The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes.

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.

SummaryBackgroundOsteoarthritis is the most common form of arthritis worldwide and is a major cause of pain and disability in elderly people. The health economic burden of osteoarthritis is increasing commensurate with obesity prevalence and longevity. Osteoarthritis has a strong genetic component but the success of previous genetic studies has been restricted due to insufficient sample sizes and phenotype heterogeneity.MethodsWe undertook a large genome-wide association study (GWAS) in 7410 unrelated and retrospectively and prospectively selected patients with severe osteoarthritis in the arcOGEN study, 80% of whom had undergone total joint replacement, and 11 009 unrelated controls from the UK. We replicated the most promising signals in an independent set of up to 7473 cases and 42 938 controls, from studies in Iceland, Estonia, the Netherlands, and the UK. All patients and controls were of European descent.FindingsWe identified five genome-wide significant loci (binomial test p≤5·0×10−8) for association with osteoarthritis and three loci just below this threshold. The strongest association was on chromosome 3 with rs6976 (odds ratio 1·12 [95% CI 1·08–1·16]; p=7·24×10−11), which is in perfect linkage disequilibrium with rs11177. This SNP encodes a missense polymorphism within the nucleostemin-encoding gene GNL3. Levels of nucleostemin were raised in chondrocytes from patients with osteoarthritis in functional studies. Other significant loci were on chromosome 9 close to ASTN2, chromosome 6 between FILIP1 and SENP6, chromosome 12 close to KLHDC5 and PTHLH, and in another region of chromosome 12 close to CHST11. One of the signals close to genome-wide significance was within the FTO gene, which is involved in regulation of bodyweight—a strong risk factor for osteoarthritis. All risk variants were common in frequency and exerted small effects.InterpretationOur findings provide insight into the genetics of arthritis and identify new pathways that might be amenable to future therapeutic intervention.FundingarcOGEN was funded by a special purpose grant from Arthritis Research UK.

Background Accurate diagnosis and early detection of complex disease has the potential to be of enormous benefit to clinical trialists, patients, and researchers alike. We sought to create a non-invasive, low-cost, and accurate classification model for diagnosing Parkinson’s disease risk to serve as a basis for future disease prediction studies in prospective longitudinal cohorts. Methods We developed a simple disease classifying model within 367 patients with Parkinson’s disease and phenotypically typical imaging data and 165 controls without neurological disease of the Parkinson’s Progression Marker Initiative (PPMI) study. Olfactory function, genetic risk, family history of PD, age and gender were algorithmically selected as significant contributors to our classifying model. This model was developed using the PPMI study then tested in 825 patients with Parkinson’s disease and 261 controls from five independent studies with varying recruitment strategies and designs including the Parkinson’s Disease Biomarkers Program (PDBP), Parkinson’s Associated Risk Study (PARS), 23andMe, Longitudinal and Biomarker Study in PD (LABS-PD), and Morris K. Udall Parkinson’s Disease Research Center of Excellence (Penn-Udall). Findings Our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0.923 (95% CI = 0.900 – 0.946) with high sensitivity (0.834, 95% CI = 0.711 – 0.883) and specificity (0.903, 95% CI = 0.824 – 0.946) in PPMI at its optimal AUC threshold (0.655). The model is also well-calibrated with all Hosmer-Lemeshow simulations suggesting that when parsed into random subgroups, the actual data mirrors that of the larger expected data, demonstrating that our model is robust and fits well. Likewise external validation shows excellent classification of PD with AUCs of 0.894 in PDBP, 0.998 in PARS, 0.955 in 23andMe, 0.929 in LABS-PD, and 0.939 in Penn-Udall. Additionally, when our model classifies SWEDD as PD, they convert within one year to typical PD more than would be expected by chance, with 4 out of 17 classified as PD converting to PD during brief follow-up; while SWEDD not classified as PD showed one conversion to PD out of 38 participants (test of proportions, p-value = 0.003). Interpretation This model may serve as a basis for future investigations into the classification, prediction and treatment of Parkinson’s disease, particularly those planning on attempting to identify prodromal or preclinical etiologically typical PD cases in prospective cohorts for efficient interventional and biomarker studies. Funding Please see the acknowledgements and funding section at the end of the manuscript.

Background Several reports have identified different patterns of Parkinson's disease progression in individuals carrying missense variants in GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinson's disease, however, has not been well studied. Objectives To test the association between genetic variants and the clinical features of Parkinson's disease on a genomewide scale. Methods We accumulated individual data from 12 longitudinal cohorts in a total of 4093 patients with 22,307 observations for a median of 3.81 years. Genomewide associations were evaluated for 25 cross‐sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently identified disease‐risk variants, were also investigated post hoc for candidate associations with these phenotypes. Results Two variants were genomewide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (hazard ratio 2.04 [1.58–2.62]; P value = 3.46E‐8). Rs61863020(G>A), an intergenic variant and expression quantitative trait loci for α‐2A adrenergic receptor, was associated with a lower prevalence of insomnia at baseline (odds ratio 0.63 [0.52–0.75]; P value = 4.74E‐8). In the targeted analysis, we found 9 associations between known Parkinson's risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K; rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and an APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients. Conclusions We identified novel genetic factors associated with heterogeneity of Parkinson's disease. The results can be used for validation or hypothesis tests regarding Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society