New Strategies and Tools in Quantitative Genetics: How to Go from the Phenotype to the Genotype

Abstract

Quantitative genetics has a long history in plants: It has been used to study specific biological processes, identify the factors important for trait evolution, and breed new crop varieties. These classical approaches to quantitative trait locus mapping have naturally improved with technology. In this review, we show how quantitative genetics has evolved recently in plants and how new developments in phenotyping, population generation, sequencing, gene manipulation, and statistics are rejuvenating both the classical linkage mapping approaches (for example, through nested association mapping) as well as the more recently developed genome-wide association studies. These strategies are complementary in most instances, and indeed, one is often used to confirm the results of the other. Despite significant advances, an emerging trend is that the outcome and efficiency of the different approaches depend greatly on the genetic architecture of the trait in the genetic material under study.

A phylogenetically informed search for an alternative Macrostomum model species, with notes on taxonomy, mating behavior, karyology, and genome size

“…In spite of its general appeal as a flatworm model, the further establishment of M. lignano as a broadly employed genetic and genomic model has recently run into a roadblock. Such models should ideally have small and stable genomes, facilitating the establishment of (a) highly contiguous genome assemblies, (b) high‐resolution genetic maps for forward genetics using quantitative trait loci (QTL) and genome‐wide association studies (GWAS; Bazakos, Hanemian, Trontin, Jiménez‐Gómez, & Loudet, ), and (c) efficient genome editing for reverse genetics (e.g., CRISPR/Cas9; Brooks & Gaj, ). In this light, the recent discovery that M. lignano has an unusual genome organization is problematic.…”

GWAS for Fusarium Head Blight Traits in a Soft Red Winter Wheat Mapping Panel

“…Thus, GWAS can be used to identify the genetic architecture of the trait, providing information on the number and the contribution of each locus in the phenotypic response (Korte and Farlow, 2013). Two limitations of GWAS are the difficulty in detecting rare alleles and accounting for population structure (Korte and Farlow, 2013; Huang and Han, 2014; Bazakos et al, 2017). However, GWAS is still a promising method to identify QTL that have an effect across large and diverse population (Jannink, 2007; Ogbonnaya et al, 2017).…”

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

“…This recognition, together with the development of chlorophyll fluorescence technologies that allow large numbers of plants to be evaluated within a short timeframe, has led to an increasing number of forward genetic studies aimed at detecting the genetic loci that contribute towards improving photosynthetic efficiency of crop plants. The introduction of GWAS has accelerated the speed with which candidate genes can be identified in photosynthesis‐related traits when compared with bi‐parental mapping population studies, but combining these population types to elucidate functional genetic variation and traits is likely to be the most rewarding approach (Bazakos et al ., ).…”

“…Most make only a small contribution to the total phenotypic variation. These findings illustrate the classic dichotomy between GWAS and bi‐parental mapping studies; the former are more accurate at pinpointing causal variants and the latter are more powerful at detecting phenotypic differences caused by such variants (Bazakos et al ., ). Nevertheless, QTLs in GWAS often identify SNPs that locate in or near genes that have a predicted role in photosynthesis‐related traits following Gene Ontology enrichment analyses, which sets the precedent for a closer investigation of those genetic loci (Dhanapal et al ., ; Herritt et al ., ; van Rooijen et al ., ; Wang et al ., ).…”