Ville Huikari

Genome-wide association and longitudinal analyses reveal genetic loci linking pubertal height growth, pubertal timing and childhood adiposity

The pubertal height growth spurt is a distinctive feature of childhood growth reflecting both the central onset of puberty and local growth factors. Although little is known about the underlying genetics, growth variability during puberty correlates with adult risks for hormone-dependent cancer and adverse cardiometabolic health. The only gene so far associated with pubertal height growth, LIN28B, pleiotropically influences childhood growth, puberty and cancer progression, pointing to shared underlying mechanisms. To discover genetic loci influencing pubertal height and growth and to place them in context of overall growth and maturation, we performed genome-wide association meta-analyses in 18 737 European samples utilizing longitudinally collected height measurements. We found significant associations (P < 1.67 × 10(-8)) at 10 loci, including LIN28B. Five loci associated with pubertal timing, all impacting multiple aspects of growth. In particular, a novel variant correlated with expression of MAPK3, and associated both with increased prepubertal growth and earlier menarche. Another variant near ADCY3-POMC associated with increased body mass index, reduced pubertal growth and earlier puberty. Whereas epidemiological correlations suggest that early puberty marks a pathway from rapid prepubertal growth to reduced final height and adult obesity, our study shows that individual loci associating with pubertal growth have variable longitudinal growth patterns that may differ from epidemiological observations. Overall, this study uncovers part of the complex genetic architecture linking pubertal height growth, the timing of puberty and childhood obesity and provides new information to pinpoint processes linking these traits.

A genome-wide association study of body mass index across early life and childhood

Background: Several studies have investigated the effect of known adult body mass index (BMI) associated single nucleotide polymorphisms (SNPs) on BMI in childhood. There has been no genome-wide association study (GWAS) of BMI trajectories over childhood. Methods: We conducted a GWAS meta-analysis of BMI trajectories from 1 to 17 years of age in 9377 children (77 967 measurements) from the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Western Australian Pregnancy Cohort (Raine) Study. Genome-wide significant loci were examined in a further 3918 individuals (48 530 measurements) from Northern Finland. Linear mixed effects models with smoothing splines were used in each cohort for longitudinal modelling of BMI. Results: A novel SNP, downstream from the FAM120AOS gene on chromosome 9, was detected in the meta-analysis of ALSPAC and Raine. This association was driven by a difference in BMI at 8 years (T allele of rs944990 increased BMI; PSNP = 1.52 × 10−8), with a modest association with change in BMI over time (PWald(Change) = 0.006). Three known adult BMI-associated loci (FTO, MC4R and ADCY3) and one childhood obesity locus (OLFM4) reached genome-wide significance (PWald < 1.13 × 10−8) with BMI at 8 years and/or change over time. Conclusions: This GWAS of BMI trajectories over childhood identified a novel locus that warrants further investigation. We also observed genome-wide significance with previously established obesity loci, making the novel observation that these loci affected both the level and the rate of change in BMI. We have demonstrated that the use of repeated measures data can increase power to allow detection of genetic loci with smaller sample sizes.

Adiposity as a cause of cardiovascular disease: a Mendelian randomization study

BACKGROUND Adiposity, as indicated by body mass index (BMI), has been associated with risk of cardiovascular diseases in epidemiological studies. We aimed to investigate if these associations are causal, using Mendelian randomization (MR) methods. METHODS The associations of BMI with cardiovascular outcomes [coronary heart disease (CHD), heart failure and ischaemic stroke], and associations of a genetic score (32 BMI single nucleotide polymorphisms) with BMI and cardiovascular outcomes were examined in up to 22,193 individuals with 3062 incident cardiovascular events from nine prospective follow-up studies within the ENGAGE consortium. We used random-effects meta-analysis in an MR framework to provide causal estimates of the effect of adiposity on cardiovascular outcomes. RESULTS There was a strong association between BMI and incident CHD (HR = 1.20 per SD-increase of BMI, 95% CI, 1.12-1.28, P = 1.9.10(-7)), heart failure (HR = 1.47, 95% CI, 1.35-1.60, P = 9.10(-19)) and ischaemic stroke (HR = 1.15, 95% CI, 1.06-1.24, P = 0.0008) in observational analyses. The genetic score was robustly associated with BMI (β = 0.030 SD-increase of BMI per additional allele, 95% CI, 0.028-0.033, P = 3.10(-107)). Analyses indicated a causal effect of adiposity on development of heart failure (HR = 1.93 per SD-increase of BMI, 95% CI, 1.12-3.30, P = 0.017) and ischaemic stroke (HR = 1.83, 95% CI, 1.05-3.20, P = 0.034). Additional cross-sectional analyses using both ENGAGE and CARDIoGRAMplusC4D data showed a causal effect of adiposity on CHD. CONCLUSIONS Using MR methods, we provide support for the hypothesis that adiposity causes CHD, heart failure and, previously not demonstrated, ischaemic stroke.

Genetic variation in the 15q25 nicotinic acetylcholine receptor gene cluster (CHRNA5–CHRNA3–CHRNB4) interacts with maternal self-reported smoking status during pregnancy to influence birth weight

Maternal smoking during pregnancy is associated with low birth weight. Common variation at rs1051730 is robustly associated with smoking quantity and was recently shown to influence smoking cessation during pregnancy, but its influence on birth weight is not clear. We aimed to investigate the association between this variant and birth weight of term, singleton offspring in a well-powered meta-analysis. We stratified 26 241 European origin study participants by smoking status (women who smoked during pregnancy versus women who did not smoke during pregnancy) and, in each stratum, analysed the association between maternal rs1051730 genotype and offspring birth weight. There was evidence of interaction between genotype and smoking (P = 0.007). In women who smoked during pregnancy, each additional smoking-related T-allele was associated with a 20 g [95% confidence interval (95% CI): 4–36 g] lower birth weight (P = 0.014). However, in women who did not smoke during pregnancy, the effect size estimate was 5 g per T-allele (95% CI: −4 to 14 g; P = 0.268). To conclude, smoking status during pregnancy modifies the association between maternal rs1051730 genotype and offspring birth weight. This strengthens the evidence that smoking during pregnancy is causally related to lower offspring birth weight and suggests that population interventions that effectively reduce smoking in pregnant women would result in a reduced prevalence of low birth weight.

Activity recognition using a wrist-worn inertial measurement unit: A case study for industrial assembly lines

As wearable sensors are becoming more common, their utilization in real-world applications is also becoming more attractive. In this study, a single wrist-worn inertial measurement unit was attached to the active wrist of a worker and acceleration and angular speed information was used to decide what activity the worker was performing at certain time intervals. This activity information can then be used for proactive instruction systems or to ensure that all the needed work phases are performed. In this study, the selected activities were basic tasks of hammering, screwing, spanner use and using a power drill for screwing. In addition, a null activity class consisting of other activities (moving around the post, staying still, changing tools) was defined. The performed activity could then be recognized online by using a sliding window method to divide the data into two-second intervals and overlapping two adjacent windows by 1.5 seconds. Thus, the activity was recognized every half second. The method used for the actual recognition was the k nearest neighbor method with a specific distance boundary for classifying completely new events as null data. In addition, the final class was decided by using a majority vote to classifications of three adjacent windows. The results showed that almost 90 percent accuracy can be achieved with this kind of setting; the activity-specific accuracies for hammering, screwing, spanner use, power drilling and null data were 96.4%, 89.7%, 89.5%, 77.6% and 89.0%, respectively. In addition, in a case with completely new null events, use of the specific distance measure improved accuracy from 68.6% to 82.3%.

Dietary Intake, FTO genetic variants, and adiposity: A combined analysis of over 16,000 children and adolescents

The FTO gene harbors variation with the strongest effect on adiposity and obesity risk. Previous data support a role for FTO variation in influencing food intake. We conducted a combined analysis of 16,094 boys and girls aged 1–18 years from 14 studies to examine the following: 1) the association between the FTO rs9939609 variant (or a proxy) and total energy and macronutrient intake; and 2) the interaction between the FTO variant and dietary intake, and the effect on BMI. We found that the BMI-increasing allele (minor allele) of the FTO variant was associated with increased total energy intake (effect per allele = 14.3 kcal/day [95% CI 5.9, 22.7 kcal/day], P = 6.5 × 10−4), but not with protein, carbohydrate, or fat intake. We also found that protein intake modified the association between the FTO variant and BMI (interactive effect per allele = 0.08 SD [0.03, 0.12 SD], P for interaction = 7.2 × 10−4): the association between FTO genotype and BMI was much stronger in individuals with high protein intake (effect per allele = 0.10 SD [0.07, 0.13 SD], P = 8.2 × 10−10) than in those with low intake (effect per allele = 0.04 SD [0.01, 0.07 SD], P = 0.02). Our results suggest that the FTO variant that confers a predisposition to higher BMI is associated with higher total energy intake, and that lower dietary protein intake attenuates the association between FTO genotype and adiposity in children and adolescents.

Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors

Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10−107) and stratified analyses (all P < 3.3 × 10−30). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.

Serum 25-Hydroxyvitamin D Status and Longitudinal Changes in Weight and Waist Circumference: Influence of Genetic Predisposition to Adiposity.

Studies of the relationship between serum 25-hydroxyvitamin D (25(OH)D) and changes in measures of adiposity have shown inconsistent results, and interaction with genetic predisposition to obesity has rarely been examined. We examined whether 25(OH)D was associated with subsequent annual changes in body weight (ΔBW) or waist circumference (ΔWC), and whether the associations were modified by genetic predisposition to a high BMI, WC or waist-hip ratio adjusted for BMI (WHRBMI). The study was based on 10,898 individuals from the Danish Inter99, the 1958 British Birth Cohort and the Northern Finland Birth Cohort 1966. We combined 42 adiposity-associated Single Nucleotide Polymorphisms (SNPs) into four scores indicating genetic predisposition to BMI, WC and WHRBMI, or all three traits combined. Linear regression was used to examine the association between serum 25(OH)D and ΔBW or ΔWC, SNP-score × 25(OH)D interactions were examined, and results from the individual cohorts were meta-analyzed. In the meta-analyses, we found no evidence of an association between 25(OH)D and ΔBW (-9.4 gram/y per 10 nmol/L higher 25(OH)D [95% CI: -23.0, +4.3; P = 0.18]) or ΔWC (-0.06 mm/y per 10 nmol/L higher 25(OH)D [95% CI: -0.17, +0.06; P = 0.33]). Furthermore, we found no statistically significant interactions between the four SNP-scores and 25(OH)D in relation to ΔBW or ΔWC. Thus, in view of the narrow CIs, our results suggest that an association between 25(OH)D and changes in measures of adiposity is absent or marginal. Similarly, the study provided evidence that there is either no or very limited dependence on genetic predisposition to adiposity.

Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics

Abstract Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother–child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P < 5 × 10−8. In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.

User-independent activity recognition for industrial assembly lines-feature vs. instance selection

This study concentrated on real-time monitoring of a worker using wearable-sensor-based activity recognition. An inertial measurement unit was attached to both wrists of the worker and, by using acceleration and angle speed information, the activities performed by the worker were recognized. Online recognition was done using the sliding window method to divide the data into two-second intervals, and the activity performed in each window was recognized using a knn classifier. Moreover, especially the real-time aspect of the system was considered by studying ways to decrease the amount of training data needed in knn recognition. The studied methods for decreasing the data set included feature and instance selection methods. The results show that instance selection is the best solution for data reduction, while the overall recognition accuracy of the system is high enough for use on industrial assembly lines. When an independent test set is used, the recognition rates using an instance selection method are on average 79.2% which is six percentage units better than the best result using feature selection methods.