Pheno2Geno - High-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains

Background

Genetic markers and maps are instrumental in quantitative trait locus (QTL) mapping in segregating populations. The resolution of QTL localization depends on the number of informative recombinations in the population and how well they are tagged by markers. Larger populations and denser marker maps are better for detecting and locating QTLs. Marker maps that are initially too sparse can be saturated or derived de novo from high-throughput omics data, (e.g. gene expression, protein or metabolite abundance). If these molecular phenotypes are affected by genetic variation due to a major QTL they will show a clear multimodal distribution. Using this information, phenotypes can be converted into genetic markers.

Results

The Pheno2Geno tool uses mixture modeling to select phenotypes and transform them into genetic markers suitable for construction and/or saturation of a genetic map. Pheno2Geno excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping.We demonstrate the use of Pheno2Geno on gene expression data of 370,000 probes in 148 A. thaliana recombinant inbred lines. Pheno2Geno is able to saturate the existing genetic map, decreasing the average distance between markers from 7.1 cM to 0.89 cM, close to the theoretical limit of 0.68 cM (with 148 individuals we expect a recombination every 100/148=0.68 cM); this pinpointed almost all of the informative recombinations in the population.

Conclusion

The Pheno2Geno package makes use of genome-wide molecular profiling and provides a tool for high-throughput de novo map construction and saturation of existing genetic maps. Processing of the showcase dataset takes less than 30 minutes on an average desktop PC. Pheno2Geno improves QTL mapping results at no additional laboratory cost and with minimum computational effort. Its results are formatted for direct use in R/qtl, the leading R package for QTL studies. Pheno2Geno is freely available on CRAN under “GNU GPL v3”. The Pheno2Geno package as well as the tutorial can also be found at: http://pheno2geno.nl.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0475-6) contains supplementary material, which is available to authorized users.     

Relationship of aerobic and anaerobic parameters with 400 m front crawl swimming performance

The aims of the present study were to investigate the relationship of aerobic and anaerobic parameters with 400 m performance, and establish which variable better explains long distance performance in swimming. Twenty-two swimmers (19.1±1.5 years, height 173.9±10.0 cm, body mass 71.2±10.2 kg; 76.6±5.3% of 400 m world record) underwent a lactate minimum test to determine lactate minimum speed (LMS) (i.e., aerobic capacity index). Moreover, the swimmers performed a 400 m maximal effort to determine mean speed (S400m), peak oxygen uptake (V.O2PEAK) and total anaerobic contribution (C_ANA). The C_ANA was assumed as the sum of alactic and lactic contributions. Physiological parameters of 400 m were determined using the backward extrapolation technique (V.O2PEAK and alactic contributions of C_ANA) and blood lactate concentration analysis (lactic anaerobic contributions of C_ANA). The Pearson correlation test and backward multiple regression analysis were used to verify the possible correlations between the physiological indices (predictor factors) and S400m (independent variable) (p < 0.05). Values are presented as mean ± standard deviation. Significant correlations were observed between S400m (1.4±0.1 m·s^-1) and LMS (1.3±0.1 m·s^-1; r = 0.80), V.O2PEAK (4.5±3.9 L·min^-1; r = 0.72) and C_ANA (4.7±1.5 L·O₂; r= 0.44). The best model constructed using multiple regression analysis demonstrated that LMS and V.O2PEAK explained 85% of the 400 m performance variance. When backward multiple regression analysis was performed, C_ANA lost significance. Thus, the results demonstrated that both aerobic parameters (capacity and power) can be used to predict 400 m swimming performance.     

Variation in nutrient characteristics of surface soils from the Luquillo Experimental Forest of Puerto Rico: A multivariate perspective

We assessed the effects of landscape features (vegetation type and topography), season, and spatial hierarchy on the nutrient content of surface soils in the Luquillo Experimental Forest (LEF) of Puerto Rico. Considerable spatial variation characterized the soils of the LEF, and differences between replicate sites within each combination of vegetation type (tabonuco vs. palo colorado vs. dwarf vs. pasture) and topographic position (ridge vs valley) accounted for 11–60% of the total variation in soil properties. Nevertheless, mean soil properties differed significantly among vegetation types, between topographic positions, and between seasons (wet vs dry). Differences among vegetation types reflected soil properties (e.g., bulk density, soil moisture, Na, P, C, N, S) that typically are related to biological processes and inputs of water. In forests, differences between topographic positions reflected elements (e.g., Ca, Mg, K, and Al) that typically are associated with geochemical processes; however, the nutrients and elements responsible for topographic differences in dwarf forest were different from those in other forest types. In pastures, differences between topographic positions were associated with the same soil properties responsible for differences among the other vegetation types. Pastures also had reduced N levels and different soil characteristics compared to undisturbed tabonuco forest. The only soil parameter that differed significantly between seasons was soil moisture. Soils of the LEF do not support the contention that N becomes limiting with an increase in elevation, and suggest that absolute pool sizes of N and P are not responsible for the reduction in productivity with elevation.     

Ovule,fruit and seed development in Abolboda (Xyridaceae,Poales): implications for taxonomy and phylogeny

Xyridaceae belongs to the xyrid clade of Poales, but the phylogenetic position of the xyrid families is only weakly supported. Xyridaceae is divided into two subfamilies and five genera, the relationships of which remain unclear. The development of the ovule, fruit and seed of Abolboda spp. was studied to identify characteristics of taxonomic and phylogenetic value. All of the studied species share anatropous, tenuinucellate and bitegmic ovules with a micropyle formed by the inner and outer integuments, megagametophyte development of the Polygonum type, seeds with a tanniferous hypostase, a helobial and starchy endosperm and an undifferentiated embryo, seed coat derived from both integuments with a tanniferous tegmen and a micropylar operculum, and fruits with a parenchymatous endocarp and mesocarp and a sclerenchymatous exocarp. Most of the ovule and seed characteristics described for Abolboda are also present in Xyris and may represent a pattern for the family. Abolboda is distinguished by the ovule type, endosperm formation and the number of layers in the seed coat, in agreement with its classification in Abolbodoideae. The following characteristics link Xyridaceae to Eriocaulaceae and Mayacaceae, supporting the xyrid clade: tenuinucellate, bitegmic ovules; seeds with a tanniferous hypostase, a starchy endosperm and an undifferentiated embryo; and a seed coat with a tanniferous tegmen. A micropylar operculum in the seeds of Abolboda is described for the first time here and may represent a synapomorphy for the xyrids. © 2014 The Linnean Society of London, Botanical Journal of the Linnean Society, 2014, 175 , 144–154.     

Twelve testable hypotheses on the geobiology of weathering

Critical Zone (CZ) research investigates the chemical, physical, and biological processes that modulate the Earth's surface. Here, we advance 12 hypotheses that must be tested to improve our understanding of the CZ: (1) Solar-to-chemical conversion of energy by plants regulates flows of carbon, water, and nutrients through plant-microbe soil networks, thereby controlling the location and extent of biological weathering. (2) Biological stoichiometry drives changes in mineral stoichiometry and distribution through weathering. (3) On landscapes experiencing little erosion, biology drives weathering during initial succession, whereas weathering drives biology over the long term. (4) In eroding landscapes, weathering-front advance at depth is coupled to surface denudation via biotic processes. (5) Biology shapes the topography of the Critical Zone. (6) The impact of climate forcing on denudation rates in natural systems can be predicted from models incorporating biogeochemical reaction rates and geomorphological transport laws. (7) Rising global temperatures will increase carbon losses from the Critical Zone. (8) Rising atmospheric P(CO2) will increase rates and extents of mineral weathering in soils. (9) Riverine solute fluxes will respond to changes in climate primarily due to changes in water fluxes and secondarily through changes in biologically mediated weathering. (10) Land use change will impact Critical Zone processes and exports more than climate change. (11) In many severely altered settings, restoration of hydrological processes is possible in decades or less, whereas restoration of biodiversity and biogeochemical processes requires longer timescales. (12) Biogeochemical properties impart thresholds or tipping points beyond which rapid and irreversible losses of ecosystem health, function, and services can occur.