Aaron G. Day-Williams

Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways

New players in Lou Gehrigs disease Amyotrophic lateral sclerosis (ALS), often referred to as “Lou Gehrigs disease,” is a progressive neurodegenerative disease that affects nerve cells in the brain and the spinal cord. Cirulli et al. sequenced the expressed genes of nearly 3000 ALS patients and compared them with those of more than 6000 controls (see the Perspective by Singleton and Traynor). They identified several proteins that were linked to disease in patients. One such protein, TBK1, is implicated in innate immunity and autophagy and may represent a therapeutic target. Science, this issue p. 1436; see also p. 1422 Analysis of the expressed genes of nearly 2900 patients with amyotrophic lateral sclerosis and about 6400 controls reveals a disease predisposition–associated gene. [Also see Perspective by Singleton and Traynor] Amyotrophic lateral sclerosis (ALS) is a devastating neurological disease with no effective treatment. We report the results of a moderate-scale sequencing study aimed at increasing the number of genes known to contribute to predisposition for ALS. We performed whole-exome sequencing of 2869 ALS patients and 6405 controls. Several known ALS genes were found to be associated, and TBK1 (the gene encoding TANK-binding kinase 1) was identified as an ALS gene. TBK1 is known to bind to and phosphorylate a number of proteins involved in innate immunity and autophagy, including optineurin (OPTN) and p62 (SQSTM1/sequestosome), both of which have also been implicated in ALS. These observations reveal a key role of the autophagic pathway in ALS and suggest specific targets for therapeutic intervention.

Identification of new susceptibility loci for osteoarthritis (arcOGEN): A genome-wide association study

Summary Background Osteoarthritis 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. Methods We 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. Findings We 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. Interpretation Our findings provide insight into the genetics of arthritis and identify new pathways that might be amenable to future therapeutic intervention. Funding arcOGEN was funded by a special purpose grant from Arthritis Research UK.

Insights into the genetic architecture of osteoarthritis from stage 1 of the arcOGEN study

Objectives The genetic aetiology of osteoarthritis has not yet been elucidated. To enable a well-powered genome-wide association study (GWAS) for osteoarthritis, the authors have formed the arcOGEN Consortium, a UK-wide collaborative effort aiming to scan genome-wide over 7500 osteoarthritis cases in a two-stage genome-wide association scan. Here the authors report the findings of the stage 1 interim analysis. Methods The authors have performed a genome-wide association scan for knee and hip osteoarthritis in 3177 cases and 4894 population-based controls from the UK. Replication of promising signals was carried out in silico in five further scans (44 449 individuals), and de novo in 14 534 independent samples, all of European descent. Results None of the association signals the authors identified reach genome-wide levels of statistical significance, therefore stressing the need for corroboration in sample sets of a larger size. Application of analytical approaches to examine the allelic architecture of disease to the stage 1 genome-wide association scan data suggests that osteoarthritis is a highly polygenic disease with multiple risk variants conferring small effects. Conclusions Identifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.

A meta-analysis of genome-wide association studies identifies novel variants associated with osteoarthritis of the hip

Objectives Osteoarthritis (OA) is the most common form of arthritis with a clear genetic component. To identify novel loci associated with hip OA we performed a meta-analysis of genome-wide association studies (GWAS) on European subjects. Methods We performed a two-stage meta-analysis on more than 78 000 participants. In stage 1, we synthesised data from eight GWAS whereas data from 10 centres were used for ‘in silico’ or ‘de novo’ replication. Besides the main analysis, a stratified by sex analysis was performed to detect possible sex-specific signals. Meta-analysis was performed using inverse-variance fixed effects models. A random effects approach was also used. Results We accumulated 11 277 cases of radiographic and symptomatic hip OA. We prioritised eight single nucleotide polymorphism (SNPs) for follow-up in the discovery stage (4349 OA cases); five from the combined analysis, two male specific and one female specific. One locus, at 20q13, represented by rs6094710 (minor allele frequency (MAF) 4%) near the NCOA3 (nuclear receptor coactivator 3) gene, reached genome-wide significance level with p=7.9×10−9 and OR=1.28 (95% CI 1.18 to 1.39) in the combined analysis of discovery (p=5.6×10−8) and follow-up studies (p=7.3×10−4). We showed that this gene is expressed in articular cartilage and its expression was significantly reduced in OA-affected cartilage. Moreover, two loci remained suggestive associated; rs5009270 at 7q31 (MAF 30%, p=9.9×10−7, OR=1.10) and rs3757837 at 7p13 (MAF 6%, p=2.2×10−6, OR=1.27 in male specific analysis). Conclusions Novel genetic loci for hip OA were found in this meta-analysis of GWAS.

Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study

BACKGROUND Accurate diagnosis and early detection of complex diseases, such as Parkinsons disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinsons disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. METHODS We developed a model for disease classification using data from the Parkinsons Progression Marker Initiative (PPMI) study for 367 patients with Parkinsons disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinsons disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinsons disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinsons Disease Biomarkers Program (PDBP), the Parkinsons Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinsons Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). FINDINGS In the population from PPMI, our initial model correctly distinguished patients with Parkinsons 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) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinsons disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinsons disease converted to Parkinsons disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinsons disease underwent conversion (test of proportions, p=0·003). INTERPRETATION Our model provides a potential new approach to distinguish participants with Parkinsons disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinsons disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. FUNDING National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.

Genome-wide association study in a Chinese population identifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4.

Aims/hypothesisMost genetic variants identified for type 2 diabetes have been discovered in European populations. We performed genome-wide association studies (GWAS) in a Chinese population with the aim of identifying novel variants for type 2 diabetes in Asians.MethodsWe performed a meta-analysis of three GWAS comprising 684 patients with type 2 diabetes and 955 controls of Southern Han Chinese descent. We followed up the top signals in two independent Southern Han Chinese cohorts (totalling 10,383 cases and 6,974 controls), and performed in silico replication in multiple populations.ResultsWe identified CDKN2A/B and four novel type 2 diabetes association signals with p < 1 × 10−5 from the meta-analysis. Thirteen variants within these four loci were followed up in two independent Chinese cohorts, and rs10229583 at 7q32 was found to be associated with type 2 diabetes in a combined analysis of 11,067 cases and 7,929 controls (pmeta = 2.6 × 10−8; OR [95% CI] 1.18 [1.11, 1.25]). In silico replication revealed consistent associations across multiethnic groups, including five East Asian populations (pmeta = 2.3 × 10−10) and a population of European descent (p = 8.6 × 10−3). The rs10229583 risk variant was associated with elevated fasting plasma glucose, impaired beta cell function in controls, and an earlier age at diagnosis for the cases. The novel variant lies within an islet-selective cluster of open regulatory elements. There was significant heterogeneity of effect between Han Chinese and individuals of European descent, Malaysians and Indians.Conclusions/interpretationOur study identifies rs10229583 near PAX4 as a novel locus for type 2 diabetes in Chinese and other populations and provides new insights into the pathogenesis of type 2 diabetes.

The effect of next-generation sequencing technology on complex trait research

Eur J Clin Invest 2011; 41 (5): 561–567

ARIEL and AMELIA: Testing for an Accumulation of Rare Variants Using Next-Generation Sequencing Data

Objectives: There is increasing evidence that rare variants play a role in some complex traits, but their analysis is not straightforward. Locus-based tests become necessary due to low power in rare variant single-point association analyses. In addition, variant quality scores are available for sequencing data, but are rarely taken into account. Here, we propose two locus-based methods that incorporate variant quality scores: a regression-based collapsing approach and an allele-matching method. Methods: Using simulated sequencing data we compare 4 locus-based tests of trait association under different scenarios of data quality. We test two collapsing-based approaches and two allele-matching-based approaches, taking into account variant quality scores and ignoring variant quality scores. We implement the collapsing and allele-matching approaches accounting for variant quality in the freely available ARIEL and AMELIA software. Results: The incorporation of variant quality scores in locus-based association tests has power advantages over weighting each variant equally. The allele-matching methods are robust to the presence of both protective and risk variants in a locus, while collapsing methods exhibit a dramatic loss of power in this scenario. Conclusions: The incorporation of variant quality scores should be a standard protocol when performing locus-based association analysis on sequencing data. The ARIEL and AMELIA software implement collapsing and allele-matching locus association analysis methods, respectively, that allow the incorporation of variant quality scores.

Evaluation of the genetic overlap between osteoarthritis with body mass index and height using genome-wide association scan data

Objectives Obesity as measured by body mass index (BMI) is one of the major risk factors for osteoarthritis. In addition, genetic overlap has been reported between osteoarthritis and normal adult height variation. We investigated whether this relationship is due to a shared genetic aetiology on a genome-wide scale. Methods We compared genetic association summary statistics (effect size, p value) for BMI and height from the GIANT consortium genome-wide association study (GWAS) with genetic association summary statistics from the arcOGEN consortium osteoarthritis GWAS. Significance was evaluated by permutation. Replication of osteoarthritis association of the highlighted signals was investigated in an independent dataset. Phenotypic information of height and BMI was accounted for in a separate analysis using osteoarthritis-free controls. Results We found significant overlap between osteoarthritis and height (p=3.3×10−5 for signals with p≤0.05) when the GIANT and arcOGEN GWAS were compared. For signals with p≤0.001 we found 17 shared signals between osteoarthritis and height and four between osteoarthritis and BMI. However, only one of the height or BMI signals that had shown evidence of association with osteoarthritis in the arcOGEN GWAS was also associated with osteoarthritis in the independent dataset: rs12149832, within the FTO gene (combined p=2.3×10−5). As expected, this signal was attenuated when we adjusted for BMI. Conclusions We found a significant excess of shared signals between both osteoarthritis and height and osteoarthritis and BMI, suggestive of a common genetic aetiology. However, only one signal showed association with osteoarthritis when followed up in a new dataset.

Linkage Analysis without Defined Pedigrees

The need to collect accurate and complete pedigree information has been a drawback of family‐based linkage and association studies. Even in case‐control studies, investigators should be aware of, and condition on, familial relationships. In single nucleotide polymorphism (SNP) genome scans, relatedness can be directly inferred from the genetic data rather than determined through interviews. Various methods of estimating relatedness have previously been implemented, most notably in PLINK. We present new fast and accurate algorithms for estimating global and local kinship coefficients from dense SNP genotypes. These algorithms require only a single pass through the SNP genotype data. We also show that these estimates can be used to cluster individuals into pedigrees. With these estimates in hand, quantitative trait locus linkage analysis proceeds via traditional variance components methods without any prior relationship information. We demonstrate the success of our algorithms on simulated and real data sets. Our procedures make linkage analysis as easy as a typical genomewide association study. Genet. Epidemiol. 2011.