Low Copy Number of the AMY1 Locus Is Associated with Early-Onset Female Obesity in Finland

Background The salivary alpha-amylase locus (AMY1) is located in a highly polymorphic multi allelic copy number variable chromosomal region. A recent report identified an association between AMY1 copy numbers and BMI in common obesity. The present study investigated the relationship between AMY1 copy number, BMI and serum amylase in childhood-onset obesity. Patients Sixty-one subjects with a history of childhood-onset obesity (mean age 19.1 years, 54% males) and 71 matched controls (19.8 yrs, 45% males) were included. All anthropometric measures were greater in the obese; their mean BMI was 40 kg/m(2) (range 25-62 kg/m(2)) compared with 23 kg/m(2) in the controls (15-32 kg/m(2)). Results Mean AMY1 copy numbers did not differ between the obese and control subjects, but gender differences were observed; obese men showed the highest and obese women the lowest number of AMY1 copies (p=0.045). Further, only in affected females, AMY1 copy number correlated significantly with whole body fat percent (r=-0.512, p=0.013) and BMI (r=-0.416, p=0.025). Finally, a clear linear association between AMY1 copy number and serum salivary amylase was observed in all subgroups but again differences existed between obese males and females. Conclusions In conclusion, our findings suggest that AMY1 copy number differences play a role in childhood-onset obesity but the effect differs between males and females. Further studies in larger cohorts are needed to confirm these observations.Peer reviewe

Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study

Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. Methods In this multinational retrospective observational study we enrolled adult participants (aged $\ge$18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) were female, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was 4$\bullet$7 kg/m${}^2$ (4$\bullet$4-5$\bullet$0), and the mean difference between predicted and observed BMI was-0$\bullet$3 kg/m${}^2$ (SD 4$\bullet$7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. InterpretationWe developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.Comment: The Lancet Digital Health, 202

AMYCNE: Confident copy number assessment using whole genome sequencing data

<div><p>Copy number variations (CNVs) within the human genome have been linked to a diversity of inherited diseases and phenotypic traits. The currently used methodology to measure copy numbers has limited resolution and/or precision, especially for regions with more than 4 copies. Whole genome sequencing (WGS) offers an alternative data source to allow for the detection and characterization of the copy number across different genomic regions in a single experiment. A plethora of tools have been developed to utilize WGS data for CNV detection. None of these tools are designed specifically to accurately estimate copy numbers of complex regions in a small cohort or clinical setting. Herein, we present AMYCNE (automatic modeling functionality for copy number estimation), a CNV analysis tool using WGS data. AMYCNE is multifunctional and performs copy number estimation of complex regions, annotation of VCF files, and CNV detection on individual samples. The performance of AMYCNE was evaluated using <i>AMY1A</i> ddPCR measurements from 86 unrelated individuals. In addition, we validated the accuracy of AMYCNE copy number predictions on two additional genes (<i>FCGR3A</i> and <i>FCGR3B)</i> using datasets available through the 1000 genomes consortium. Finally, we simulated levels of mosaic loss and gain of chromosome X and used this dataset for benchmarking AMYCNE. The results show a high concordance between AMYCNE and ddPCR, validating the use of AMYCNE to measure tandem <i>AMY1</i> repeats with high accuracy. This opens up new possibilities for the use of WGS for accurate copy number determination of other complex regions in the genome in small cohorts or single individuals.</p></div

Copy Number Variants Are Enriched in Individuals With Early-Onset Obesity and Highlight Novel Pathogenic Pathways

Context: Only a few genetic causes for childhood obesity have been identified to date. Copy number variants (CNVs) are known to contribute to obesity, both syndromic (15q11.2 deletions, Prader-Willi syndrome) and nonsyndromic (16p11.2 deletions) obesity. Objective: To study the contribution of CNVs to early-onset obesity and evaluate the expression of candidate genes in subcutaneous adipose tissue. Design and Setting: A case-control study in a tertiary academic center. Participants: CNV analysis was performed on 90 subjects with early-onset obesity and 67 normalweight controls. Subcutaneous adipose tissue from body mass index-discordant siblings was used for the gene expression analyses. Main Outcome Measures: We used custom high-density array comparative genomic hybridization with exon resolution in 1989 genes, including all known obesity loci. The expression of candidate genes was assessed using microarray analysis of messenger RNA from subcutaneous adipose tissue. Results: We identified rare CNVs in 17 subjects (19%) with obesity and 2 controls (3%). In three cases (3%), the identified variant involved a known syndromic lesion (22q11.21 duplication, 1q21.1 deletion, and 16p11.2 deletion, respectively), although the others were not known. Seven CNVs in 10 families were inherited and segregated with obesity. Expression analysis of 37 candidate genes showed discordant expression for 10 genes (PCM1, EFEMP1, MAMLD1, ACP6, BAZ2B, SORBS1, KLF15, MACROD2, ATR, and MBD5). Conclusions: Rare CNVs contribute possibly pathogenic alleles to a substantial fraction of children with early-onset obesity. The involved genes might provide insights into pathogenic mechanisms and involved cellular pathways. These findings highlight the importance of CNV screening in children with early-onset obesity.Peer reviewe

Comparison of AMYCNE, DCNE of <i>AMY1</i>, and CNVnator against the <i>AMY1</i> ddPCR measurements.

<p>Comparison of AMYCNE, DCNE of <i>AMY1</i>, and CNVnator against the <i>AMY1</i> ddPCR measurements.</p

Copy number distributions of the AMY genes.

<p>Histograms of the copy number distributions of the three genes in the AMY locus: (a) <i>AMY1</i>, (b) <i>AMY2A</i> and (c) <i>AMY2B</i>.</p

Comparison of AMYCNE and CNVnator against the truth-set presented in [16].

<p>Comparison of AMYCNE and CNVnator against the truth-set presented in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189710#pone.0189710.ref016" target="_blank">16</a>].</p

A comparison of three WGS copy number estimation methods.

<p>A comparison of the three WGS copy number estimation methods: (a) AMYCNE, (b) Diploid copy number estimation of <i>AMY1</i>, and (c) CNVnator. In all heat maps the number of samples per point is illustrated by color from red (low numbers) to blue (high numbers) as shown in the color key.</p

Copy number of pancreatic polypeptide receptor gene NPY4R correlates with body mass index and waist circumference

Multiple genetic studies have linked copy number variation (CNV) in different genes to body mass index (BMI) and obesity. A CNV on chromosome 10q11.22 has been associated with body weight. This CNV region spans NPY4R, the gene encoding the pancreatic polypeptide receptor Y4, which has been described as a satiety-stimulating receptor. We have investigated CNV of the NPY4R gene and analysed its relationship to BMI, waist circumference and self-reported dietary intake from 558 individuals (216 men and 342 women) representing a wide BMI range. The copy number for NPY4R ranged from 2 to 8 copies (average 4.6 +/- 0.8). Rather than the expected negative correlation, we observed a positive correlation between NPY4R copy number and BMI as well as waist circumference (r = 0.267, p = 2.65x 10(-7) and r = 0.256, p = 8x10(-7), respectively). Each additional copy of NPY4R correlated with 2.6 kg/m(2) increase in BMI and 5.67 cm increase in waist circumference (p = 3.3x10(-7) and p = 1x10(-6), respectively) for women. For men, there was no statistically significant correlation between CNV and BMI. Our results suggest that NPY4R genetic variation influences body weight in women, but the exact role of this receptor appears to be more complex than previously proposed