Estivill, X. | Search bioRxiv

bioRxiv2017

Genetic architecture of early childhood growth phenotypes gives insights into their link with later obesity

Early childhood growth patterns are associated with adult metabolic health, but the underlying mechanisms are unclear. We performed genome-wide meta-analyses and follow-up in up to 22,769 European children for six early growth phenotypes derived from longitudinal data: peak height and weight velocities, age and body mass index (BMI) at adiposity peak (AP ~9 months) and rebound (AR ~5-6 years). We identified four associated loci (P< 5x10-8): LEPR/LEPROT with BMI at AP, FTO and TFAP2B with Age at AR and GNPDA2 with BMI at AR. The observed AR-associated SNPs at FTO, TFAP2B and GNPDA2 represent known adult BMI-associated variants. The common BMI at AP associated variant at LEPR/LEPROT was not associated with adult BMI but was associated with LEPROT gene expression levels, especially in subcutaneous fat (P<2x10-51). We identify strong positive genetic correlations between early growth and later adiposity traits, and analysis of the full discovery stage results for Age at AR revealed enrichment for insulin-like growth factor 1 (IGF-1) signaling and apolipoprotein pathways. This genome-wide association study suggests mechanistic links between early childhood growth and adiposity in later childhood and adulthood, highlighting these early growth phenotypes as potential targets for the prevention of obesity.

genomics↗

bioRxiv2017

Bayesian Inference Of Cancer Driver Genes Using Signatures Of Positive Selection

Tumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian inference model for driver prediction. In an in-depth benchmark, we demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.

cancer biology↗