a GWAS approach, we gain power and precision using WGS data, presumably due to more accurate determination of genotypes. The lack of a comparable dataset for replication of our results is a limitation in our study. However, this further highlights that there is a need for more genetic epidemiological studies based on WGS data.