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Key Points DNA sequence variation that influences gene expression is an important source of variation in higher-order organismal traits. Variants that influence transcription factor binding often affect multiple levels of chromatin organization and gene expression. Genetic variation in protein levels is studied much less frequently than that in mRNA levels. Based on current knowledge, most loci that influence a given mRNA also seem to affect the abundance of the corresponding protein. Model organisms show that the interplay between regulatory variation and higher-order traits can be highly complex. Rapidly growing data sets and detailed functional follow-up studies show that regulatory variation contributes to disease risk in humans, sometimes in surprisingly complex ways. Genetic variation in gene expression among individuals is an important contributor to differences in organismal phenotypes. This Review describes key concepts of such variation and provides an update on recent research on the effects of regulatory variants on the transcriptome, proteome and complex traits, including human disease risk. We are in a phase of unprecedented progress in identifying genetic loci that cause variation in traits ranging from growth and fitness in simple organisms to disease in humans. However, a mechanistic understanding of how these loci influence traits is lacking for the majority of loci. Studies of the genetics of gene expression have emerged as a key tool for linking DNA sequence variation to phenotypes. Here, we review recent insights into the molecular nature of regulatory variants and describe their influence on the transcriptome and the proteome. We discuss conceptual advances from studies in model organisms and present examples of complete chains of causality that link individual polymorphisms to changes in gene expression, which in turn result in physiological changes and, ultimately, disease risk.

DNA variants that alter gene expression contribute to variation in many phenotypic traits. In particular, trans-acting variants, which are often located on different chromosomes from the genes they affect, are an important source of heritable gene expression variation. However, our knowledge about the identity and mechanism of causal trans-acting variants remains limited. Here, we developed a fine-mapping strategy called CRISPR-Swap and dissected three expression quantitative trait locus (eQTL) hotspots known to alter the expression of numerous genes in trans in the yeast Saccharomyces cerevisiae. Causal variants were identified by engineering recombinant alleles and quantifying the effects of these alleles on the expression of a green fluorescent protein-tagged gene affected by the given locus in trans. We validated the effect of each variant on the expression of multiple genes by RNA-sequencing. The three variants differed in their molecular mechanism, the type of genes they reside in, and their distribution in natural populations. While a missense leucine-to-serine variant at position 63 in the transcription factor Oaf1 (L63S) was almost exclusively present in the reference laboratory strain, the two other variants were frequent among S. cerevisiae isolates. A causal missense variant in the glucose receptor Rgt2 (V539I) occurred at a poorly conserved amino acid residue and its effect was strongly dependent on the concentration of glucose in the culture medium. A noncoding variant in the conserved fatty acid regulated (FAR) element of the OLE1 promoter influenced the expression of the fatty acid desaturase Ole1 in cis and, by modulating the level of this essential enzyme, other genes in trans. The OAF1 and OLE1 variants showed a non-additive genetic interaction, and affected cellular lipid metabolism. These results demonstrate that the molecular basis of trans-regulatory variation is diverse, highlighting the challenges in predicting which natural genetic variants affect gene expression.

Protein degradation is an essential biological process that regulates protein abundance and removes misfolded and damaged proteins from cells. In eukaryotes, most protein degradation occurs through the stepwise actions of two functionally distinct entities, the ubiquitin system and the proteasome. Ubiquitin system enzymes attach ubiquitin to cellular proteins, targeting them for degradation. The proteasome then selectively binds and degrades ubiquitinated substrate proteins. Genetic variation in ubiquitin system genes creates heritable differences in the degradation of their substrates. However, the challenges of measuring the degradative activity of the proteasome independently of the ubiquitin system in large samples have limited our understanding of genetic influences on the proteasome. Here, using the yeast Saccharomyces cerevisiae , we built and characterized reporters that provide high-throughput, ubiquitin system-independent measurements of proteasome activity. Using single-cell measurements of proteasome activity from millions of genetically diverse yeast cells, we mapped 15 loci across the genome that influence proteasomal protein degradation. Twelve of these 15 loci exerted specific effects on the degradation of two distinct proteasome substrates, revealing a high degree of substrate-specificity in the genetics of proteasome activity. Using CRISPR-Cas9-based allelic engineering, we resolved a locus to a causal variant in the promoter of RPT6 , a gene that encodes a subunit of the proteasome’s 19S regulatory particle. The variant increases RPT6 expression, which we show results in increased proteasome activity. Our results reveal the complex genetic architecture of proteasome activity and suggest that genetic influences on the proteasome may be an important source of variation in the many cellular and organismal traits shaped by protein degradation.

Changes in gene expression are thought to underlie many of the phenotypic differences between species. However, large-scale analyses of gene expression evolution were until recently prevented by technological limitations. Here we report the sequencing of polyadenylated RNA from six organs across ten species that represent all major mammalian lineages (placentals, marsupials and monotremes) and birds (the evolutionary outgroup), with the goal of understanding the dynamics of mammalian transcriptome evolution. We show that the rate of gene expression evolution varies among organs, lineages and chromosomes, owing to differences in selective pressures: transcriptome change was slow in nervous tissues and rapid in testes, slower in rodents than in apes and monotremes, and rapid for the X chromosome right after its formation. Although gene expression evolution in mammals was strongly shaped by purifying selection, we identify numerous potentially selectively driven expression switches, which occurred at different rates across lineages and tissues and which probably contributed to the specific organ biology of various mammals. Gene expression and species difference Genome analyses can uncover protein-coding changes that potentially underlie the differences between species, but many of the phenotypic differences between species are the result of regulatory mutations affecting gene expression. Brawand et al . use high-throughput RNA sequencing to study the evolutionary dynamics of mammalian transcriptomes in six major tissues (cortex, cerebellum, heart, kidney, liver and testis) from ten species from all major mammalian lineages. Among the findings is the extent of transcriptome variation between organs and species, as well as the identification of potentially selectively driven expression switches that may have shaped specific organ biology.

Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL–QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL–QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies. This study uses a large number of crosses between a common lab strain and vineyard-isolated strain of yeast, and estimates the phenotypic variance for various quantitative traits. Using this data set, the authors show additive quantitative trait loci (QTL) and QTL–QTL interactions to be on average 43% and 9%, respectively.

The establishment of the gut microbiome in early life is critical for healthy infant development. Although human milk is recommended as sole nutrition for the infant, little is known about how variation in the milk microbiome shapes the microbial communities in the infant gut. Here, we quantified the similarity between the maternal milk and the infant gut microbiomes using 507 metagenomic samples collected from 195 mother-infant pairs at one, three, and six months postpartum. Microbial taxonomic overlap between milk and the infant gut was driven by Bifidobacterium longum , and infant microbiomes dominated by B. longum showed greater temporal stability than those dominated by other species. We identified numerous instances of strain sharing between milk and the infant gut, involving both commensal (e.g. B. longum ) and pathobiont species (e.g. K. pneumoniae ). Shared strains also included typically oral species such as S. salivarius and V. parvula , suggesting possible transmission from the infant’s oral cavity to the mother’s milk. At one month, the infant gut microbiome was enriched in biosynthetic pathways, suggesting that early colonisers might be more metabolically independent than those present at six months. Lastly, we observed significant overlap in antimicrobial resistance gene carriage within mother-infant pairs. Together, our results suggest that the human milk microbiome has an important role in the assembly, composition, and stability of the infant gut microbiome. Here, with metagenomic analyses on longitudinal samples collected from 195 mother-infant pairs, the authors show that the breast milk microbiome contributes to infant gut assembly through bacterial strain sharing and antimicrobial resistance gene overlap during the first six months of life.

The analysis of introgression of genomic regions between divergent populations provides an excellent opportunity to determine the genetic basis of reproductive isolation during the early stages of speciation. However, hybridization and subsequent gene flow must be relatively common in order to localize individual loci that resist introgression. In this study, we used next-generation sequencing to study genome-wide patterns of genetic differentiation between two hybridizing subspecies of rabbits (Oryctolagus cuniculus algirus and O. c. cuniculus) that are known to undergo high rates of gene exchange. Our primary objective was to identify specific genes or genomic regions that have resisted introgression and are likely to confer reproductive barriers in natural conditions. On the basis of 326,000 polymorphisms, we found low to moderate overall levels of differentiation between subspecies, and fewer than 200 genomic regions dispersed throughout the genome showing high differentiation consistent with a signature of reduced gene flow. Most differentiated regions were smaller than 200 Kb and contained very few genes. Remarkably, 30 regions were each found to contain a single gene, facilitating the identification of candidate genes underlying reproductive isolation. This gene-level resolution yielded several insights into the genetic basis and architecture of reproductive isolation in rabbits. Regions of high differentiation were enriched on the X-chromosome and near centromeres. Genes lying within differentiated regions were often associated with transcription and epigenetic activities, including chromatin organization, regulation of transcription, and DNA binding. Overall, our results from a naturally hybridizing system share important commonalities with hybrid incompatibility genes identified using laboratory crosses in mice and flies, highlighting general mechanisms underlying the maintenance of reproductive barriers.

A new method for identifying genetic loci that influence protein expression in budding yeast reveals considerable complexity in how genetic variation shapes the proteome. Mapping gene variance in yeast Many DNA variants influence phenotypes by altering the expression level of one or several genes, hence the current interest in the mapping of these expression quantitative trait loci (eQTL). This paper presents a new method for eQTL mapping, designed to overcome the limitations of the existing approaches that focus either on RNA or protein abundance. The new approach relies on GFP (green florescent protein) tags to measure single-cell protein abundance in the yeast Saccharomyces cerevisiae . Pooled sequencing is then used to compare allele frequencies across the genome in thousands of individuals with high versus low protein abundance. The authors report close correspondence between loci that influence mRNA and protein abundance for a given gene and identify hotspot locations that influence multiple proteins; the latter have profound effects on the gene regulatory network. Variation among individuals arises in part from differences in DNA sequences, but the genetic basis for variation in most traits, including common diseases, remains only partly understood. Many DNA variants influence phenotypes by altering the expression level of one or several genes. The effects of such variants can be detected as expression quantitative trait loci (eQTL) 1 . Traditional eQTL mapping requires large-scale genotype and gene expression data for each individual in the study sample, which limits sample sizes to hundreds of individuals in both humans and model organisms and reduces statistical power 2 , 3 , 4 , 5 , 6 . Consequently, many eQTL are probably missed, especially those with smaller effects 7 . Furthermore, most studies use messenger RNA rather than protein abundance as the measure of gene expression. Studies that have used mass-spectrometry proteomics 8 , 9 , 10 , 11 , 12 , 13 reported unexpected differences between eQTL and protein QTL (pQTL) for the same genes 9 , 10 , but these studies have been even more limited in scope. Here we introduce a powerful method for identifying genetic loci that influence protein expression in the yeast Saccharomyces cerevisiae . We measure single-cell protein abundance through the use of green fluorescent protein tags in very large populations of genetically variable cells, and use pooled sequencing to compare allele frequencies across the genome in thousands of individuals with high versus low protein abundance. We applied this method to 160 genes and detected many more loci per gene than previous studies. We also observed closer correspondence between loci that influence protein abundance and loci that influence mRNA abundance of a given gene. Most loci that we detected were clustered in ‘hotspots’ that influence multiple proteins, and some hotspots were found to influence more than half of the proteins that we examined. The variants that underlie these hotspots have profound effects on the gene regulatory network and provide insights into genetic variation in cell physiology between yeast strains.

Domestication has led to similar changes in morphology and behavior in several animal species, raising the question whether similarities between different domestication events also exist at the molecular level. We used mRNA sequencing to analyze genome-wide gene expression patterns in brain frontal cortex in three pairs of domesticated and wild species (dogs and wolves, pigs and wild boars, and domesticated and wild rabbits). We compared the expression differences with those between domesticated guinea pigs and a distant wild relative (Cavia aperea) as well as between two lines of rats selected for tameness or aggression towards humans. There were few gene expression differences between domesticated and wild dogs, pigs, and rabbits (30-75 genes (less than 1%) of expressed genes were differentially expressed), while guinea pigs and C. aperea differed more strongly. Almost no overlap was found between the genes with differential expression in the different domestication events. In addition, joint analyses of all domesticated and wild samples provided only suggestive evidence for the existence of a small group of genes that changed their expression in a similar fashion in different domesticated species. The most extreme of these shared expression changes include up-regulation in domesticates of SOX6 and PROM1, two modulators of brain development. There was almost no overlap between gene expression in domesticated animals and the tame and aggressive rats. However, two of the genes with the strongest expression differences between the rats (DLL3 and DHDH) were located in a genomic region associated with tameness and aggression, suggesting a role in influencing tameness. In summary, the majority of brain gene expression changes in domesticated animals are specific to the given domestication event, suggesting that the causative variants of behavioral domestication traits may likewise be different.

Human cytomegalovirus (CMV) is a highly prevalent herpesvirus that is often transmitted to the neonate via breast milk. Postnatal CMV transmission can have negative health consequences for preterm and immunocompromised infants, but any effects on healthy term infants are thought to be benign. Furthermore, the impact of CMV on the composition of the hundreds of bioactive factors in human milk has not been tested. Here, we utilize a cohort of exclusively breastfeeding full-term mother-infant pairs to test for differences in the milk transcriptome and metabolome associated with CMV, and the impact of CMV in breast milk on the infant gut microbiome and infant growth. We find upregulation of the indoleamine 2,3-dioxygenase (IDO) tryptophan-to-kynurenine metabolic pathway in CMV+ milk samples, and that CMV+ milk is associated with decreased Bifidobacterium in the infant gut. Our data indicate two opposing CMV-associated effects on infant growth; with kynurenine positively correlated, and CMV viral load negatively correlated, with infant weight-for-length at 1 month of age. These results suggest CMV transmission, CMV-related changes in milk composition, or both may be modulators of full-term infant development. Cytomegalovirus (CMV) is often transmitted to infants through breast milk. Here, in a cohort of exclusively breastfeeding full-term mother-infant pairs, the authors identify changes in milk composition, infant growth, and the infant gut microbiome associated with the presence of CMV in milk.