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Genetic architecture describes the characteristics of genetic variation that are responsible for heritable phenotypic variability. It depends on the number of genetic variants affecting a trait, their frequencies in the population, the magnitude of their effects and their interactions with each other and the environment. Defining the genetic architecture of a complex trait or disease is central to the scientific and clinical goals of human genetics, which are to understand disease aetiology and aid in disease screening, diagnosis, prognosis and therapy. Recent technological advances have enabled genome-wide association studies and emerging next-generation sequencing studies to begin to decipher the nature of the heritable contribution to traits and disease. Here, we describe the types of genetic architecture that have been observed, how architecture can be measured and why an improved understanding of genetic architecture is central to future advances in the field.

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets. Genome-wide association studies comprising 1,091 metabolites and 309 metabolite ratios in 8,299 individuals from the Canadian Longitudinal Study on Aging provide insights into the genetic architecture of metabolites and their role in human diseases.

Observational studies have demonstrated an association between decreased vitamin D level and risk of multiple sclerosis (MS); however, it remains unclear whether this relationship is causal. We undertook a Mendelian randomization (MR) study to evaluate whether genetically lowered vitamin D level influences the risk of MS. We identified single nucleotide polymorphisms (SNPs) associated with 25-hydroxyvitamin D (25OHD) level from SUNLIGHT, the largest (n = 33,996) genome-wide association study to date for vitamin D. Four SNPs were genome-wide significant for 25OHD level (p-values ranging from 6 × 10-10 to 2 × 10-109), and all four SNPs lay in, or near, genes strongly implicated in separate mechanisms influencing 25OHD. We then ascertained their effect on 25OHD level in 2,347 participants from a population-based cohort, the Canadian Multicentre Osteoporosis Study, and tested the extent to which the 25OHD-decreasing alleles explained variation in 25OHD level. We found that the count of 25OHD-decreasing alleles across these four SNPs was strongly associated with lower 25OHD level (n = 2,347, F-test statistic = 49.7, p = 2.4 × 10-12). Next, we conducted an MR study to describe the effect of genetically lowered 25OHD on the odds of MS in the International Multiple Sclerosis Genetics Consortium study, the largest genetic association study to date for MS (including up to 14,498 cases and 24,091 healthy controls). Alleles were weighted by their relative effect on 25OHD level, and sensitivity analyses were performed to test MR assumptions. MR analyses found that each genetically determined one-standard-deviation decrease in log-transformed 25OHD level conferred a 2.0-fold increase in the odds of MS (95% CI: 1.7-2.5; p = 7.7 × 10-12; I2 = 63%, 95% CI: 0%-88%). This result persisted in sensitivity analyses excluding SNPs possibly influenced by population stratification or pleiotropy (odds ratio [OR] = 1.7, 95% CI: 1.3-2.2; p = 2.3 × 10-5; I2 = 47%, 95% CI: 0%-85%) and including only SNPs involved in 25OHD synthesis or metabolism (ORsynthesis = 2.1, 95% CI: 1.6-2.6, p = 1 × 10-9; ORmetabolism = 1.9, 95% CI: 1.3-2.7, p = 0.002). While these sensitivity analyses decreased the possibility that pleiotropy may have biased the results, residual pleiotropy is difficult to exclude entirely. A genetically lowered 25OHD level is strongly associated with increased susceptibility to MS. Whether vitamin D sufficiency can delay, or prevent, MS onset merits further investigation in long-term randomized controlled trials.

Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program. A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed \"gSOS\", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk. Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.

This randomized trial showed that two different doses of oral cannabidiol resulted in greater reductions in drop-seizure and total-seizure frequencies than placebo among patients with the Lennox–Gastaut syndrome, a severe developmental epileptic encephalopathy.

To identify circulating proteins influencing Coronavirus Disease 2019 (COVID-19) susceptibility and severity, we undertook a two-sample Mendelian randomization (MR) study, rapidly scanning hundreds of circulating proteins while reducing bias due to reverse causation and confounding. In up to 14,134 cases and 1.2 million controls, we found that an s.d. increase in OAS1 levels was associated with reduced COVID-19 death or ventilation (odds ratio (OR) = 0.54, P  = 7 × 10 −8 ), hospitalization (OR = 0.61, P  = 8 × 10 −8 ) and susceptibility (OR = 0.78, P  = 8 × 10 −6 ). Measuring OAS1 levels in 504 individuals, we found that higher plasma OAS1 levels in a non-infectious state were associated with reduced COVID-19 susceptibility and severity. Further analyses suggested that a Neanderthal isoform of OAS1 in individuals of European ancestry affords this protection. Thus, evidence from MR and a case–control study support a protective role for OAS1 in COVID-19 adverse outcomes. Available pharmacological agents that increase OAS1 levels could be prioritized for drug development. A variant of the OAS1 gene, which encodes an enzyme that is critical for the innate immune response to viral infections, is associated with decreased risk of death in patients with COVID-19.

Background Many different methods exist to adjust for variability in cell-type mixture proportions when analyzing DNA methylation studies. Here we present the result of an extensive simulation study, built on cell-separated DNA methylation profiles from Illumina Infinium 450K methylation data, to compare the performance of eight methods including the most commonly used approaches. Results We designed a rich multi-layered simulation containing a set of probes with true associations with either binary or continuous phenotypes, confounding by cell type, variability in means and standard deviations for population parameters, additional variability at the level of an individual cell-type-specific sample, and variability in the mixture proportions across samples. Performance varied quite substantially across methods and simulations. In particular, the number of false positives was sometimes unrealistically high, indicating limited ability to discriminate the true signals from those appearing significant through confounding. Methods that filtered probes had consequently poor power. QQ plots of p values across all tested probes showed that adjustments did not always improve the distribution. The same methods were used to examine associations between smoking and methylation data from a case–control study of colorectal cancer, and we also explored the effect of cell-type adjustments on associations between rheumatoid arthritis cases and controls. Conclusions We recommend surrogate variable analysis for cell-type mixture adjustment since performance was stable under all our simulated scenarios.

Dysembryoplastic neuroepithelial tumor (DNET) is a benign brain tumor associated with intractable drug-resistant epilepsy. In order to identify underlying genetic alterations and molecular mechanisms, we examined three family members affected by multinodular DNETs as well as 100 sporadic tumors from 96 patients, which had been referred to us as DNETs. We performed whole-exome sequencing on 46 tumors and targeted sequencing for hotspot FGFR1 mutations and BRAF p.V600E was used on the remaining samples. FISH, copy number variation assays and Sanger sequencing were used to validate the findings. By whole-exome sequencing of the familial cases, we identified a novel germline FGFR1 mutation, p.R661P. Somatic activating FGFR1 mutations (p.N546K or p.K656E) were observed in the tumor samples and further evidence for functional relevance was obtained by in silico modeling. The FGFR1 p.K656E mutation was confirmed to be in cis with the germline p.R661P variant. In 43 sporadic cases, in which the diagnosis of DNET could be confirmed on central blinded neuropathology review, FGFR1 alterations were also frequent and mainly comprised intragenic tyrosine kinase FGFR1 duplication and multiple mutants in cis (25/43; 58.1 %) while BRAF p.V600E alterations were absent (0/43). In contrast, in 53 cases, in which the diagnosis of DNET was not confirmed, FGFR1 alterations were less common (10/53; 19 %; p  < 0.0001) and hotspot BRAF p.V600E (12/53; 22.6 %) ( p  < 0.001) prevailed. We observed overexpression of phospho-ERK in FGFR1 p.R661P and p.N546K mutant expressing HEK293 cells as well as FGFR1 mutated tumor samples, supporting enhanced MAP kinase pathway activation under these conditions. In conclusion, constitutional and somatic FGFR1 alterations and MAP kinase pathway activation are key events in the pathogenesis of DNET. These findings point the way towards existing targeted therapies.

Purpose Identifying rare genetic causes of common diseases can improve diagnostic and treatment strategies, but incurs high costs. We tested whether individuals with common disease and low polygenic risk score (PRS) for that disease generated from less expensive genome-wide genotyping data are more likely to carry rare pathogenic variants. Methods We identified patients with one of five common complex diseases among 44,550 individuals who underwent exome sequencing in the UK Biobank. We derived PRS for these five diseases, and identified pathogenic rare variant heterozygotes. We tested whether individuals with disease and low PRS were more likely to carry rare pathogenic variants. Results While rare pathogenic variants conferred, at most, 5.18-fold (95% confidence interval [CI]: 2.32–10.13) increased odds of disease, a standard deviation increase in PRS, at most, increased the odds of disease by 5.25-fold (95% CI: 5.06–5.45). Among diseased patients, a standard deviation decrease in the PRS was associated with, at most, 2.82-fold (95% CI: 1.14–7.46) increased odds of identifying rare variant heterozygotes. Conclusion Rare pathogenic variants were more prevalent among affected patients with a low PRS. Therefore, prioritizing individuals for sequencing who have disease but low PRS may increase the yield of sequencing studies to identify rare variant heterozygotes.