Biology subjects

Didion, J. P.

Publications and source records attributed to Didion, J. P..

4 recordsLinked to original sources

bioRxiv2022

Population structure and inbreeding in wild house mice (Mus musculus) at different geographic scales

House mice (Mus musculus) have spread globally as a result of their commensal relationship with humans. In the form of laboratory strains, both inbred and outbred, they are also among the most widely-used model organisms in biomedical research. Although the general outlines of house mouse dispersal and population structure are well known, details have been obscured by either limited sample size or small numbers of markers. Here we examine ancestry, population structure and inbreeding using SNP microarray genotypes in a cohort of 814 wild mice spanning five continents and all major subspecies of Mus, with a focus on M. m. domesticus. We find that the major axis of genetic variation in M. m. domesticus is a south-to-north gradient within Europe and the Mediterranean. The dominant ancestry component in North America, Australia, New Zealand and various small offshore islands is of northern European origin. Next we show that inbreeding is surprisingly pervasive and highly variable, even between nearby populations. By inspecting the length distribution of homozygous segments in individual genomes, we find that inbreeding in commensal populations is mostly due to consanguinity. Our results offer new insight into the natural history of an important model organism for medicine and evolutionary biology.

genetics↗

bioRxiv2019

Single cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures

ObjectiveType 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome wide association studies (GWAS) have identified >400 independent signals that encode genetic predisposition. More than 90% of the associated single nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, and so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity.\n\nMethodsWe present genome-wide single cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell-combinatorial-indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on the U-Net architecture to accurately predict open chromatin peak calls in rare cell populations.\n\nResultsWe show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, we find that T2D GWAS SNPs are significantly enriched in beta cell-specific and cross cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep-learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals.\n\nConclusionsCollectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways.

genomics↗

bioRxiv2018

Genetic characterization of invasive house mouse populations on small islands

House mice (Mus musculus) have dispersed to nearly every major landmass around the globe as a result of human activity. They are a highly successful invasive species, but their presence can be devastating for native ecosystems. This is particularly true on small offshore islands where mouse populations may grow unchecked by predators. Here we use genome-wide SNP genotypes to examine ancestry and population structure on two islands of ecological interest - Southeast Farallon Island, near San Francisco, CA; and Floreana Island in the Galapagos - in the context of a total cohort of 520 mice with diverse geographic origins, as a first step towards genetically-based eradication campaigns. We show that Farallon and Floreana mice, like those from previously-studied islands in both the Atlantic and Pacific Oceans, are of admixed European ancestry. We find that these populations are on average more inbred than mainland ones and passed through a strong colonization bottleneck with little subsequent genetic exchange. Finally we show that rodenticide resistance alleles present in parts of Europe are absent from all island populations studied. Our results add nuance to previous studies of island populations based on mitochondrial sequences or small numbers of microsatellites and will be useful for future eradication and monitoring efforts.

genetics↗

bioRxiv2017

BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues

BackgroundBisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to variability in depth of coverage. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features and motifs associated with predictive power.\n\nResultsHere we describe BoostMe, a novel method for imputation of DNA methylation within whole-genome bisulfite sequencing (WGBS) data based on a gradient boosting algorithm. Importantly, we designed a new feature that leverages information from multiple samples in the same tissue and disease state, enabling BoostMe to outperform existing imputation methods in speed and accuracy. We show that imputation improves WGBS concordance with the Infinium MethylationEPIC array at low WGBS sequencing depth, suggesting improvement in WGBS accuracy after imputation. Furthermore, we compare the ability of BoostMe and DeepCpG - a deep neural network method - to identify interesting features and motifs associated with methylation in three human tissues implicated in type 2 diabetes (T2D) etiology. We find that while BoostMe only identifies features important to general methylation levels across tissues, DeepCpG is able to learn differences in methylation-associated sequence motifs among different tissues and identify tissue-specific regulators of differentiation such as EBF1 in adipose, ASCL2 in muscle, and FOXA1, TCF12, and NRF1 in islets. Neither algorithm readily identified T2D-associated features.\n\nConclusionsOur findings demonstrate the current power and limitations of machine and deep learning algorithms to both improve the quality of, and infer biological meaning from, genome-wide DNA methylation data.

genomics↗