The microbiome shapes many host traits, yet the biology of microbiomes challenges traditional evolutionary models. Here, we illustrate how integrating the microbiome into quantitative genetics can help untangle complexities of host-microbiome evolution. We describe two general ways in which the microbiome may affect host evolutionary potential: by shifting the mean host phenotype and by changing the variance in host phenotype in the population. We synthesize the literature across diverse taxa and discuss how these scenarios could shape the host response to selection. We conclude by outlining key avenues of research to improve our understanding of the complex interplay between hosts and microbiomes.

As Arabidopsis thaliana has colonized a wide range of habitats across the world it is an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we used public data from two collections of A. thaliana accessions to associate genetic variability at individual loci with differences in climates at the sampling sites. We use a novel method to screen the genome for plastic alleles that tolerate a broader climate range than the major allele. This approach reduces confounding with population structure and increases power compared to standard genome-wide association methods. Sixteen novel loci were found, including an association between Chromomethylase 2 (CMT2) and temperature seasonality where the genome-wide CHH methylation was different for the group of accessions carrying the plastic allele. Cmt2 mutants were shown to be more tolerant to heat-stress, suggesting genetic regulation of epigenetic modifications as a likely mechanism underlying natural adaptation to variable temperatures, potentially through differential allelic plasticity to temperature-stress. A central problem when studying adaptation to a new environment is the interplay between genetic variation and phenotypic plasticity. Arabidopsis thaliana has colonized a wide range of habitats across the world and it is therefore an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we study two collections of A. thaliana accessions from across Eurasia to identify loci associated with differences in climates at the sampling sites. A new genome-wide association analysis method was developed to detect adaptive loci where the alleles tolerate different climate ranges. Sixteen novel such loci were found including a strong association between Chromomethylase 2 (CMT2) and temperature seasonality. The reference allele dominated in areas with less seasonal variability in temperature, and the alternative allele existed in both stable and variable regions. Our results thus link natural variation in CMT2 and epigenetic changes to temperature adaptation. We showed experimentally that plants with a defective CMT2 gene tolerate heat-stress better than plants with a functional gene. Together this strongly suggests a role for genetic regulation of epigenetic modifications in natural adaptation to temperature and illustrates the importance of re-analyses of existing data using new analytical methods to obtain deeper insights into the underlying biology from available data.

Örjan Carlborg and colleagues analyze genotype and phenotype data from 4,390 yeast segregants from a cross between laboratory and vineyard strains to estimate how higher-order genetic interactions contribute to complex trait variation. They find networks of epistatic loci and show that accounting for these interactions leads to more accurate phenotypic predictions. Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, the notion that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multilocus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection.

Genome-wide association (GWA) analyses have generally been used to detect individual loci contributing to the phenotypic diversity in a population by the effects of these loci on the trait mean. More rarely, loci have also been detected based on variance differences between genotypes. Several hypotheses have been proposed to explain the possible genetic mechanisms leading to such variance signals. However, little is known about what causes these signals, or whether this genetic variance-heterogeneity reflects mechanisms of importance in natural populations. Previously, we identified a variance-heterogeneity GWA (vGWA) signal for leaf molybdenum concentrations in Arabidopsis thaliana. Here, fine-mapping of this association reveals that the vGWA emerges from the effects of three independent genetic polymorphisms that all are in strong LD with the markers displaying the genetic variance-heterogeneity. By revealing the genetic architecture underlying this vGWA signal, we uncovered the molecular source of a significant amount of hidden additive genetic variation or “missing heritability”. Two of the three polymorphisms underlying the genetic variance-heterogeneity are promoter variants for Molybdate transporter 1 (MOT1), and the third a variant located ~25 kb downstream of this gene. A fourth independent association was also detected ~600 kb upstream of MOT1. Use of a T-DNA knockout allele highlights Copper Transporter 6; COPT6 (AT2G26975) as a strong candidate gene for this association. Our results show that an extended LD across a complex locus including multiple functional alleles can lead to a variance-heterogeneity between genotypes in natural populations. Further, they provide novel insights into the genetic regulation of ion homeostasis in A. thaliana, and empirically confirm that variance-heterogeneity based GWA methods are a valuable tool to detect novel associations of biological importance in natural populations. Most biological traits vary in natural populations, and understanding the genetic basis of this variation remains an important challenge. Genome-wide association (GWA) studies have emerged as a powerful tool to address this challenge by dissecting the genetic architecture of trait variation into the contribution of individual genes. This contribution has traditionally been measured as the difference in the phenotypic means between groups of individuals with alternative genotypes at one, or multiple loci. However, instead of altering the trait mean, certain loci alter the variability of the trait. Here, we describe the genetic dissection of one such variance-controlling locus that drives variation in leaf molybdenum concentrations amongst natural accessions of Arabidopsis thaliana. The variance-controlling locus was found to result from the contributions of multiple alleles at multiple loci that are closely linked on the chromosome and is a major contributor to the “missing heritability” for this trait identified in previous studies. This illustrates that multi-allelic genetic architectures can hide large amounts of additive genetic variation, and that it is possible to uncover this hidden variation using the appropriate experimental designs and statistical methods described here.

Biomarkers for early detection of breast cancer may complement population screening approaches to enable earlier and more precise treatment. The blood proteome is an important source for biomarker discovery but so far, few proteins have been identified with breast cancer risk. Here, we measure 2929 unique proteins in plasma from 598 women selected from the Karolinska Mammography Project to explore the association between protein levels, clinical characteristics, and gene variants, and to identify proteins with a causal role in breast cancer. We present 812 cis-acting protein quantitative trait loci for 737 proteins which are used as instruments in Mendelian randomisation analyses of breast cancer risk. Of those, we present five proteins (CD160, DNPH1, LAYN, LRRC37A2 and TLR1) that show a potential causal role in breast cancer risk with confirmatory results in independent cohorts. Our study suggests that these proteins should be further explored as biomarkers and potential drug targets in breast cancer.