Biology subjects

Diaz-Papkovich, A.

Publications and source records attributed to Diaz-Papkovich, A..

8 recordsLinked to original sources

bioRxiv2026

From Wikipedia to AI: Measuring 25 years of synthesis of human genetics research in the public-facing information ecosystem

Genetics research frequently intersects with ethnicity, nationality, and race, making it uniquely vulnerable to misrepresentation. Yet, 25 years after the initial sequencing of the human genome, there is little understanding of how human genetics research exists in the public-facing information ecosystem. We analyze 3,050,422 historical revisions from 6,738 Wikipedia pages about ethnicity, nationality, and race spanning 25 years. We find genetics terminology is present in 14.8% of these pages (55.5% in the top 1,000 pages) and in 67.8% of pages about nationalities, suggesting research is synthesized to present a biological element to ethnicity and nationality. We also find that 10.1% of 56,908 discussions from these pages contain genetics terminology. We further analyze responses from three popular chatbots queried about nationalities and find that they commonly reference both genetics and Wikipedia. Lastly, we analyze 133 pages from Grokipedia, an AI-generated encyclopedia, and find it mentions genetics more frequently than Wikipedia and hallucinates or misrepresents human genetics research.

genetics↗

bioRxiv2025

Using the ancestral recombination graph to study the history of rare variants in founder populations

Gene genealogies represent the ancestry of a sample and are often encoded as ancestral recombination graphs (ARG). It has recently become possible to infer these gene genealogies from sequencing or genotyping data and use them for evolutionary and statistical genetics. Unfortunately, inferred gene genealogies can be noisy and subject to biases, making their applications more challenging. This project aims to study the application of ARG methods to systematically impute and trace the transmission of all disease variants in founder populations where long-shared haplotypes allow for accurate timing of relatedness. We applied these methods to the population of Quebec, where multiple founder events led to an uneven distribution of pathogenic variants across regions and where extensive population pedigrees are available. We validated our approach with nine founder mutations for the SLSJ region, demonstrating high accuracy for mutation age, imputation, and regional frequency estimation. Moreover, we showed that this subset of high-quality carriers is sufficient to capture previously described associations with pathogenic variants in the LPL gene. This method systematically characterizes rare variants in founder populations, establishing a fast and accurate approach to inform genetic screening programs.

genetics↗

bioRxiv2023

Topological stratification of continuous genetic variation in large biobanks

Biobanks now contain genetic data from millions of individuals. Dimensionality reduction, visualization and clustering are standard when exploring data at these scales; while efficient and tractable methods exist for the first two, clustering remains challenging because of uncertainty about sources of population structure. In practice, clustering is commonly performed by drawing shapes around dimensionally reduced data or assuming populations have a "type" genome. We propose a method of clustering data with topological analysis that is fast, easy to implement, and integrates with existing pipelines. The approach is robust to the presence of sub-populations of varying sizes and wide ranges of population structure patterns. We use UMAP and HDBSCAN, respectively methods of dimensionality reduction and density clustering, on data from three biobanks. We illustrate how topological genetic strata can help us understand structure within biobanks, evaluate distributions of genotypic and phenotypic data, examine polygenic score transferability, identify potential influential alleles, and perform quality control.

genomics↗

bioRxiv2022

Conserved whole-brain spatiomolecular gradients shape adult brain functional organization

Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, how these gradients relate to adult brain function, and whether they are maintained in the adult brain, remains unknown. Here we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral and medial-lateral axes of early developmental patterning. The interaction of these spatiomolecular gradients i) accurately predicts the location of unseen brain tissue samples, ii) delineates known functional territories, and iii) explains the topographical variation of diverse cortical features. The spatiomolecular gradients are distinct from canonical cortical functional hierarchies differentiating primary sensory cortex from association cortex, but radiate in parallel with the axes traversed by local field potentials along the cortex. We replicate all three molecular gradients in three independent human datasets as well as two non-human primate datasets, and find that each gradient shows a distinct developmental trajectory across the lifespan. The gradients are composed of several well known morphogens (e.g., PAX6 and SIX3), and a small set of genes shared across gradients are strongly enriched for multiple diseases. Together, these results provide insight into the developmental sculpting of functionally distinct brain regions, governed by three robust transcriptomic axes embedded within brain parenchyma.

neuroscience↗

bioRxiv2022

On the Genes, Genealogies, and Geographies of Quebec

Population genetic models only provide coarse representations of real-world ancestry. We use a pedigree compiled from four million parish records and genotype data from 2,276 French and 20,451 French Canadian (FC) individuals, to finely model and trace FC ancestry through space and time. The loss of ancestral French population structure and the appearance of spatial and regional structure highlights a wide range of population expansion models. Geographic features shaped migrations throughout, and we find enrichments for migration, genetic and genealogical relatedness patterns within river networks across Quebec regions. Finally, we provide a freely accessible simulated whole-genome sequence dataset with spatiotemporal metadata for 1,426,749 individuals reflecting intricate FC population structure. Such realistic populations-scale simulations provide new opportunities to investigate population genetics at an unprecedented resolution. Lay SummaryWe all share common ancestors ranging from a couple generations ago to hundreds of thousands of years ago. The genetic differences between individuals today mostly depends on how closely related they are. The only problem is that the actual genealogies that relate all of us are often forgotten over time. Some geneticists have tried to come up with simple models of our shared ancestry but they dont really explain the full, rich history of humanity. Our study uses a multi-institutional project in Quebec that has digitized parish records into a single unified genealogical database that dates back to the arrival of the first French settlers four hundred years ago. This genealogy traces the ancestry of millions of French-Canadian and we have used it to build a very high resolution genetic map. We used this genetic map to study in detail how certain historical events, and landscapes have influenced the genomes of French-Canadians today. One-Sentence SummaryWe present an accurate and high resolution spatiotemporal model of genetic variation in a founder population.

genomics↗

bioRxiv2020

Recent fluctuations in Mexican American genomes have altered the genetic architecture of biomedical traits

People in the Americas represent a diverse group of populations with varying degrees of admixture among African, European, and Amerindigenous ancestries. In the United States, many populations with non-European ancestry remain understudied, and thus little is known about the genetic architecture of phenotypic variation in these populations. Using genome-wide genotype data from the Hispanic Community Health Study/Study of Latinos, we find that Amerindigenous ancestry has increased over time across Hispanic/Latino populations, particularly in Mexican Americans where Amerindigenous ancestry increased by an average of [~]20% over the 50-year period spanning 1940s-1990s. We find similar patterns across American cities, and replicate our observations in an independent sample of Mexican Americans. These dynamic ancestry patterns are a result of a complex interaction of several population and cultural factors, including strong ancestry-related assortative mating and subtle shifts in migration with differences in subcontinental Amerindigenous ancestry over time. These factors have shaped patterns of genetic variation, including an increase in runs of homozygosity in Amerindigenous ancestral tracts, and also influenced the genetic architecture of complex traits within the Mexican American population. We show for height, a trait correlated with ancestry, polygenic risk scores based on summary statistics from a European-based genome-wide association study perform poorly in Mexican Americans. Our findings reveal temporal changes in population structure within Hispanics/Latinos that may influence biomedical traits, demonstrating a crucial need to improve our understanding of the genetic diversity of admixed populations.

genetics↗

bioRxiv2019

A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems

The functional organization of the hippocampus is distributed as a gradient along its longitudinal axis that explains its differential interaction with diverse brain systems. We show that the location of human tissue samples extracted along the longitudinal axis of the hippocampus can be predicted within 2mm using the expression pattern of less than 100 genes. When variation in this specific gene expression pattern was observed across the whole brain, a distinct anterioventral-posteriodorsal gradient was observed. Frontal, anterior temporal and brainstem regions involved in social and motivational behaviors, selectively vulnerable to frontotemporal dementia and more functionally connected to the anterior hippocampus could be clearly differentiated from posterior parieto-occipital and cerebellar regions involved in spatial cognition, selectively vulnerable to Alzheimers disease, and more functionally connected to the posterior hippocampus. These findings place the human hippocampus at the interface of two major brain systems defined by a single distinct molecular gradient. (148/150)

neuroscience↗

bioRxiv2018

Revealing multi-scale population structure in large cohorts

Genetic structure in large cohorts results from technical, sampling and demographic variation. Visualisation is therefore a first step in most genomic analyses. However, existing data exploration methods struggle with unbalanced sampling and the many scales of population structure. We investigate an approach to dimension reduction of genomic data that combines principal components analysis (PCA) with uniform manifold approximation and projection (UMAP) to succinctly illustrate population structure in large cohorts and capture their relationships on local and global scales. Using data from large-scale genomic datasets, we demonstrate that PCA-UMAP effectively clusters closely related individuals while placing them in a global continuum of genetic variation. This approach reveals previously overlooked subpopulations within the American Hispanic population and fine-scale relationships between geography, genotypes, and phenotypes in the UK population. This opens new lines of investigation for demographic research and statistical genetics. Given its small computational cost, PCA-UMAP also provides a general-purpose approach to exploratory analysis in population-scale datasets. Author summaryBecause of geographic isolation, individuals tend to be more genetically related to people living nearby than to people living far. This is an example of population structure, a situation where a large population contains subgroups that share more than the average amount of DNA. This structure can tell us about human history, and it can also have a large effect on medical studies. We use a newly developed method (UMAP) to visualize population structure from three genomic datasets. Using genotype data alone, we reveal numerous subgroups related to ancestry and correlated with traits such as white blood cell count, height, and FEV1, a measure used to detect airway obstruction. We demonstrate that UMAP reveals previously unobserved patterns and fine-scale structure. We show that visualizations work especially well in large datasets containing populations with diverse backgrounds, which are rapidly becoming more common, and that unlike other visualization methods, we can preserve intuitive connections between populations that reflect their shared ancestries. The combination of these results and the effectiveness of the strategy on large and diverse datasets make this an important approach for exploratory analysis for geneticists studying ancestral events and phenotype distributions.

genomics↗