Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations.

The selective pressures that shape clonal evolution in healthy individuals are largely unknown. Here we investigate 8,342 mosaic chromosomal alterations, from 50 kb to 249 Mb long, that we uncovered in blood-derived DNA from 151,202 UK Biobank participants using phase-based computational techniques (estimated false discovery rate, 6-9%). We found six loci at which inherited variants associated strongly with the acquisition of deletions or loss of heterozygosity in cis. At three such loci (MPL, TM2D3-TARSL2, and FRA10B), we identified a likely causal variant that acted with high penetrance (5-50%). Inherited alleles at one locus appeared to affect the probability of somatic mutation, and at three other loci to be objects of positive or negative clonal selection. Several specific mosaic chromosomal alterations were strongly associated with future haematological malignancies. Our results reveal a multitude of paths towards clonal expansions with a wide range of effects on human health.

Sequential regulatory activity prediction across chromosomes with convolutional neural networks.

Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell-type-specific epigenetic and transcriptional profiles in large mammalian genomes from DNA sequence alone. By use of convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions. We show that model predictions for the influence of genomic variants on gene expression align well to causal variants underlying eQTLs in human populations and can be useful for generating mechanistic hypotheses to enable fine mapping of disease loci.

Estimating cross-population genetic correlations of causal effect sizes.

Recent studies have examined the genetic correlations of single-nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated ρ g , the cross-population correlation of joint-fit effect sizes at genotyped SNPs. However, the value of ρ g depends both on the cross-population correlation of true causal effect sizes ( ρ b ) and on the similarity in linkage disequilibrium (LD) patterns in the two populations, which drive tagging effects. Here, we derive the value of the ratio ρ g / ρ b as a function of LD in each population. By applying existing methods to obtain estimates of ρ g , we can use this ratio to estimate ρ b . Our estimates of ρ b were equal to 0.55 ( SE = 0.14) between Europeans and East Asians averaged across nine traits in the Genetic Epidemiology Research on Adult Health and Aging data set, 0.54 ( SE = 0.18) between Europeans and South Asians averaged across 13 traits in the UK Biobank data set, and 0.48 ( SE = 0.06) and 0.65 ( SE = 0.09) between Europeans and East Asians in summary statistic data sets for type 2 diabetes and rheumatoid arthritis, respectively. These results implicate substantially different causal genetic architectures across continental populations.

Interaction Patterns of Men Who Have Sex With Men on a Geosocial Networking Mobile App in Seven United States Metropolitan Areas: Observational Study.

BACKGROUND: The structure of the sexual networks and partnership characteristics of young black men who have sex with men (MSM) may be contributing to their high risk of contracting HIV in the United States. Assortative mixing, which refers to the tendency of individuals to have partners from one's own group, has been proposed as a potential explanation for disparities. OBJECTIVE: The objective of this study was to identify the age- and race-related search patterns of users of a diverse geosocial networking mobile app in seven metropolitan areas in the United States to understand the disparities in sexually transmitted infection and HIV risk in MSM communities. METHODS: Data were collected on user behavior between November 2015 and May 2016. Data pertaining to behavior on the app were collected for men who had searched for partners with at least one search parameter narrowed from defaults or used the app to send at least one private chat message and used the app at least once during the study period. Newman assortativity coefficient (R) was calculated from the study data to understand assortativity patterns of men by race. Pearson correlation coefficient was used to assess assortativity patterns by age. Heat maps were used to visualize the relationship between searcher's and candidate's characteristics by age band, race, or age band and race. RESULTS: From November 2015 through May 2016, there were 2,989,737 searches in all seven metropolitan areas among 122,417 searchers. Assortativity by age was important for looking at the profiles of candidates with correlation coefficients ranging from 0.284 (Birmingham) to 0.523 (San Francisco). Men tended to look at the profiles of candidates that matched their race in a highly assortative manner with R ranging from 0.310 (Birmingham) to 0.566 (Los Angeles). For the initiation of chats, race appeared to be slightly assortative for some groups with R ranging from 0.023 (Birmingham) to 0.305 (Los Angeles). Asian searchers were most assortative in initiating chats with Asian candidates in Boston, Los Angeles, New York, and San Francisco. In Birmingham and Tampa, searchers from all races tended to initiate chats with black candidates. CONCLUSIONS: Our results indicate that the age preferences of MSM are relatively consistent across cities, that is, younger MSM are more likely to be chatted with and have their profiles viewed compared with older MSM, but the patterns of racial mixing are more variable. Although some generalizations can be made regarding Web-based behaviors across all cities, city-specific usage patterns and trends should be analyzed to create targeted and localized interventions that may make the most difference in the lives of MSM in these areas.

Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics.

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space-termed neighborhoods-that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis.

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk.

Biological interpretation of genome-wide association study data frequently involves assessing whether SNPs linked to a biological process, for example, binding of a transcription factor, show unsigned enrichment for disease signal. However, signed annotations quantifying whether each SNP allele promotes or hinders the biological process can enable stronger statements about disease mechanism. We introduce a method, signed linkage disequilibrium profile regression, for detecting genome-wide directional effects of signed functional annotations on disease risk. We validate the method via simulations and application to molecular quantitative trait loci in blood, recovering known transcriptional regulators. We apply the method to expression quantitative trait loci in 48 Genotype-Tissue Expression tissues, identifying 651 transcription factor-tissue associations including 30 with robust evidence of tissue specificity. We apply the method to 46 diseases and complex traits (average n = 290 K), identifying 77 annotation-trait associations representing 12 independent transcription factor-trait associations, and characterize the underlying transcriptional programs using gene-set enrichment analyses. Our results implicate new causal disease genes and new disease mechanisms.

Detecting novel associations in large data sets.

Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R(2)) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.