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Plant growth, development, and nutritional quality depends upon amino acid homeostasis, especially in seeds. However, our understanding of the underlying genetics influencing amino acid content and composition remains limited, with only a few candidate genes and quantitative trait loci identified to date. Improved knowledge of the genetics and biological processes that determine amino acid levels will enable researchers to use this information for plant breeding and biological discovery. Toward this goal, we used genomic prediction to identify biological processes that are associated with, and therefore potentially influence, free amino acid (FAA) composition in seeds of the model plant Arabidopsis thaliana Markers were split into categories based on metabolic pathway annotations and fit using a genomic partitioning model to evaluate the influence of each pathway on heritability explained, model fit, and predictive ability. Selected pathways included processes known to influence FAA composition, albeit to an unknown degree, and spanned four categories: amino acid, core, specialized, and protein metabolism. Using this approach, we identified associations for pathways containing known variants for FAA traits, in addition to finding new trait-pathway associations. Markers related to amino acid metabolism, which are directly involved in FAA regulation, improved predictive ability for branched chain amino acids and histidine. The use of genomic partitioning also revealed patterns across biochemical families, in which serine-derived FAAs were associated with protein related annotations and aromatic FAAs were associated with specialized metabolic pathways. Taken together, these findings provide evidence that genomic partitioning is a viable strategy to uncover the relative contributions of biological processes to FAA traits in seeds, offering a promising framework to guide hypothesis testing and narrow the search space for candidate genes.

A major goal in the analysis of complex traits is to partition the observed genetic variation in a trait into components due to individual loci and perhaps variants within those loci. However, in both QTL mapping and genetic association studies, the estimated percent variation attributable to a QTL is upwardly biased conditional on it being discovered. This bias was first described in two-way QTL mapping experiments by William Beavis, and has been referred to extensively as "the Beavis effect." The Beavis effect is likely to occur in multiparent population (MPP) panels as well as collections of sequenced lines used for genome-wide association studies (GWAS). However, the strength of the Beavis effect is unknown-and often implicitly assumed to be negligible-when "hits" are obtained from an association panel consisting of hundreds of inbred lines tested across millions of SNPs, or in multiparent mapping populations where mapping involves fitting a complex statistical model with several d.f. at thousands of genetic intervals. To estimate the size of the effect in more complex panels, we performed simulations of both biallelic and multiallelic QTL in two major Drosophila melanogaster mapping panels, the GWAS-based Drosophila Genetic Reference Panel (DGRP), and the MPP the Drosophila Synthetic Population Resource (DSPR). Our results show that overestimation is determined most strongly by sample size and is only minimally impacted by the mapping design. When < 100, 200, 500, and 1000 lines are employed, the variance attributable to hits is inflated by factors of 6, 3, 1.5, and 1.1, respectively, for a QTL that truly contributes 5% to the variation in the trait. This overestimation indicates that QTL could be difficult to validate in follow-up replication experiments where additional individuals are examined. Further, QTL could be difficult to cross-validate between the two Drosophila resources. We provide guidelines for: (1) the sample sizes necessary to accurately est

A major goal in the analysis of complex traits is to partition the observed genetic variation in a trait into components due to individual loci and perhaps variants within those loci. However, in both QTL mapping and genetic association studies, the estimated percent variation attributable to a QTL is upwardly biased conditional on it being discovered. This bias was first described in two-way QTL mapping experiments by William Beavis, and has been referred to extensively as "the Beavis effect." The Beavis effect is likely to occur in multiparent population (MPP) panels as well as collections of sequenced lines used for genome-wide association studies (GWAS). However, the strength of the Beavis effect is unknown-and often implicitly assumed to be negligible-when "hits" are obtained from an association panel consisting of hundreds of inbred lines tested across millions of SNPs, or in multiparent mapping populations where mapping involves fitting a complex statistical model with several d.f. at thousands of genetic intervals. To estimate the size of the effect in more complex panels, we performed simulations of both biallelic and multiallelic QTL in two major Drosophila melanogaster mapping panels, the GWAS-based Drosophila Genetic Reference Panel (DGRP), and the MPP the Drosophila Synthetic Population Resource (DSPR). Our results show that overestimation is determined most strongly by sample size and is only minimally impacted by the mapping design. When < 100, 200, 500, and 1000 lines are employed, the variance attributable to hits is inflated by factors of 6, 3, 1.5, and 1.1, respectively, for a QTL that truly contributes 5% to the variation in the trait. This overestimation indicates that QTL could be difficult to validate in follow-up replication experiments where additional individuals are examined. Further, QTL could be difficult to cross-validate between the two Drosophila resources. We provide guidelines for: (1) the sample sizes necessary to accurately est

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.

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.

The severity of the toxic side effects of chemotherapy shows a great deal of interindividual variability, and much of this variation is likely genetically based. Simple DNA tests predictive of toxic side effects could revolutionize the way chemotherapy is carried out. Due to the challenges in identifying polymorphisms that affect toxicity in humans, we use Drosophila fecundity following oral exposure to carboplatin, gemcitabine and mitomycin C as a model system to identify naturally occurring DNA variants predictive of toxicity. We use the Drosophila Synthetic Population Resource (DSPR), a panel of recombinant inbred lines derived from a multiparent advanced intercross, to map quantitative trait loci affecting chemotoxicity. We identify two QTL each for carboplatin and gemcitabine toxicity and none for mitomycin. One QTL is associated with fly orthologs of a priori human carboplatin candidate genes ABCC2 and MSH2, and a second QTL is associated with fly orthologs of human gemcitabine candidate genes RRM2 and RRM2B. The third, a carboplatin QTL, is associated with a posteriori human orthologs from solute carrier family 7A, INPP4A&B, and NALCN. The fourth, a gemcitabine QTL that also affects methotrexate toxicity, is associated with human ortholog GPx4. Mapped QTL each explain a significant fraction of variation in toxicity, yet individual SNPs and transposable elements in the candidate gene regions fail to singly explain QTL peaks. Furthermore, estimates of founder haplotype effects are consistent with genes harboring several segregating functional alleles. We find little evidence for nonsynonymous SNPs explaining mapped QTL; thus it seems likely that standing variation in toxicity is due to regulatory alleles.

Natural populations exhibit a great deal of interindividual genetic variation in the response to toxins, exemplified by the variable clinical efficacy of pharmaceutical drugs in humans, and the evolution of pesticide resistant insects. Such variation can result from several phenomena, including variable metabolic detoxification of the xenobiotic, and differential sensitivity of the molecular target of the toxin. Our goal is to genetically dissect variation in the response to xenobiotics, and characterize naturally-segregating polymorphisms that modulate toxicity. Here, we use the Drosophila Synthetic Population Resource (DSPR), a multiparent advanced intercross panel of recombinant inbred lines, to identify QTL (Quantitative Trait Loci) underlying xenobiotic resistance, and employ caffeine as a model toxic compound. Phenotyping over 1,700 genotypes led to the identification of ten QTL, each explaining 4.5-14.4% of the broad-sense heritability for caffeine resistance. Four QTL harbor members of the cytochrome P450 family of detoxification enzymes, which represent strong a priori candidate genes. The case is especially strong for Cyp12d1, with multiple lines of evidence indicating the gene causally impacts caffeine resistance. Cyp12d1 is implicated by QTL mapped in both panels of DSPR RILs, is significantly upregulated in the presence of caffeine, and RNAi knockdown robustly decreases caffeine tolerance. Furthermore, copy number variation at Cyp12d1 is strongly associated with phenotype in the DSPR, with a trend in the same direction observed in the DGRP (Drosophila Genetic Reference Panel). No additional plausible causative polymorphisms were observed in a full genomewide association study in the DGRP, or in analyses restricted to QTL regions mapped in the DSPR. Just as in human populations, replicating modest-effect, naturally-segregating causative variants in an association study framework in flies will likely require very large sample sizes.

Natural populations exhibit a great deal of interindividual genetic variation in the response to toxins, exemplified by the variable clinical efficacy of pharmaceutical drugs in humans, and the evolution of pesticide resistant insects. Such variation can result from several phenomena, including variable metabolic detoxification of the xenobiotic, and differential sensitivity of the molecular target of the toxin. Our goal is to genetically dissect variation in the response to xenobiotics, and characterize naturally-segregating polymorphisms that modulate toxicity. Here, we use the Drosophila Synthetic Population Resource (DSPR), a multiparent advanced intercross panel of recombinant inbred lines, to identify QTL (Quantitative Trait Loci) underlying xenobiotic resistance, and employ caffeine as a model toxic compound. Phenotyping over 1,700 genotypes led to the identification of ten QTL, each explaining 4.5-14.4% of the broad-sense heritability for caffeine resistance. Four QTL harbor members of the cytochrome P450 family of detoxification enzymes, which represent strong a priori candidate genes. The case is especially strong for Cyp12d1, with multiple lines of evidence indicating the gene causally impacts caffeine resistance. Cyp12d1 is implicated by QTL mapped in both panels of DSPR RILs, is significantly upregulated in the presence of caffeine, and RNAi knockdown robustly decreases caffeine tolerance. Furthermore, copy number variation at Cyp12d1 is strongly associated with phenotype in the DSPR, with a trend in the same direction observed in the DGRP (Drosophila Genetic Reference Panel). No additional plausible causative polymorphisms were observed in a full genomewide association study in the DGRP, or in analyses restricted to QTL regions mapped in the DSPR. Just as in human populations, replicating modest-effect, naturally-segregating causative variants in an association study framework in flies will likely require very large sample sizes.

Animals in nature are frequently challenged by toxic compounds, from those that occur naturally in plants as a defense against herbivory, to pesticides used to protect crops. On exposure to such xenobiotic substances, animals mount a transcriptional response, generating detoxification enzymes and transporters that metabolize and remove the toxin. Genetic variation in this response can lead to variation in the susceptibility of different genotypes to the toxic effects of a given xenobiotic. Here we use Drosophila melanogaster to dissect the genetic basis of larval resistance to nicotine, a common plant defense chemical and widely used addictive drug in humans. We identified quantitative trait loci (QTL) for the trait using the DSPR (Drosophila Synthetic Population Resource), a panel of multiparental advanced intercross lines. Mapped QTL collectively explain 68.4% of the broad-sense heritability for nicotine resistance. The two largest-effect loci-contributing 50.3 and 8.5% to the genetic variation-map to short regions encompassing members of classic detoxification gene families. The largest QTL resides over a cluster of ten UDP-glucuronosyltransferase (UGT) genes, while the next largest QTL harbors a pair of cytochrome P450 genes. Using RNAseq we measured gene expression in a pair of DSPR founders predicted to harbor different alleles at both QTL and showed that Ugt86Dd, Cyp28d1, and Cyp28d2 had significantly higher expression in the founder carrying the allele conferring greater resistance. These genes are very strong candidates to harbor causative, regulatory polymorphisms that explain a large fraction of the genetic variation in larval nicotine resistance in the DSPR.

For most complex traits we have a poor understanding of the positions, phenotypic effects, and population frequencies of the underlying genetic variants contributing to their variation. Recently, several groups have developed multi-parent advanced intercross mapping panels in different model organisms in an attempt to improve our ability to characterize causative genetic variants. These panels are powerful and are particularly well suited to the dissection of phenotypic variation generated by rare alleles and loci segregating multiple functional alleles. We describe studies using one such panel, the Drosophila Synthetic Population Resource (DSPR), and the implications for our understanding of the genetic basis of complex traits. In particular, we note that many loci of large effect appear to be multiallelic. If multiallelism is a general rule, analytical approaches designed to identify multiallelic variants should be a priority for both genome-wide association studies (GWASs) and multi-parental panels.

In dryland ecosystems, understanding the effects of heterogeneity in soil moisture and geophysical properties on vegetation structure and dynamics poses a suite of challenging research questions. Heterogeneity in soil depth can affect resource availability and the subsequent organization of woody vegetation, while spatiotemporal variation in soil moisture can reveal important ecohydrological feedbacks that govern the outcome of anthropogenic activities on the organization of dryland vegetation. In this research we investigate two cases of soil resource heterogeneity that affect the organization of dryland vegetation patterns by expanding previous electromagnetic induction (EMI) imaging techniques. In the first case we examine the influence of soil depth as a control on soil resource availability on hillslopes in tree-grass systems in central Kenya. Our results indicate that woody vegetation clumping occurs where soil depth changes, and the deeper rooted Acacia tortilis occurs on deep soils while the drought tolerant Acacia etbaica occurs on shallow soils. In the second case we examine daily patch–interpatch scale moisture dynamics following two different-sized rain events in a degraded landscape. With the aid of a numerical subsurface flow model, EMI, and soil moisture data, we have identified a possible positive feedback mechanism (‘soil moisture halo effect’) that we believe may have contributed to the proliferation and two-phase pattern formation of a native succulent Sansevieria volkensii in degraded ecosystems of Kenya. By determining how different plants respond to, and modify, the soil environment, we can better understand resource capture and dynamics, which in the longterm will help to develop management strategies.

In dryland ecosystems, understanding the effects of heterogeneity in soil moisture and geophysical properties on vegetation structure and dynamics poses a suite of challenging research questions. Heterogeneity in soil depth can affect resource availability and the subsequent organization of woody vegetation, while spatiotemporal variation in soil moisture can reveal important ecohydrological feedbacks that govern the outcome of anthropogenic activities on the organization of dryland vegetation. In this research we investigate two cases of soil resource heterogeneity that affect the organization of dryland vegetation patterns by expanding previous electromagnetic induction (EMI) imaging techniques. In the first case we examine the influence of soil depth as a control on soil resource availability on hillslopes in tree-grass systems in central Kenya. Our results indicate that woody vegetation clumping occurs where soil depth changes, and the deeper rooted Acacia tortilis occurs on deep soils while the drought tolerant Acacia etbaica occurs on shallow soils. In the second case we examine daily patch–interpatch scale moisture dynamics following two different-sized rain events in a degraded landscape. With the aid of a numerical subsurface flow model, EMI, and soil moisture data, we have identified a possible positive feedback mechanism (‘soil moisture halo effect’) that we believe may have contributed to the proliferation and two-phase pattern formation of a native succulent Sansevieria volkensii in degraded ecosystems of Kenya. By determining how different plants respond to, and modify, the soil environment, we can better understand resource capture and dynamics, which in the longterm will help to develop management strategies.