Chromosome 16p11.2 deletions: another piece in the genetic puzzle of childhood obesity

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

Key Words Collapse

MESH Headings Collapse

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

Collapse

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

Collapse

Health supervision for children and adolescents with 16p11.2 deletion syndrome

Rare genetic conditions are challenging for the primary care provider to manage without proper guidelines. This clinical review is designed to assist the pediatrician, family physician, or internist in the primary care setting to manage the complexities of 16p11.2 deletion syndrome. A multidisciplinary medical home with the primary care provider leading the care and armed with up-to-date guidelines will prove most helpful to the rare genetic patient population. A special focus on technology to fill gaps in deficits, review of case studies on novel medical treatments, and involvement with the educational system for advocacy with an emphasis on celebrating diversity will serve the rare genetic syndrome population well.

Reviewed and updated Algorithm for Genetic Characterization of Syndromic Obesity Phenotypes

Background: Individuals with a phenotype of early-onset severe obesity associated with intellectual disability can have molecular diagnoses ranging from monogenic to complex genetic traits. Severe overweight is the major sign of a syndromic physical appearance and predicting the influence of a single gene and/or polygenic risk profile is extremely complicated among the majority of the cases. At present, considering rare monogenic bases as the principal etiology for the majority of obesity cases associated with intellectual disability is scientifically poor. The diversity of the molecular bases responsible for the two entities makes the appliance of the current routinely powerful genomics diagnostic tools essential. Objective: Clinical investigation of these difficult-to-diagnose patients requires pediatricians and neurologists to use optimized descriptions of signs and symptoms to improve genotype correlations. Methods: The use of modern integrated bioinformatics strategies which are conducted by experienced multidisciplinary clinical teams. Evaluation of the phenotype of the patient's family is also of importance. Results: The next step involves discarding the monogenic canonical obesity syndromes and considering infrequent unique molecular cases, and/or then polygenic bases. Adequate management of the application of the new technique and its diagnostic phases is essential for achieving good cost/efficiency balances. Conclusion: With the current clinical management, it is necessary to consider the potential coincidence of risk mutations for obesity in patients with genetic alterations that induce intellectual disability. In this review, we describe an updated algorithm for the molecular characterization and diagnosis of patients with a syndromic obesity phenotype.

Copy number variant syndromes are frequent in schizophrenia: Progressing towards a CNV-schizophrenia model

The genetic underpinnings of schizophrenia (SCZ) remain unclear. SCZ genetic studies thus far have only identified numerous single nucleotide polymorphisms with small effect sizes and a handful of copy number variants (CNVs). This study investigates the prevalence of well-characterized CNV syndromes and candidate CNVs within a cohort of 348 SCZ patients, and explores correlations to their phenotypic findings. There was an enrichment of syndromic CNVs in the cohort, as well as brain-related and immune pathway genes within the detected CNVs. SCZ patients with brain-related CNVs had increased CNV burden, neurodevelopmental features, and types of hallucinations. Based on these results, we propose a CNV-SCZ model wherein specific phenotypic profiles should be prioritized for CNV screening within the SCZ patient population.

Phosphorylation of the Unique C-Terminal Tail of the Alpha Isoform of the Scaffold Protein SH2B1 Controls the Ability of SH2B1α To Enhance Nerve Growth Factor Function

The scaffold protein SH2B1, a major regulator of body weight, is recruited to the receptors of multiple cytokines and growth factors, including nerve growth factor (NGF). The β isoform but not the α isoform of SH2B1 greatly enhances NGF-dependent neurite outgrowth of PC12 cells. Here, we asked how the unique C-terminal tails of the α and β isoforms modulate SH2B1 function. We compared the actions of SH2B1α and SH2B1β to those of the N-terminal 631 amino acids shared by both isoforms. In contrast to the β tail, the α tail inhibited the ability of SH2B1 to both cycle through the nucleus and enhance NGF-mediated neurite outgrowth, gene expression, phosphorylation of Akt and phospholipase C-gamma (PLC-γ), and autophosphorylation of the NGF receptor TrkA. These functions were restored when Tyr753 in the α tail was mutated to phenylalanine. We provide evidence that TrkA phosphorylates Tyr753 in SH2B1α, as well as tyrosines 439 and 55 in both SH2B1α and SH2B1β. Finally, coexpression of SH2B1α but not SH2B1α with a mutation of Y to F at position 753 (Y753F) inhibited the ability of SH2B1β to enhance neurite outgrowth. These results suggest that the C-terminal tails of SH2B1 isoforms are key determinants of the cellular role of SH2B1. Furthermore, the function of SH2B1α is regulated by phosphorylation of the α tail.

Understanding the pathophysiological mechanisms in the pediatric non-alcoholic fatty liver disease: The role of genetics

Classically, the non-alcoholic fatty liver disease (NAFLD) physiopathology and progression has been summarized in the two hits hypothesis. The first hit is represented by the action of hyperinsulinemia and insulin resistance, accompanying obesity, that leads to liver steatosis increasing the absolute non esterified fatty acids uptake in the liver and the esterification to form triacylglycerol. The oxidative stress is involved in the second hit leading to the progression to nonalcoholic steatohepatitis (NASH) because of its harmful action on steatosic hepatocytes. However, at the present time, the two hits hypothesis needs to be updated because of the discover of genetic polymorphisms involved both in the liver fat accumulation and progression to NASH that make more intriguing understanding the NAFLD pathophysiological mechanisms. In this editorial, we want to underline the role of PNPLA3 I148M, GPR120 R270H and TM6SF2 E167K in the pediatric NAFLD development because they add new pieces to the comprehension of the NAFLD pathophysiological puzzle. The PNPLA3 I148M polymorphism encodes for an abnormal protein which predisposes to intrahepatic triglycerides accumulation both for a loss-of-function of its triglyceride hydrolase activity and for a gain-of-function of its lipogenic activity.

Therefore, it is involved in the first hit, such as TM6SF2 E167K polymorphisms that lead to intrahepatic fat accumulation through a reduced very low density lipoprotein secretion. On the other hand, the GPR120 R270H variant, reducing the anti-inflammatory action of the GPR120 receptor expressed by Kuppfer cells, is involved in the second hit leading to the liver injury.

Fine Mapping of a GWAS-Derived Obesity Candidate Region on Chromosome 16p11.2

INTRODUCTION

Large-scale genome-wide association studies (GWASs) have identified 97 chromosomal loci associated with increased body mass index in population-based studies on adults. One of these SNPs, rs7359397, tags a large region (approx. 1MB) with high linkage disequilibrium (r2>0.7), which comprises five genes (SH2B1, APOBR, sulfotransferases: SULT1A1 and SULT1A2, TUFM). We had previously described a rare mutation in SH2B1 solely identified in extremely obese individuals but not in lean controls.

METHODS

The coding regions of the genes APOBR, SULT1A1, SULT1A2, and TUFM were screened for mutations (dHPLC, SSCP, Sanger re-sequencing) in 95 extremely obese children and adolescents. Detected non-synonymous variants were genotyped (TaqMan SNP Genotyping, MALDI TOF, PCR-RFLP) in independent large study groups (up to 3,210 extremely obese/overweight cases, 485 lean controls and 615 obesity trios). In silico tools were used for the prediction of potential functional effects of detected variants.

RESULTS

Except for TUFM we detected non-synonymous variants in all screened genes. Two polymorphisms rs180743 (APOBR p.Pro428Ala) and rs3833080 (APOBR p.Gly369_Asp370del9) showed nominal association to (extreme) obesity (uncorrected p = 0.003 and p = 0.002, respectively). In silico analyses predicted a functional implication for rs180743 (APOBR p.Pro428Ala). Both APOBR variants are located in the repetitive region with unknown function.

CONCLUSION

Variants in APOBR contributed as strongly as variants in SH2B1 to the association with extreme obesity in the chromosomal region chr16p11.2. In silico analyses implied no functional effect of several of the detected variants. Further in vitro or in vivo analyses on the functional implications of the obesity associated variants are warranted.

Novel association between a nonsynonymous variant (R270H) of the G-protein-coupled receptor 120 and liver injury in children and adolescents with obesity

The G-protein-coupled receptor 120 (GPR120) is a receptor for polyunsaturated fatty acids with anti-inflammatory activity. The R270H variant of GPR120 enhances inflammation in adipose and hepatic tissues. We investigated whether the R270H variant could play a role in determining liver injury in children and adolescents with obesity. Five hundred eighty-one children with obesity were studied. No homozygotes and 20 heterozygotes for the 270H allele were found. Heterozygotes showed higher alanine transaminase (ALT) levels (P = 0.01) than wild-type subjects, and also showed an odds ratio to have pathologic ALT of 3.2 (95% confidence interval [CI] 1.2-8.0, P < 0.05). Moreover, we genotyped the same patients for the patatin-like phospholipase-containing domain 3 (PNPLA3) I148M polymorphism, which is implicated in the development of liver steatosis. Stratifying the patients with the GPR120 270H variant on the basis of their PNPLA3 polymorphism, we demonstrated a significant interaction effect on ALT levels (P = 0.00001), suggesting a driving effect of the PNPLA3 148M allele on liver injury in children with obesity carrying this variant.

Recent advances in obesity: genetics and beyond

The prevalence of obesity, which is a heritable trait that arises from the interactions of multiple genes and lifestyle factors, continues to increase worldwide, causing serious health problems and imposing a substantial economic burden on societies. For the past several years, various genetic epidemiological approaches have been utilized to identify genetic loci for obesity. Recent evidence suggests that development of obesity involves hormones and neurotransmitters (such as leptin, cocaine- and amphetamine-regulated transcript (CART), and ghrelin) that regulate appetite and energy expenditure. These hormones act on specific centers in the brain that regulate the sensations of satiety. Mutations in these hormones or their receptors can lead to obesity. Aberrant circadian rhythms and biochemical pathways in peripheral organs or tissues have also been implicated in the pathology of obesity. More interestingly, increasing evidence indicates a potential relation between obesity and central nervous system disorders (such as cognitive deficits). This paper discusses recent advances in the field of genetics of obesity with an emphasis on several established loci that influence obesity. These recently identified loci may hold the promise to substantially improve our insights into the pathophysiology of obesity and open up new therapeutic strategies to combat growing obesity epidemic facing the human population today.