Integrating 3D genomic and epigenomic data to enhance target gene discovery and drug repurposing in transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) are popular approaches to test for association between imputed gene expression levels and traits of interest. Here, we propose an integrative method PUMICE (Prediction Using Models Informed by Chromatin conformations and Epigenomics) to integrate 3D genomic and epigenomic data with expression quantitative trait loci (eQTL) to more accurately predict gene expressions. PUMICE helps define and prioritize regions that harbor cis-regulatory variants, which outperforms competing methods. We further describe an extension to our method PUMICE +, which jointly combines TWAS results from single- and multi-tissue models. Across 79 traits, PUMICE + identifies 22% more independent novel genes and increases median chi-square statistics values at known loci by 35% compared to the second-best method, as well as achieves the narrowest credible interval size. Lastly, we perform computational drug repurposing and confirm that PUMICE + outperforms other TWAS methods.

Development and applications of a concentrating membrane osmometer for colloid solutions.

The membrane concentration osmometer coupled with multiple sample preparations has been used for over a century to determine a number of colloidal properties. At the dilute region, this method has been used to determine solute molecular mass. When the solution is proteinaceous, in the intermediate region, the osmotic pressure profile provides the second virial coefficient, useful for estimating protein crystallization and salting out. At the most crowded concentrations, it provides insight into protein hydration and protein-ion interaction. One of the most critical factors in generating the osmotic pressure profile is minimizing the quantity of protein used and reducing the error in preparing samples. Here, we introduce a membrane concentrating osmometer that allows one to measure osmotic pressure over a wide concentration range from a single sample. A test study was performed using the osmotic pressure profile of self-crowded bovine serum albumin solutions. The resulting profile was in good agreement with previous data in the literature obtained from multiple sample studies. The osmotic pressure profile was further used with a free solvent-based osmotic pressure model to determine protein hydration and ion binding. These results were in excellent agreement with literature values. This concentrating osmometer has several advantages over a conventional concentration osmometer for obtaining the osmotic pressure profile for proteinaceous solutions: (1) the amount of protein required is significantly decreased, (2) the potential for experimental error in sample preparation diminishes, and (3) the time for generating the osmotic pressure profile is substantially reduced.

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use.

BACKGROUND: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk. METHODS: We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci. RESULTS: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals. CONCLUSIONS: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.