Targeted Recombinant Progeny: a design for ultra-high resolution mapping of Quantitative Trait Loci in crosses between inbred or pure lines

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Developmental Pleiotropy Shaped the Roots of the Domesticated Common Bean (Phaseolus vulgaris)

Roots have been omitted from previous domestication analyses owing mostly to their subterranean nature. We hypothesized that domestication-associated changes in common bean (Phaseolus vulgaris) roots were due to direct selection for some aboveground traits that also affect roots, and to indirect selection of root traits that improved aboveground plant performance. To test this hypothesis, we compared the root traits of wild and domesticated accessions and performed a multistep quantitative trait locus (QTL) analysis of an intra-Andean recombinant inbred family derived from a landrace and a wild accession. Multivariate analysis of root traits distinguished wild from domesticated accessions and showed that seed weight affects many root traits of young seedlings. Sequential and methodical scanning of the genome confirmed the significant effect of seed weight on root traits and identified QTLs that control seed weight, root architecture, shoot and root traits, and shoot traits alone. The root domestication syndrome in the common bean was associated with genes that were directly selected to increase seed weight but had a significant effect on early root growth through a developmental pleiotropic effect. The syndrome was also associated with genes that control root system architecture and that were apparently the product of indirect selection.