Complete re-sequencing of a 2Mb topological domain encompassing the FTO/IRXB genes identifies a novel obesity-associated region upstream of IRX5

Lilian E Hunt, Boris Noyvert, Leena Bhaw-Rosun, Abdul K Sesay, Lavinia Paternoster, Ellen A Nohr, George Davey Smith, Niels Tommerup, Thorkild Sorensen, Greg Elgar

Research output: Contribution to journalArticle (Academic Journal)

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Abstract

BACKGROUND: Association studies have identified a number of loci that contribute to an increased body mass index (BMI), the strongest of which is in the first intron of the FTO gene on human chromosome 16q12.2. However, this region is both non-coding and under strong linkage disequilibrium, making it recalcitrant to functional interpretation. Furthermore, the FTO gene is located within a complex cis-regulatory landscape defined by a topologically associated domain that includes the IRXB gene cluster, a trio of developmental regulators. Consequently, at least three genes in this interval have been implicated in the aetiology of obesity.

METHODS: Here, we sequence a 2 Mb region encompassing the FTO, RPGRIP1L and IRXB cluster genes in 284 individuals from a well-characterised study group of Danish men containing extremely overweight young adults and controls. We further replicate our findings both in an expanded male cohort and an independent female study group. Finally, we compare our variant data with a previous study describing IRX3 and FTO interactions in this region.

RESULTS: We obtain deep coverage across the entire region, allowing accurate and unequivocal determination of almost every single nucleotide polymorphism and short insertion/deletion. As well as confirming previous findings across the interval, we identify a further novel age-dependent association upstream of IRX5 that imposes a similar burden on BMI to the FTO locus.

CONCLUSIONS: Our findings are consistent with the hypothesis that chromatin architectures play a role in regulating gene expression levels across topological domains while our targeted sequence approach represents a widely applicable methodology for high-resolution analysis of regional variation across candidate genomic loci.

Original languageEnglish
Article number126
Number of pages14
JournalGenome Medicine
Volume7
DOIs
Publication statusPublished - 7 Dec 2015

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