Genome Wide Association Study Biology Essay


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The foundation for the Genome Wide Association Study (or GWAS) was laid only after the completion of Human Genome project in 2003 and the International HapMap Project in 2005 (Ertekin-Taner (2010) Genetics of Alzheimer disease in the pre- and post-GWAS era; Alzheimer's Research & Therapy 2:3). Genome-wide association study (GWAS) is an unbiased research tool that identifies the genetic variant across the human genome that is responsible for causing the genetic disease. It is known to be an unbiased research tool as the study design assumes no prior information of the genes and SNPs being genotyped. These are genome-wide tag SNPs selected from HapMap database that are randomly and evenly spread across the genome. GWAS are performed using a chip-based high-throughput genotyping method that could possibly genotype between 300K-1000K SNPs simultaneously across the entire genome to find an association for each SNP with the large sample sizes of cases and controls. The SNP frequencies are then compared against cases and controls with an appropriate statistical test. If the minor allele frequency (MAF) of the SNP deviates significantly between the cases and controls, then the tested SNP is likely to be associated with the disease. The commonly used arrays in GWAS are Affymetrix 500K GeneChip ( and Illumina HumanMap300 ( that provides about 65% and 75% coverage of common genetic variation across different populations respectively (Barrett JC., et al, 2006; Pe'er I., et al, 2006). Some newer arrays like, Affymetrix SNP Array 6.0 and Illumina Human 1M Beadchip have a higher coverage of genetic variants as compared to the previous arrays. The disadvantage of the GWAS is that it's very cost-effective as the study requires a large number of sample sizes. Also multiple statistical testing is required to analyze these large numbers of sample sizes thereby increasing the risk of false positives, which brought a great concern. The researchers unanimously decided to use more stringent genome-wide significance level of p<1X10-7 when used the older array and for the newer ones p<5X10-8 to reduce the false-positive rate (Pe'er I., et al, 2008), which is subject to change depending on the size of the samples.

GWAS for schizophrenia

There have been about 9 genome wide association studies done for schizophrenia, from which five studies are based on association to SNP genotypes while the remaining ones are association to copy number variants (CNVs). The first GWA for schizophrenia was published in 2007 that used a small sample size of Caucasian population with 178 cases and 144 controls. This study concluded the association of schizophrenia with a novel locus or SNP rs4129148, located near the CSF2RA (Colony Stimulating Factor, Receptor 2 Alpha) gene in the pseudoautosomal region of the Y chromosome with p-value of 3.7X10-7 (Lencz et al. 2007). Thereafter, the year 2008 was highly productive as all the remaining GWAS for schizophrenia were published. The first GWA in this year was studied with the pooled DNA samples (600 cases and 2771 controls) of Ashkenazi Jewish population and this study was based on the sex-differences noted in schizophrenia. The study did not find any genome wide significant association but reported a female-specific association between SNP rs7341475 on the reelin (RELN) gene with schizophrenia, p=1.8X10-4 (Shiftman et al. 2008). Similarly, another GWA study was done using pooled DNA samples of the parent-offspring trios. The sample size consisted of 574 schizophrenia trios and 605 unaffected controls. The study failed to find any genome-wide significant association which was estimated to be about 1.85X10-7 (Kirov et al. 2008). The next GWA study included patients diagnosed with DSM-IV classification of schizophrenia and was participants of CATIE study. The samples were mixed ancestry with the total number of 738 as cases (56.3% Whites, 29.6% African-American and 14.1% others) and 733 as group-matched controls. There was no conclusive genome-wide significant association noticed with schizophrenia however interested results were seen for the candidate genes selected for this study. A few genes were identified in the past to be associated with the etiology of schizophrenia, amongst them, DISC1, COMT and NRG1 showed significance in this study also with the p-value of 0.0011, 0.016 and 0.00091 respectively. This study also tested the gene AKT1, which is a candidate gene in our present study, but failed to show the association with p=0.0316 (Sullivan et al. 2008). Another GWA study was conducted on 479 cases and 2937 controls and the associated SNPs were then replicated with 2 sets of samples: 1664 cases + 3541 controls and 6666 cases + 7897 controls. After the meta-analysis the study concluded with a very strong association around ZNF804A (zinc finger protein 804A) gene with p-value=1.61X10-7 (O'Donovan et al. 2008).

With the high-throughput assay based technology used in GWAS it's now feasible to identify the copy number variation (CNV) in the population tested. As mentioned above, there have been 4 GWAS based of copy number variants. The two GWAS, one with 1,433 cases and 33,250 controls showed three deletions at 1q21.1 (OR: 14.8), 15q11.2 (OR: 2.7) and 15q13.3 (OR: 11.5) regions to be associated with schizophrenia (Stefanson et al. 2008), while another study conducted by the International Schizophrenia Consortium with 3,391 patients with schizophrenia and 3,181 ancestrally matched controls suggested large deletions on chromosome 15q13.3 and 1q21 to be associated with the disease (Stone et al. 2008). However, the deletions on chromosomes 1q21.1 and 15q13.3 have also been noticed in cases of mental retardation and autism though in a lesser frequencies. GWAS have also identified a deletion on chromosome 22q11 (Xu et al. 2008) which was previously been associated with schizophrenia (Liu et al. 2002, Bassett et al. 2008). The nature of these deletions has been de novo meaning that they have not been inherited from the parents and the study concluded that these deletions were more frequently observed in sporadic cases of schizophrenia (~8 times) as compared to the familial ones (Xu et al. 2008). Also, it has been noticed that large rare duplications and deletions of about more than 100kb that disrupt a gene function are more commonly seen in individuals with schizophrenia. Also these disrupted genes affect the normal neurodevelopment pathway of an individual (Walsh et al. 2008).

GWAS for type-2 diabetes

The development of genome-wide association study was most successful in identifying the genes associated with type-2 diabetes. There have been about 12 GWAS already published that have discovered new susceptibility loci for type-2 diabetes having a p-value of <5X10-8 (Ridderstra et al. 2009). There are 4 genes had have been previously associated with type-2 diabetes through linkage, candidate gene or association studies, which were validated by GWAS were PPARG, TCF7L2, KCNJ11 and WSF1. The first GWAS was published in February 2007 that covered 392,935 SNPs in a French case-control population. The study identified the following genes to be associated with type-2 diabetes, SLC30A8, IDE-KIF11-HHEX and EXT2-ALX4. Also the study could validate the association of TCF7L2 gene with the disease (Sladek et al. 2007). Another 2 GWAS was conducted on Finnish population with a large sample size of 1464 cases +1467 controls (Saxena et al. 2007) and 1161 cases +1174 controls (Scott et al. 2007) and tested about 393,453 and 315,635 SNPs respectively. The genes identified by these studies that were significantly associated with type-2 diabetes risk were IGF2BP2, CDKAL1, CDKN2A, CDKN2B, SLC30A8 and HHEX. Also, the variants near the genes TCF7L2, FTO, PPARG and KCNJ11 were identified to give a high risk to develop type-2 diabetes (Scott et al. 2007). The largest GWAS was conducted by Wellcome Trust Case Control with 1924 cases and 2938 controls on UK population. This study found similar result as mentioned in the above GWAS and confirmed the association of genes CDKAL1, CDKN2A/CDKN2B, IGF2BP2, HHEX/IDE and SLC30A8 with the etiology of type-2 diabetes. Moreover, these genes were noted to involve in the pathways influencing pancreatic β-cell development and function (Zeggini E., et al., 2008). Another GWAS study confirmed the association of variant rs7756992 in the CDKAL1 gene in European and Han Chinese ancestry (Steinthorsdottir et al. 2007). Yasuda et al. (2008) found a strong association of the C allele of SNP rs2237892 in the KCNQ1 gene with an increased risk for developing type-2 diabetes in Japanese, Chinese, Korean and European case-control samples (Yasuda et al. 2008). Similarly, SNPs rs2237895 and rs2237897 in KCNQ1 gene (lie close to the SNP rs2237892 in KCNQ1 gene) was also been identified to be associated with type-2 diabetes in the Singaporean population of East Asian descent and the Danish population of European descent (Unoki et al. 2008). The other two new genes identified were FTO (Frayling 2007) and MTNR1B (Saxena et al 2007). Strong association was noted at the locus rs10830963 at MTNR1B gene with fasting glucose concentration or insulin secretion.

In order achieve the greater power to detect the genes with modest or small effect, the sample size was increased and a meta-analysis study was conducted by the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) consortium by combining the data from three previously published GWAS (Saxena et al., 2007; Scott et al., 2007; Zeggini et al., 2007). The sample size increased enormously and this became the largest study for type-2 diabetes with about 4500 cases and 5500 controls. The consortium also used novel imputation approaches (Marchini et al., 2007) to infer genotypes at additional SNPs that were not directly typed on the commercial arrays used for the original GWAS, thereby extending the analysis to a total of ~2.2 million SNPs across the genome. The study found six signals that reached combined levels of significance at the loci rs864745 (p=5X10-14), rs12779790 (p=1.2X10-10), rs7961581 (p=1.1X10-9), rs7578597 (p=1.1X10-9), rs4607103 (1.2X10-8) and rs10923931 (p=4.1X10-8) of the genes JAZF1 (Zeggini et al 2008).

In summary, there have been 19 genes identified that contribute to the susceptibility for type-2 diabetes. Amongst the 19 genes, 5 genes (PPARG, TCF7L2, TCF2, KCNJ11 and WSF1) were identified through candidate-gene approach studies and the remaining 14 genes are newly identified in individual genome wide association studies. These genes are ADAMTS9, CDC123/CAMK1D, CDKN2A/B, CDKAL1, FTO, HHEX/IDE, IGF2BP2, JAZF1, KCNQ1, MTNR1B, NOTCH2, SLC30A8, THADA and TSPAN8/LGR5.


Full name

Chr. location



Possible association with

type-2 diabetes



Transcription factor 7-like 2




Reduce insulin-secretory capacity through beta cell function or dysfunction to increase susceptibility to type-2 diabetes

Florez et al 2007

Sanghera et al 2008


Peroxisome proliferator-activated receptor gamma




Impaired insulin sensitivity and involved in adipocyte development

Ludovico et al 2007


Potassium inwardly-rectifying channel, subfamily J, member 11




Regulator of

glucose-induced insulin secretion in pancreatic beta cells

Sanghera et al 2008


ADAM metallopeptidase with thrombospondin type 1 motif 9




Encodes a protease responsible for cleaving of proteoglycans

Zeggini et al 2008


CDK5 regulatory subunit-associated protein1-like1




Glucotoxicity signaling in pancreatic β-cell

Frayling et al 2007



Cell division cycle 123 homologue (Saccharomyces cerevisiae)/ Calcium/calmodulin-dependent protein kinase 1D




Might be involved through a T2D pathogenetic mechanism with cell cycle dysregulation

Zeggini et al. 2008


Cyclin-dependent kinase inhibitor 2A/2B




Regeneration of pancreatic β-cell

Frayling et al 2007


Fat mass and obesity associated




Altered BMI, Obesity

Zeggini et al. 2008


Haematopoietically expressed homeobox





Pancreatic development

Insulin signaling & Islet function

Frayling et al 2007


Insulin like growth factor 2 mRNA binding protein 2




Pancreatic development

Frayling et al 2007


Juxtaposed with another zinc finger gene 1




Peri- and postnatal hypoglycemia

Zeggini et al 2008


Potassium voltage-gated channel, KQT-like subfamily, member 1




β-cell dysfunction

Yasuda et al 2008


Melatonin receptor 1B



2.2 x 10-50

β-cell dysfunction

Prokopenko et al 2009


Notch homologue (Drosophila)




Pancreatic development

Zeggini et al 2008


Solute carrier family 30 (zinc transporter), member 8




Effect insulin storage, stability and secretion

Frayling et al 2007


Thyroid adenoma associated




β-cell apoptosis

Zeggini et al. 2008


Wolfram syndrome 1




Involved in the regulation of membrane trafficking, protein processing and homeostasis in the

endoplasmic reticulum of pancreatic β-cell

Sandhu et al. 2007


Tetraspanin 8




Encodes a glycoprotein expressed on the cell surface of pancreatic carcinomas

Zeggini et al. 2008

Table X.: Detail of genes associated with type-2 diabetes that has been identified by GWAS. Function of the genes taken from Pattin & Moore (2010)GWAS of Obesity

There has been a good progress made by the genome-wide association studies in identifying the loci associated with obesity and BMI. Till date there has been about 7 GWAS published at a significance level of p-value= 5X10-7 or better, identifying about 20 genes for obesity that have been associated with BMI. The fat mass- and obesity-associated (FTO) gene has been the most remarkable discovery so far that was observed in a few recent GWAS and also seen in different populations (Frayling et al 2007; Scuteri et al 2007; Loos et al 2008; Willer et al, 2009; Thorleifsson et al, 2009; Meyre et al 2009). Interesting thing to note here is that, the FTO gene was initially found significant in a GWAS for type-2 diabetes but the significance disappeared after adjusting the BMI. This could probably mean that the association between FTO and type-2 diabetes was mediated through BMI (Frayling et al 2007). Another study found an association of MC4R gene with BMI in Asian Indians and European ancestry with the frequency of the risk allele of 36% and 27% respectively with the same effect size (Chambers et al 2008).The first large GWAS for obesity was conducted by an international research group called the GIANT (Genomic Investigation of Anthropometric Traits) consortium. They performed a meta-analysis with a large sample size of about 32387 and confirmed the association of FTO gene and MC4R gene to be associated with BMI in humans. Additionally, they found 6 novel loci (p-value<5X10-8): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 to be associated with BMI (Willer et al. 2009). The second large meta-analysis was conducted by deCODE Genetics group on multiple population comprising of Icelanders (n=25,344), Dutch (n=25,344), European Americans (n=1,890) and African American (n=1,160) and found new loci, SEC16B, ATP2A1 and BDNF in addition to the previously identified genes NEGRI, KCTD15 & TMEM18. The study also successfully identified regions between ETV5-DGKG genes and BCDIN3D-FAIM2 genes to be associated with obesity (Thorleifsson et al 2009).

In our study we could only test the above mentioned genes identified by GWAS due to limitation of time in my PhD project. However, there have 3 more GWAS published recently which have identified new loci, LYPLAL1, TFAP2B, MSRA (Lindgren et al 2009); PTER, MAR, NPC1 (Meyre et al 2009) and NEUREXIN 3 (Heard et al 2009) respectively. A detailed description of the genes identified by GWAS until now has been summarized in table X.


Full name

Chr. location



Gene function



Fat-mass- and obesity associated






8.6 Ã-10-7

Alters BMI in general population.

Highly expressed in the hypothalamus, involved in regulation of energy balance (food intake).

Frayling et al 2007

Scuter et al 2007


Melanocortin 4 receptor




Regulator of

food intake and energy homeostasis

Loos et al, 2008


Neuronal growth regulator 1




Neuronal outgrowth

Willer etal 2009

Thorleifsson et al 2009


Transmembrane protein 18






Neural development

Willer etal 2009

Thorleifsson et al 2009



deaminase 2




Unknown function, expressed in the hypothalamus

Willer etal 2009


Mitochondrial carrier homologue

2 (Caenorhabditis elegans)



7.1 Ã- 10-6

Promotes food intake

Willer etal 2009


SH2B adaptor protein 1




Implicated in leptin signaling

Willer etal 2009


Potassium channel

tetramerisation domain

containing 15




Unknown function, expressed in the hypothalamus

Willer etal 2009

Thorleifsson et al 2009


SEC16 homologue B




Unknown function

Thorleifsson et al 2009


Brain-derived neurotrophic





Involved in eating behaviour, body weight regulation and hyperactivity. Expressed in the hypothalamus

Thorleifsson et al 2009


ATPase, Ca2+ transporting,

cardiac muscle, fast twitch 1




Involved in muscular contraction and excitation

Thorleifsson et al 2009


Ets variant gene 5




A transcription factor

Lindgren et al 2009




FAS apoptotic inhibitory

molecule 2



1.2 Ã- 10-7

Involved in apoptosis

Thorleifsson et al 2009


Lysophospholipase-like 1




Unknown function

Lindgren et al 2009


Transcription factor AP-2 b




Function as transcriptional activator and repressor. They are thought to stimulate cell proliferation and suppress terminal differentiation of specific cell types during embryonic development.

Lindgren et al 2009


Methionine sulfoxide

reductase A




Possibly play an important role in aging and neurological disorders

Lindgren et al 2009


Phosphotriesterase related




Highly expressed in the hypothalamus, have a role in


Meyre et al 2009


v-Maf musculoaponeurotic

fibrosarcoma oncogene





Involved in developmental and cellular differentiation

processes, notably of the immune system, pancreas and

adipose tissue

Meyre et al 2009


Niemann-Pick disease, type C1




Highly expressed in the hypothalamus, involved in

lipid transport in the central nervous system, liver and



Neurexin 3




May be involved in cell recognition molecules in the nerve terminal

Heard et al 2009

Table X. Genes associated with obesity and BMI in humans, identified by GWAS. Modified table from Vimaleswaran and Loos, 2010. Functions of the genes have taken from NCBI & GeneAtlas.

Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Metaanalysis

of genome-wide association data and large-scale replication identifies

additional susceptibility loci for type 2 diabetes. Nat Genet2008 May;40(5):638-45.

Ludovico O, Pellegrini F, Di Paola R, Minenna A, Mastroianno S, Cardellini M,

et al. Heterogeneous effect of peroxisome proliferator-activated receptor gamma2 Ala12

variant on type 2 diabetes risk. Obesity (Silver Spring)2007 May;15(5):1076-81.

Frayling TM. Genome-wide association studies provide new insights into type 2

diabetes aetiology. Nat Rev Genet2007 Sep;8(9):657-62.

Florez JC. The new type 2 diabetes gene TCF7L2. Curr Opin Clin Nutr Metab

Care2007 Jul;10(4):391-6.

Sanghera DK, Ortega L, Han S, Singh J, Ralhan SK, Wander GS, et al. Impact

of nine common type 2 diabetes risk polymorphisms in Asian Indian Sikhs: PPARG2

(Pro12Ala), IGF2BP2, TCF7L2 and FTO variants confer a significant risk. BMC Med


Hani EH, Boutin P, Durand E, Inoue H, Permutt MA, Velho G, et al. Missense

mutations in the pancreatic islet beta cell inwardly rectifying K+ channel gene

(KIR6.2/BIR): a meta-analysis suggests a role in the polygenic basis of Type II diabetes

mellitus in Caucasians. Diabetologia1998 Dec;41(12):1511-5.

Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G,

et al. Variants in MTNR1B influence fasting glucose levels. Nat Genet2009


Sandhu MS, Weedon MN, Fawcett KA, Wasson J, Debenham SL, Daly A, et al.

Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet2007


The first wave, in 2007, comprised two high-density genome-wide association studies. Each confirmed FTO (fat-mass- and obesity-associated gene) as the first gene in controvertibly associated with common obesity and related traits. The first study was a genome-wide association scan for type-2 diabetes in which variants in the first intron of the FTO gene showed a highly significant association with type-2 diabetes mediated through BMI (Ref. 49).

Subsequently, the association with BMI and obesity was clearly replicated in 13 cohorts comprising more than 38,000 individuals. The second study (Ref. 50) was the first large-scale high-density genome-wide association study of BMI, conducted in more than 4000 Sardinians. In the initial analyses, variants in the FTO and PFKP (platelet-type phosphor fructo kinase) genes showed the strongest association, but only those in FTO were significantly replicated in European Americans and Hispanic Americans. A third study published at the same time as the first two studies identified FTO while testing for population stratification (Ref. 51). Each risk allele increased BMI by 0.10-0.13 standard deviation (equivalent to about 0.40-0.66 kg/m2) and the risks increased by 1.18-fold and 1.32-fold for overweight and obesity, respectively. Taken together, homozygotes for the risk allele weighed about 3 kg more and had a 1.67-fold increased risk for obesity than those who did

not inherit a risk allele (Refs 49, 50). The frequency of the FTO risk genotypes is high in populations of European descent; 63% carry at least one risk allele and 16% are homozygous. Although the population attributable risk for overweight (13%) and obesity (20%) is high, the FTO locus explains only ,1% of the variation in BMI (Ref. 49).

Second wave

As part of the second wave of discoveries, individual genome-wide association studies were combined through collaborative efforts in order to increase sample size and thus power to identify more common variants with small effects. The GIANT (Genomic Investigation of Anthropometric Traits) consortium is such an international collaborative initiative that brings together research groups focusing on anthropometric traits from across Europe and the USA. Data from seven genome-wide association scans for BMI (n=16876) were combined in their first meta-analysis (Ref. 52). Despite a quadrupling increase in sample size compared with the first wave, only FTO and one new locus [188 kb downstream of MC4R ('near-MC4R')], out of ten loci that were taken forward for replication, were unequivocally confirmed. The near-MC4R locus was identified in another study in 2684 Asian Indians, and confirmed in 11 955 individuals of Asian Indian and European ancestry (Ref. 53). The effect size was the same in both ethnic groups but the frequency of the risk allele in Asian Indians (36%) was greater than in white Europeans (27%), which might explain why this locus could be identified with a relatively small sample of Asian Indians in the discovery stage.

Third wave

In the third wave of discoveries, the sample size was increased to 32 387 adults of European ancestry from 15 cohorts (GIANT consortium) (Ref. 54) (Fig. 2). Of the 35 loci identified in the first stage of the genome-wide scan, eight loci were firmly replicated in an independent series of 59 082 individuals. These include the previously established FTO and near-MC4R loci and six new loci: near-NEGR1 (neuronal growth regulator 1), near-TMEM18 (transmembrane protein 18), in SH2B1 (SH2B adaptor protein 1), near-KCTD15 (potassium channel tetramerisation domain containing 15), near-GNPDA2 (glucosamine-6-phosphate deaminase 2), and in MTCH2 (mitochondrial carrier homologue 2). In parallel to these analyses, deCODE Genetics performed a meta-analysis of four genome-wide association

studies for BMI, including 34 416 individuals comprising Europeans and African Americans

(Ref. 39). A total of 43 single-nucleotide polymorphisms (SNPs) in 19 chromosomal regions were taken forward for replication in 5586 Danish individuals and for confirmation in discovery stage data of the GIANT consortium. Besides the FTO and near-MC4R loci, eight

additional loci reached genome-wide significance. Of these, four loci (near-NEGR1, near-TMEM18, in SH2B1, near-KCTD15) had also been identified by the GIANT consortium, whereas four loci were novel: SEC16B (SEC16 homologue B), between ETV5 (Ets variant gene 5) and DGKG (diacylglycerol kinase), in BDNF, and between BCDIN3D (BCDIN3-domaincontaining) and FAIM2 (FAS apoptotic inhibitory molecule 2). Variation in BAT2 (HLA-Bassociated transcript 2) was associated with weight, but not BMI, suggesting that this locus might contribute to overall size rather than adiposity. A recent study that genotyped the 12 obesity-susceptibility variants identified by the GIANT consortium and deCODE Genetics group in 20 431 individuals of a population-based study of white Europeans showed that these loci had a cumulative effect on BMI, with each additional risk-allele increasing BMI by 0.149 units, or weight by 444 g (Ref. 55) (Fig. 3). Nevertheless, together these 12 obesity-susceptibility loci explained less than 1% of the variation in BMI and had only limited predictive value of obesity. While the studies by the GIANT consortium and deCODE Genetics focused on BMI as the main outcome, genome-wide association studies exploring the association with other obesity related traits have successfully led to the discovery of seven additional loci. One study examined association with the risk of early-onset and morbid adult obesity in 1380 cases and 1416 controls (Ref. 56). Of the 38 loci showing association, three new loci - NPC1 (Niemann-Pick disease, type C1), near-MAF (v-Maf musculoaponeurotic fibrosarcoma oncogene homologue) and near-PTER (phosphotriesterase related) - in addition to FTO and near-MC4R were identified and firmly

replicated in 14 186 adults and children. A study involving a meta-analysis of 16 genome-wide association studies (N=38 580) from the GIANT consortium and a follow-up in 70 689 individuals for adult waist circumference and waist:hip ratio discovered two novel loci - TFAP2B (transcription factor AP-2 b) and MSRA (methionine sulfoxide reductase A) - associated with waist circumference and one locus -LYPLAL1 (lysophospholipase-like 1) - associated with waist:hip ratio only in women (Ref. 57). Another two-stage genome-wide association analysis for waist circumference (Ref. 58) from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium identified a novel locus - NRXN3 (neurexin 3) - in addition to FTO and MC4R based on 31 373 individuals of Caucasian descent from eight cohort studies in the stage 1 and 38 641 individuals in the stage 2 analysis. The discovery of these loci has initiated a series of experiments to explore the pathophysiological mechanisms and pathways that underlie obesity development, in particular for the FTO gene. FTO encodes a Fe(II)- and 2-oxoglutarate-dependent oxygenase putatively involved in DNA demethylation (Refs 59, 60). Studies in rodents

indicated that Fto mRNA is most abundant in the hypothalamic nuclei, which govern energy

balance (Ref. 61). Another study has shown that loss of Fto in mice leads to a significant reduction in adipose tissue and lean body mass, which was found to develop as a consequence of increased energy expenditure despite decreased spontaneous locomotor activity and relative hyperphagia (Ref. 62). The FTO protein shows wide expression patterns in peripheral as well as central tissues with a high expression in the brain (Refs 59, 61, 63). A study in healthy women demonstrated that FTO mRNA in adipose tissue increases with BMI, and carriers of the risk allele had reduced lipolytic activity, independent of BMI (Ref. 64). For other loci, except SH2B1 (Ref. 65), BDNF (Ref. 66) and MC4R (Ref. 21), the physiological role in relation to obesity risk is not or poorly understood.

In summary, the three waves of high-density multistage genome-wide association scans have so far identified 19 novel loci convincingly associated with obesity traits (Table 2), hence proving this approach more productive than any of the other gene-discovery methods

previously used for common traits. To date, of all identified loci, the genetic variation in FTO

has still the largest effect on obesity susceptibility.

H. Liu et al., Proc. Natl. Acad. Sci. U.S.A. 99, 16859 (2002)

Anne S. Bassett, Christian R. Marshall, Anath C. Lionel, Eva W.C. Chow and Stephen W. Scherer (2008) Copy number variations and risk for schizophrenia in 22q11.2 deletion syndrome. Hum Mol Genet. 2008 December 15; 17(24): 4045-4053.

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