Andrew G Clark

Unique patterns of inheritance and selection on Y chromosomes have led to the evolution of specialized gene functions. We report CRISPR mutants in Drosophila of the Y-linked gene, WDY, which is required for male fertility. We demonstrate that the sperm tails of WDY mutants beat approximately half as fast as those of wild-type and that mutant sperm do not propel themselves within the male ejaculatory duct or female reproductive tract. Therefore, although mature sperm are produced by WDY mutant males, and are transferred to females, those sperm fail to enter the female sperm storage organs. We report genotype-dependent and regional differences in sperm motility that appear to break the correlation between sperm tail beating and propulsion. Furthermore, we identify a significant change in hydrophobicity at a residue at a putative calcium-binding site in WDY orthologs at the split between the melanogaster …

Background Mendelian randomization (MR) studies suggest a causal effect of iron (Fe) status on cardiovascular disease (CVD) risk, but it is unknown if these associations are confounded by pleiotropic effects of the instrumental variables (IV) on CVD risk factors. We aimed to investigate the effect of Fe status on CVD risk controlling for CVD risk factors. Methods Fe biomarker IVs (total Fe binding capacity (TIBC, n=208,422), transferrin saturation (TSAT, n=198,516), serum Fe (SI, n=236,612), ferritin (n=257,953)) were selected from a European GWAS meta-analysis. We performed two-sample univariate (UV) MR of each Fe trait on CVD outcomes (all-cause ischemic stroke (IS), cardioembolic IS (CES), large artery IS (LAS), small vessel IS (SVS), and coronary heart disease (CHD)) from MEGASTROKE (n=440,328) and CARDIoGRAMplusC4D (n=183,305). We then implemented multivariate (MV) MR conditioning on six CVD risk factors from independent European samples to evaluate their potential confounding and/or mediating effects on the observed Fe-CVD associations. Results With UVMR analyses, we found higher genetically predicted Fe status to be associated with a greater risk of CES (TSAT: OR 1.17 [95%CI 1.03, 1.33], SI: OR 1.21 [ 95%CI 1.02, 1.44]; TIBC: OR 0.81 [95%CI 0.69, 0.94]). The detrimental effects of Fe status on CES risk remained unaffected when adjusting for CVD risk factors (all P<0.05). Additionally, we found diastolic blood pressure (DBP) to mediate between 7.1-8.8% of the total effect of Fe status on CES incidence. While UVMR initially suggested a protective effect of Fe status on LAS and CHD, MVMR analyses …

Drosophila melanogaster is a widely used model organism for studying environmental adaptation. However, the genetic diversity of populations in Asia is poorly understood, leaving a notable gap in our knowledge of the global evolution and adaptation of this species. We sequenced genomes of 292 D. melanogaster strains from various ecological settings in China and analyzed them along with previously published genome sequences. We have identified six global genetic ancestry groups, despite the presence of widespread genetic admixture. The strains from China represent a unique ancestry group, although detectable differentiation exists among populations within China. We deciphered the global migration and demography of D. melanogaster, and identified widespread signals of adaptation, including genetic changes in response to insecticides. We validated the effects of insecticide resistance variants …

Background:Time-course multi-omics experiments have been highly informative for obtaining a comprehensive understanding of the dynamic relationships between molecules in a biological process, especially if the different profiles are obtained from the same samples. A fundamental step in analyzing time-course multi-omics data involves selecting a short list of genes or gene regions (“sites”) that warrant further study. Two important criteria for site selection are the magnitude of change and the temporal dynamic consistency. However, existing methods only consider one of these criteria, while neglecting the other.Results:In our study, we propose a framework called MINT-DE (Multi-omics INtegration of Time-course for Diffierential Expression analysis) to address this limitation. MINT-DE is capable of selecting sites based on summarized measures of both aforementioned aspects. We calculate evidence measures …

High-quality genome assemblies across a range of nontraditional model organisms can accelerate the discovery of novel aspects of genome evolution. The Drosophila virilis group has several attributes that distinguish it from more highly studied species in the Drosophila genus, such as an unusual abundance of repetitive elements and extensive karyotype evolution, in addition to being an attractive model for speciation genetics. Here, we used long-read sequencing to assemble five genomes of three virilis group species and characterized sequence and structural divergence and repetitive DNA evolution. We find that our contiguous genome assemblies allow characterization of chromosomal arrangements with ease and can facilitate analysis of inversion breakpoints. We also leverage a small panel of resequenced strains to explore the genomic pattern of divergence and polymorphism in this species and …

Mendelian randomization (MR) studies suggest a causal effect of Fe status on ischemic stroke (IS). It is currently unknown if these associations are confounded by pleiotropic effects of the instrumental variables (IV) on cardiovascular disease (CVD) risk factors, such as low-density lipoprotein cholesterol (LDL), diastolic blood pressure (DBP), and body mass index (BMI). We aimed to investigate the effect of iron (Fe) status on risk of IS subtypes (cardioembolic (CES), small vessel (SVS), and large artery atherosclerotic (LAS)) controlling for CVD risk factors. Fe biomarker IVs (total Fe binding capacity (TIBC), transferrin saturation (TSAT), serum Fe (SI), serum ferritin (SF)) were selected from a European GWAS (n=198K-258K) and filtered based on genome-wide significance (P<5e-8), F-statistic>10, R2<0.001, minor allele frequency >1%, and consistent effect direction for all Fe traits. We performed univariate two-sample …

Arctic coasts are vast, representing 30-34% of Earth’s coastline and exhibit some of the highest rates of erosion in the World due to the presence of permafrost. Rates of erosion are expected to increase with warming air and water temperatures, reductions in Arctic sea ice extent and duration, sea level rise, and increased storm severity and frequency. Erosion of Arctic coasts can lead to rapid land loss threatening habitat, archaeologically significant sites, modern infrastructure, and communities. Rapid erosion and permafrost degradation also leads to the liberation of previously frozen sediment and organic carbon into the nearshore zone which affects marine ecosystems and contributes to ocean acidification. Further, the release of organic carbon from frozen sediment contribute to global greenhouse gas release which are not well understood nor included in current Earth System Models. This thesis focuses on the use of emerging technologies to further our understanding of Arctic coastal processes, specifically, volumetric erosion, and broad scale delineation of multiple shoreline proxies for monitoring and quantification of erosion. UAV-SfM provides aerial and DSM imagery at unprecedented spatial and temporal resolution that provides perspectives and quantitative measures that are unachievable using conventional methods and very high resolution satellite imagery enables broader scale multiple proxy analysis while image classifications derived from OBIA, or GEOBIA, provide opportunities to systematically create boundary features (ie shoreline proxies). Overall, this doctoral research develops and evaluates techniques that enhance our …

Time-course gene expression datasets provide insight into the dynamics of complex biological processes, such as immune response and organ development. It is of interest to identify genes with similar temporal expression patterns because such genes are often biologically related. However, this task is challenging due to the high dimensionality of these datasets and the nonlinearity of gene expression time dynamics. We propose an empirical Bayes approach to estimating ordinary differential equation (ODE) models of gene expression, from which we derive a similarity metric between genes called the Bayesian lead-lag R2 (LLR2). Importantly, the calculation of the LLR2 leverages biological databases that document known interactions amongst genes; this information is automatically used to define informative prior distributions on the ODE model’s parameters. As a result, the LLR2 is a biologically-informed …