First genome-wide association study of 99 body measures derived from 3-dimensional body scans.
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ABSTRACT:
SUBMITTER: Kuhnapfel A
PROVIDER: S-EPMC9243350 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Body segment parameters are inputs for a range of applications. Participant-specific estimates of body segment parameters are desirable as this requires fewer prior assumptions and can reduce outcome measurement errors. Commonly used methods for estimating participant-specific body segment parameters are either expensive and out of reach (medical imaging), have many underlying assumptions (geometrical modelling) or are based on a specific subset of a population (regression models). Our objective was to develop a participant-specific 3D scanning and body segmentation method that estimates body segment parameters without any assumptions about the geometry of the body, ethnic background, and gender, is low-cost, fast, and can be readily available. Using a Microsoft Kinect Version 2 camera, we developed a 3D surface scanning protocol that enabled the estimation of participant-specific body segment parameters. To evaluate our system, we performed repeated 3D scans of 21 healthy participants (10 male, 11 female). We used open source tools to segment each body scan into 16 segments (head, torso, abdomen, pelvis, left and right hand, forearm, upper arm, foot, shank and thigh) and wrote custom software to estimate each segment's mass, mass moment of inertia in the three principal orthogonal axes relevant to the center of the segment, longitudinal length, and center of mass. We compared our body segment parameter estimates to those obtained using two comparison methods and found that our system was consistent in estimating total body volume between repeated scans (male p = 0.1194, female p = 0.2240), estimated total body mass without significant differences when compared to our comparison method and a medical scale (male p = 0.8529, female p = 0.6339), and generated consistent and comparable estimates across a range of the body segment parameters of interest. Our work here outlines and provides the code for an inexpensive 3D surface scanning method for estimating a range of participant-specific body segment parameters.
| S-EPMC8730461 | biostudies-literature
Project description:Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h2 = .25 vs. .13, p = 1.8x10-7). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (rg = .49, p = 2.7x10-22). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.
| S-EPMC9675774 | biostudies-literature
Project description:Genome-wide association studies (GWAS) require considerable investment, so researchers often study multiple traits collected on the same set of subjects to maximize return. However, many GWAS have adopted a case-control design; improperly accounting for case-control ascertainment can lead to biased estimates of association between markers and secondary traits. We show that under the null hypothesis of no marker-secondary trait association, naïve analyses that ignore ascertainment or stratify on case-control status have proper Type I error rates except when both the marker and secondary trait are independently associated with disease risk. Under the alternative hypothesis, these methods are unbiased when the secondary trait is not associated with disease risk. We also show that inverse-probability-of-sampling-weighted (IPW) regression provides unbiased estimates of marker-secondary trait association. We use simulation to quantify the Type I error, power and bias of naïve and IPW methods. IPW regression has appropriate Type I error in all situations we consider, but has lower power than naïve analyses. The bias for naïve analyses is small provided the marker is independent of disease risk. Considering the majority of tested markers in a GWAS are not associated with disease risk, naïve analyses provide valid tests of and nearly unbiased estimates of marker-secondary trait association. Care must be taken when there is evidence that both the secondary trait and tested marker are associated with the primary disease, a situation we illustrate using an analysis of the relationship between a marker in FGFR2 and mammographic density in a breast cancer case-control sample.
| S-EPMC2790028 | biostudies-literature
Project description:Obesity is a major public health problem with strong genetic determination; however, the genetic factors underlying obesity are largely unknown. In this study, we performed a genome-wide association scan for obesity by examining approximately 500 000 single-nucleotide polymorphisms (SNPs) in a sample of 1000 unrelated US Caucasians. We identified a novel gene, CTNNBL1, which has multiple SNPs associated with body mass index (BMI) and fat mass. The most significant SNP, rs6013029, achieved experiment-wise P-values of 2.69 x 10(-7) for BMI and of 4.99 x 10(-8) for fat mass, respectively. The SNP rs6013029 minor allele T confers an average increase in BMI and fat mass of 2.67 kg/m(2) and 5.96 kg, respectively, compared with the alternative allele G. We further genotyped the five most significant CTNNBL1 SNPs in a French case-control sample comprising 896 class III obese adults (BMI > or = 40 kg/m(2)) and 2916 lean adults (BMI < 25 kg/m(2)). All five SNPs showed consistent associations with obesity (8.83 x 10(-3) < P < 6.96 x 10(-4)). Those subjects who were homozygous for the rs6013029 T allele had 1.42-fold increased odds of obesity compared with those without the T allele. The protein structure of CTNNBL1 is homologous to beta-catenin, a family of proteins containing armadillo repeats, suggesting similar biological functions. beta-Catenin is involved in the Wnt/beta-catenin-signaling pathway which appears to contribute to maintaining the undifferentiated state of pre-adipocytes by inhibiting adipogenic gene expression. Our study hence suggests a novel mechanism for the development of obesity, where CTNNBL1 may play an important role. Our study also provided supportive evidence for previously identified associations between obesity and INSIG2 and PFKP, but not FTO.
| S-EPMC2900891 | biostudies-literature
Project description:As genome-wide association studies (GWAS) are becoming more popular, two approaches, among others, could be considered in order to improve statistical power for identifying genes contributing subtle to moderate effects to human diseases. The first approach is to increase sample size, which could be achieved by combining both unrelated and familial subjects together. The second approach is to jointly analyze multiple correlated traits. In this study, by extending generalized estimating equations (GEEs), we propose a simple approach for performing univariate or multivariate association tests for the combined data of unrelated subjects and nuclear families. In particular, we correct for population stratification by integrating principal component analysis and transmission disequilibrium test strategies. The proposed method allows for multiple siblings as well as missing parental information. Simulation studies show that the proposed test has improved power compared to two popular methods, EIGENSTRAT and FBAT, by analyzing the combined data, while correcting for population stratification. In addition, joint analysis of bivariate traits has improved power over univariate analysis when pleiotropic effects are present. Application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility and applicability of the proposed method.
| S-EPMC2715864 | biostudies-literature
Project description:IntroductionDigital tools like 3D laser-based photonic scanners, which can assess external anthropometric measurements for population based studies, and predict body composition, are gaining in importance. Here we focus on a) systematic deviation between manually determined and scanned standard measurements, b) differences regarding the strength of association between these standard measurements and body composition, and c) improving these predictions of body composition by considering additional scan measurements.MethodsWe analysed 104 men aged 19-23. Bioelectrical Impedance Analysis was used to estimate whole body fat mass, visceral fat mass and skeletal muscle mass (SMM). For the 3D body scans, an Anthroscan VITUSbodyscan was used to automatically obtain 90 body shape measurements. Manual anthropometric measurements (height, weight, waist circumference) were also taken.ResultsScanned and manually measured height, waist circumference, waist-to-height-ratio, and BMI were strongly correlated (Spearman Rho>0.96), however we also found systematic differences. When these variables were used to predict body fat or muscle mass, explained variation and prediction standard errors were similar between scanned and manual measurements. The univariable predictions performed well for both visceral fat (r2 up to 0.92) and absolute fat mass (AFM, r2 up to 0.87) but not for SMM (r2 up to 0.54). Of the 90 body scanner measures used in the multivariable prediction models, belly circumference and middle hip circumference were the most important predictors of body fat content. Stepwise forward model selection using the AIC criterion showed that the best predictive power (r2 up to 0.99) was achieved with models including 49 scanner measurements.ConclusionThe use of a 3D full body scanner produced results that strongly correlate to manually measured anthropometric measures. Predictions were improved substantially by including multiple measurements, which can only be obtained with a 3D body scanner, in the models.
| S-EPMC7289400 | biostudies-literature
Project description:The P-value approach has been employed to prioritizing genome-wide association (GWA) scan signals, with a genome-wide significance defined by a prior P-value threshold, although this is not ideal. A rationale put forward is that the association signals rather should be expected to give less support for single nucleotide polymorphisms (SNPs) that are rare (with associated low-power tests) than for common SNPs with equivalent P-values, unless investigators believe, a priori, that rare causative variants contribute to the disease and have more pronounced effects.Using data from a GWA scan for type 2 diabetes (1924 cases, 2938 controls, 393 453 SNPs), we compared P-values with four alternative signal measures: likelihood ratio (LR), Bayes factor (BF; with a specified prior distribution for true effects), 'frequentist factor' (FF; reflecting the ratio between estimated--post-data-- 'power' and P-value) and probability of pronounced effect size (PrPES).The 19 common SNPs [minor allele frequency (MAF) among the controls >29%] yielding strong P-value signals (P < 5 x 10(-7)) were also top ranked by the other approaches. There was a strong similarity between the P-values, LR and BF signals, in terms of ranking SNPs. In contrast, FF and PrPES signals down-weighted rare SNPs (control MAF <10%) with low P-values.For prioritization of signals that do not achieve compelling levels of evidence for association, the main driving force behind observed differences between the various association signals appears to be SNP MAF. The statistical power afforded by follow-up samples for establishing replication should be taken into account when tailoring the signal selection strategy.
| S-EPMC3072755 | biostudies-literature
Project description:Anterior cruciate ligament (ACL) rupture is a common condition that can be devastating and life changing, particularly in young adults. A non-contact mechanism is typical. Second ACL ruptures through rupture of the contralateral ACL or rupture of a graft repair is also common. Risk of rupture is increased in females. ACL rupture is also common in dogs. Disease prevalence exceeds 5% in several dog breeds, ~100 fold higher than human beings. We provide insight into the genetic etiology of ACL rupture by genome-wide association study (GWAS) in a high-risk breed using 98 case and 139 control Labrador Retrievers. We identified 129 single nucleotide polymorphisms (SNPs) within 99 risk loci. Associated loci (P<5E-04) explained approximately half of phenotypic variance in the ACL rupture trait. Two of these loci were located in uncharacterized or non-coding regions of the genome. A chromosome 24 locus containing nine genes with diverse functions met genome-wide significance (P = 3.63E-0.6). GWAS pathways were enriched for c-type lectins, a gene set that includes aggrecan, a gene set encoding antimicrobial proteins, and a gene set encoding membrane transport proteins with a variety of physiological functions. Genotypic risk estimated for each dog based on the risk contributed by each GWAS locus showed clear separation of ACL rupture cases and controls. Power analysis of the GWAS data set estimated that ~172 loci explain the genetic contribution to ACL rupture in the Labrador Retriever. Heritability was estimated at 0.48. We conclude ACL rupture is a moderately heritable highly polygenic complex trait. Our results implicate c-type lectin pathways in ACL homeostasis.
| S-EPMC5381864 | biostudies-literature
Project description:ObjectiveThis study aimed to uncover genetic contributors to adiposity in early life.MethodsA genome-wide association study of childhood body fatness in 34,401 individuals within the Nurses' Health Studies and the Health Professionals Follow-up Study was conducted. Data were imputed to the 1000 Genomes Phase 3 version 5 reference panel.ResultsA total of 1,354 single-nucleotide polymorphisms (P < 10-4 ) were selected for replication in a previously published genome-wide association study of childhood BMI. Nineteen significant genome-wide (P < 5 × 10-8 ) regions were observed, fourteen of which were previously associated with childhood obesity and five were novel: BNDF (P = 7.58 × 10-13 ), PRKD1 (P = 1.43 × 10-10 ), 20p13 (P = 2.05 × 10-10 ), FHIT (P = 1.77 × 10-8 ), and LOC101927575 (P = 3.22 × 10-8 ). The BNDF, FHIT, and PRKD1 regions were previously associated with adult BMI. LOC101927575 and 20p13 regions have not previously been associated with adiposity phenotypes. In a transcriptome-wide analysis, associations for POMC at 2p23.3 (P = 3.36 × 10-6 ) and with TMEM18 at 2p25.3 (P = 3.53 × 10-7 ) were observed. Childhood body fatness was genetically correlated with hip (rg = 0.42, P = 4.44 × 10-16 ) and waist circumference (rg = 0.39, P = 5.56 × 10-16 ), as well as age at menarche (rg = -0.37, P = 7.96 × 10-19 ).ConclusionsAdditional loci that contribute to childhood adiposity were identified, further explicating its genetic architecture.
| S-EPMC7842657 | biostudies-literature
Project description:Osteonecrosis of the femoral head (ONFH) is a multifactorial disease and is associated with genetic predisposition, and exposure to certain risk factors. In particular, idiopathic ONFH in twins and the clustering of cases in families have indicated that genetic factors are involved. However, the majority of cases of ONFH are sporadic and various studies have demonstrated that differences in the study design and/or the ethnic groups analyzed leads to different results. The present study performed one of the first genome‑wide association studies to identify genetic loci that may increase the risk of idiopathic ONFH. In total, 217 patients with idiopathic ONFH and 217 control samples, without ONFH, were genotyped using Axiom™ chips. Following quality control, 509,886 single‑nucleotide polymorphisms (SNPs) were included in the association analysis to identify genetic variants that may influence susceptibility to idiopathic ONFH. The lowest P‑value identified by the current study was for an association with rs220324 (P=3.57x10‑7), an SNP that is located near to the uromodulin‑like 1 gene region on chromosome 21q22.3, although none of the SNPs reached the traditional genome‑wide significance level of 5x10‑8. However, the DnaJ heat shock protein family (Hsp40) member C6 (DNAJC6) locus, a region between 65.37 and 65.67 Mb located on chromosome 1p31.3, harbored a cluster of SNPs that were associated with idiopathic ONFH at a significance level of P<1x10‑5. Four variants, rs10493374, rs12032616, rs17127529 and rs6679032, with marginal associations were located in and around the DNAJC6 locus and were in strong linkage disequilibrium with each other. In conclusion, the current study did not identify any SNPs that were associated with idiopathic ONFH at a genome‑wide significance level, however, the results suggest that future studies should investigate the effects of SNPs in the DNAJC6 gene on the idiopathic ONFH risk.
| S-EPMC5364851 | biostudies-literature