SummaryBackgroundAmyotrophic lateral sclerosis shares characteristics with some cancers, such as onset being more common in later life, progression usually being rapid, the disease affecting a particular cell type, and showing complex inheritance. We used a model originally applied to cancer epidemiology to investigate the hypothesis that amyotrophic lateral sclerosis is a multistep process.MethodsWe generated incidence data by age and sex from amyotrophic lateral sclerosis population registers in Ireland (registration dates 1995–2012), the Netherlands (2006–12), Italy (1995–2004), Scotland (1989–98), and England (2002–09), and calculated age and sex-adjusted incidences for each register. We regressed the log of age-specific incidence against the log of age with least squares regression. We did the analyses within each register, and also did a combined analysis, adjusting for register.FindingsWe identified 6274 cases of amyotrophic lateral sclerosis from a catchment population of about 34 million people. We noted a linear relationship between log incidence and log age in all five registers: England r2=0·95, Ireland r2=0·99, Italy r2=0·95, the Netherlands r2=0·99, and Scotland r2=0·97; overall r2=0·99. All five registers gave similar estimates of the linear slope ranging from 4·5 to 5·1, with overlapping confidence intervals. The combination of all five registers gave an overall slope of 4·8 (95% CI 4·5–5·0), with similar estimates for men (4·6, 4·3–4·9) and women (5·0, 4·5–5·5).InterpretationA linear relationship between the log incidence and log age of onset of amyotrophic lateral sclerosis is consistent with a multistage model of disease. The slope estimate suggests that amyotrophic lateral sclerosis is a six-step process. Identification of these steps could lead to preventive and therapeutic avenues.FundingUK Medical Research Council; UK Economic and Social Research Council; Ireland Health Research Board; The Netherlands Organisation for Health Research and Development (ZonMw); the Ministry of Health and Ministry of Education, University, and Research in Italy; the Motor Neurone Disease Association of England, Wales, and Northern Ireland; and the European Commission (Seventh Framework Programme).

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We conducted a genome-wide association study among 2,323 individuals with sporadic amyotrophic lateral sclerosis (ALS) and 9,013 control subjects and evaluated all SNPs with P < 1.0 x 10(-4) in a second, independent cohort of 2,532 affected individuals and 5,940 controls. Analysis of the genome-wide data revealed genome-wide significance for one SNP, rs12608932, with P = 1.30 x 10(-9). This SNP showed robust replication in the second cohort (P = 1.86 x 10(-6)), and a combined analysis over the two stages yielded P = 2.53 x 10(-14). The rs12608932 SNP is located at 19p13.3 and maps to a haplotype block within the boundaries of UNC13A, which regulates the release of neurotransmitters such as glutamate at neuromuscular synapses. Follow-up of additional SNPs showed genome-wide significance for two further SNPs (rs2814707, with P = 7.45 x 10(-9), and rs3849942, with P = 1.01 x 10(-8)) in the combined analysis of both stages. These SNPs are located at chromosome 9p21.2, in a linkage region for familial ALS with frontotemporal dementia found previously in several large pedigrees.

Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, several treatment methods are proposed in the literature: model-based methods within the framework of exponential random graph models, and imputation methods. In this paper we focus on the latter group of methods, and investigate the use of some simple imputation procedures to handle missing network data. The results of a simulation study show that ignoring the missing data can have large negative effects on structural properties of the network. Missing data treatment based on simple imputation procedures, however, does also have large negative effects and simple imputations can only successfully correct for non-response in a few specific situations.

Population based epidemiology of amyotrophic lateral sclerosis using capture-recapture methodology Huisman, M.H.B.; de Jong, S.W.; van Doormaal, P.T.C.; Weinreich, S.S.; Schelhaas, H.J.; van der Kooi, A.J.; Visser, M.; Veldink, J.H.; Berg, L.H.