Age- and gender-dependent obesity in individuals with 16p11.2 deletion

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA Collapse

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Grants

• R01 HL143885 NHLBI NIH HHS
• R01 HL163262 NHLBI NIH HHS
• R01 HL142302 NHLBI NIH HHS
• R01 HL151152 NHLBI NIH HHS
• N01HC25195 NHLBI NIH HHS
• R01 HG010297 NHGRI NIH HHS
• R01 HL092577 NHLBI NIH HHS
• P20 MD000170 NIMHD NIH HHS
• 75N92019D00031 NHLBI NIH HHS
• R01 HD057194 NICHD NIH HHS
• UL1 TR000371 NCATS NIH HHS
• R01 DK122503 NIDDK NIH HHS

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Affiliation(s)

Hanfei Xu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Shreyash Gupta Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ian Dinsmore Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Abbey Kollu Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599, USA
Anne Marie Cawley Marsico Lung Institute, University of North Carolina, 125 Mason Farm Rd, Chapel Hill, NC, 27599, USA
Mohammad Y. Anwar Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Hung-Hsin Chen Institute of Biomedical Sciences, Academia Sinica, No. 128, Section 2, Academia Rd., Taipei, Nangang District, 115201, Taiwan Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Lauren E. Petty Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Sudha Seshadri Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, 8300 Floyd Curl Drive, San Antonio, TX, 78229, USA
Misa Graff Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Piper Below Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Jennifer A. Brody Department of Medicine, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98101, USA
Geetha Chittoor Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Susan P. Fisher-Hoch Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Nancy L. Heard-Costa Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, 01702, USA Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA
Daniel Levy Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
Honghuang Lin Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, USA
Ruth JF. Loos Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
Joseph B. Mccormick Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Jerome I. Rotter Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA
Tooraj Mirshahi Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Christopher D. Still Center for Obesity and Metabolic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Anita Destefano Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
L. Adrienne Cupples Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Karen L Mohlke Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
Kari E. North Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Anne E. Justice Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ching-Ti Liu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA

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Experiences and concerns of parents of children with a 16p11.2 deletion or duplication diagnosis: a reflexive thematic analysis

BACKGROUND

16p11.2 proximal deletion and duplication syndromes (Break points 4-5) (593KB, Chr16; 29.6-30.2mb - HG38) are observed to have highly varied phenotypes, with a known propensity for lifelong psychiatric problems. This study aimed to contribute to a research gap by qualitatively exploring the challenges families with 16p11.2 deletion and duplication face by answering three research questions: (1) What are parents' perceptions of the ongoing support needs of families with children who have 16p11.2 living in the UK?; (2) What are their experiences in trying to access support?; (3) In these regards, do the experiences of parents of children with duplication converge or vary from those of parents of children with 16p11.2 deletion?

METHODS

33 parents with children (aged 7-17 years) with 16p11.2 deletion or duplication participated in structured interviews, including the Autism Diagnostic Interview- Revised (ADI-R). Their answers to the ADI-R question 'what are your current concerns' were transcribed and subsequently analysed using Braun and Clarke's six step reflexive thematic analysis framework.

RESULTS

Three themes were identified: (1) Child is Behind Peers (subthemes: developmentally; academically; socially; emotionally); (2) Metabolism and Eating Patterns and; (3) Support (subthemes: insufficient support available; parent has to fight to access support; COVID-19 was a barrier to accessing support; 16p11.2 diagnosis can be a barrier to support, child is well-supported).

CONCLUSIONS

Parents of children with either 16p11.2 deletion or duplication shared similar experiences. However, metabolism concerns were specific to parents of children with 16p11.2 deletion. The theme Child is Behind Peers echoed concerns raised in previous Neurodevelopmental Copy Number Variant research. However, there were some key subthemes relating to research question (2) which were specific to this study. This included parents' descriptions of diagnostic overshadowing and the impact of a lack of eponymous name and scant awareness of 16p11.2.

Resting Energy Expenditure and Body Composition in Children and Adolescents With Genetic, Hypothalamic, Medication-Induced or Multifactorial Severe Obesity

BACKGROUND

Pediatric obesity is a multifactorial disease which can be caused by underlying medical disorders arising from disruptions in the hypothalamic leptin-melanocortin pathway, which regulates satiety and energy expenditure.

AIM

To investigate and compare resting energy expenditure (REE) and body composition characteristics of children and adolescents with severe obesity with or without underlying medical causes.

METHODS

This prospective observational study included pediatric patients who underwent an extensive diagnostic workup in our academic centre that evaluated endocrine, non-syndromic and syndromic genetic, hypothalamic, and medication-induced causes of obesity. REE was assessed by indirect calorimetry; body composition by air displacement plethysmography. The ratio between measured REE (mREE) and predicted REE (Schofield equations), REE%, was calculated, with decreased mREE defined as REE% ≤90% and elevated mREE ≥110%. Additionally, the influence of fat-free-mass (FFM) on mREE was evaluated using multiple linear regression.

RESULTS

We included 292 patients (146 [50%] with body composition measurements), of which 218 (75%) patients had multifactorial obesity and 74 (25%) an underlying medical cause: non-syndromic and syndromic genetic (n= 29 and 28, respectively), hypothalamic (n= 10), and medication-induced (n= 7) obesity. Mean age was 10.8 ± 4.3 years, 59% were female, mean BMI SDS was 3.8 ± 1.1, indicating severe obesity. Mean REE% was higher in children with non-syndromic genetic obesity (107.4% ± 12.7) and lower in children with hypothalamic obesity (87.6% ± 14.2) compared to multifactorial obesity (100.5% ± 12.6, both p<0.01). In 9 children with pseudohypoparathyroidism type 1a, mean REE% was similar (100.4 ± 5.1). Across all patients, mREE was decreased in 60 (21%) patients and elevated in 69 (24%) patients. After adjustment for FFM, mREE did not differ between patients within each of the subgroups of underlying medical causes compared to multifactorial obesity (all p>0.05).

CONCLUSIONS

In this cohort of children with severe obesity due to various etiologies, large inter-individual differences in mREE were found. Consistent with previous studies, almost half of patients had decreased or elevated mREE. This knowledge is important for patient-tailored treatment, e.g. personalized dietary and physical activity interventions and consideration of pharmacotherapy affecting central energy expenditure regulation in children with decreased mREE.

Neurodevelopmental trajectory and modifiers of 16p11.2 microdeletion: A follow-up study of four Chinese children carriers

Background

Neurodevelopmental disorders (NDDs) are a group of disorders with high genetic and phenotypic heterogeneities. The 16p11.2 microdeletion has been implicated as an important genetic risk factor for NDDs.

Methods

Multiple genetic tests were used to detect the 16p11.2 microdeletion from 918 Chinese children with NDDs. Targeted sequencing of genes in the 16p11.2 interval was performed in all carriers of the 16p11.2 microdeletion, and whole‐genome expression profiling analysis was performed for the patient carriers and normal carriers in their intra‐family.

Results

Three patients carrying the 16p11.2 microdeletion were screened out, indicating a frequency of 0.33% for the 16p11.2 microdeletion in this cohort. We reviewed the neurodevelopmental trajectories of the 16p11.2 microdeletion carriers from childhood to puberty and confirmed that this microdeletion was associated with abnormal neurodevelopment, with varied neurodevelopmental phenotypes. A differential PRRT2 genotype (rs10204, T>C) was identified between patients and normal carriers of the 16p11.2 microdeletion. Moreover, the determination of differential whole‐genome expression profiling demonstrated the destruction of the top‐ranked network in neurogenesis and accounted for observation of abnormal neurodevelopmental phenotypes in the 16p11.2 microdeletion carriers.

Conclusions

We have provided the frequency of the 16p11.2 microdeletion in a Chinese pediatric NDD cohort with a variable NDD phenotype from childhood to puberty, which is useful for Chinese geneticists/pediatricians to conduct the 16p11.2 microdeletion testing in children with NDDs.

Kctd13-deficient mice display short-term memory impairment and sex-dependent genetic interactions

The 16p11.2 BP4-BP5 deletion and duplication syndromes are associated with a complex spectrum of neurodevelopmental phenotypes that includes developmental delay and autism spectrum disorder, with a reciprocal effect on head circumference, brain structure and body mass index. Mouse models of the 16p11.2 copy number variant have recapitulated some of the patient phenotypes, while studies in flies and zebrafish have uncovered several candidate contributory genes within the region, as well as complex genetic interactions. We evaluated one of these loci, KCTD13, by modeling haploinsufficiency and complete knockout in mice. In contrast to the zebrafish model, and in agreement with recent data, we found normal brain structure in heterozygous and homozygous mutants. However, recapitulating previously observed genetic interactions, we discovered sex-specific brain volumetric alterations in double heterozygous Kctd13xMvp and Kctd13xLat mice. Behavioral testing revealed a significant deficit in novel object recognition, novel location recognition and social transmission of food preference in Kctd13 mutants. These phenotypes were concomitant with a reduction in density of mature spines in the hippocampus, but potentially independent of RhoA abundance, which was unperturbed postnatally in our mutants. Furthermore, transcriptome analyses from cortex and hippocampus highlighted the dysregulation of pathways important in neurodevelopment, the most significant of which was synaptic formation. Together, these data suggest that KCTD13 contributes to the neurocognitive aspects of patients with the BP4-BP5 deletion, likely through genetic interactions with other loci.

The importance of gene-environment interactions in human obesity

The worldwide obesity epidemic has been mainly attributed to lifestyle changes. However, who becomes obese in an obesity-prone environment is largely determined by genetic factors. In the last 20 years, important progress has been made in the elucidation of the genetic architecture of obesity. In parallel with successful gene identifications, the number of gene-environment interaction (GEI) studies has grown rapidly. This paper reviews the growing body of evidence supporting gene-environment interactions in the field of obesity. Heritability, monogenic and polygenic obesity studies provide converging evidence that obesity-predisposing genes interact with a variety of environmental, lifestyle and treatment exposures. However, some skepticism remains regarding the validity of these studies based on several issues, which include statistical modelling, confounding, low replication rate, underpowered analyses, biological assumptions and measurement precision. What follows in this review includes (1) an introduction to the study of GEI, (2) the evidence of GEI in the field of obesity, (3) an outline of the biological mechanisms that may explain these interaction effects, (4) methodological challenges associated with GEI studies and potential solutions, and (5) future directions of GEI research. Thus far, this growing body of evidence has provided a deeper understanding of GEI influencing obesity and may have tremendous applications in the emerging field of personalized medicine and individualized lifestyle recommendations.

A systematic review of genetic syndromes with obesity

Syndromic monogenic obesity typically follows Mendelian patterns of inheritance and involves the co-presentation of other characteristics, such as mental retardation, dysmorphic features and organ-specific abnormalities. Previous reviews on obesity have reported 20 to 30 syndromes but no systematic review has yet been conducted on syndromic obesity. We searched seven databases using terms such as 'obesity', 'syndrome' and 'gene' to conduct a systematic review of literature on syndromic obesity. Our literature search identified 13,719 references. After abstract and full-text review, 119 relevant papers were eligible, and 42 papers were identified through additional searches. Our analysis of these 161 papers found that 79 obesity syndromes have been reported in literature. Of the 79 syndromes, 19 have been fully genetically elucidated, 11 have been partially elucidated, 27 have been mapped to a chromosomal region and for the remaining 22, neither the gene(s) nor the chromosomal location(s) have yet been identified. Interestingly, 54.4% of the syndromes have not been assigned a name, whereas 13.9% have more than one name. We report on organizational inconsistencies (e.g. naming discrepancies and syndrome classification) and provide suggestions for improvements. Overall, this review illustrates the need for increased clinical and genetic research on syndromes with obesity.

Interactions between obesity-related copy number variants and dietary behaviors in childhood obesity

Copy number variants (CNVs) have been implicated as an important genetic marker of obesity, and gene-environment interaction has been found to modulate risk of obesity. To evaluate the associations between CNVs and childhood obesity, as well as the interactions between CNVs and dietary behaviors, we recruited 534 obese children and 508 controls from six cities in China and six candidate CNVs were screened through published genome-wide studies (GWAS) on childhood obesity. We found three loci (10q11.22, 4q25 and 11q11) to be significantly associated with obesity after false discovery rate (FDR) correction (all the p ≤ 0.05). Cumulative effect of the three positive loci was measured by the genetic risk score (GRS), showing a significant relationship with the risk of obesity (Ptrend < 0.001). The OR of obesity increased to 21.38 (95% CI = 21.19-21.55) among the 10q11.22 deletion carriers who had meat-based diets, indicating prominent multiplicative interaction (MI) between deletions of 10q11.22 and preference for a meat-based diet. Simultaneous deletions of 5q13.2 and duplications of 6q14.1 had significant MI with a preference for salty foods. Our results suggested that CNVs may contribute to the genetic susceptibility of childhood obesity, and the CNV-diet interactions modulate the risk of obesity.

Eating in the absence of hunger but not loss of control behaviors are associated with 16p11.2 deletions

OBJECTIVE

The ∼600-kb BP4-BP5 16p11.2 deletion has been consistently associated with obesity. We studied two heritable disinhibited eating behaviors, eating in the absence of hunger (EAH) and loss of control (LOC), to better characterize the relationship between the deletion and obesity.

METHODS

Our study population included ninety-three 16p11.2 CNV carriers (64 with deletions and 29 with duplications) and their families. We performed analyses using linear mixed models and focused on deletion carriers.

RESULTS

We confirmed previous associations between the 16p11.2 deletion and obesity (P < 0.0001) and between all EAH subscales and obesity (P < 0.05), after adjusting for confounders. We found significant associations between the deletion and EAH due to external cues (P = 0.004) and EAH due to boredom (P = 0.003), but not EAH due to fatigue/anxiety or negative affect. Conditioning BMI on the 16p11.2 deletion and each EAH behavior did not abolish the association between the deletion and obesity. LOC was underrepresented and not associated with the deletion.

CONCLUSIONS

We report evidence that the 16p11.2 deletion may influence specific obesity-associated disinhibited eating behaviors: EAH due to external trigger and EAH due to boredom. Prospective studies are needed to confirm the temporal order of EAH behaviors and obesity related to the deletion.

Obesity in children with autism spectrum disorder

Research suggests that the prevalence of obesity in children with autism spectrum disorder (ASD) is at least as high as that seen in typically developing children. Many of the risk factors for children with ASD are likely the same as for typically developing children, especially within the context of today's obesogenic environment. The particular needs and challenges that this population faces, however, may render them more susceptible to the adverse effects of typical risk factors, and they may also be vulnerable to additional risk factors not shared by children in the general population, including psychopharmacological treatment, genetics, disordered sleep, atypical eating patterns, and challenges for engaging in sufficient physical activity. For individuals with ASD, obesity and its sequelae potentially represent a significant threat to independent living, self-care, quality of life, and overall health.

From obesity genetics to the future of personalized obesity therapy

Obesity is a disorder characterized by an excess accumulation of body fat resulting from a mismatch between energy intake and expenditure. Incidence of obesity has increased dramatically in the past few years, almost certainly fuelled by a shift in dietary habits owing to the widespread availability of low-cost, hypercaloric foods. However, clear differences exist in obesity susceptibility among individuals exposed to the same obesogenic environment, implicating genetic risk factors. Numerous genes have been shown to be involved in the development of monofactorial forms of obesity. In genome-wide association studies, a large number of common variants have been associated with adiposity levels, each accounting for only a small proportion of the predicted heritability. Although the small effect sizes of obesity variants identified in genome-wide association studies currently preclude their utility in clinical settings, screening for a number of monogenic obesity variants is now possible. Such regular screening will provide more informed prognoses and help in the identification of at-risk individuals who could benefit from early intervention, in evaluation of the outcomes of current obesity treatments, and in personalization of the clinical management of obesity. This Review summarizes current advances in obesity genetics and discusses the future of research in this field and the potential relevance to personalized obesity therapy.

Gene-gene interaction between PPARδ and PPARγ is associated with abdominal obesity in a Chinese population

The peroxisome proliferator-activated receptors (PPARs) -α, -δ/β and -γ are the ligand-activated transcription factors that function as the master regulators of glucose, fatty acid and lipoprotein metabolism, energy balance, cell proliferation and differentiation, inflammation, and atherosclerosis. The objective of the current study was to examine the main and interactive effect of seven single nucleotide polymorphisms (SNPs) of PPARδ/γ in contribution to abdominal obesity. A total of 820 subjects were randomly selected and no individuals were related. The selected SNPs in PPARδ (rs2016520 and rs9794) and PPARγ (rs10865710, rs1805192, rs709158, rs3856806, and rs4684847) were genotyped. Mean difference and 95% confident interval were calculated. Interactions were explored by the method of generalized multifactor dimensionality reduction. After adjustment for gender, age, and smoking status, it was found that the carriers of the C allele (TC + CC) of rs2016520 were associated with a decreased risk of abdominal obesity compared to the carriers of the TT genotype (mean difference = -2.63, 95% CI = -3.61--1.64, P < 0.0001). A significant two-locus model (P = 0.0107) involving rs2016520 and rs10865710 and a significant three-locus model (P = 0.0107) involving rs2016520, rs9794, and rs1805192 were observed. Overall, the three-locus model had the highest level of testing accuracy (59.85%) and showed a better cross-validation consistency (9/10) than two-locus model. Therefore, for abdominal obesity defined by waist circumference, we chose the three-locus model as the best interaction model. In conclusion, the C allele in rs2016520 was significantly associated with a lower abdominal obesity. Moreover, an interaction among rs2016520, rs1805192, and rs9794 on incident abdominal obesity could be demonstrated.