Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Scaling probabilistic models of genetic variation to millions of humans.

ABSTRACT: A major goal of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. The aggregated number of genotyped humans is currently on the order of millions of individuals, and existing methods do not scale to data of this size. To solve this problem, we developed TeraStructure, an algorithm to fit Bayesian models of genetic variation in structured human populations on tera-sample-sized data sets (1012 observed genotypes; for example, 1 million individuals at 1 million SNPs). TeraStructure is a scalable approach to Bayesian inference in which subsamples of markers are used to update an estimate of the latent population structure among individuals. We demonstrate that TeraStructure performs as well as existing methods on current globally sampled data, and we show using simulations that TeraStructure continues to be accurate and is the only method that can scale to tera-sample sizes.

SUBMITTER: Gopalan P

PROVIDER: S-EPMC5127768 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Scaling probabilistic models of genetic variation to millions of humans.

Gopalan Prem P Hao Wei W Blei David M DM Storey John D JD

Nature genetics 20161107 12

A major goal of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. The aggregated number of genotyped humans is currently on the order of millions of individuals, and existing methods do not scale to data of this size. To solve this problem, we developed TeraStructure, an algorithm to fit Bayesian models of genetic variation in structured human populations on tera-sample-sized data sets (10<sup>12</sup> observed genotypes; for example, 1 mil ...[more]

PMID: 27819665

Similar Datasets

Probabilistic models of genetic variation in structured populations applied to global human studies.

Project description:MotivationModern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important problem is how to formulate and estimate probabilistic models of observed genotypes that account for complex population structure. The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual. Here, we instead focus on modeling variation of the genotypes without requiring a higher-level admixture interpretation.ResultsWe formulate two general probabilistic models, and we propose computationally efficient algorithms to estimate them. First, we show how principal component analysis can be utilized to estimate a general model that includes the well-known Pritchard-Stephens-Donnelly admixture model as a special case. Noting some drawbacks of this approach, we introduce a new 'logistic factor analysis' framework that seeks to directly model the logit transformation of probabilities underlying observed genotypes in terms of latent variables that capture population structure. We demonstrate these advances on data from the Human Genome Diversity Panel and 1000 Genomes Project, where we are able to identify SNPs that are highly differentiated with respect to structure while making minimal modeling assumptions.Availability and implementationA Bioconductor R package called lfa is available at http://www.bioconductor.org/packages/release/bioc/html/lfa.htmlContactjstorey@princeton.eduSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC4795615 | biostudies-literature

Scaling computational genomics to millions of individuals with GPUs.

Project description:Current genomics methods are designed to handle tens to thousands of samples but will need to scale to millions to match the pace of data and hypothesis generation in biomedical science. Here, we show that high efficiency at low cost can be achieved by leveraging general-purpose libraries for computing using graphics processing units (GPUs), such as PyTorch and TensorFlow. We demonstrate > 200-fold decreases in runtime and ~ 5-10-fold reductions in cost relative to CPUs. We anticipate that the accessibility of these libraries will lead to a widespread adoption of GPUs in computational genomics.

| S-EPMC6823959 | biostudies-literature

Scalable probabilistic PCA for large-scale genetic variation data.

Project description:Principal component analysis (PCA) is a key tool for understanding population structure and controlling for population stratification in genome-wide association studies (GWAS). With the advent of large-scale datasets of genetic variation, there is a need for methods that can compute principal components (PCs) with scalable computational and memory requirements. We present ProPCA, a highly scalable method based on a probabilistic generative model, which computes the top PCs on genetic variation data efficiently. We applied ProPCA to compute the top five PCs on genotype data from the UK Biobank, consisting of 488,363 individuals and 146,671 SNPs, in about thirty minutes. To illustrate the utility of computing PCs in large samples, we leveraged the population structure inferred by ProPCA within White British individuals in the UK Biobank to identify several novel genome-wide signals of recent putative selection including missense mutations in RPGRIP1L and TLR4.

| S-EPMC7286535 | biostudies-literature

SC3s: efficient scaling of single cell consensus clustering to millions of cells.

Project description:BackgroundToday it is possible to profile the transcriptome of individual cells, and a key step in the analysis of these datasets is unsupervised clustering. For very large datasets, efficient algorithms are required to ensure that analyses can be conducted with reasonable time and memory requirements.ResultsHere, we present a highly efficient k-means based approach, and we demonstrate that it scales favorably with the number of cells with regards to time and memory.ConclusionsWe have demonstrated that our streaming k-means clustering algorithm gives state-of-the-art performance while resource requirements scale favorably for up to 2 million cells.

| S-EPMC9743492 | biostudies-literature

Scaling metagenome sequence assembly with probabilistic de Bruijn graphs.

Project description:Deep sequencing has enabled the investigation of a wide range of environmental microbial ecosystems, but the high memory requirements for de novo assembly of short-read shotgun sequencing data from these complex populations are an increasingly large practical barrier. Here we introduce a memory-efficient graph representation with which we can analyze the k-mer connectivity of metagenomic samples. The graph representation is based on a probabilistic data structure, a Bloom filter, that allows us to efficiently store assembly graphs in as little as 4 bits per k-mer, albeit inexactly. We show that this data structure accurately represents DNA assembly graphs in low memory. We apply this data structure to the problem of partitioning assembly graphs into components as a prelude to assembly, and show that this reduces the overall memory requirements for de novo assembly of metagenomes. On one soil metagenome assembly, this approach achieves a nearly 40-fold decrease in the maximum memory requirements for assembly. This probabilistic graph representation is a significant theoretical advance in storing assembly graphs and also yields immediate leverage on metagenomic assembly.

| S-EPMC3421212 | biostudies-literature

Genetic architecture of transcript-level variation in humans.

Project description:We report here the results of testing the pairwise association of 12,747 transcriptional gene-expression values with more than two million single-nucleotide polymorphisms (SNPs) in samples of European (CEPH from Utah; CEU) and African (Yoruba from Ibadan; YRI) ancestry. We found 4,677 and 5,125 significant associations between expression quantitative nucleotides (eQTNs) and transcript clusters in the CEU and the YRI samples, respectively. The physical distance between an eQTN and its associated transcript cluster was referred to as the intrapair distance. An association with 4 Mb or less intrapair distance was defined as local; otherwise, it was defined as distant. The enrichment analysis of functional categories shows that genes harboring the local eQTNs are enriched in the categories related to nucleosome and chromatin assembly; the genes harboring the distant eQTNs are enriched in the categories related to transmembrane signal transduction, suggesting that these biological pathways are likely to play a significant role in regulation of gene expression. We highlight in the EPHX1 gene a deleterious nonsynonymous SNP that is distantly associated with gene expression of ORMDL3, a susceptibility gene for asthma.

| S-EPMC2651622 | biostudies-literature

Variation and genetic control of chromatin architecture in humans (RNAseq)

Project description:This data set comprises population (46 samples) measurements of RNA expression levels for a subset of the 1000 Genomes Project CEPH samples. This data was generated as part of the following study: - Population Variation and Genetic Control of Modular Chromatin Architecture in Humans. Cell. 2015 Aug 27;162(5):1039-50. doi: 10.1016/j.cell.2015.08.001. Epub 2015 Aug 20. An additional set of 111 samples from the 1000 Genomes Project (GBR and TSI populations) were also assayed using RNA-seq. This data was generated as part of the following study: - Chromatin 3D interactions mediate genetic effects on regulatory networks.

2016-05-27 | E-MTAB-3656 | biostudies-arrayexpress

Variation and genetic control of chromatin architecture in humans (ChIPseq)

Project description:This data set comprises population (47 samples) measurements of transcription factor DNA binding (PU.1 and RPB2) and histone modification (H3K27ac, H3K4me1 and H3k4me3) levels for a subset of the 1000 Genomes Project CEPH samples. This data was generated as part of the following study: - Population Variation and Genetic Control of Modular Chromatin Architecture in Humans. Cell. 2015 Aug 27;162(5):1039-50. doi: 10.1016/j.cell.2015.08.001. Epub 2015 Aug 20. An additional set of 111 samples from the 1000 Genomes Project (GBR and TSI populations) were also assayed for three histone modifications (H3K27ac, H3K4me1 and H3k4me3). This data was generated as part of the following study: - Chromatin 3D interactions mediate genetic effects on regulatory networks.

2019-03-14 | E-MTAB-3657 | biostudies-arrayexpress

Analysis of protein-coding genetic variation in 60,706 humans.

Project description:Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

| S-EPMC5018207 | biostudies-literature

Natural genetic variation caused by transposable elements in humans.

Project description:Transposons and transposon-like repetitive elements collectively occupy 44% of the human genome sequence. In an effort to measure the levels of genetic variation that are caused by human transposons, we have developed a new method to broadly detect transposon insertion polymorphisms of all kinds in humans. We began by identifying 606,093 insertion and deletion (indel) polymorphisms in the genomes of diverse humans. We then screened these polymorphisms to detect indels that were caused by de novo transposon insertions. Our method was highly efficient and led to the identification of 605 nonredundant transposon insertion polymorphisms in 36 diverse humans. We estimate that this represents 25-35% of approximately 2075 common transposon polymorphisms in human populations. Because we identified all transposon insertion polymorphisms with a single method, we could evaluate the relative levels of variation that were caused by each transposon class. The average human in our study was estimated to harbor 1283 Alu insertion polymorphisms, 180 L1 polymorphisms, 56 SVA polymorphisms, and 17 polymorphisms related to other forms of mobilized DNA. Overall, our study provides significant steps toward (i) measuring the genetic variation that is caused by transposon insertions in humans and (ii) identifying the transposon copies that produce this variation.

| S-EPMC1448813 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data