**7. Genetic approach to obesity**

Recognizing the monogenic syndromic and not syndromic obesity is really very important for at least two reasons: firstly, because it is hoped that, in the near future, making use of the results of other research in the field of obesity, obese patients can benefit from specific treatment (such as leptin administration and MC4R receptor agonists); secondly, because it is hoped that they will benefit from a multidisciplinary approach to the management of the symptoms, however, the clinical features of patients with genetic obesity are often very blurred, so that diagnosis can escape at first. **Figure 5** shows a diagnostic classification algorithm which can be useful in territorial pediatrics to suspect monogenic obesity and in the second and third levels in hospitals to orientate themselves in the execution of all the diagnostic tests in order to confirm the final diagnosis [12].

The genetic contribution to common obesity has been established initially through family, twin and adoption studies. Twin studies have shown a relatively high heritability ranging from 40 to 77% [6]. Gene identification for the last 15 years has been based on two genetic epidemio‐ logical approaches (candidate gene and genome‐wide linkage methods). Recently, genome‐ wide association studies have brought great information on obesity‐related genes.

*Candidate‐gene studies*: The design of the candidate gene approach is simple; candidate genes are genes that, according to their characteristics, can be considered causally related to the

(GCKRP) that inhibits the glucokinase (GCK) activity competing with the glucose, substrate of GCK. It has been demonstrated that the GCKRP L466 variant encodes for a protein that indirectly increased GCK activity. This increase in GCK hepatic activity promotes hepatic glucose metabolism, raises the concentrations of malonyl coenzyme A, a substrate for de novo lipogenesis, and contributes in liver fat accumulation [160, 163]. In addition, a study conducted in Chinese children has shown that that the polymorphism *rs11235972* of the *uncoupling protein 3* (*UCP3*) gene is associated with the occurrence of NAFLD. *UCP3* is a mitochondrial protein with a highly selective expression in skeletal muscle, a major site of thermogenesis in humans.

*Apolipoprotein C3* gene (*APOC3*) *rs2854117* and *rs2854116* variants and *farnesyl‐diphosphate farnesyltransferase 1* (*FDFT1*) gene *rs2645424* variant have been also associated with NAFLD in adult [160]. Also in the recent years, genetic studies have demonstrated that single‐nucleotide polymorphisms (SNPs) in genes involved in lipid metabolism (*Lipin 1, LPIN1*), oxidative stress (*superoxide dismutase 2, —SOD2*), insulin signaling (*insulin receptor substrate‐1, IRS‐1*) and fibrogenesis (*Kruppel‐like factor 6, KLF6*) have been associated with a high risk for NAFLD development and progression [154]. Finally, a recent study evaluated the combined effect of four‐polymorphisms genetic risk score in predicting NASH in NAFLD obese children with increased liver enzymes to help NASH diagnosis with the other non‐invasive diagnostic tests

In conclusion, obesity and fatty liver disease often go hand in hand even in the pediatric

Recognizing the monogenic syndromic and not syndromic obesity is really very important for at least two reasons: firstly, because it is hoped that, in the near future, making use of the results of other research in the field of obesity, obese patients can benefit from specific treatment (such as leptin administration and MC4R receptor agonists); secondly, because it is hoped that they will benefit from a multidisciplinary approach to the management of the symptoms, however, the clinical features of patients with genetic obesity are often very blurred, so that diagnosis can escape at first. **Figure 5** shows a diagnostic classification algorithm which can be useful in territorial pediatrics to suspect monogenic obesity and in the second and third levels in hospitals to orientate themselves in the execution of all the diagnostic tests in order to confirm

The genetic contribution to common obesity has been established initially through family, twin and adoption studies. Twin studies have shown a relatively high heritability ranging from 40 to 77% [6]. Gene identification for the last 15 years has been based on two genetic epidemio‐ logical approaches (candidate gene and genome‐wide linkage methods). Recently, genome‐

*Candidate‐gene studies*: The design of the candidate gene approach is simple; candidate genes are genes that, according to their characteristics, can be considered causally related to the

wide association studies have brought great information on obesity‐related genes.

population, and both are pathologies related to genetic and environmental factors.

Genetic variants of *UCP3* have been associated with NIDDM and obesity [164].

[165].

**7. Genetic approach to obesity**

236 Adiposity - Omics and Molecular Understanding

the final diagnosis [12].

**Figure 5.** Diagnostic approach to genetic obesity. Adapted with permission from Farooqi and O'Rahilly [12].

disease. This method is based on the following resources: animal models using gene knockout and transgenic approaches and cellular model systems showing their role in metabolic pathways involved in glucose metabolism. There are two main types of candidates that are generally considered in such studies: functional and positional. Functional candidates are genes with products that are in some way involved in the pathogenesis of the disease. Positional candidates are genes that are identified because they lie within genomic regions that have been shown to be genetically important in linkage or association studies, or by the detection of chromosomal translocations that disrupt the gene [2, 3]. The latest update of the Human Obesity GeneMap reported 127 candidate genes for obesity‐related traits. Results of large‐scale studies suggest that obesity is strongly associated with genetic variants in the *MC4R* gene, *adrenergic β3 receptor* (*ADRB3*) gene, *PCSK1* gene, *BDNF* gene and *endocannabinoid receptor 1* (*CNR1*) gene [16].

*Genome‐wide linkage studies*: Genome‐wide linkage studies (GWLS) identify new, unforeseen genetic variants associated with a disease or a feature of interest. They rely on kinship of study participants and seek to identify chromosomal regions that tend to be co‐inherited by indi‐ viduals. The limit of genome‐wide linkage studies is that they have a rather coarse resolution and typically identify broad intervals that require follow‐up genotyping to pinpoint the genes that underlie the linkage signal [17]. The latest Human Obesity Gene Map update reported 253 loci from 61 genome‐wide linkage scans, of which 15 loci have been replicated in at least three studies [16].

*Genome‐wide association studies*: Genome‐wide association studies (GWAS) are used in genetic research to look for associations between many (typically hundreds of thousands) specific genetic variations (more commonly, single‐nucleotide polymorphisms, SNP) and particular diseases or traits. Genome‐wide association studies have a higher resolution levels and are able to narrow down the locus associated with greater accuracy, so this approach took place in the genome‐wide linkage studies for common disease [3]. This new approach has found about 30 loci associated with obesity and high BMI. The strongest association is with FTO gene (the fat‐mass and obesity‐related gene) mutations. Also BDNF, SH2B1 e NEGR1 mutations are associated with obesity and support that obesity is a disorder of hypothalamic function [17].

Since the beginning of the genome‐wide association study (GWAS) era in 2005, a number of large GWASs have been conducted on obesity‐related traits in humans. A large meta‐analysis from 46 studies conducted by the Genetic Investigation of Anthropometric Traits (GIANT) [166] consortium identified 32 SNPs robustly associated with adult BMI. The majority of these SNPs demonstrated directionally consistent effects in age‐ and sex‐adjusted BMI in children and adolescents. However, even in combination, the 32 established SNPs explain <2% of the variation in BMI in either adults or children. The mismatch between the high heritability estimates from twin and other family studies (40–70%) and the small percentage of variation explained through GWAS (<2%) is called the problem of "missing heritability" [167, 168]. A portion of the missing heritability appears to be due to rare genetic variants and some non‐ additive genetic effects that are not found in analyses GWAS that showed only additional effects of common SNPs with minor allele frequencies (MAF) of >5%. Another part of the missing heritability can be explained by the fact that multiple additional common genetic mutations contribute to obesity, but they have a small effect that cannot be found by GWAS analyses [168].

New types of analyses, such as genome‐wide complex trait analysis (GCTA), analysis of uncommon (MAF 0.5–1%) or rare (MAF 0.5%) variants and structural variants not detected by GWAS arrays, epigenetic analysis and gene–gene interactions (epistasis), are helping to fill that gap [167]. The purpose of the novel approach called genome‐wide complex trait analysis (GCTA) is not to identify specific SNPs related to the target phenotype, but rather to estimate the total additive genetic effect of the common SNPs used on currently available DNA arrays [168].

The rare variant—common disease hypothesis—suggests that rare variants contribute signifi‐ cantly to complex traits. Probably, the obese phenotype is the consequence of additive effects and interactions among multiple alleles with varying magnitude of effect. Actually, we know that only 1% of the human genome is transcribed into mRNA and translated into proteins. An additional 0.5% is regulatory regions that control gene expression. Functions of the remaining 98.5% of the genome remain unknown. Rare variants might be identified by massive genotyping or deep sequencing in large families thanks to novel techniques that sequence millions of DNA strands in parallel and at low cost such as next‐generation sequencing techniques [169].

Copy number variants (CNVs) represent another source of the heritability that is missed by GWAS studies. Copy number variants (CNVs) are products of genomic rearrangements, resulting in deletions, duplications, inversions and translocations [167, 170]. The most established CNV in the obesity field is a large, rare chromosomal deletion at 16p11.2; this deletion includes a small number of genes, one of which is *SH2B1*, known to be involved in leptin and insulin signaling. The search for CNVs in the context of obesity has proved fruitful, and it has become quite clear they play a role in the missing heritability that still needs to be explained for the disease [19, 170].
