Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Effectiveness of three interventions for secondary prevention of low back pain in the occupational health setting - a randomised controlled trial with a natural course control

Abstract: We assessed the effectiveness of three interventions that were aimed to reduce non-acute low back pain (LBP) related symptoms in the occupational health setting. Based on a survey (n = 2480; response rate 71%) on LBP, we selected a cohort of 193 employees who reported moderate LBP (Visual Analogue Scale VAS > 34 mm) and fulfilled at least one of the following criteria during the past 12 months: sciatica, recurrence of LBP ≥ 2 times, LBP ≥ 2 weeks, or previous sickness absence. A random sample was extracted from the cohort as a control group (Control, n = 50), representing the natural course of LBP. The remaining 143 employees were invited to participate in a randomised controlled trial (RCT) of three 1:1:1 allocated parallel intervention arms: multidisciplinary rehabilitation (Rehab, n = 43); progressive exercises (Physio, n = 43) and self-care advice (Advice, n = 40). Seventeen employees declined participation in the intervention. The primary outcome measures were physical impairment (PHI), LBP intensity (Visual Analogue Scale), health related quality of life (QoL), and accumulated sickness absence days. We imputed missing values with multiple imputation procedure. We assessed all comparisons between the intervention groups and the Control group by analysing questionnaire outcomes at 2 years with ANOVA and sickness absence at 4 years by using negative binomial model with a logarithmic link function. Mean differences between the Rehab and Control groups were − 3 [95% CI -5 to − 1] for PHI, − 13 [− 24 to − 1] for pain intensity, and 0.06 [0.00 to 0.12] for QoL. Mean differences between the Physio and Control groups were − 3 [95% CI -5 to − 1] for PHI, − 13 [− 29 to 2] for pain intensity, and 0.07 [0.01 to 0.13] for QoL. The main effects sizes were from 0.4 to 0.6. The interventions were not effective in reducing sickness absence. Rehab and Physio interventions improved health related quality of life, decreased low back pain and physical impairment in non-acute, moderate LBP, but we found no differences between the Advice and Control group results. No effectiveness on sickness absence was observed. Number NCT00908102 Clinicaltrials.gov

An empirical comparison of case-control and trio based study designs in high throughput association mapping

Abstract: Motivated by high throughput genotyping technology, our aim in this study was to experimentally compare the power and accuracy of case‐control and family trio based approaches for haplotype based, large scale, association gene mapping. We compared trio based and case‐control study designs in different disease models, and partitioned the performance differences into separate components: those from the sample ascertainment, the effective sample size, and the haplotyping approaches. For systematic and controlled tests, we simulated a rapidly expanding and relatively young isolated population. The experiments were also replicated with real asthma data. We used computationally efficient methods that scale up to large amounts of both markers and individuals. Mapping is based on a haplotype association test for haplotypes of 1–10 markers. For population based haplotype reconstruction, we use HaploRec, and compare it to both a simple trio based inference and true haplotypes. Firstly and surprisingly, statistically inferred population based haplotypes can be equally powerful as true haplotypes. Secondly, as expected, the effective sample size has a clear effect on both gene detection power and mapping accuracy. Thirdly, the sample ascertainment method does not have much effect on mapping accuracy. Finally, an interesting side result is that the simple haplotype association test clearly outperformed exhaustive allelic transmission disequilibrium tests. The results suggest that the case‐control design is a powerful alternative to the more laborious family based ascertainment approach, especially for large datasets, and wherever population stratification can be controlled.

Geographic Variation and Bias in the Polygenic Scores of Complex Diseases and Traits in Finland

Abstract: Polygenic scores (PSs) are becoming a useful tool to identify individuals with high genetic risk for complex diseases, and several projects are currently testing their utility for translational applications. It is also tempting to use PSs to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how the population genetic properties of the training and target samples affect the geographic distribution of PSs. Here, we evaluate geographic differences, and related biases, of PSs in Finland in a geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PSs for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waist-hip ratio (WHR), body-mass index (BMI), and height, but not for Crohn disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PSs and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulations of geographic differences for CAD, WHR, BMI, and height, suggesting bias arising from the population's genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PSs are for small biases even within relatively homogeneous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PSs is essential for translational applications of PSs.

Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models

Abstract: We present new measures of the causal direction between two non-gaussian random variables. They are based on the likelihood ratio under the linear non-gaussian acyclic model (LiNGAM). We also develop simple first-order approximations and analyze them based on related cumulant-based measures. The cumulant-based measures can be shown to give the right causal directions, and they are statistically consistent even in the presence of measurement noise. We further show how to apply these measures to estimate LiNGAM for more than two variables, and even in the case of more variables than observations. The proposed framework is statistically at least as good as existing ones in the cases of few data points or noisy data, and it is computationally and conceptually very simple.