Dissecting Genetic Networks Underlying Complex Phenotypes: The Theoretical Framework

Abstract

Great progress has been made in genetic dissection of quantitative trait variation during the past two decades, but many studies still reveal only a small fraction of quantitative trait loci (QTLs), and epistasis remains elusive. We integrate contemporary knowledge of signal transduction pathways with principles of quantitative and population genetics to characterize genetic networks underlying complex traits, using a model founded upon one-way functional dependency of downstream genes on upstream regulators (the principle of hierarchy) and mutual functional dependency among related genes (functional genetic units, FGU). Both simulated and real data suggest that complementary epistasis contributes greatly to quantitative trait variation, and obscures the phenotypic effects of many ‘downstream’ loci in pathways. The mathematical relationships between the main effects and epistatic effects of genes acting at different levels of signaling pathways were established using the quantitative and population genetic parameters. Both loss of function and “co-adapted” gene complexes formed by multiple alleles with differentiated functions (effects) are predicted to be frequent types of allelic diversity at loci that contribute to the genetic variation of complex traits in populations. Downstream FGUs appear to be more vulnerable to loss of function than their upstream regulators, but this vulnerability is apparently compensated by different FGUs of similar functions. Other predictions from the model may account for puzzling results regarding responses to selection, genotype by environment interaction, and the genetic basis of heterosis.

Genome-wide response to selection and genetic basis of cold tolerance in rice (Oryza sativaL.)

“…Unfortunately, genome-wide characterization of non-random associations between or among unlinked loci due to strong directional selection has rarely been carried out in experimental populations. Statistically, strong epistasis is implicated by strong non-random associations between or among alleles at unlinked loci in individuals of extreme phenotypes [26,30]. Indeed, six epistatic QTL pairs conferring CT (bin 3.5-bin 6.3, bin 6.3-bin 9.10, bin 4.12-bin 4.17, bin 4.17-bin 10.10, bin 6.13-bin 9.10, and bin 7.2-bin 11.10) that were previously reported in different rice mapping populations [12,13,16] were identified either as AGs or associated FGUs within the same branches of the CT genetic networks in this study (Figure 2; Additional file 1: Table S4).…”

“…The criterion used for delineating these groups was that individual FGUs of different IFs within each group were all significantly and positively associated, with $\stackrel{true^}{\mathrm{italicD}}{{\mathit{s}}_{\mathit{AB}}}^{\text{'}}$= 1.0, and were either independent or negatively associated with FGUs in different groups. Second, all FGUs within a given group were connected, forming multiple layers based on the principle of hierarchy according to their progressively reduced functional genotype frequencies and inclusive relationships [26]. In addition, to confirm the detected loci, we used the two-sample test for proportions in SAS to compare the IF at each identified CT locus between two segregating groups of BC 2 F 6 ILs with contrasting CT phenotypes, either high SP or low SP, all derived from the same selected BC 2 F 2 plants [27].…”

Mapping quantitative trait loci in selected breeding populations: A segregation distortion approach

“…The small populations after selection are too small to be utilized for QTL mapping using a conventional marker-trait association model. Nevertheless, efforts have recently been made to map QTL in single selected populations by detecting segregation distortion loci using simple Chisquare tests (Li et al, 2005;Venuprasad et al, 2009;Zhang et al, 2011Zhang et al, , 2014. Obviously, the simple Chi-square tests are not the optimal methods because they cannot take advantage of the unique feature of large number of small advanced breeding progenies to perform a joint analysis in most plant and animal breeding programs.…”

Adaptabilitas Galur-galur Green Super Rice pada Kondisi Sawah Tadah Hujan saat Musim Kemarau di Kabupaten Pati

“…Keunggulan untuk beragam karakter tersebut telah dihimpun dari berbagai donor dengan beragam latar belakang genetik dan telah dikombinasikan (pyramiding) dalam galur-galur GSR (Susanto et al 2013). Melalui studi komprehensif interaksi berbagai gen dalam pemunculan suatu sifat (genetic network) (Zhang et al 2011). Green Super Rice diharapkan mampu memberikan hasil yang tinggi pada kondisi input yang relatif rendah.…”