Biology subjects

Timshel, P. N.

Publications and source records attributed to Timshel, P. N..

3 recordsLinked to original sources

bioRxiv2020

Mapping heritability of obesity by brain cell types

The underlying cell types mediating predisposition to obesity remain largely obscure. Here we first integrated recently published single-cell RNA-sequencing (scRNA-seq) data from >380 peripheral and nervous system cell types spanning 19 mouse organs with body mass index (BMI) genome-wide association study (GWAS) data from >450,000 individuals. Leveraging a novel strategy for integrating scRNA-seq data with GWAS data, we identified 22, exclusively neuronal, cell types from the subthalamus, midbrain, hippocampus, thalamus, cortex, pons, medulla, pallidum that were significantly enriched for BMI heritability (P<1.6x10-4). Using genes harboring coding mutations leading to syndromic forms of obesity, we replicate four midbrain cell types from the anterior pretectal nucleus, superior nucleus, periaqueductal gray and pallidum (P<1.7x10-4). Testing an additional set of 347 hypothalamic cell types, ventromedial hypothalamic steroidogenic-factor 1 (SF1) and cholecystokinin b receptor (CCKBR)-expressing neurons (P=4.9x10-5) previously implicated in energy homeostasis and glucose control and three cell types from the preoptic area of the hypothalamus and the lateral hypothalamus enriched for BMI GWAS associations (P<4.9x10-5). Together, our results suggest brain nuclei regulating integration of sensory stimuli, learning and memory are likely to play a key role in obesity and provide testable hypotheses for mechanistic follow-up studies.

genetics↗

bioRxiv2018

scVAE: Variational auto-encoders for single-cell gene expression data

MotivationModels for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual data-processing steps, each with their own set of hyperparameter choices. With deep generative models one can work directly with count data, make likelihood-based model comparison, learn a latent representation of the cells and capture more of the variability in different cell populations.\n\nResultsWe propose a novel method based on variational auto-encoders (VAEs) for analysis of single-cell RNA sequencing (scRNA-seq) data. It avoids data preprocessing by using raw count data as input and can robustly estimate the expected gene expression levels and a latent representation for each cell. We tested several count likelihood functions and a variant of the VAE that has a priori clustering in the latent space. We show for several scRNA-seq data sets that our method outperforms recently proposed scRNA-seq methods in clustering cells and that the resulting clusters reflect cell types.\n\nAvailability and implementationOur method, called scVAE, is implemented in Python using the TensorFlow machine-learning library, and it is freely available at https://github.com/scvae/scvae.

bioinformatics↗

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↗