Admixture mapping in interspecific Populus hybrids identifies classes of genomic architectures for phytochemical, morphological and growth traits

The genomic architecture of functionally important traits is key to understanding the maintenance of reproductive barriers and trait differences when divergent populations or species hybridize. We conducted a genome-wide association study (GWAS) to study trait architecture in natural hybrids of two ecologically divergent Populus species. We genotyped 472 seedlings from a natural hybrid zone of Populus alba and Populus tremula for genome-wide markers from reduced representation sequencing, phenotyped the plants in common gardens for 46 phytochemical (phenylpropanoid), morphological and growth traits, and used a Bayesian polygenic model for mapping. We detected three classes of genomic architectures: traits with finite, detectable associations of genetic loci with phenotypic variation in addition to highly polygenic heritability; traits with indications for polygenic heritability only; and traits with no detectable heritability. For the first class, we identified genome regions with plausible candidate genes for phenylpropanoid biosynthesis or its regulation, including MYB transcription factors and glycosyl transferases. GWAS in natural, recombinant hybrids represent a promising step towards resolving the genomic architecture of phenotypic traits in long-lived species. This facilitates the fine-mapping and subsequent functional characterization of genes and networks causing differences in hybrid performance and fitness.

Genomic architecture of pharmacological efficacy and adverse events

The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene-gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.

Poly-omic prediction of complex traits: OmicKriging

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).