An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data, bioRxiv, 2019-03-29

AbstractMost current methods for detecting natural selection from DNA sequence data are limited in that they are either based on summary statistics or a composite likelihood, and as a consequence, do not make full use of the information available in DNA sequence data. We here present a new importance sampling approach for approximating the full likelihood function for the selection coefficient. The method treats the ancestral recombination graph (ARG) as a latent variable that is integrated out using previously published Markov Chain Monte Carlo (MCMC) methods. The method can be used for detecting selection, estimating selection coefficients, testing models of changes in the strength of selection, estimating the time of the start of a selective sweep, and for inferring the allele frequency trajectory of a selected or neutral allele. We perform extensive simulations to evaluate the method and show that it uniformly improves power to detect selection compared to current popular methods such as nSL and SDS, under various demographic models and can provide reliable inferences of allele frequency trajectories under many conditions. We also explore the potential of our method to detect extremely recent changes in the strength of selection. We use the method to infer the past allele frequency trajectory for a lactase persistence SNP (MCM6) in Europeans. We also study a set of 11 pigmentation-associated variants. Several genes show evidence of strong selection particularly within the last 5,000 years, including ASIP, KITLG, and TYR. However, selection on OCA2HERC2 seems to be much older and, in contrast to previous claims, we find no evidence of selection on TYRP1.Author summaryCurrent methods to study natural selection using modern population genomic data are limited in their power and flexibility. Here, we present a new method to infer natural selection that builds on recent methodological advances in estimating genome-wide genealogies. By using importance sampling we are able to efficiently estimate the likelihood function of the selection coefficient. We show our method improves power to test for selection over competing methods across a diverse range of scenarios, and also accurately infers the selection coefficient. We also demonstrate a novel capability of our model, using it to infer the allele’s frequency over time. We validate these results with a study of a lactase persistence SNP in Europeans, and also study a set of 11 pigmentation-associated variants.

biorxiv genetics 100-200-users 2019

Research grade marijuana supplied by the National Institute on Drug Abuse is genetically divergent from commercially available Cannabis, bioRxiv, 2019-03-29

AbstractPublic comfort with Cannabis (marijuana and hemp) has recently increased, resulting in previously strict Cannabis regulations now allowing hemp cultivation, medical use, and in some states, recreational consumption. There is a growing interest in the potential medical benefits of the various chemical constituents produced by the Cannabis plant. Currently, the University of Mississippi, funded through the National Institutes of HealthNational Institute on Drug Abuse (NIHNIDA), is the sole Drug Enforcement Agency (DEA) licensed facility to cultivate Cannabis for research purposes. Hence, most federally funded research where participants consume Cannabis for medicinal purposes relies on NIDA-supplied product. Previous research found that cannabinoid levels in research grade marijuana supplied by NIDA did not align with commercially available Cannabis from Colorado, Washington and California. Given NIDA chemotypes were misaligned with commercial Cannabis, we sought to investigate where NIDA’s research grade marijuana falls on the genetic spectrum of Cannabis groups. NIDA research grade marijuana was found to genetically group with Hemp samples along with a small subset of commercial drug-type Cannabis. A majority of commercially available drug-type Cannabis was genetically very distinct from NIDA samples. These results suggest that subjects consuming NIDA research grade marijuana may experience different effects than average consumers.

biorxiv genetics 100-200-users 2019

Research grade marijuana supplied by the National Institute on Drug Abuse is genetically divergent from commercially availableCannabis, bioRxiv, 2019-03-29

AbstractPublic comfort withCannabis(marijuana and hemp) has recently increased, resulting in previously strictCannabisregulations now allowing hemp cultivation, medical use, and in some states, recreational consumption. There is a growing interest in the potential medical benefits of the various chemical constituents produced by theCannabisplant. Currently, the University of Mississippi, funded through the National Institutes of HealthNational Institute on Drug Abuse (NIHNIDA), is the sole Drug Enforcement Agency (DEA) licensed facility to cultivateCannabisfor research purposes. Hence, most federally funded research where participants consumeCannabisfor medicinal purposes relies on NIDA-supplied product. Previous research found that cannabinoid levels in research grade marijuana supplied by NIDA did not align with commercially availableCannabisfrom Colorado, Washington and California. Given NIDA chemotypes were misaligned with commercialCannabis, we sought to investigate where NIDA’s research grade marijuana falls on the genetic spectrum ofCannabisgroups. NIDA research grade marijuana was found to genetically group with Hemp samples along with a small subset of commercial drug-typeCannabis. A majority of commercially available drug-typeCannabiswas genetically very distinct from NIDA samples. These results suggest that subjects consuming NIDA research grade marijuana may experience different effects than average consumers.

biorxiv genetics 100-200-users 2019

 

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