Neural ADMIXTURE: rapid population clustering with autoencoders

Ad, Mantes; Dm, Montserrat; Bustamante, Carlos D.; Giró-i-Nieto, Xavier; Ioannidis, Alexander G.

doi:10.1101/2021.06.27.450081

Cited by 10 publications

(15 citation statements)

References 34 publications

(49 reference statements)

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“…We use a subset of the dataset presented in [29]. This dataset includes human labeled genomic sequences from several publicly available databases.…”

Section: Resultsmentioning

confidence: 99%

Generative Moment Matching Networks for Genotype Simulation

Perera

Montserrat

Barrabes

et al. 2022

Preprint

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The generation of synthetic genomic sequences using neural networks has potential to ameliorate privacy and data sharing concerns and to mitigate potential bias within datasets due to under-representation of some population groups. However, there is not a consensus on which architectures, training procedures, and evaluation metrics should be used when simulating single nucleotide polymorphism (SNP) sequences with neural networks. In this paper, we explore the use of Generative Moment Matching Networks (GMMNs) for SNP simulation, we present some architectural and procedural changes to properly train the networks, and we introduce an evaluation scheme to qualitatively and quantitatively assess the quality of the simulated sequences.

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“…We use a subset of the dataset presented in [29]. This dataset includes human labeled genomic sequences from several publicly available databases.…”

Section: Resultsmentioning

confidence: 99%

Generative Moment Matching Networks for Genotype Simulation

Perera

Montserrat

Barrabes

et al. 2022

Preprint

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“…For the correction of PRS models, we make use of estimates of global ancestry. For this purpose, we use Neural ADMIXTURE [56], a faster adaptation of the ADMIXTURE algorithm [57] with similar (or better) clustering results. Utilizing the Python implementation of Neural ADMIXTURE, we use data from the 1000 Genomes Project Consortium [58] for training a model in the supervised mode of Neural ADMIXTURE with the default parameters.…”

Section: Genetic Ancestrymentioning

confidence: 99%

“…For the correction of PRS models, we make use of estimates of global ancestry. For this purpose, we use Neural ADMIXTURE [56], a faster adaptation of the ADMIXTURE algorithm [57]…”

Section: Genetic Ancestrymentioning

confidence: 99%

Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

Pineda

Vernekar

Grau³

et al. 2022

Preprint

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IntroductionA major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate. Here, we present a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions including Type 2 diabetes (T2D) and hypertension.MethodsWe collected genotyped data from a novel DTC platform where participants upload their genotype data files, and were invited to answer general health questionnaires regarding cardiometabolic traits over a period of 6 months. Quality control, imputation and genome-wide association studies were performed on this dataset, and polygenic risk scores were built in a case-control setting using the BASIL algorithm.ResultsWe collected data on N=4,550 (389 cases / 4,161 controls) who reported being affected or previously affected for T2D; and N=4,528 (1,027 cases / 3,501 controls) for hypertension. We identified 164 out of 272 variants showing identical effect direction to previously reported genome-significant findings in Europeans. Performance metric of the PRS models was AUC=0.68, which is comparable to previously published PRS models obtained with larger datasets including clinical biomarkers.DiscussionDTC platforms have the potential of inverting research models of genome sequencing and phenotypic data acquisition. Quality control (QC) mechanisms proved to successfully enable traditional GWAS and PRS analyses. The direct participation of individuals has shown the potential to generate rich datasets enabling the creation of PRS cardiometabolic models. More importantly, federated learning of PRS from reuse of DTC data provides a mechanism for scaling precision health care delivery beyond the small number of countries who can afford to finance these efforts directly.ConclusionsThe genetics of T2D and hypertension have been studied extensively in controlled datasets, and various polygenic risk scores (PRS) have been developed. We developed predictive tools for both phenotypes trained with heterogeneous genotypic and phenotypic data generated outside of the clinical environment and show that our methods can recapitulate prior findings with fidelity. From these observations, we conclude that it is possible to leverage DTC genetic repositories to identify individuals at risk of debilitating diseases based on their unique genetic landscape so that informed, timely clinical interventions can be incorporated.

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“…Direct computation on compressed genotypes, including all GPU kernels, is also implemented in OpenADMIXTURE. Thus, OpenADMIX-TURE can analyze 16-32 times more data per GPU than GPU-based admixture software such as Mantes et al 38 , whose GPU kernels require a single precision or double precision genotype matrix as input.…”

Section: Direct Computation On the Plink Bed Filementioning

confidence: 99%

Unsupervised Discovery of Ancestry Informative Markers and Genetic Admixture Proportions in Biobank-Scale Data Sets

Chu

Peterson

et al. 2022

Preprint

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Admixture estimation plays a crucial role in ancestry inference and genomewide association studies (GWAS). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 10^5 to 10^6 samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank data sets. Our implementation of the method is called OpenADMIXTURE.

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Neural ADMIXTURE: rapid population clustering with autoencoders

Cited by 10 publications

References 34 publications

Generative Moment Matching Networks for Genotype Simulation

Generative Moment Matching Networks for Genotype Simulation

Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

Unsupervised Discovery of Ancestry Informative Markers and Genetic Admixture Proportions in Biobank-Scale Data Sets

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