Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).

Salmoninae Commensal Bacteria Show a Positive Correlation Between Antibiotic Resistance and Mercury Resistance Profiles

Bacterial isolates from natural sites with high toxic and heavy metal contamination more frequently contain determinants for resistance to antimicrobials. Natural strains were isolated from the ingesta and external slime of Salmo salar (Linnaeus, 1758) and Salvelinusjontinalis (Mitchell, 1814). Fish specimens were acquired from Casco Bay hatcheries, Casco, ME where there is no history of antibiotic use. Seventy-nine bacterial strains, including many well-documented salmonid commensals (an association from which the fish derives no benefit), were identified using 165 rRNA gene sequencing. Mercury resistant isolates were selected for initially on 25μM HgCI2. Strains were then grown at 20-24°C on Trypticase Soy Agar (TSA) plates containing 0-1000μM HgCl2 or 0-130μM Phenyl Mercuric Acetate (PMA). Mercury in the hatchery feed water due to ubiquitous non-point source deposition has selected for the mercury resistance observed in bacterial strains. Antibiotic resistance determinations, as measured by Minimum Inhibitory Concentration MIC) assays were performed on the 79 bacterial isolates using Sensititrel antimicrobial susceptibility panels. A positive linear correlation between the mercury (pMA and HgCl2) MIC's and antibiotic resistance for all observed strains was demonstrated. Conjugation experiments with Pseudomonas, Aeromonas, and Azomonas donors confirmed phenotypic transfer of penicillin and cephem resistances to Escherichia coli DH5a recipients. Conjugation experiments with Pseudomonas donors showed minimal transfer of tetracycline and minoglycoside resistances to Escherichia coli DH5a recipients. Our study suggests that the accumulation of antimicrobial resistances observed in these natural bacterial populations may be due to the indirect selective pressure exerted by environmental mercury.