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Biology subjects

Raj, S. M.

Publications and source records attributed to Raj, S. M..

3 recordsLinked to original sources

FORGEPHAST: A fast, haplotype-first complex population and phenotype simulator for biobank-scale simulation

MotivationSimulation frameworks for statistical and population genetics often separate reference-panel genotype generation, admixture modeling, phenotype simulation, and scalable genomic storage. This limits construction and evaluation of large synthetic cohorts with controlled donor composition, local ancestry, and phenotype architecture. ResultsWe developed FORGEPHAST, a Python framework for reference-panel-based synthetic haplotype and phenotype generation. FORGEPHAST supports homogeneous mosaic and pulse-admixture generation from user-defined donor panels, integrated genotype quality control, and phenotype simulation with population- and local-ancestry-specific genetic effects. Its HapStore backend provides haplotype-native, Zarr-based storage for scalable numerical analysis. We demonstrate preservation of key donor-panel population-genetic properties and use FORGEPHAST to compare polygenic risk score methods across 54 simulated phenotypic architectures at biobank scales, showing that relative performance depends on phenotype architecture, ancestry, and implementation-specific variant coverage. Availability and implementationFORGEPHAST is implemented in Python and is available as an open-source package at https://github.com/sriraj-lab/forgephast.

genomics↗

Regulatory Landscape Enrichment Analysis (RLEA) using gaiaAssociation

MotivationTo understand whether sets of genomic loci are enriched at the regulatory loci of one or more cell types, we developed the gaiaAssociation package to perform Regulatory Landscape Enrichment Analysis (RLEA). RLEA is a novel analytical process that tests for enrichment of sets of loci in cell type-specific open chromatin regions (OCRs) in the genome. ResultsWe demonstrate that the application of RLEA to genome-wide association study (GWAS) data reveals cell types likely to be mediating the phenotype studied, and clusters OCRs based on their shared regulatory profiles. GaiaAssociation is Python code that is freely available for use in functional genomics studies. Availability and ImplementationGaia Association is available on PyPi (https://pypi.org/project/gaiaAssociation/0.6.0/#description) for pip download and use on the command line or as an inline Python package. Gaia Association can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation. Contactjohn.greally@einsteinmed.edu

bioinformatics↗

Reconstruction of nine thousand years of agriculture-based diet and impact on human genetic diversity in Asia

Domestication of crops and animals during the Holocene epoch played a critical role in shaping human culture, diet and genetic variation. This domestication process took place across a span of time and space, especially in Asia. We hypothesize that domestication of plants and animals around the world must have influenced the human genome differentially among human populations to a far greater degree than has been appreciated previously. The range of domesticated foods that were available in different regions can be expected to have created regionally distinct nutrient intake profiles and deficiencies. To capture this complexity, we used archaeobotanical evidence to construct two models of dietary nutrient composition over a 9000 year time span in Asia: one based on Larson et al. (2014) and measured through composition of 8 nutrients, and another taking into account a wider range of crops, cooking and lifestyle variation, and the dietary variables glycemic index and carbohydrate content. We hypothesize that the subtle dietary shifts through time and space have also influenced current human genetic variation among Asians. We used statistical methods BayeScEnv, BayeScan and Baypass, to examine the impact of our reconstructed long-term dietary habits on genome-wide genetic variation in 29 current-day Asian populations (Figure S1, Figure 1, Figure 2). Our results show that genetic variation in diet-related pathways is correlated with dietary differences among Asian populations. SNPs in five genes, GHR, LAMA1, SEMA3A, CAST and TCF7L2, involved in the gene ontologies salivary gland morphogenesis and negative regulation of type B pancreatic cell apoptotic process suggest that metabolism may have been primary targets of selection driven by dietary shifts. These shifts may have influenced biological pathways in ways that have a lasting impact on health. We present a case that archaeobotanical evidence can provide valuable insight for understanding how historical human niche construction might have influenced modern human genetic variation.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC=\"FIGDIR/small/747709v1_fig1.gif\" ALT=\"Figure 1\">\nView larger version (26K):\norg.highwire.dtl.DTLVardef@1aa917corg.highwire.dtl.DTLVardef@135990forg.highwire.dtl.DTLVardef@1d3e0d3org.highwire.dtl.DTLVardef@14d5112_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Organization of the study and analysis pipeline. The two figures in column Archaeological data refer to the two constructed dietary models, with the boxes beside them referring to the dietary variables from each model.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC=\"FIGDIR/small/747709v1_fig2a.gif\" ALT=\"Figure 2\">\nO_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC=\"FIGDIR/small/747709v1_fig2b.gif\" ALT=\"Figure 2\">\nView larger version (52K):\norg.highwire.dtl.DTLVardef@1059950org.highwire.dtl.DTLVardef@94662dorg.highwire.dtl.DTLVardef@c01de2org.highwire.dtl.DTLVardef@ea07cb_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Summary of Diet Model 1. a) Levels of nutrients contributed by domestication, based on Diet Model 1, as a sum total of values across all three population groups. b) Average levels of nutrients across the three time intervals (9000 YBP-present) in each region, based on Diet Model 1. Supplementary Tables 1-8 contain the data used to generate these figures.\n\nC_FIG

genetics↗