Biology subjects

Gonzalez, J. R.

Publications and source records attributed to Gonzalez, J. R..

4 recordsLinked to original sources

bioRxiv2018

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

We conducted genome-wide association study (GWAS) meta-analyses of relative caloric intake from fat, protein, carbohydrates and sugar in over 235,000 individuals. We identified 21 approximately independent lead SNPs. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg {approx} 0.15 - 0.5). Relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood poverty (|rg| {approx} 0.1 - 0.3). Overall, our results show that the relative intake of each macronutrient has a distinct genetic architecture and pattern of genetic correlations suggestive of health implications beyond caloric content.

genetics↗

bioRxiv2018

Genome-wide study identifies 611 loci associated with risk tolerance and risky behaviors

Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated ([Formula] to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

genetics↗

bioRxiv2017

A measure of agreement to distinguish between condition levels: Reproducibility of co-expression networks across tissues

There is great interest to study how co-expression gene networks change across tissues. However, the reproducibility assessment of these studies is challenged by a lack of fully confirmatory experiments from independent researchers. While an increment in the number of studies with expression data for several tissues is expected, statistical measures are still needed to assess the reproducibility between studies. We identified a gap in the statistical literature concerning the assessment of agreement between studies that independently measure the effect of a varying condition on a population sample. The gap precluded us to test, using standard statistics, the level of agreement between the GTEX (RNAseq) and BRAINEAC (microarray) studies to distinguish the structure of co-expression networks across four brain tissues. We, therefore, generalized a classical measure of agreement, Cohens{kappa} , derived its distributional characteristics and determined its reliability properties. In the gene expression studies, we found full agreement of genome-wide networks in BRAINEAC benchmarked against GTEX, and highest agreement for brain specific pathways. Our highly interpretable measure can contribute to anticipated efforts on reproducibility research.

systems biology↗

bioRxiv2017

A Comprehensive Reanalysis Of Publicly Available GWAS Datasets Reveals An X Chromosome Rare Regulatory Variant Associated With High Risk For Type 2 Diabetes.

The reanalysis of publicly available GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics and pathophysiology of complex diseases. We demonstrate this by gathering and reanalyzing public type 2 diabetes (T2D) GWAS data for 70,127 subjects, using an innovative imputation and association strategy based on multiple reference panels (1000G and UK10K). This approach led us replicate and fine map 50 known T2D loci, and identify seven novel associated regions: five driven by common variants in or near LYPLAL1, NEUROG3, CAMKK2, ABO and GIP genes; one by a low frequency variant near EHMT2; and one driven by a rare variant in chromosome Xq23, associated with a 2.7-fold increased risk for T2D in males, and located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a known modulator of insulin sensitivity. We further show that the risk T allele reduces binding of a nuclear protein, resulting in increased enhancer activity in muscle cells. Beyond providing novel insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel analytical approaches.

genetics↗