Bevan Emma Huang

Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array

High-density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker–trait associations in mapping experiments. We developed a genotyping array including about 90 000 gene-associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome-wide distributed SNPs that are represented in populations of diverse geographical origin. We used density-based spatial clustering algorithms to enable high-throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model-free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low-intensity clusters can provide insight into the distribution of presence–absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat.

Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars

Domesticated crops experience strong human-mediated selection aimed at developing high-yielding varieties adapted to local conditions. To detect regions of the wheat genome subject to selection during improvement, we developed a high-throughput array to interrogate 9,000 gene-associated single-nucleotide polymorphisms (SNP) in a worldwide sample of 2,994 accessions of hexaploid wheat including landraces and modern cultivars. Using a SNP-based diversity map we characterized the impact of crop improvement on genomic and geographic patterns of genetic diversity. We found evidence of a small population bottleneck and extensive use of ancestral variation often traceable to founders of cultivars from diverse geographic regions. Analyzing genetic differentiation among populations and the extent of haplotype sharing, we identified allelic variants subjected to selection during improvement. Selective sweeps were found around genes involved in the regulation of flowering time and phenology. An introgression of a wild relative-derived gene conferring resistance to a fungal pathogen was detected by haplotype-based analysis. Comparing selective sweeps identified in different populations, we show that selection likely acts on distinct targets or multiple functionally equivalent alleles in different portions of the geographic range of wheat. The majority of the selected alleles were present at low frequency in local populations, suggesting either weak selection pressure or temporal variation in the targets of directional selection during breeding probably associated with changing agricultural practices or environmental conditions. The developed SNP chip and map of genetic variation provide a resource for advancing wheat breeding and supporting future population genomic and genome-wide association studies in wheat.

A multiparent advanced generation inter‐cross population for genetic analysis in wheat

We present the first results from a novel multiparent advanced generation inter-cross (MAGIC) population derived from four elite wheat cultivars. The large size of this MAGIC population (1579 progeny), its diverse genetic composition and high levels of recombination all contribute to its value as a genetic resource. Applications of this resource include interrogation of the wheat genome and the analysis of gene-trait association in agronomically important wheat phenotypes. Here, we report the utilization of a MAGIC population for the first time for linkage map construction. We have constructed a linkage map with 1162 DArT, single nucleotide polymorphism and simple sequence repeat markers distributed across all 21 chromosomes. We benchmark this map against a high-density DArT consensus map created by integrating more than 100 biparental populations. The linkage map forms the basis for further exploration of the genetic architecture within the population, including characterization of linkage disequilibrium, founder contribution and inclusion of an alien introgression into the genetic map. Finally, we demonstrate the application of the resource for quantitative trait loci mapping using the complex traits plant height and hectolitre weight as a proof of principle.

Multi-parent advanced generation inter-cross in barley: high-resolution quantitative trait locus mapping for flowering time as a proof of concept

The choice of mapping population is one of the key factors in understanding the genetic effects of complex traits and determines the power and precision of quantitative trait locus (QTL) mapping. We present the results of the first eight-way multi-parent advanced generation inter-cross (MAGIC) doubled haploid (DH) population in barley (Hordeum vulgare ssp. vulgare) applied to mapping complex traits. The results of the genetic architecture within the barley MAGIC population allowed QTL mapping in 533 DH lines with 4,550 single nucleotide polymorphisms (SNPs) with a newly developed mixed linear model in SAS v9.2, incorporating multi-locus analysis and cross validation for flowering time. Two QTL mapping approaches, the binary approach (BA), which is widely used in QTL and association mapping, and a novel haplotype approach (HA) were compared based on their efficiency, precision for QTL detection and estimation of genetic effects. The analysis detected 17 QTLs, five of which were shared between the two approaches; five and two were specifically found with the BA and HA approaches, respectively. The combination of the two mapping approaches enabled high-precision QTL mapping for flowering time. The QTLs corresponded to the genomic regions of major flowering-time genes Vrn-H1, Vrn-H3, HvGI, Ppd-H1, HvFT2, HvFT4, Co1 and linked genes for plant height (sdw1). These results confirm the proof of concept of QTL mapping in a multi-parent population, highlight the advantages and demonstrate that the barley MAGIC DH lines in combination with an advanced QTL mapping approach are valuable resources for mapping complex traits.

Postprandial total and HMW adiponectin following a high-fat meal in lean, obese and diabetic men.

Background/Objectives:Recent work suggests that macronutrients are pro-inflammatory and promote oxidative stress. Reports of postprandial regulation of total adiponectin have been mixed, and there is limited information regarding postprandial changes in high molecular weight (HMW) adiponectin. The aim of this study was to assess the effect of a standardised high-fat meal on metabolic variables, adiponectin (total and HMW), and markers of inflammation and oxidative stress in: (i) lean, (ii) obese non-diabetic and (iii) men with type 2 diabetes mellitus (T2DM).Subjects/Methods:Male subjects: lean (n=10), obese (n=10) and T2DM (n=10) were studied for 6 h following both a high-fat meal and water control. Metabolic variables (glucose, insulin, triglycerides), inflammatory markers (interleukin-6 (IL6), tumour necrosis factor (TNF)α, high-sensitivity C-reactive protein (hsCRP), nuclear factor (NF)κB expression in peripheral blood mononuclear cells (p65)), indicators of oxidative stress (oxidised low density lipoprotein (oxLDL), protein carbonyl) and adiponectin (total and HMW) were measured.Results:No significant changes in TNFα, p65, oxLDL or protein carbonyl concentrations were observed. Overall, postprandial IL6 decreased in subjects with T2DM but increased in lean subjects, whereas hsCRP decreased in the lean cohort and increased in obese subjects. There was no overall postprandial change in total or HMW adiponectin in any group. Total adiponectin concentrations changed over time following the water control, and the response was significantly different in lean subjects compared with subjects with T2DM (P=0.04).Conclusions:No consistent significant postprandial inflammation, oxidative stress or regulation of adiponectin was observed in this study. Findings from the water control suggest differential basal regulation of total adiponectin in T2DM compared with lean controls.

The path from big data to precision medicine

ABSTRACT Precision medicine aims to combine comprehensive data collected over time about an individual’s genetics, environment, and lifestyle, to advance disease understanding and interception, aid drug discovery, and ensure delivery of appropriate therapies. Considerable public and private resources have been deployed to harness the potential value of big data derived from electronic health records, ‘omics technologies, imaging, and mobile health in advancing these goals. While both technical and sociopolitical challenges in implementation remain, we believe that consolidating these data into comprehensive and coherent bodies will aid in transforming healthcare. Overcoming these challenges will see the effective, efficient, and secure use of big data disrupt the practice of medicine. It will have significant implications for drug discovery and development as well as in the provisioning, utilization and economics of health care delivery going forward; ultimately, it will enhance the quality of care for the benefit of patients.

The effect of a high-fat meal on postprandial arterial stiffness in men with obesity and Type 2 diabetes

CONTEXT Postprandial dysmetabolism is emerging as an important cardiovascular risk factor. Augmentation index (AIx) is a measure of systemic arterial stiffness and independently predicts cardiovascular outcome. OBJECTIVE The objective of this study was to assess the effect of a standardized high-fat meal on metabolic parameters and AIx in 1) lean, 2) obese nondiabetic, and 3) subjects with type 2 diabetes mellitus (T2DM). DESIGN AND SETTING Male subjects (lean, n = 8; obese, n = 10; and T2DM, n = 10) were studied for 6 h after a high-fat meal and water control. Glucose, insulin, triglycerides, and AIx (radial applanation tonometry) were measured serially to determine the incremental area under the curve (iAUC). RESULTS AIx decreased in all three groups after a high-fat meal. A greater overall postprandial reduction in AIx was seen in lean and T2DM compared with obese subjects (iAUC, 2251 +/- 1204, 2764 +/- 1102, and 1187 +/- 429% . min, respectively; P < 0.05). The time to return to baseline AIx was significantly delayed in subjects with T2DM (297 +/- 68 min) compared with lean subjects (161 +/- 88 min; P < 0.05). There was a significant correlation between iAUC AIx and iAUC triglycerides (r = 0.50; P < 0.05). CONCLUSIONS Obesity is associated with an attenuated overall postprandial decrease in AIx. Subjects with T2DM have a preserved, but significantly prolonged, reduction in AIx after a high-fat meal. The correlation between AIx and triglycerides suggests that postprandial dysmetabolism may impact on vascular dynamics. The markedly different response observed in the obese subjects compared with those with T2DM was unexpected and warrants additional evaluation.

Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library.

BackgroundThe determination and regulation of cell morphology are critical components of cell-cycle control, fitness, and development in both single-cell and multicellular organisms. Understanding how environmental factors, chemical perturbations, and genetic differences affect cell morphology requires precise, unbiased, and validated measurements of cell-shape features.ResultsHere we introduce two software packages, Morphometrics and BlurLab, that together enable automated, computationally efficient, unbiased identification of cells and morphological features. We applied these tools to bacterial cells because the small size of these cells and the subtlety of certain morphological changes have thus far obscured correlations between bacterial morphology and genotype. We used an online resource of images of the Keio knockout library of nonessential genes in the Gram-negative bacterium Escherichia coli to demonstrate that cell width, width variability, and length significantly correlate with each other and with drug treatments, nutrient changes, and environmental conditions. Further, we combined morphological classification of genetic variants with genetic meta-analysis to reveal novel connections among gene function, fitness, and cell morphology, thus suggesting potential functions for unknown genes and differences in modes of action of antibiotics.ConclusionsMorphometrics and BlurLab set the stage for future quantitative studies of bacterial cell shape and intracellular localization. The previously unappreciated connections between morphological parameters measured with these software packages and the cellular environment point toward novel mechanistic connections among physiological perturbations, cell fitness, and growth.

The effect of microbial colonization on the host proteome varies by gastrointestinal location

Endogenous intestinal microbiota have wide-ranging and largely uncharacterized effects on host physiology. Here, we used reverse-phase liquid chromatography-coupled tandem mass spectrometry to define the mouse intestinal proteome in the stomach, jejunum, ileum, cecum and proximal colon under three colonization states: germ-free (GF), monocolonized with Bacteroides thetaiotaomicron and conventionally raised (CR). Our analysis revealed distinct proteomic abundance profiles along the gastrointestinal (GI) tract. Unsupervised clustering showed that host protein abundance primarily depended on GI location rather than colonization state and specific proteins and functions that defined these locations were identified by random forest classifications. K-means clustering of protein abundance across locations revealed substantial differences in host protein production between CR mice relative to GF and monocolonized mice. Finally, comparison with fecal proteomic data sets suggested that the identities of stool proteins are not biased to any region of the GI tract, but are substantially impacted by the microbiota in the distal colon.

A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data.

Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.