Determination of the obesity-associated gene variants within the entire FTO gene by ultra-deep targeted sequencing in obese and lean children

Sällman Almén, M; Rask-Andersen, M; Jacobsson, J A; Ameur, A; Kalnina, I; Moschonis, G; Juhlin, S; Bringeland, N; Hedberg, L A; Ignatovica, V; Chrousos, G P; Manios, Y; Klovins, J; Marcus, C; Gyllensten, U; Fredriksson, R; Schiöth, H B

doi:10.1038/ijo.2012.57

Determination of the obesity-associated gene variants within the entire FTO gene by ultra-deep targeted sequencing in obese and lean children

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Published: 24 April 2012

Volume 37, pages 424–431, (2013)
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Determination of the obesity-associated gene variants within the entire FTO gene by ultra-deep targeted sequencing in obese and lean children

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M Sällman Almén¹,
M Rask-Andersen¹,
J A Jacobsson¹,
A Ameur²,
I Kalnina³,
G Moschonis⁴,
S Juhlin¹,
N Bringeland¹,
L A Hedberg¹,
V Ignatovica³,
G P Chrousos⁵,
Y Manios⁴,
J Klovins³,
C Marcus⁶,
U Gyllensten²,
R Fredriksson¹ &
…
H B Schiöth¹

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Abstract

Background:

The Fat mass and obesity-associated gene (FTO) was the first gene reliably associated with body mass index in genome-wide association studies on a population level. At present, the genetic variations within the FTO gene are still the common variants that have the largest influence on body mass index.

Methods:

In the current study, we amplified the entire FTO gene, in total 412 Kbp, in over 200 long-range PCR fragments from each individual, from 524 severely obese and 527 lean Swedish children, and sequenced the products as two DNA pools using massive parallel sequencing (SOLiD).

Results:

The sequencing achieved very high coverage (median 18 000 reads) and we detected and estimated allele frequencies for 705 single nucleotide polymorphisms (SNPs) (19 novel) and 40 indels (24 novel) using a sophisticated statistical approach to remove false-positive SNPs. We identified 19 obesity-associated SNPs within intron one of the FTO gene, and validated our findings with genoty**. Ten of the validated obesity-associated SNPs have a stronger obesity association (P<0.007) than the commonly studied rs9939609 SNP (P<0.012).

Conclusions:

This study provides a comprehensive obesity-associated variation map of FTO, identifies novel lead SNPs and evaluates putative causative variants. We conclude that intron one is the only region within the FTO gene associated with obesity, and finally, we establish next generation sequencing of pooled DNA as a powerful method to investigate genetic association with complex diseases and traits.

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Introduction

Genetic variants of the FTO gene were the first common genetic polymorphisms to be associated with increased body weight and obesity.¹ This association was replicated in several distinct populations,^{2, 3, 4, 5, 6, 7, 8, 9, Full size image}

SOLiD sequencing

Libraries were prepared from each pool and emulsion PCR was carried out according to the instructions from Applied Biosystems (Foster City, CA, USA). Sequencing was performed with Applied Biosystems' SOLiD 3 platform using a 50-bp read length on standard slides according to the protocol of the manufacturer. The reads were mapped to the FTO reference sequence (hg18, chr16:52 285 069-52 716 675) using the corona lite algorithm (Applied Biosystems) with default settings.

Variant calling and filtering

The methodology used for identification and filtration of false-positives SNPs, insertions and deletion (indels) are covered in greater detail in Supplementary Information 1. Briefly, SNPs are reported by the SOLiD system as valid adjacent miss-matches (that is, not sequencing errors) compared with the reference sequence. However, to limit the number of false-positive SNPs, we implemented a strategy that we previously used on data from SOLiD sequencing of pooled DNA.²⁶ This method focuses on filtering excessively amplified reads containing valid adjacent miss-matches, probably derived from errors in previous PCR amplification steps and not true polymorphic sites. A quality score of unique valid adjacent mismatches (UVAM) was calculated for each candidate polymorphic site and a UVAM threshold was determined by comparing the score distribution of the detected candidate sites to sites that were found in the dbSNP database and the 1000 genomes project (Figures 2a–d). Sites that had a lower quality score than the threshold was considered false positives and excluded. The filtration step was validated by genoty** 48 candidate SNPs before filtration with a 48-plex GoldenGate assay from Illumina (San Diego, CA, USA).²⁸

Indels with an estimated minor allele frequency >1% were detected using two methods for gapped read map** and included for further analysis.

Data analysis

Association tests based on allele frequencies were performed using Fisher's exact test together with approximation of 95% confidence interval off odds ratios for each SNP and indel using the software R (http://www.r-project.org, R Foundation for statistical computing, Vienna, Austria). To adjust for multiple testing, the significance level was adjusted for the 44 haplotype blocks that we identified across the sequenced region in a previous study of a Swedish population, which contains SNPs in linkage disequilibrium.²⁹ Consequently, P-values below 1.14 × 10⁻³ were considered significant.

Results from the individual genoty** (GoldenGate and TaqMan assay) were analyzed with PLINK (http://pngu.mgh.harvard.edu/purcell/plink/). The validated SNPs were checked for deviation from Hardy–Weinberg equilibrium using a Pearson's χ²-test (1 d.f) before being tested for association with obesity by using logarithmic regression to calculate 95% confidence intervals of odds ratios, controlled for sex and assuming an additive model. For details on the information with ENCODE data see Supplementary Information 1.

Genoty**

For details on genoty** of candidate SNPs and validation in Latvian and Greek populations see Supplementary Information 1.

Results

Sequencing and map**

In this study, we sequenced 95% of the entire human FTO gene and surrounding sequence, a region spanning 411 606 bases. The DNA was collected from a pool of 524 severely obese children and a pool of 527 lean controls (Figure 1). In total, we successfully sequenced 389 501 bases; the non-sequenced regions were excluded owing to difficulties in the PCR enrichment, which was primarily due to genomic repeats. After map**, a high sequencing depth was obtained across the FTO region, with a median read depth of 17 385 and 18 385 in the obese and control pools, respectively. This corresponds to an ∼16-fold read depth per allele in each pool for 50% of the positions (Figure 3). Furthermore, ∼95% of all positions had at least 1000 in read depth in both pools, which corresponds to an average ∼1-fold read depth for each FTO allele in the pools (∼1000 alleles/pool) for most sites. The coverage correlates well for each position between the two pools (r=0.92), and the same pattern of coverage peaks were reproduced in each independently sequenced pool (Figure 4). Characteristic peaks were found close to the primers of each amplicon, with peaks reaching a maximum of more than 345 000 fold coverage. Therefore, both primer regions, and the overlap between PCR amplicons, were excluded from further analysis. These regions add up to 40 944 bases, which leaves 348 557 bases for inclusion in the variant calling and analysis.

SNP calling and estimation of allele frequencies

Candidate SNPs were identified in reads containing valid adjacent mismatches, as defined by the SOLiD system, and allele frequencies were estimated by counting the number of reads containing the reference allele and the number of reads containing the putative SNP. To remove potential false-positive SNPs, primarily from amplification errors during the PCR enrichment, we used a statistical approach to determine a threshold for the minimum number of UVAM required for a SNP candidate to be considered reliable (Figure 2).²⁶ The UVAM thresholds were 21 for the obese pool and 23 for the control pool. This difference was caused by the higher coverage obtained in the control pool. In addition, SNPs were only called if they were detected in both pools, if the number of reads were distributed equally between the DNA strands (at least 23% for the strand with the lowest coverage) and if they were located outside of amplicon overlaps. Based on these conditions, 705 SNPs with a minor allele frequency above 1% were called, and out of these we found 19 SNPs that were not present in dbSNP(v132) and, hence, considered novel (Supplementary Information 2). In comparison, 5091 SNPs were detected when not applying the above filtration steps.

To test the validity of the UVAM filtration, we selected 48 high-frequency SNPs without first applying the UVAM threshold that had a strong obesity association for genoty** with custom-designed array. Out of the SNPs selected for genoty**, 25 were found in dbSNP, whereas the remainders were candidate novel polymorphisms. The genoty** failed for two SNPs (rs11076004 and rs3751814) and these were removed from further analysis. However, the genoty** clearly showed that all of the candidate SNPs not found in dbSNP were monoallelic, and thereby erroneously called SNPs (Supplementary Information 3). In contrast, when applying the UVAM filtration, all these false candidate SNPs were removed, which strongly validates the applied method. Among the genotyped variations found in dbSNP, four (rs11076004, rs28665159, rs34270264 and rs9928425) were found to be monoallelic. However, our filtration removed all these SNPs. Of the 17 true genotyped SNPs, four (rs1558902, rs12933996, rs9939609 and rs8063057) were removed as they were found in amplicon overlap regions or did not achieve good enough strand distribution.

The 13 genotyped true SNP correlations, between estimated allele frequencies from the SOLiD sequencing and the array had a Pearson's correlation coefficient of 0.76 (P<2.26 × 10⁻³) in the obese pool and 0.89 (P<3.25 × 10⁻⁵) in the control pool. Furthermore, the absolute error of the SOLiD sequencing was 5% and 8% (relative error 0.10 and 0.17) in the obese and control pool, respectively.

Indel calling and allele frequency estimation

Small insertions and deletions (indels) were called with two methods: SplitSeek, which realigns unmapped reads from corona lite; and gapped alignments using bwa and samtools. Indels were only considered valid if they were called by both approaches and frequencies, were estimated from the coverage of the alternate and reference sequence allele that were achieved by bwa. With this approach, 13 insertions and 27 deletions were called, ranging from 1 to 9 residues in length (Supplementary Information 4). Sixteen of these overlap known indels in dbSNP, whereas the remaining twenty-four are considered potential novel variants. The frequency differences between the pools were very small, with an average absolute deviation of 0.6% and a Pearson’s correlation coefficient equal to 0.999.

The obesity-associated variation pattern

Fisher’s exact test was performed, based on the allele frequencies for each SNP and indel with an estimated minor allele frequency above 5%, to test for association with the obese versus the lean group (Supplementary Information 2). First, 22 SNPs from the SOLiD data were considered associated with obesity (P<1.14 × 10⁻³) and were found in three different regions of the gene: 20 SNPs were found within the well-known obesity-associated haplotype block of intron one; 2 in intron eight and 1 in intron nine. However, after validation by genoty** with a TaqMan assay, one SNP in intron one and one in intron nine were found to be monoalleic and were excluded (Supplementary Information 3). In addition, the two SNPs in intron eight were far from association with obesity when the calculation was based on the genoty**. All of the 19 candidate obesity-associated SNPs were found within a 31 794-bp-long region (hg18 chr16:52 357 478-52 389 272) that lies within the known obesity-associated haplotype block of intron one. Ten of the 19 putative obesity-associated SNPs from the SOLiD data in intron one were genotyped with the 48-plex GoldenGate array and analyzed for association with obesity using logarithmic regression and controlling for sex (See Table 1 for a summary of the association tests for all true SNPs associated with obesity calculated on the SOLiD data). Based on the genoty**, all 10 SNPs were associated with obesity, when not adjusting for multiple testing. However, when adjusting for the 44 haplotype blocks in the FTO region, 3 SNPs (rs55872725, rs11642015 and rs62048402) were associated (P<1.1 × 10⁻³). The SNPs, rs11642015 and rs62048402, were the top-associated SNPs, when calculating association tests based on SOLiD estimates and genoty**. No indel was considered associated with obesity, even when not adjusting for multiple testing (P>0.05). For results from the validation in Greek and Lativan populations, see Supplementary Information 1 and 5.

Table 1 Obesity-associated SNPs from the sequencing of the FTO gene

Full size table

By using the UCSC browser, the genetic data were integrated with ENCODE data of gene regulation and DNA sequence conservation between vertebrate species, to try to identify candidate functional variants among the obesity-associated SNPs (Figure 5 and Supplementary Information 1).

Discussion

Here we provide a uniquely high resolution of the obesity-associated variation pattern of the FTO gene, identifying 19 obesity-associated SNPs within a haplotype block of intron one (Table 1). In total, we sequenced ∼400 Kbp with very high coverage (median ∼18 000 reads), and identified 705 SNPs and 40 indels across the gene (Figure 5). We conclude that it is likely that intron one is the only obesity-associated region of the FTO gene, as we do not detect any strongly associated variants elsewhere. None of the three SNPs, having the strongest association with obesity that we validated with genoty**, have previously been reported in any previous genetic association studies.

The SNP rs62048402 (P<0.0007; OR=1.35 (confidence interval 95%: 1.13–1.60)) has a stronger association with obesity than the most studied FTO marker rs9939609 (P<0.012; OR=1.25 (confidence interval 95%: 1.05–1.48)). Moreover, an additional nine of the SNPs that we validated have a stronger association with obesity than rs9939609 (Table 1). Intriguingly, this top obesity-associated SNP rs62048402, lies within a potential regulatory region of intron one, together with the additional three top obesity-associated SNPs (Figure 5). This region is distinguished by high DNAse sensitivity and modifications of Histone 3 (mono-methylation and acetylation), which are characteristic for accessible chromatin in enhancer sites. Further, CHiP-seq data suggests that several transcription factors bind to this region, of which the glucorticoid receptor has the strongest signal (maximum score 1000/1000, Figure 5). Thus, rs62048402, and the other SNPs, in this glucorticoid receptor-binding region could be the most likely candidate causative variants for the obesity association of the FTO gene, as they are the only associated SNPs in our comprehensive map, which are found within a known functional region. The potential importance of this region is emphasized by replication of the stronger association of rs62048402 and rs11642101 over rs9939609 in 2280 Greek children and 760 Latvian adults (Supplementary Information 5).

For the first time, sequencing allows us to test if small indels contribute, and could be causative, for the association of the FTO gene with obesity. By using two different algorithms to avoid calling false-positives, we identified 13 insertions and 27 deletions that range between 1 and 9 residues of length, of which 24 are potential novel indels. However, these indels do not seem to contribute to the obesity association of the FTO gene, as we did not find any association; although three insertions with a minor allele frequency below 5% reside within the obesity-associated haplotype of intron one. The average frequency for the indels is relatively low (∼6% in both groups) and the allele frequency correlates almost perfectly between the pools. However, as the short read sequencing technology only allows us to identify small indels with a reasonable accuracy, it is possible that we are not detecting longer structural variants that could be associated with obesity.

The number of new SNPs that we detected can be considered to be low, as only 19 of the total number of 705 were not found in databases. This clearly suggests that recent progress in map** of genetic variations, most notably through the 1000 genomes project, has provided a nearly complete catalog of frequent SNPs.³⁰ As expected, the median minor allele frequency of the novel SNPs is low (∼3%). However, five of them are more common, having more than 10% in minor allele frequency. Interestingly, we only find two SNPs, over 1% minor allele frequency, in exons (both in the 5' UTR). Hence, we conclude that mutations causing amino-acid shifts in FTO are very rare. This is in line with the fact that known defects in the FTO gene lead to gross developmental defects.³¹ Moreover, our results are in good agreement with previous limited sequencing in Europeans, of FTO exons, that only identified rare mutations;³² and also results in Europeans from the 1000 genomes project (http://browser.1000genomes.org/). In a perspective of the whole gene, we identified around 2 SNPs/Kbp, which can be compared with the results in Europeans of the 1000 genomes project for the whole genome with about 3 SNPs/Kbp.³⁰

Targeted large-scale sequencing of many individuals simultaneously, in high-complexity DNA pools, is a new cost-effective approach to provide a high-resolution variation map. We have clearly demonstrated that sequencing of pooled DNA from different groups gives high enough accuracy to detect associated common variants in complex traits, providing appropriate data management. We show that our filtration approach, which relies on UVAM scores and additional strict filters, while retaining all true SNPs, was able to appropriately exclude all 20 false positives that were erroneously called using a tolerant approach, relying mainly on the SOLiD systems definition of valid SNPs. This clearly shows that data from massive parallel sequencing technologies demands careful and specific implementation of data processing and analysis to give reliable results. Interestingly, we identified two SNPs (rs2388452 and rs1421086), found in dbSNP and the 1000 genomes project, which were not excluded by the UVAM filtration, but are monoallelic sites according to the TaqMan assay. Assuming that the traditional genoty** is reliable, this could indicate that the novel sequencing technologies used in this study and the 1000 genome project may cause reproducible artifacts that erroneously are called as SNPs. Although powerful, this suggests that massive parallel sequencing technologies needs to be more extensively compared with traditional genoty** methods to prove their definite accuracy.

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Acknowledgements

The study was supported by the Swedish Research Council, the Brain Research Foundation, Novo Nordisk Foundation and the Åhlens Foundation. We thank Dr Michael J Williams for valuable comments on the article. Genoty** was performed by the SNP&SEQ Technology Platform in Uppsala, which is supported by Uppsala University, Uppsala University Hospital, Science for Life Laboratory - Uppsala and the Swedish Research Council (Contracts 80576801 and 70374401). The SOLiD sequencing was performed by the Uppsala Genome Center that is part of the Science for Life Laboratory Uppsala Genomics Platform. We thank the Healthy Growth Study Group for their contribution in this study. The Healthy Growth Study Group consists of (1) Harokopio University Research Team/Department of Nutrition and Dietetics: Yannis Manios (Coordinator), George Moschonis (Project manager), Katerina P Skenderi, Evangelia Grammatikaki, Odysseas Androutsos, Sofia Tanagra, Alexandra Koumpitski, Paraskevi-Eirini Siatitsa, Anastasia Vandorou, Aikaterini-Efstathia Kyriakou, Vasiliki Dede, Maria Kantilafti, Aliki-Eleni Farmaki, Aikaterini Siopi, Sofia Micheli, Louiza Damianidi, Panagiota Margiola, Despoina Gakni, Vasiliki Iatridi, Christina Mavrogianni, Kelaidi Michailidou, Aggeliki Giannopoulou, Efstathoula Argyri, Konstantina Maragkopoulou, Maria Spyridonos, Eirini Tsikalaki, Panagiotis Kliasios, Anthi Naoumi, Konstantinos Koutsikas, Katerina Kondaki, Epistimi Aggelou, Zoi Krommyda, Charitini Aga, Manolis Birbilis, Ioanna Kosteria, Amalia Zlatintsi, Elpida Voutsadaki, Eleni-Zouboulia Papadopoulou, Zoi Papazi, Maria Papadogiorgakaki, Fanouria Chlouveraki, Maria Lyberi, Nora Karatsikaki-Vlami, Eva Dionysopoulou and Efstratia Daskalou; (2) Aristotle University of Thessaloniki/School of Physical Education and Sports Sciences: Vassilis Mougios, Anatoli Petridou, Konstantinos Papaioannou, Georgios Tsalis, Ananis Karagkiozidis, Konstantinos Bougioukas, Afroditi Sakellaropoulou and Georgia Skouli; (3) University of Athens/ Medical School: George P Chrousos, Maria Drakopoulou and Evangelia Charmandari.

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Department of Neuroscience, Functional Pharmacology, Uppsala University, BMC, Uppsala, Sweden
M Sällman Almén, M Rask-Andersen, J A Jacobsson, S Juhlin, N Bringeland, L A Hedberg, R Fredriksson & H B Schiöth
Department of Immunology, Genetics, and Pathology, SciLifeLab Uppsala, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
A Ameur & U Gyllensten
Latvian Biomedical Research and Study Centre, Riga, Latvia
I Kalnina, V Ignatovica & J Klovins
Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
G Moschonis & Y Manios
First Department of Pediatrics, Athens University Medical School, Aghia Sophia Children’s Hospital, Athens, Greece
G P Chrousos
Department for Clinical Science, Division of Pediatrics, Intervention and Technology, Karolinska Institutet, National Childhood Obesity Centre, Stockholm, Sweden
C Marcus

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M Sällman Almén
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J A Jacobsson
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I Kalnina
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Sällman Almén, M., Rask-Andersen, M., Jacobsson, J. et al. Determination of the obesity-associated gene variants within the entire FTO gene by ultra-deep targeted sequencing in obese and lean children. Int J Obes 37, 424–431 (2013). https://doi.org/10.1038/ijo.2012.57

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Received: 11 January 2012
Revised: 08 March 2012
Accepted: 11 March 2012
Published: 24 April 2012
Issue Date: March 2013
DOI: https://doi.org/10.1038/ijo.2012.57
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Determination of the obesity-associated gene variants within the entire FTO gene by ultra-deep targeted sequencing in obese and lean children