Genetic dissection of complex, polygenic trait variation is a key goal of medical and evolutionary genetics. Attempts to identify genetic variants underlying complex traits have been plagued by low mapping resolution in traditional linkage studies, and an inability to identify variants that cumulatively explain the bulk of standing genetic variation in genome-wide association studies (GWAS). Thus, much of the heritability remains unexplained for most complex traits. Here we describe a novel, freely available resource for the Drosophila community consisting of two sets of recombinant inbred lines (RILs), each derived from an advanced generation cross between a different set of eight highly inbred, completely resequenced founders. The Drosophila Synthetic Population Resource (DSPR) has been designed to combine the high mapping resolution offered by multiple generations of recombination, with the high statistical power afforded by a linkage-based design. Here, we detail the properties of the mapping panel of >1600 genotyped RILs, and provide an empirical demonstration of the utility of the approach by genetically dissecting alcohol dehydrogenase (ADH) enzyme activity. We confirm that a large fraction of the variation in this classic quantitative trait is due to allelic variation at the Adh locus, and additionally identify several previously unknown modest-effect trans-acting QTL (quantitative trait loci). Using a unique property of multiparental linkage mapping designs, for each QTL we highlight a relatively small set of candidate causative variants for follow-up work. The DSPR represents an important step toward the ultimate goal of a complete understanding of the genetics of complex traits in the Drosophila model system.

The Drosophila Synthetic Population Resource (DSPR) is a newly developed multifounder advanced intercross panel consisting of .1600 recombinant inbred lines (RILs) designed for the genetic dissection of complex traits. Here, we describe the inference of the underlying mosaic founder structure for the full set of RILs from a dense set of semicodominant restriction-siteassociated DNA (RAD) markers and use simulations to explore how variation in marker density and sequencing coverage affects inference. For a given sequencing effort, marker density is more important than sequence coverage per marker in terms of the amount of genetic information we can infer. We also assessed the power of the DSPR by assigning genotypes at a hidden QTL to each RIL on the basis of the inferred founder state and simulating phenotypes for different experimental designs, different genetic architectures, different sample sizes, and QTL of varying effect sizes. We found the DSPR has both high power (e.g., 84% power to detect a 5% QTL) and high mapping resolution (e.g., $1.5 cM for a 5% QTL).T HE ultimate goal of modern genetics is to determine how molecular genetic variation is translated into organismal phenotypes. The vast majority of continuously varying phenotypes are influenced by many genetic variants that often interact with one another and with environmental factors (Falconer and Mackay 1996;Roff 1997;Lynch and Walsh 1998). This underlying complexity has made identifying causative genetic variants for most traits a steep challenge for which the scientific community has only had limited, albeit increasing, success (Mackay 2001;Chanock et al. 2007; Wellcome Trust Case Control Consortium 2007;Mccarthy et al. 2008;Stranger et al. 2011). As a result, there is a large discrepancy between the known heritability of most traits and the fraction of that heritability that can be explained by known causative genetic variants (Manolio et al. 2009;Stranger et al. 2011). This discrepancy has spurred the development of new mapping panels designed to address the shortcomings of existing genome-wide association studies and QTL mapping panels derived from only two parents.The Drosophila Synthetic Population Resource (DSPR) is one such panel (King et al. 2012) similar in concept to other available linkage-based resources: the mouse Collaborative Cross (Churchill et al. 2004;Aylor et al. 2011;Philip et al. 2011), the Arabidopsis multiparent recombinant inbred line population (AMPRIL) (Huang et al. 2011), the Arabidopsis multiparent advanced generation intercross lines (MAGIC) (Kover et al. 2009), and the maize nested associated mapping population (NAM) (Yu et al. 2008;Buckler et al. 2009;Mcmullen et al. 2009;Li et al. 2011). The DSPR is a linkage-based panel that uses a synthetic population approach (Macdonald and Long 2007). To create the DSPR, two separate synthetic populations were created each from a 50-generation intercross of 8 inbred founder lines with one founder line shared between the two populations. From these two synthetic popula...

Modern genetic mapping is plagued by the “missing heritability” problem, which refers to the discordance between the estimated heritabilities of quantitative traits and the variance accounted for by mapped causative variants. One major potential explanation for the missing heritability is allelic heterogeneity, in which there are multiple causative variants at each causative gene with only a fraction having been identified. The majority of genome-wide association studies (GWAS) implicitly assume that a single SNP can explain all the variance for a causative locus. However, if allelic heterogeneity is prevalent, a substantial amount of genetic variance will remain unexplained. In this paper, we take a haplotype-based mapping approach and quantify the number of alleles segregating at each locus using a large set of 7922 eQTL contributing to regulatory variation in the Drosophila melanogaster female head. Not only does this study provide a comprehensive eQTL map for a major community genetic resource, the Drosophila Synthetic Population Resource, but it also provides a direct test of the allelic heterogeneity hypothesis. We find that 95% of cis-eQTLs and 78% of trans-eQTLs are due to multiple alleles, demonstrating that allelic heterogeneity is widespread in Drosophila eQTL. Allelic heterogeneity likely contributes significantly to the missing heritability problem common in GWAS studies.

Abstract. Drylands cover about 40 % of the terrestrial land surface and account for approximately 40 % of global net primary productivity. Water is fundamental to the biophysical processes that sustain ecosystem function and food production, particularly in drylands where a tight coupling exists between ecosystem productivity, surface energy balance, biogeochemical cycles, and water resource availability. Currently, drylands support at least 2 billion people and comprise both natural and managed ecosystems. In this synthesis, we identify some current critical issues in the understanding of dryland systems and discuss how arid and semiarid environments are responding to the changes in climate and land use. The issues range from societal aspects such as rapid population growth, the resulting food and water security, and development issues, to natural aspects such as ecohydrological consequences of bush encroachment and the causes of desertification. To improve current understanding and inform upon the needed research efforts to address these critical issues, we identify some recent technical advances in terms of monitoring dryland water dynamics, water budget and vegetation water use, with a focus on the use of stable isotopes and remote sensing. These technological advances provide new tools that assist in addressing critical issues in dryland ecohydrology under climate change.

The partitioning of surface vapor flux (F ET ) into evaporation (F E ) and transpiration (F T ) is theoretically possible because of distinct differences in end-member stable isotope composition. In this study, we combine high-frequency laser spectroscopy with eddy covariance techniques to critically evaluate isotope flux partitioning of F ET over a grass field during a 15 day experiment. Following the application of a 30 mm water pulse, green grass coverage at the study site increased from 0 to 10% of ground surface area after 6 days and then began to senesce. Using isotope flux partitioning, transpiration increased as a fraction of total vapor flux from 0% to 40% during the green-up phase, after which this ratio decreased while exhibiting hysteresis with respect to green grass coverage. Daily daytime leaf-level gas exchange measurements compare well with daily isotope flux partitioning averages (RMSE 5 0.0018 g m 22 s 21 ). Overall the average ratio of F T to F ET was 29%, where uncertainties in Keeling plot intercepts and transpiration composition resulted in an average of uncertainty of $5% in our isotopic partitioning of F ET . Flux-variance similarity partitioning was partially consistent with the isotope-based approach, with divergence occurring after rainfall and when the grass was stressed. Over the average diurnal cycle, local meteorological conditions, particularly net radiation and relative humidity, are shown to control partitioning. At longer time scales, green leaf area and available soil water control F T /F ET . Finally, we demonstrate the feasibility of combining isotope flux partitioning and flux-variance similarity theory to estimate water use efficiency at the landscape scale.