Best Practice & Research Clinical Endocrinology & Metabolism

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1 Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) Contents lists available at SciVerse ScienceDirect Best Practice & Research Clinical Endocrinology & Metabolism journal homepage: 8 Genetic determinants of common obesity and their value in prediction Ruth J.F. Loos, PhD, Programme Leader * MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke s Hospital, Box 285, Cambridge, UK Keywords: genome-wide association obesity body mass index (BMI) prediction personal genome profile Genome-wide association studies (GWAS) have revolutionised the discovery of genes for common traits and diseases, including obesity-related traits. In less then four years time, 52 genetic loci were identified to be unequivocally associated with obesityrelated traits. This vast success raised hope and expectations that genetic information would become soon an integral part of personalised medicine. However, these loci have only small effects on obesity-susceptibility and explain just a fraction of the total variance. As such, their accuracy to predict obesity is poor and not competitive with the predictive ability of traditional risk factors. Nevertheless, some of these loci are being used in commercially available personal genome tests to estimate individuals lifetime risk of obesity. While proponents believe that personal genome profiling could have beneficial effects on behaviour, early reports do not support this hypothesis. To conclude, the most valuable contribution of GWAS-identified loci lies in their contribution to elucidating new physiological pathways that underlie obesitysusceptibility. Ó 2011 Elsevier Ltd. All rights reserved. Introduction The prevalence of obesity has been increasing steadily over the past four decades, fuelled by an abundance of inexpensive, palatable and energy-dense foods and by a lack of daily physical activity. 1 At first, the rise was seen in high-income countries and adults only, but in more recent years also middleand low-income countries and children alike have fallen victim to the rapid globalisation of the * Tel.: þ ; Fax: þ address: ruth.loos@mrc-epid.cam.ac.uk X/$ see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi: /j.beem

2 212 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) westernized lifestyle. 1 However, not only lifestyle factors are to blame for the growing obesity problem. This is evidenced by the fact that efforts to loose weight at the individual level and policy strategies to reverse the epidemic at the population level have typically been unsuccessful. 2 Furthermore, despite being exposed to an obesogenic environment, a large proportion of the population remains of normal weight. 3 These observations suggest that innate, non-environmental, factors make some individuals more susceptible to obesity and prevent them from loosing weight, while others seem to be protected against weight gain providing support for biological mechanisms, and thus genetic factors, to underlie the individual s response to the obesogenic environment. Indeed, family and twin studies have shown that 40 70% of the inter-individual variation in obesity-susceptibility can be attributed to genetic differences in the population. 4 The search for genetic variants contributing to obesity-susceptibility started in the mid-1990 s with candidate gene studies. Such studies define the candidacy of genes based on their role in body weight regulation observed in extreme and early-onset obesity cases or in transgenic animal models. Variation in these genes is then tested for association with obesity-related traits in the general population. Although hundreds of genes were proposed as candidates, only a handful has shown convincing association with obesity-susceptibility. 5,6 Towards the end of the 1990s, genome-wide linkage studies became available, a hypothesis-generating approach that screens the whole genome of related individuals with around polymorphic markers aiming to identify chromosomal regions that cosegregate with obesity-related traits. More than 80 genome-wide linkage studies have been performed so far, identifying more than 300 chromosomal loci showing some evidence of linkage with obesity. However, none of these loci have been successfully fine-mapped to pinpoint the causal gene or variant that links to obesity. 5,6 Taken together, 15 years of research using candidate gene and genome-wide linkage approaches to search for obesity-susceptibility genes has been only marginally successful. However, the advent of genome-wide association studies (GWAS) has revolutionised the discovery of genes for common traits and diseases, including obesity-related traits. 7,8 This paper reviews the discovery of loci unequivocally associated with obesity-related traits, and subsequently focuses on the body mass index (BMI) loci in particular to explore whether there is sufficient evidence for these loci to be used in personal genome profiling tests to predict one s lifetime risk of obesity. Gene discovery through GWAS Similar to genome-wide linkage studies, GWAS are hypothesis generating. The three main reasons for the tremendous success of GWAS are the high-density of the scans, the large sample size and the two-stage study design. More specifically, the human genome is screened at a very high resolution as more than two million genetic variants are tested for association with the trait of interest. Furthermore, study participants in GWAS do not have to be related, such that sample sizes tend to be much larger than for linkage studies, which are family-based. Key to GWAS is also the two-stage study design; loci for which associations reach high significance levels in the genome-wide scan (first stage) are taken forward to be tested for association in an independent series of samples (second stage). A locus is considered as established when the association reaches a significance of < in the subsequent meta-analysis of first and second stage results. This study design, combined with a stringent significance level, result in highly credible and robust association results. Since the start of the GWAS era in 2005, more than 1440 genetic loci have been identified to be associated with at least 235 traits at genome-wide significance levels (P ). 8,9 GWAS has also been fruitful for obesity-related traits, with the discovery of 52 loci in the past four years. Traits examined so far include BMI, waist circumference, waist-to-hip ratio (WHR), body fat percentage, and extreme and early-onset obesity (Fig. 1). Genetic loci for BMI So far, four waves of large-scale high-density GWAS for BMI, a marker of overall adiposity, have been performed, identifying 32 loci that reached genome-wide significance. The large majority of these studies have been performed in white Europeans and in adults only.

3 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) Fig. 1. Obesity-susceptibility loci discovered in four waves of GWAS for BMI (blue), 10,11,34,36,45,46,48 in one genome-wide metaanalysis for body fat percentage (orange), 49 in two waves of GWAS for waist circumference and WHR (pink), and in two GWAS for extreme and early-onset of obesity (green). 59,60 Each Venn-diagram represents the loci of one paper, except for papers that discovered only one locus, i.e. the FTO 10,11 and the near-mc4r loci, 34,36 for which no Venn-diagram was drawn. Adapted from Loos. 6 First wave of discoveries. The first convincing GWAS discovery for any obesity-related trait was made in 2007 for BMI. Two studies, each comprising w4800 individuals of white Europeans descent, independently identified genetic variants in the first intron of FTO (fat mass and obesity associated gene) to be robustly associated with BMI. 10,11 Over the past four years, the association of the FTO locus has been repeatedly replicated, not only for BMI, but also for obesity risk, body fat percentage, waist circumference and other obesity-related traits. Furthermore, although discovered in adults of white Europeans origin, the FTO locus has been found to be associated with obesity-related traits in children and adolescents, as well as in individuals of African, East Asian, 18,24 29 and Indian Asian descent. Second wave of discoveries. Following the discovery of the FTO locus, scientists realised that studies needed to increase sample size to improve statistical power to identify more loci, which led to the formation of a large-scale international consortium, called the GIANT (Genetic Investigation of ANtropometric Traits) consortium. As such, association data of 16,876 white Europeans from seven GWAS for BMI were combined in a meta-analysis. 34 This study confirmed the strong association with variation in FTO, and identified one new locus near the melanocortin 4 receptor (MC4R), a gene in which mutations are known to be the commonest cause of extreme childhood obesity. 35 At the same time, a GWAS in individuals of Indian Asian origin identified the same locus to be associated with waist circumference and related traits. 36 As the locus identified in the GWAS meta-analysis is located at w180 kb downstream of MC4R, it remains to be established whether the observed association of this locus with BMI is related the MC4R s role in monogenic obesity, or whether it has an independent role in body weight regulation. Of interest is that this locus is not only associated with increased BMI, but also with increased height, which is consistent with the phenotype of individuals with mutations in MC4R. 35 Subsequent to its discovery, the near-mc4r locus has been replicated in various populations of

4 214 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) white European descent, but also in South Asians, 37 East Asians, 27,28,38 40 and African Americans, 41,42 including children and adolescents ,43,44 Third wave of discoveries. In the third wave, the GIANT consortium doubled the sample size of its GWAS meta-analysis to 32,387 individuals of white European descent, identifying six new loci robustly associated with BMI. 45 At the same time, decode genetics performed a similar-sized GWAS of 31,392 individuals, predominantly from Iceland, and identified seven new BMI-associated loci. 46 As four of the newly identified loci were common to both studies (Fig. 1), the total number of new loci identified in the third wave adds up to 10; i.e. near TMEM18, near KCTD15, near GNPDA2, in SH2B1, in MTCH2, near NEGR1, near FAIM2, near SEC16B, near ETV5, and in BDNF. Although for many of these 10 loci association with BMI has been observed in children and adolescents, 12,44,45 and in populations of non-white origin, 18,27,40,41,47 their replication has been less consistent than for the FTO and near-mc4r loci. This inconsistency is likely attributable to the relatively small sample size of the replication studies. After all, more than 31,000 individuals were needed for their initial discovery. 45,46 Fourth wave of discoveries. For the most recent meta-analysis, the GIANT consortium expanded its GWAS stage to comprise 123,865 individuals from 46 populations of white European descent. 48 Besides confirming all 12 BMI-associated loci established in the three previous waves, this meta-analysis identified 20 loci that had not previously been associated with BMI (Fig. 1). 48 While there is some evidence that some of these loci show association with BMI in childhood, 48 no results have been reported for non-white populations. Replication of loci discovered in this fourth wave is hampered by the need for very large samples. Taken together, by the end of the fourth wave, GWAS had identified 32 loci unequivocally associated with BMI. Effect size, prevalence and explained variance. Despite the highly significant associations, the effects of these 32 established loci on BMI and obesity risk are small. 48 The firstly identified locus, FTO, has the largest effect on obesity-susceptibility with each FTO risk allele increasing BMI by on average 0.39 kg m 2 (equivalent to w1,100 g for a person of 1.70 m tall) and obesity risk by 1.20 fold (Fig. 2). FTO was the most easily identified locus as not only its effect size is largest, but also the frequency of the BMI-increasing allele is high in white Europeans (i.e. 46%). As a consequence, of all 32 BMI-associated loci, the FTO locus explains the most of the inter-individual variation in BMI, yet only a mere 0.34%. 48 The identification of the locus near MC4R required a quadrupling of the sample size, because its effect on BMI (0.23 kg m 2 /allele) and obesity risk (1.11/allele) was much smaller, and because the BMIincreasing allele frequency (24%) was much lower than that of the FTO locus. The doubling and subsequent quadrupling of sample sizes in the third and fourth waves allowed identifying more loci with even smaller effects and also with lower allele frequencies. More specifically, the per-allele effects of loci identified in the third wave ranged between 0.06 and 0.31 kg m 2 for BMI (or between 170 and 900 g for a person of 1.70 m tall) and between 1.02 and 1.13 fold for obesity risk, with allele frequencies ranging from 24% to 83%. Loci identified in the fourth wave had smaller per-allele effects, ranging from 0.06 and 0.19 kg m 2 for BMI (or between 170 and 550 g for a person 1.70 m tall) and between 1.02 and 1.10 fold for obesity risk, with allele frequencies ranging from 4% to 87% (Fig. 2). 48 While the number of discovered loci increases linearly with sample size from wave 1 to wave 4 (Fig. 3a), the explained variance lags behind substantially as effect sizes decrease and minor allele frequencies are lower (Fig. 3b). More specifically, from wave 1 to wave 4, sample size increases w25 fold, the number of established loci increases 32-fold, whereas the explained variance increases only 4 fold. Most importantly, the 32 loci combined explain only 1.45% of the phenotypic variation in BMI, equivalent to 2 4% of the heritability. 48 Genetic loci for body fat percentage While BMI is generally a good proxy for overall adiposity, non-invasive, inexpensive and easily obtained, it does not allow distinguishing between lean mass and fat mass. Therefore, a GWAS for body fat percentage, a more accurate proxy of adiposity, may reveal genetic associations that are not necessarily discovered when BMI is used as the outcome. A recent meta-analysis of 15 GWAS of body fat percentage, including 36,626 individuals of white European and Indian Asian descent, confirmed FTO as an obesity-susceptibility locus and identified two loci not previously linked to body composition (Fig. 1). 49 One locus locates near SPRY2, which is involved in Ras/mitogen-activated protein kinase

5 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) Fig. 2. Per-allele effect of BMI-associated loci on body weight (left axis) and obesity risk (right axis). Loci are sorted by wave of discovery (first wave in dark blue, second in red, third in green, and fourth in light blue) and subsequently by effect on body weight. Data were derived from Speliotes et al. 48 pathway, and which was also found to be associated with type 2 diabetes in East Asians. 50,51 The second locus locates near IRS1, showing more pronounced associations in men than in women. This locus was previously identified in GWAS for type 2 diabetes, 52 cardiovascular disease 53 and plasma lipid levels. 54 Most intriguingly, it was the fat percentage decreasing allele that was associated with increased risk of type 2 diabetes 52 and cardiovascular disease, 53 and with an adverse lipid profile. 49,54 Further analyses showed that the fat percentage decreasing allele lowered the subcutaneous fat, but not the more harmful visceral fat, suggesting that this locus is implicated in fat distribution and/or adipocyte biology. a 35 b 35 7% Number of loci discovered (n) Number of loci discovered (n) % 5% 4% 3% 2% 1% Explained variation in BMI (%) ,000 40,000 60,000 80, ,000 Sample size (discovery stage) 120, ,000 0 Wave 1 (2007) Wave 2 (2008) Wave 3 (2009) Waves of discovery Wave 4 (2011) 0% Fig. 3. Relationship between the number of loci identified and the stage-1 sample size across the four waves of GWAS for BMI (panel a), and increase in number of BMI loci identified (left axis) and increase in explained BMI variance (right axis) from wave 1 through wave 4 (panel b).

6 216 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) Genetic loci for waist and WHR So far, two waves of GWAS meta-analyses for waist and WHR have been undertaken to learn about the biological pathways that underlie abdominal obesity (Fig. 1). First wave of discoveries. In 2009, the GIANT and CHARGE consortium each performed a GWAS metaanalysis for abdominal obesity. 55,56 The study by the GIANT consortium included data of 35,580 individuals with GWAS data and identified four loci; three loci were found to be associated with waist circumference and one locus with WHR. 55 The meta-analysis by the CHARGE consortium, comprising 31,373 individuals at the genome-wide association stage, focussed on waist circumference only and identified three loci. 56 Because waist circumference and BMI are highly correlated, it is no surprise that all loci identified in a GWAS for waist circumference were also identified in a GWAS for BMI (Fig. 1), suggesting that these loci are associated with overall obesity, rather than with abdominal obesity in particular. Second wave of discoveries. In a subsequent meta-analysis, the GIANT consortium extended their GWAS stage to include 77,167 individuals from 29 populations of white European descent and focused more specifically on abdominal obesity by testing for association with WHR adjusted for BMI. 57 A total of 14 loci reached genome-wide significance of which seven were significantly more pronounced in women than in men. 57 None of the WHR loci overlapped with any of the BMI loci, suggesting that they likely influence abdominal obesity in particular, rather overall obesity (Fig. 1). Genetic loci for extreme and early-onset obesity GWAS for extreme and early-onset obesity have been proposed as an approach to find genetic variants associated with common obesity-susceptibility in the general population. This approach is based on the hypothesis that extremely obese individuals or those who develop obesity early on in life are enriched for genetic variants that also predispose to obesity in the general population. So far, five GWAS studies on extreme and/or early-onset obesity have been performed, but only two have identified loci that had not yet been identified by GWAS for BMI (Fig. 1). 59,60 The first GWAS, which compared 1380 cases with early-onset or morbid obesity with 1416 lean controls, identified three new loci and confirmed the FTO and near-mc4r loci. 59 The second GWAS, of which part of the samples overlapped with those of the first GWAS, focussed on early-onset obesity only, comparing data from 1138 cases and 1120 normal weight controls. 60 Besides confirming the FTO and near-mc4r loci, one additional locus reached genome-wide significance, which located close to a locus previously identified for waist circumference. 60 Taken together, GWAS of extreme and early-onset obesity have identified four new loci. While each of the GWAS showed that these four loci were also associated with BMI in the original study, replication of these loci with either obesity 61,63,64 or BMI in follow-up studies 48,64 has been variable, suggesting that larger-scale studies are needed for a more robust replication. GWAS in individuals of non-white European origin Over the past six years, the large majority of GWAS have been carried out in individuals of white European descent. However, more recently, a growing number of GWAS are being performed in populations of non-white origin, including GWAS for obesity-related traits. 28,38,42,65,66 The advantage of studying diverse populations is that such GWAS may identify loci that are specific to one particular ethnic group, or loci that may affect all ethnicities but that are more easily identified in a particular ethnic group because of its genotypic and/or phenotypic characteristics. A GWAS for obesity-related traits in 8842 Koreans identified one new locus in C12orf12 to be associated with WHR. 38 This WHR locus, which is located near ALDH2 and is in moderate linkage disequilibrium (LD r 2 ¼ 0.58 in CHBþJPT) with the functional Glu504Lys variant in ALDH2, which encodes the enzyme involved in alcohol metabolism. This locus seems East Asians specific and not polymorphic in white Europeans. A low-density GWAS in 413 Pima Indians identified a locus in A2BP1 to be associated with body fat percentage, which was not replicated in Old Order Amish or white Europeans. 65 Other GWAS for BMI in Filipino women 28 and in individuals of African descent 42,66

7 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) confirmed some of the loci previously identified in GWAS of white Europeans, but did not discover any new obesity-susceptibility loci. Collaborative efforts that combine the ethnic-specific GWAS in large-scale meta-analyses will be needed for the discovery of more obesity-susceptibility loci. Taken together, GWAS for BMI, body fat percentage, waist circumference, WHR and extreme and early-onset obesity in predominantly individuals of white Europeans descent have identified at least 52 obesity-susceptibility loci in the past four years. Obesity-susceptibility loci and their contribution to personalised health care The use of genetic information in a clinical setting has been common practice for more than 15 years and, so far, its application has been limited to the diagnosis and prediction of mainly rare, often monogenic, conditions. More specifically, at the population level, genetic information is being used as a screening tool to identify individuals at risk to allow careful follow-up and early intervention (e.g. screening of BRCA genes in women with a family history of breast cancer). At the individual level, genetic information is used for the diagnosis of disease (e.g. LEP mutations in extreme and early-onset obesity), and also to predict people s risk of certain conditions to allow a personalised treatment (e.g. phenylketonuria). The flurry of genetic loci discovered through GWAS, coupled with the increased affordability of genome-wide screening, has raised hope and expectations that soon genetic information will become an integral part of mainstream health care. The prospect is that genetic information would be used to estimate a person s risk of common diseases and that recommendations to improve health or to prevent disease are tailored to their genotype. Already, a substantial number of companies provide personal genome profiling services, which are offered directly-to-consumers (DTC) often via the internet without the involvement of health care professionals. These personal genetic tests are increasingly based on genome-wide information and estimate the clients lifetime risk for a variety of common diseases and conditions, ranging from obesity and type 2 diabetes, to Graves disease and several types of cancers, typically without accounting for established non-genetic predictors. However, these personalised genetic tests have been the subject of a growing debate; not only has their predictive validity been widely questioned 67,68 but also their clinical utility remains uncertain. 69,70 Although data that concentrates specifically on the genetic prediction of obesity is still sparse, we review the available literature that examined the predictive value of the established obesitysusceptibility loci and their ability to discriminate between individuals who are at high risk of obesity in adult life versus those who are at low risk. We focus on the 32 BMI-associated loci only, as obesity is defined by BMI and, more pragmatically, as currently no data is available on the predictive ability of any of the other obesity-susceptibility loci. To properly assess the predictive ability of genetic information, we first consider the predictive ability of traditional, non-genetic, risk factors of obesity, which can serve as a reference or a basis for genetic prediction. Traditional prediction of obesity risk Longitudinal studies with life-course data have identified parental obesity and childhood obesity as the strongest traditional predictors of obesity in adulthood, along with other predictors such as ethnicity, gestational diabetes, birth weight and rapid early growth that affect in particular obesity risk during childhood. 78 The effect of family history and childhood obesity on obesity risk during adulthood is fairly consistent across studies To illustrate the predictive ability of these traditional risk factors, we have chosen the study by Whitaker et al., which assesses the influence of both family history and childhood obesity on obesity risk in young adulthood. 71 Height and weight of 854 individuals, born between 1965 and 1971, were measured throughout childhood and adolescence, and once more during adult life (21 29 yrs). Parental height and weight were measured at around the same time their offspring s data was collected. This study shows that the influence of having one obese parent throughout childhood and adolescence, increases an individual s risk of adult obesity by 2.2- to 3.2-fold compared to someone whose parents were not obese (Fig. 4). Having two obese parents during

8 218 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) childhood and adolescence, increases the risk of being an obese adult substantially more, with ORs (odds ratios) ranging from 5 to 15.30, compared to someone who had no obese parents, with the exception of parental obesity at the age of yrs (Fig. 4). The influence of obesity during childhood and adolescence on the risk of adult obesity increases steadily with age; while obesity during infancy (1 2 yrs) does not increase one s risk of being an obese adult, an obese child at the age of yrs has a fold increased risk of being an obese adult compared to someone who was not obese as a child (Fig. 4). While these results clearly demonstrate that family history and one s own childhood obesity substantially increase the relative risk of being obese as an adult, they do not predict who will be obese and who will not be obese. To evaluate the predictive ability of a test, the area under the receiver operating characteristic curve (AUC ROC ) (see Glossary) is calculated. The AUC ROC is based on the specificity and sensitivity of the test (see Glossary) and assesses to which degree the test results can discriminate between who will be obese and who will not be obese later in life. Data on the discriminatory ability of tests like where you obese as a child or did you have an obese parent to predict adult obesity are limited. A study in 12,327 individuals of the British 1958 Birth Cohort estimated that BMI at age 11 yrs could predict obesity at age 33 yrs with an AUC ROC of 0.78 for men and 0.80 for women, 75 which is considered as a very high discriminative accuracy and clinically useful. The results in the paper by Whitaker et al. allowed calculating the sensitivity and specificity of parental obesity (as binary tests do not allow computing AUC ROC ). 71 For example, the specificity (ability to identify non-obese Risk factors during childhood Age 1-2 yrs 1 vs 0 obese parents 2 vs 0 obese parents Obese in childhood Risk of obesity in adult life Odds Ratio (95% CI) 3.20 (1.80, 5.70) (3.70, 50.40) 1.30 (0.60, 3.00) Age 3-5 yrs 1 vs 0 obese parents 2 vs 0 obese parents Obese in childhood 3.20 (1.80, 5.70) (5.70, 41.30) 4.70 (2.50, 8.80) Age 6-9 yrs 1 vs 0 obese parents 2 vs 0 obese parents Obese in childhood 2.60 (1.40, 4.60) 5.00 (2.10, 12.10) 8.80 (4.70, 16.50) Age yrs 1 vs 0 obese parents 2 vs 0 obese parents Obese in childhood 2.20 (1.20, 3.80) 2.00 (0.80, 5.20) (10.50, 47.10) Age yrs 1 vs 0 obese parents 2 vs 0 obese parents Obese in childhood 2.20 (1.10, 4.30) 5.60 (2.50, 12.40) (7.70, 39.50) Fig. 4. Risk of obesity in adult life (21 29 yrs) given one parent (blue) or both parents (red) were obese during the individuals childhood or adolescence, or given the individual was themselves obese during childhood or adolescence (green). For example, if both parents were obese when the individual was 3 5 yrs old, their risk of obesity was fold increased compared to someone whose parents were not obese. Note that adult obesity was defined when BMI was 27.8 kg m 2. Data were derived from Whitaker et al. 71

9 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) individuals) and sensitivity (ability to identify obese individuals) of the question was at least one parent obese was 0.72 and 0.75, respectively, for individuals who were obese at the age of 1 2 yrs. The specificity of this question decreased as the age to which the question refers increased (from 1 2 yrs to yrs), whereas the sensitivity increased at the same time. The sensitivity and specificity of parental obesity as a test to predict adult obesity for the five age groups and according to childhood obesity are shown in Fig. 5. Taken together, two traditional predictors, i.e. childhood obesity and parental obesity appear good predictors of obesity risk in adult life. The advantage of these traditional predictors is that data on family history and childhood obesity is typically easy to obtain and that both capture genetic as well as familial environmental susceptibility. Genetic prediction of obesity risk Personal genomic profiling, which is more expensive, invasive and cumbersome to establish than parental and childhood obesity, faces the challenge to either substantially improve the predictive ability of traditional risk factors or to provide an acceptable predictive ability on its own. Given that genetic factors account for 40 70% of the variation in obesity-susceptibility and that the remainder is explained by lifestyle factors, we know a priori that prediction of obesity solely based on genetic information will never reach a perfect discriminatory accuracy. 79 Therefore, the value of genetic prediction should ideally be considered in the context of supplementing traditional predictors of obesity, rather than replacing them. Nevertheless, the commercially available DTC tests are currently exclusively based on people s genotypes, typically of only one (FTO) locus and occasionally of up to 12 BMI-associated loci. So far, only two studies have examined the ability of the BMI-associated loci to Sensitivity Obese in childhood Non-obese in childhood AUC 32 SNPs = Specificity Fig. 5. The AUC ROC for the 32 BMI-associated loci to predict obesity in 8,120 individuals from the ARIC study. 48 For comparison, sensitivity and 1-specific are shown for parental obesity as a test at various ages during childhood and adolescence (1 2 yrs in dark blue, 3 5 yrs in green, 6 9 yrs in red, yrs in light blue, yrs in grey) to predict obesity in adult life, with data derived from Whitaker et al. 71

10 220 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) predict adult obesity. 48,80 As these studies had no data on parental or childhood obesity their results reflect the predictive ability of the commercially available tests that are also based on genotypes only. The predictive value of the loci identified in the first three waves of GWAS discoveries was examined in a population-based study of 20,431 British men and women of the EPIC-Norfolk study. 80 This study showed that if only FTO were to be used in the prediction model, as is often the case for commercially available DTC tests, the discrimination between obese and non-obese individuals would be very poor, with an AUC ROC of (Table 1). 80 Adding the locus near MC4R (AUC ROC of 0.554), or even combining the 12 loci identified by the end of the third wave in a genetic predisposition score (see Glossary) (AUC ROC of 0.574) hardly improved the predictive ability (Table 1). Interestingly, the AUC ROC for a genetic score of the 32 loci, calculated in 8120 individuals of the ARIC study, 48 was exactly the same as that of a score of the 12 loci assessed in the EPIC-Norfolk study, suggesting that the additional 20 BMI loci did not further improve the genetic prediction of adult obesity (Fig. 5, Table 1). It should be noted that, while the predictive ability of the two genetic predisposition scores (12 loci vs. 32 loci) is the same (i.e. AUC ROC of 0.574), the outcome at the individual level might be different. For example, an individual who carries many of the first 12 BMI-associated risk-alleles but much fewer of the subsequent 20 BMI-associated risk-alleles may be classified as at high risk of obese when the score based on 12 loci is used, but as at low risk if the score of 32 loci is used. Thus, tests based on a different configuration of genetic variants might provide discrepant risk predictions for the same person. As shown in Fig. 5, the specificity and sensitivity for parental obesity on its own, as derived from Whitaker et al., 71 are better than that of the genetic prediction score based on 32 BMI loci. Furthermore, also the AUC ROC for childhood BMI at age 11 (AUC ROC of w0.80) is substantially higher than that of the genetic predisposition scores. 75 Unfortunately, neither the ARIC nor the EPIC-Norfolk study had data available on parental obesity and/or childhood obesity to allow comparing the predictive ability of traditional risk factors versus genetic risk factors or to assess the added accuracy of including genetic risk factors in a traditional risk prediction model. Noteworthy is that the 32 BMI loci used to calculate the AUC ROC were identified in white Europeans and their effect sizes and allele frequencies may be different in populations of a different ethnicity, such that their predictive ability may be very different from non-europeans. As currently little is known about the ethnicity-specificity of obesity-susceptibility loci, companies offering DTC genomic profiling cannot tailor services according to their clients ethnic background. Taken together, the predictive ability of the currently available genetic information is poor and does not allow accurate discrimination between those at high risk of obese and those at low risk. The utility of genetic prediction of obesity Irrespective of the (poor) predictive ability, personalised genomic profiling might affect people s behaviour. Proponents of genetic testing believe that a person whose genetic profile suggests an Table 1 Explained variance in BMI and predictive ability of obesity (AUC ROC ) by SNPs in BMI-associated loci. BMI-associated loci included in the model Explained variance in BMI AUC ROC for obesity Other covariates in References the prediction model FTO No covariates Li et al. 80 FTO, near MC4R No covariates Li et al. 80 FTO, near MC4R, near TMEM18, near SEC16B, BDNF, near GNPDA2, SH2B1, near ETV5, near NEGR1, near FAIM2, near KCTD15 FTO, near MC4R, near TMEM18, near SEC16B, BDNF, near GNPDA2, SH2B1, near ETV5, near NEGR1, near FAIM2, near KCTD15, TFAP2B, NRXN3, SLC39A8, near GPRC5B, near PRKD1, QPCTL, near RBJ, MAP25K, LRRN6C, near FANCL, near FLJ35779, CADM2, near TMEM160, near LRP1B, MTIF3, TNNI3K, near ZNF608, near PTBP2, near RPL27A, NUDT No covariates Li et al Age and sex Li et al No covariates Speliotes et al Age, age 2 and sex Speliotes et al. 48

11 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) increased lifetime risk for a given disease will be motivated to adopt health-promoting behaviours. Sceptics fear that the same information might cause anxiety and depression in others. The behavioural and psychological responses to personal genetic risk estimates will likely depend on the condition; e.g. a high genetic risk estimate for lung cancer will affect a person in a different way compared to a high genetic risk estimate for restless leg syndrome. For obesity in particular, we would speculate that a personal genetic risk estimate will have little effect on a person s lifestyle or anxiety levels. After all, unlike many other common conditions, obesity and overweight develop early in adulthood or even during childhood or adolescence, such that by the time a genetic test is undertaken, a person has already a good appreciation of their susceptibility and have adopted a lifestyle that keeps them relatively weight-stable. While data is still limited, the first insights from early reports suggest that genetic testing evokes limited or no beneficial or harmful behavioural responses. A recent Cochrane review identified 13 studies that examined the effects of communicating genotype-based disease risk estimates on riskreducing behaviours and on the motivation to undertake such behaviours. 69 Overall, the results suggest that the communication of such genotype-based disease risk estimates has little or no effect on smoking cessation or physical activity, while there may be a small effect on improved dietary habits and on intentions to change behaviour. Only two studies examined the effects of genotype-based risk information for obesity and found only suggestive evidence that higher risk estimates might increase people s motivation to a healthier lifestyle. 81,82 The 13 studies were typically small and cross-sectional, and their quality was considered to be generally poor. 69 Furthermore, the behaviours and diseases studied differed across studies, impeding meta-analyses of the data. 69 A recent large-scale longitudinal cohort study tried to address some of the shortcomings of previous studies. Bloss et al. 70 examined whether DTC genome-wide profiling, used to estimate individuals lifetime risk for a variety of health conditions, results in psychological and behavioural changes. A total of 3639 participants, who were mainly recruited from health and technology companies, purchased the genetic test at a reduced rate and were informed about their estimated lifetime risk for 23 conditions, including obesity. At baseline and five months later at follow up, their anxiety levels, dietary fat intake, and exercise behaviour was assessed by self-report using a web-based survey. Only 2037 of the 3639 enrolled participants completed the assessment at the follow-up. Overall, the participants anxiety level, dietary fat intake or exercise behaviour had not changed during the follow-up period. Furthermore, the composite lifetime risk score, which combines the risk estimates of the 23 conditions, was not associated with changes in anxiety level, diet, and exercise. However, for obesity in particular, there was some evidence for a higher estimated lifetime risk score to be associated with a significant increase in dietary fat intake. Thus, rather than motivating to risk-reducing behaviour, the genetic information seems to have given participants a sense of lack of control over their obesity-susceptibility. The fact that this is only seen for obesity may not be a surprise as, more than for any of the other 22 condition, many of the participants will already by affected (i.e. be obese or overweight) at the time of the genetic testing, which may have exacerbated a fatalistic perception. Educating the population that a healthy lifestyle can reduce disease risk even in those with a high genetic susceptibility, as shown for obesity, 83 may reduce unintended effects of personal genomic profiling. Although the study by Bloss et al. has some weaknesses, such as non-representativeness of the sample, low response rate, short-term follow-up, and a limited number of behaviours examined, 84 it is the first large-scale prospective study to provide valuable insights in the behavioural and psychological responses to genotyped-based lifetime risk estimates. More large-scale prospective studies, including randomised control trials, will be needed to examine the effects of communicating personal genetic information in greater detail. The contribution of obesity-susceptibility loci to the understanding of biology and future avenues While much of the public s interest has been in the immediate implementation of the GWASidentified loci in genetic prediction of disease, the primary outputs of GWAS discoveries are in gaining new insights in the biological pathways that underlie disease. Each new genetic discovery is the end

12 222 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) of a new beginning, being a series of functional and epidemiological follow-up studies that will likely take many years to fully disentangle the mechanisms involved. The physiological pathways that link more than 50 established obesity-susceptibility loci to weight gain and increased obesity risk are not yet well-understood. FTO, the firstly identified obesitysusceptibility locus, has been the focus of most functional experiments, which have started to shed light on its role in body weight regulation. Studies in rodents have shown that Fto expression is ubiquitous and, in the arcuate nucleus, it is dependent on energy state The effects of overexpression or knockdown of Fto on food intake and adiposity seem tissue specific These observations illustrate that disentangling a gene s biology is complex and will require further collaborative work across disciplines. Some of the other obesity-susceptibility loci harbour genes (such as MC4R, BDNF and POMC) in which rare variants and mutations have previously been identified to cause monogenic obesity 35,91,92 or which have been shown to have a role in body weight regulation in animal models. For example, one locus harbours SH2B1, which encodes a protein that is implicated in leptin signalling and Sh2b1-null mice are obese. 93 Furthermore, a copy number variant in the SH2B1 locus has been linked to underweight and morbid obesity in humans. 94,95 One locus harbours NPC1 in which mutations have been linked to Niemann Pick type C, a lipid storage disease. 96 The Npc1-null mice display late-onset weight loss and poor food intake. 97 Of interest is that many of the BMI-associated loci harbour genes are highly expressed in the brain, supporting a role for a central regulation of energy balance and body weight, 48 whereas the WHR-associated loci seem more often involved in body fat distribution and in adipocyte metabolism. 57 For most of the recently identified obesity-susceptibility loci, however, their role in body weight regulation and fat distribution remains elusive and it will take many years of experimental and epidemiological research to determine the pathways in which they are involved to eventually lead to the identification of therapeutic targets. Despite the enormous success of GWAS, the established loci explain only a fraction of the heritability. Therefore, it has been speculated that more loci remain to be discovered and that the established loci may harbour low-frequency variants, not currently captured by the genome-wide genotyping arrays that have larger effects. Various approaches have been proposed to identify more genetic loci 6 ; these include studying children, adolescents, and individuals of non-european descent, using more refined outcomes of adiposity or traits intermediate to obesity (such as dietary intake and physical activity), examining the interaction between genes and between genes and environments, searching for low-frequency and structural variants through exome or whole genome sequencing or through implementation of the 1000 Genomes data. 6 While searching for new loci, we should not neglect the fact that for many of the established loci, the causal gene or variant is not yet known. Pinpointing the causal genes remains a major challenge, but a prerequisite for subsequent molecular and physiological studies. Fine-mapping endeavours are ongoing and include taking advantage of the differences in the genetic architecture of populations of non-european origin, as well as deep-sequencing of the established loci in populations or families with more extreme phenotypes. Conclusions GWAS have identified at least 52 genetic loci unequivocally associated obesity-susceptibility in less than four years time. Despite highly significant associations and repeated replication, the effects of the established loci on obesity-susceptibility are small and explain only a fraction of the total variance. Although hopes were high to implement genetic information in personalised medicine, the ability of these loci to predict obesity is poor and not competitive with the predictive ability of traditional risk factors such as parental and childhood obesity. Nevertheless, some of these loci are currently being used in DTC personal genome profiling to estimate individuals lifetime risk of obesity. While advocates speculate that personal genome profiling could have beneficial effects by motivating people to adopt risk-reducing behaviours, early reports do not support this hypothesis and even suggest that high genetic obesity risk estimates lead to a feeling of loss of control and a letting-go of a healthy lifestyle. While using genetic information in personalised health care is an honourable aim, it seems that the most valuable contribution of the GWAS-identified loci lies in their contribution to elucidating new

13 R.J.F. Loos / Best Practice & Research Clinical Endocrinology & Metabolism 26 (2012) physiological pathways that underlie obesity-susceptibility, which in turn could lead to the identification of therapeutic targets and leads its way into mainstream health care. Practice points GWAS have identified at least 52 loci robustly associated with obesity-related traits. Parental obesity and childhood obesity are good predictors of adult obesity, most likely because they capture genetic background and lifestyle simultaneously. The predictive ability of the currently established BMI-associated loci is poor; i.e. they do not accurately discriminate between obese and non-obese individuals. As obesity-susceptibility is not only determined by genetic factors, but to a substantial extent also by lifestyle, a prediction test that is based on genetic information only will never reach perfect discrimination between obese and non-obese individuals. Irrespective of the poor predictive ability, genotype-based disease risk estimates could evoke behavioural and psychological changes that are beneficial (risk-reducing lifestyle) or harmful (anxiety, depression). In general, evidence from early reports does not support either of such changes. However, one study suggest a worsening of dietary habits in individuals with increased genetic risk estimates of obesity, suggesting a fatalistic response to personal genome testing. Glossary AUC ROC (also called C-statistic) AUC ROC is a measure to determine the accuracy of a test to correctly classify diseased and nondiseased individuals. The AUC ROC is obtained by plotting the sensitivity against 1-specificity for all possible choices of test thresholds and is equal to the probability that a randomly selected individual with the disease has a higher score than a randomly selected individual without the disease. For example, a AUC ROC of 0.80 for a test based on a genetic predisposition score means that the probability that an obese individual has a higher genetic score than a non-obese individual is The AUC ranges from 0.50 (equal to tossing a coin) to 1.0 (perfect prediction). Genetic predisposition score A score that combines the information of multiple genetic variants that are associated with the same trait, by summing the number of risk-alleles carried by a given individual. In weighted genetic predisposition scores, the risk alleles are weighted by their effect size on the trait, with effect sizes derived from another, representative sample. Sensitivity of a test Sensitivity of a test is defined as the proportion of all individuals with the disease that are classified by the test as having the disease or at high risk. Thus, sensitivity assesses how well the test identifies obese individuals amongst all obese. Specificity of a test Specificity of a test is defined as the proportion of all individuals without the disease that are classified by the test as not having the disease or at low risk. Thus, specificity assesses how well the test identifies non-obese individuals amongst all non-obese.

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