Cis-regulatory variation is typically polyallelic in Drosophila

Abstract

Gene expression levels vary heritably, with approximately 25-35% of the loci affecting expression acting in cis. We characterized standing cis-regulatory variation among 16 wild-derived strains of Drosophila melanogaster. Our experiment's robust biological and technical replication enabled precise estimates of variation in allelic expression on a high-throughput SNP genotyping platform. We observed concordant, significant differential allelic expression (DAE) in 7/10 genes queried with multiple SNPs, and every member of a set of eight additional, one-assay genes suggest significant DAE. Four of the high-confidence, multiple-assay genes harbor three or more statistically distinguishable allelic classes, often at intermediate frequency. Numerous intermediate-frequency, detectable regulatory polymorphisms cast doubt on a model in which cis-acting variation is a product of deleterious mutations of large effect. Comparing our data to predictions of population genetics theory using coalescent simulations, we estimate that a typical gene harbors 7-15 cis-regulatory sites (nucleotides) at which a selectively neutral mutation would elicit an observable expression phenotype. If standing cis-regulatory variation is actually slightly deleterious, the true mutational target size is larger.

Figure 1.—

A high-throughput structure for measuring AE (a–e, clockwise). (a) SNPs are identified by comparing three wild-type sequences with the SS and BWST reference sequences. A subset of SNPs—those for which SS or BWST or both is the rare allele—is selected for assay design. In our “two-reference” cross, each reference strain is crossed to all tester strains, most of which have unknown genotypes. RNA is collected from offspring. (b) AE SNP assays (here, multiplex oligo ligation assays) are performed. The two axes represent log-transformed expression of the two SNP alleles. (c) AE data are first used to ascertain the genotypes of all tester strains so that the genotype of each datum can be inferred. Note that when SS and BWST are of a different genotype, every strain is heterozygous in one of the two crosses, and >50% of experimental individuals are informative. In contrast, any panel of outbred individuals will have a much lower rate of informative samples in a multi-gene, multi-SNP survey (cf. 27.7% of assayed samples that were heterozygous over 193 SNPs surveyed in 63 unrelated human subjects; Pastinen et al. 2004). (d) AE for each datum is calculated as its perpendicular distance from the first principal axis drawn through the heterozygous data, effectively rotating the data clockwise from b. Uninformative samples (gray: homozygotes, controls, and calibration samples) are culled so that heterozygotes (black) can be used to compare testers' AE. (e) Using linear mixed-effects models, the expression of each tester allele is estimated relative to the other strains of the same SNP genotype (left and right plots; unlabeled positions indicate uninformative strains). Separate P-values for the effect of the tester on AE in the two groups are calculated. Error bars indicate 95% confidence intervals.

Figure 2.—

A permutation-based heuristic assigns testers to allelic classes according to estimates of AE combined across SNP assays. Combined estimates of AE for the 10 genes with at least two working SNP assays are shown, with strains ordered consistently across plots (labeled below CG11129 and CG11674). For each gene, bar plots are color coded according to the allelic classes identified by the heuristic. Error lines indicate 95% confidence intervals for relative AE. The table summarizes the data for all genes with at least one working assay: Assays, assays attempted (assays showing significant DAE); Combined P, the P-value for a significant difference among testers in the linear model fitted to all relevant assays AE estimates; n, number of observations per tester, as a harmonic mean; k, number of allelic classes identified by the permutation-testing heuristic; Membership, number of testers assigned to each allelic class.

Figure 3.—

The effect of recombination on the number of haplotype classes in a 16-chromosome sample. Dashed lines indicate the mean number of unique haplotypes (conservatively assuming each class is phenotypically distinguishable) calculated from 1000 simulations of i = 2, 3, 4 … 26 mutable sites; corresponding solid lines indicate splines fit to these means. The rectangle indicates the 80% confidence interval calculated from bootstrapping the number of haplotype classes observed in our original 10 genes.