Genetics of Childhood Obesity

Obesity is a major health problem and an immense economic burden on the health care systems both in the United States and the rest of the world. The prevalence of obesity in children and adults in the United States has increased dramatically over the past decade. Besides environmental factors, genetic factors are known to play an important role in the pathogenesis of obesity. Genome-wide association studies (GWAS) have revealed strongly associated genomic variants associated with most common disorders; indeed there is general consensus on these findings from generally positive replication outcomes by independent groups. To date, there have been only a few GWAS-related reports for childhood obesity specifically, with studies primarily uncovering loci in the adult setting instead. It is clear that a number of loci previously reported from GWAS analyses of adult BMI and/or obesity also play a role in childhood obesity

Canonical Notch signaling is required for bone morphogenetic protein‐mediated human osteoblast differentiation

Osteoblast differentiation of bone marrow‐derived human mesenchymal stem cells (hMSC) can be induced by stimulation with canonical Notch ligand, Jagged1, or bone morphogenetic proteins (BMPs). However, it remains elusive how these two pathways lead to the same phenotypic outcome. Since Runx2 is regarded as a master regulator of osteoblastic differentiation, we targeted Runx2 with siRNA in hMSC. This abrogated both Jagged1 and BMP2 mediated osteoblastic differentiation, confirming the fundamental role for Runx2. However, while BMP stimulation increased Runx2 and downstream Osterix protein expression, Jagged1 treatment failed to upregulate either, suggesting that canonical Notch signals require basal Runx2 expression. To fully understand the transcriptomic profile of differentiating osteoblasts, RNA sequencing was performed in cells stimulated with BMP2 or Jagged1. There was common upregulation of ALPL and extracellular matrix genes, such as ACAN, HAS3, MCAM, and OLFML2B. Intriguingly, genes encoding components of Notch signaling (JAG1, HEY2, and HES4) were among the top 10 genes upregulated by both stimuli. Indeed, ALPL expression occurred concurrently with Notch activation and inhibiting Notch activity for up to 24 hours after BMP administration with DAPT (a gamma secretase inhibitor) completely abrogated hMSC osteoblastogenesis. Concordantly, RBPJ (recombination signal binding protein for immunoglobulin kappa J region, a critical downstream modulator of Notch signals) binding could be demonstrated within the ALPL and SP7 promoters. As such, siRNA‐mediated ablation of RBPJ decreased BMP‐mediated osteoblastogenesis. Finally, systemic Notch inhibition using diabenzazepine (DBZ) reduced BMP2‐induced calvarial bone healing in mice supporting the critical regulatory role of Notch signaling in BMP‐induced osteoblastogenesis.Bone morphogenetic protein (BMP) stimulation of bone‐marrow‐derived human mesenchymal progenitor cells (hMSCs) increases Notch proteins via increased Notch ligand Jagged1 expression. Canonical Notch signaling is required for BMP‐induced ALPL expression and osteoblastic commitment of hMSCs. Both BMP‐induced osteoblastogenesis and Notch‐induced osteoblastogenesis require Runx2.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162778/2/stem3245_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162778/1/stem3245.pd

Pathway-Wide Association Study Implicates Multiple Sterol Transport and Metabolism Genes in HDL Cholesterol Regulation

Pathway-based association methods have been proposed to be an effective approach in identifying disease genes, when single-marker association tests do not have sufficient power. The analysis of quantitative traits may be benefited from these approaches, by sampling from two extreme tails of the distribution. Here we tested a pathway association approach on a small genome-wide association study (GWAS) on 653 subjects with extremely high high-density lipoprotein cholesterol (HDL-C) levels and 784 subjects with low HDL-C levels. We identified 102 genes in the sterol transport and metabolism pathways that collectively associate with HDL-C levels, and replicated these association signals in an independent GWAS. Interestingly, the pathways include 18 genes implicated in previous GWAS on lipid traits, suggesting that genuine HDL-C genes are highly enriched in these pathways. Additionally, multiple biologically relevant loci in the pathways were not detected by previous GWAS, including genes implicated in previous candidate gene association studies (such as LEPR, APOA2, HDLBP, SOAT2), genes that cause Mendelian forms of lipid disorders (such as DHCR24), and genes expressing dyslipidemia phenotypes in knockout mice (such as SOAT1, PON1). Our study suggests that sampling from two extreme tails of a quantitative trait and examining genetic pathways may yield biological insights from smaller samples than are generally required using single-marker analysis in large-scale GWAS. Our results also implicate that functionally related genes work together to regulate complex quantitative traits, and that future large-scale studies may benefit from pathway-association approaches to identify novel pathways regulating HDL-C levels

Infant BMI or Weight-for-Length and Obesity Risk in Early Childhood

Weight-for-length (WFL) is currently used to assess adiposity under 2 years. We assessed WFL- versus BMI-based estimates of adiposity in healthy infants in determining risk for early obesity

Relation of alleles of the collagen type Ialpha1 gene to bone density and the risk of osteoporotic fractures in postmenopausal women

BACKGROUND: Osteoporosis is a common disorder with a strong genetic component. One way in which the genetic component could be expressed is through polymorphism of COLIA1, the gene for collagen type Ialpha1, a bone-matrix protein. METHODS: We determined the COLIA1 genotypes SS, Ss, and ss in a population-based sample of 177

Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies

BACKGROUND: Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory. Our aim was to provide an accessible guide along with applied examples to using four sophisticated modelling procedures for describing nonlinear growth trajectories. METHODS: This expository paper provides an illustrative guide to summarising nonlinear growth trajectories for repeatedly measured continuous outcomes using (i) linear spline and (ii) natural cubic spline linear mixed-effects (LME) models, (iii) Super Imposition by Translation and Rotation (SITAR) nonlinear mixed effects models, and (iv) latent trajectory models. The underlying model for each approach, their similarities and differences, and their advantages and disadvantages are described. Their application and correct interpretation of their results is illustrated by analysing repeated bone mass measures to characterise bone growth patterns and their sex differences in three cohort studies from the UK, USA, and Canada comprising 8500 individuals and 37,000 measurements from ages 5-40 years. Recommendations for choosing a modelling approach are provided along with a discussion and signposting on further modelling extensions for analysing trajectory exposures and outcomes, and multiple cohorts. RESULTS: Linear and natural cubic spline LME models and SITAR provided similar summary of the mean bone growth trajectory and growth velocity, and the sex differences in growth patterns. Growth velocity (in grams/year) peaked during adolescence, and peaked earlier in females than males e.g., mean age at peak bone mineral content accrual from multicohort SITAR models was 12.2 years in females and 13.9 years in males. Latent trajectory models (with trajectory shapes estimated using a natural cubic spline) identified up to four subgroups of individuals with distinct trajectories throughout adolescence. CONCLUSIONS: LME models with linear and natural cubic splines, SITAR, and latent trajectory models are useful for describing nonlinear growth trajectories, and these methods can be adapted for other complex traits. Choice of method depends on the research aims, complexity of the trajectory, and available data. Scripts and synthetic datasets are provided for readers to replicate trajectory modelling and visualisation using the R statistical computing software

Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies

BACKGROUND: Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory. Our aim was to provide an accessible guide along with applied examples to using four sophisticated modelling procedures for describing nonlinear growth trajectories. METHODS: This expository paper provides an illustrative guide to summarising nonlinear growth trajectories for repeatedly measured continuous outcomes using (i) linear spline and (ii) natural cubic spline linear mixed-effects (LME) models, (iii) Super Imposition by Translation and Rotation (SITAR) nonlinear mixed effects models, and (iv) latent trajectory models. The underlying model for each approach, their similarities and differences, and their advantages and disadvantages are described. Their application and correct interpretation of their results is illustrated by analysing repeated bone mass measures to characterise bone growth patterns and their sex differences in three cohort studies from the UK, USA, and Canada comprising 8500 individuals and 37,000 measurements from ages 5–40 years. Recommendations for choosing a modelling approach are provided along with a discussion and signposting on further modelling extensions for analysing trajectory exposures and outcomes, and multiple cohorts. RESULTS: Linear and natural cubic spline LME models and SITAR provided similar summary of the mean bone growth trajectory and growth velocity, and the sex differences in growth patterns. Growth velocity (in grams/year) peaked during adolescence, and peaked earlier in females than males e.g., mean age at peak bone mineral content accrual from multicohort SITAR models was 12.2 years in females and 13.9 years in males. Latent trajectory models (with trajectory shapes estimated using a natural cubic spline) identified up to four subgroups of individuals with distinct trajectories throughout adolescence. CONCLUSIONS: LME models with linear and natural cubic splines, SITAR, and latent trajectory models are useful for describing nonlinear growth trajectories, and these methods can be adapted for other complex traits. Choice of method depends on the research aims, complexity of the trajectory, and available data. Scripts and synthetic datasets are provided for readers to replicate trajectory modelling and visualisation using the R statistical computing software. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01542-8

Another tool in the genome-wide association study arsenal: population-based detection of somatic gene conversion

The hunt for the genetic contributors to complex disease has used a number of strategies, resulting in the identification of variants associated with many of the common diseases affecting society. However most of the genetic variants detected to date are single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) and fall far short of explaining the full genetic component of any given disease. An as yet untapped genomic mechanism is somatic gene conversion and deletion, which could be complicit in disease risk but has been challenging to detect in genome-wide datasets. In a recent publication in BMC Medicine by Kenneth Ross, the author uses existing datasets to look at somatic gene conversion and deletion in human disease. Here, we describe how Ross's recent efforts to detect such occurrences could impact the field going forward