Antagonistic pleiotropy (AP) is a genetic trade-off between different fitness components. In annual plants, a trade-off between days to flower (DTF) and reproductive capacity often determines how many individuals survive to flower in a short growing season, and also influences the seed set of survivors. We develop a model of viability and fecundity selection informed by many experiments on the yellow monkeyflower, Mimulus guttatus, but applicable to many annual species. A viability/fecundity trade-off maintains stable polymorphism under surprisingly general conditions. We also introduce both spatial heterogeneity and temporal stochasticity in environmental parameters. Neither is necessary for polymorphism, but spatial heterogeneity allows polymorphism while also generating the often observed non-negative correlations in fitness components.

The estimation of outcrossing rates in hermaphroditic species has been a major focus in the evolutionary study of reproductive strategies, and is also essential for plant breeding and conservation. Surprisingly, genomics has thus far minimally influenced outcrossing rate studies. In this article, we generalize a Bayesian inference method (BORICE) to accommodate genomic data from multiple subpopulations of a species. As an empirical demonstration, BORICE is applied to 115 maternal families of Mimulus guttatus. The analysis shows that low‐level whole genome sequencing of parents and offspring is sufficient for individualized mating system estimation: 208 offspring (88.5%) were definitively called as outcrossed, 23 (9.8%) as selfed. After mating system parameters are established (each offspring as outcrossed or selfed and the inbreeding level of maternal plants), BORICE outputs posterior genotype probabilities for each SNP genomewide. Individual SNP calls are often burdened with considerable uncertainty and distilling information from closely linked sites (within genomic windows) can be a useful strategy. For the Mimulus data, principal components based on window statistics were sufficient to diagnose inversion polymorphisms and estimate their effects on spatial structure, phenotypic and fitness measures. More generally, mating system estimation with BORICE can set the stage for population and quantitative genomic analyses, particularly researchers collect phenotypic or fitness data from maternal individuals.

Most flowering plants are hermaphroditic and experience strong pressures to evolve self‐pollination (automatic selection and reproductive assurance). Inbreeding depression (ID) can oppose selection for selfing, but it remains unclear if ID is typically strong enough to maintain outcrossing. To measure the full cost of sustained inbreeding on fitness, and its genomic basis, we planted highly homozygous, fully genome‐sequenced inbred lines of yellow monkeyflower (Mimulus guttatus) in the field next to outbred plants from crosses between the same lines. The cost of full homozygosity is severe: 65% for survival and 86% for lifetime seed production. Accounting for the unmeasured effect of lethal and sterile mutations, we estimate that the average fitness of fully inbred genotypes is only 3–4% that of outbred competitors. The genome sequence data provide no indication of simple overdominance, but the number of rare alleles carried by a line, especially within rare allele clusters nonrandomly distributed across the genome, is a significant negative predictor of fitness measurements. These findings are consistent with a deleterious allele model for ID. High variance in rare allele load among lines and the genomic distribution of rare alleles both suggest that migration might be an important source of deleterious alleles to local populations.

We measured the floral bud transcriptome of 151 fully sequenced lines of Mimulus guttatus from one natural population. Thousands of single nucleotide polymorphisms (SNPs) are implicated as transcription regulators, but there is a striking difference in the Allele Frequency Spectrum (AFS) of cis-acting and trans-acting mutations. Cis-SNPs have intermediate frequencies (consistent with balancing selection) while trans-SNPs exhibit a rare-alleles model (consistent with purifying selection). This pattern only becomes clear when transcript variation is normalized on a gene-to-gene basis. If a global normalization is applied, as is typically in RNAseq experiments, asymmetric transcript distributions combined with “rarity disequilibrium” produce a super-abundance of false positives for trans-acting SNPs. To explore the cause of purifying selection on trans-acting mutations, we identified gene expression modules as sets of co-expressed genes. The extent to which trans-acting mutations influence modules is a strong predictor of allele frequency. Mutations altering expression of genes with high “connectedness” (those that are highly predictive of the representative module expression value) have the lowest allele frequency. The expression modules can also predict whole-plant traits such as flower size. We find that a substantial portion of the genetic (co)variance among traits can be described as an emergent property of genetic effects on expression modules.