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.2008 Dec 16;105(50):19910-4.
doi: 10.1073/pnas.0810388105. Epub 2008 Dec 9.

Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis

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Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis

Haifeng Shao et al. Proc Natl Acad Sci U S A..

Abstract

The genetic architecture of complex traits underlying physiology and disease in most organisms remains elusive. We still know little about the number of genes that underlie these traits, the magnitude of their effects, or the extent to which they interact. Chromosome substitution strains (CSSs) enable statistically powerful studies based on testing engineered inbred strains that have single, unique, and nonoverlapping genetic differences, thereby providing measures of phenotypic effects that are attributable to individual chromosomes. Here, we report a study of phenotypic effects and gene interactions for 90 blood, bone, and metabolic traits in a mouse CSS panel and 54 traits in a rat CSS panel. Two key observations emerge about the genetic architecture of these traits. First, the traits tend to be highly polygenic: across the genome, many individual chromosome substitutions each had significant phenotypic effects and, within each of the chromosomes studied, multiple distinct loci were found. Second, strong epistasis was found among the individual chromosomes. Specifically, individual chromosome substitutions often conferred surprisingly large effects (often a substantial fraction of the entire phenotypic difference between the parental strains), with the result that the sum of these individual effects often dramatically exceeded the difference between the parental strains. We suggest that strong, pervasive epistasis may reflect the presence of several phenotypically-buffered physiological states. These results have implications for identification of complex trait genes, developmental and physiological studies of phenotypic variation, and opportunities to engineer phenotypic outcomes in complex biological systems.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Frequency distribution of phenotypic effects in C57BL/6J-ChrA/J/NaJ CSSs. For the 41 traits that differed significantly between the parental strains, phenotypic effects for CSSs that differed significantly from C57BL/6J are indicated in red, and those that did not differ significantly are indicated in blue. Phenotypes are normalized, so that C57BL/6J = 0% and A/J = 100%.
Fig. 2.
Fig. 2.
Cumulative combined, significant, and nonsignificant phenotypic effects in the survey of mouse CSSs. For each of the 41 traits that differed significantly between the parental strains, we calculated the cumulative phenotypic effect (sum across the CSSs) and the corresponding SEM. (A) Absolute value of the cumulative phenotypic effect for each trait, given in rank order. For additive interactions, the cumulative phenotypic effect should equal ≈100% (dashed horizontal line). Traits were termed “epistatic” (indicated in red) if the cumulative phenotypic effect exceeded 100% by more than the SEM. Of the 41 traits, 40 were epistatic (median cumulative effect, 803%; range, 164% to 1,397%.). (B andC) The analysis was repeated by using only those CSSs whose phenotypic difference from the host strain that achieved statistical significance (B), or fell short of statistical significance (C).
Fig. 3.
Fig. 3.
QTLs for final body weight in congenic strains derived from CSS-6. Genotypes are shown for various genetic markers (m, defined inSI Materials and Methods), with the genetic map at bottom (centromere at left and telomere at right). Phenotypes are shown by ovals (mean) and whiskers (SEM), with sample size (no.) indicated. Pairwise comparisons between strains are indicated, by using 2-tailedt tests with significance levels (P) corrected for multiple hypothesis testing. The locations of A/J derived segments are indicated in gray, with the breakpoints of the congenic segments arbitrarily placed midway between flanking markers. The locations of QTLs in the key congenic strains (inferred from pairwise comparisons) are indicated in boxes and in black segments on the genetic map, with breakpoints for the most likely location for the QTLs arbitrarily placed midway between flanking markers.
Fig. 4.
Fig. 4.
QTL mapping of 2 traits in CSS intercrosses with C57BL/6J. Graphs show logarithm of odds (LOD scores) along chromosomes, with dashed horizontal line denoting threshold for significance (P = 0.05, permutation test). (A) Four QTLs for final body weight (Obrq1–4) in a CSS-6 × C57BL/6J intercross. (B) Four QTLs for final body weight (Obrq9–12) in a CSS-10 × C57BL/6J intercross. (C) Two QTLs (Ltgq1 andLtgq2) controlling liver triglyceride levels in a CSS-10 × C57BL/6J intercross. Approximate locations of QTLs are derived from surveys of the same traits in 2 panels of congenic strains (Fig. 3 andFig. S2).
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