Browsing by Author "Williams, Jacob"

Now showing 1 - 5 of 5
  • Thumbnail Image
    BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data
    Xu, Shuangshuang; Williams, Jacob; Ferreira, Marco A. R. (2023-09-15)
    Background Genome-wide association studies (GWASes) aim to identify single nucleotide polymorphisms (SNPs) associated with a given phenotype. A common approach for the analysis of GWAS is single marker analysis (SMA) based on linear mixed models (LMMs). However, LMM-based SMA usually yields a large number of false discoveries and cannot be directly applied to non-Gaussian phenotypes such as count data. Results We present a novel Bayesian method to find SNPs associated with non-Gaussian phenotypes. To that end, we use generalized linear mixed models (GLMMs) and, thus, call our method Bayesian GLMMs for GWAS (BG2). To deal with the high dimensionality of GWAS analysis, we propose novel nonlocal priors specifically tailored for GLMMs. In addition, we develop related fast approximate Bayesian computations. BG2 uses a two-step procedure: first, BG2 screens for candidate SNPs; second, BG2 performs model selection that considers all screened candidate SNPs as possible regressors. A simulation study shows favorable performance of BG2 when compared to GLMM-based SMA. We illustrate the usefulness and flexibility of BG2 with three case studies on cocaine dependence (binary data), alcohol consumption (count data), and number of root-like structures in a model plant (count data).
  • Thumbnail Image
    BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies
    Williams, Jacob; Xu, Shuangshuang; Ferreira, Marco A. R. (2023-05-11)
    Background Genome-wide association studies (GWAS) seek to identify single nucleotide polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated SNPs, correlated observations, and the number of SNPs being two orders of magnitude larger than the number of observations, GWAS procedures often suffer from high false positive rates. Results We propose BGWAS, a novel Bayesian variable selection method based on nonlocal priors for linear mixed models specifically tailored for genome-wide association studies. Our proposed method BGWAS uses a novel nonlocal prior for linear mixed models (LMMs). BGWAS has two steps: screening and model selection. The screening step scans through all the SNPs fitting one LMM for each SNP and then uses Bayesian false discovery control to select a set of candidate SNPs. After that, a model selection step searches through the space of LMMs that may have any number of SNPs from the candidate set. A simulation study shows that, when compared to popular GWAS procedures, BGWAS greatly reduces false positives while maintaining the same ability to detect true positive SNPs. We show the utility and flexibility of BGWAS with two case studies: a case study on salt stress in plants, and a case study on alcohol use disorder. Conclusions BGWAS maintains and in some cases increases the recall of true SNPs while drastically lowering the number of false positives compared to popular SMA procedures.
  • Thumbnail Image
    BICOSS: Bayesian iterative conditional stochastic search for GWAS
    Williams, Jacob; Ferreira, Marco A. R.; Ji, Tieming (2022-11-12)
    Background Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control. In addition, when a few variants have large effect sizes, SMA has low statistical power to detect small and medium effect sizes, leading to low recall of true causal single nucleotide polymorphisms (SNPs). Results We present the Bayesian Iterative Conditional Stochastic Search (BICOSS) method that controls false discovery rate and increases recall of variants with small and medium effect sizes. BICOSS iterates between a screening step and a Bayesian model selection step. A simulation study shows that, when compared to SMA, BICOSS dramatically reduces false discovery rate and allows for smaller effect sizes to be discovered. Finally, two real world applications show the utility and flexibility of BICOSS. Conclusions When compared to widely used SMA, BICOSS provides higher recall of true SNPs while dramatically reducing false discovery rate.
  • Thumbnail Image
    Herbivore suppression of waterlettuce in Florida, USA
    Foley, Jeremiah R.; Williams, Jacob; Pokorny, Eileen; Tipping, Philip W. (Academic Press, 2023-04)
    Waterlettuce, Pistia stratiotes L. (Araceae: Pistieae) is an invasive free-floating aquatic weed found throughout the world that has been targeted for control using various methods including classical and conservation bio-logical control and, herbicides. In Florida, herbicides are the primary strategy employed by land managers, often without regard to the impact of herbivorous arthropods including Samea multiplicalis Guenee (Lepidoptera: Crambidae), Elophila [=Synclita] obliteralis Walker (Lepidoptera: Crambidae), Argyractis [=Petrophila] dru-malis (Dyer) (Lepidoptera: Crambidae), Draeculacephala inscripta VanDuzee (Hemiptera: Cicadellidae), Rho-palosiphum nymphaeae L. (Hemiptera: Aphididae), Orthogalumna terebrantis Wallwork (Acarina: Galumnidae), and Neohydronomus affinis Hustache (Coleoptera: Curculionoidea). A series of field experiments from 2009 to 2012 were conducted at three sites in Florida to quantify the levels of suppression by these species, using an insecticide-check approach to produce restricted and unrestricted herbivory conditions. Four of the species (E. obliteralis, S. multiplicalis, O. terebrantis, and N. affinis) were found at every field site. At the end of the experiment, plots exposed to unrestricted herbivory contained 63.1 % less biomass and covered 32.0 % less surface area compared to plots with restricted herbivory. These results indicate that naturally occurring and introduced species are suppressing the growth of waterlettuce populations in the field in Florida. Future research will examine the synergistic potential of actively managing herbicides and herbivorous arthropods to suppress waterlettuce.
  • Thumbnail Image
    Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T
    Plucani do Amaral, Fernanda; Wang, Juexin; Williams, Jacob; Tuleski, Thalita R.; Joshi, Trupti; Ferreira, Marco A. R.; Stacey, Gary (MDPI, 2023-01-28)
    Plant growth-promoting bacteria (PGPB) can enhance plant health by facilitating nutrient uptake, nitrogen fixation, protection from pathogens, stress tolerance and/or boosting plant productivity. The genetic determinants that drive the plant–bacteria association remain understudied. To identify genetic loci highly correlated with traits responsive to PGPB, we performed a genome-wide association study (GWAS) using an Arabidopsis thaliana population treated with Azoarcus olearius DQS-4T. Phenotypically, the 305 Arabidopsis accessions tested responded differently to bacterial treatment by improving, inhibiting, or not affecting root system or shoot traits. GWA mapping analysis identified several predicted loci associated with primary root length or root fresh weight. Two statistical analyses were performed to narrow down potential gene candidates followed by haplotype block analysis, resulting in the identification of 11 loci associated with the responsiveness of Arabidopsis root fresh weight to bacterial inoculation. Our results showed considerable variation in the ability of plants to respond to inoculation by A. olearius DQS-4T while revealing considerable complexity regarding statistically associated loci with the growth traits measured. This investigation is a promising starting point for sustainable breeding strategies for future cropping practices that may employ beneficial microbes and/or modifications of the root microbiome.