The role of regulatory variation in complex traits and disease

Journal name:
Nature Reviews Genetics
Volume:
16,
Pages:
197–212
Year published:
DOI:
doi:10.1038/nrg3891
Published online

Abstract

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.

At a glance

Figures

  1. Designs for genetic mapping of variation in gene expression and other molecular traits.
    Figure 1: Designs for genetic mapping of variation in gene expression and other molecular traits.

    Molecular variation is mapped in genetically variable populations. Aa | These populations can be generated through designed crosses in model organisms. For example, the genetic backgrounds of a set of yeast strains are reshuffled by mating followed by meiosis, resulting in a set of recombinant offspring. Ab | Alternatively, outbred populations can be used that carry genetic variation which was spread and recombined by historical genetic processes (illustrated by the genetic history of a hypothetical region of the genome). This is the most popular design for expression quantitative trait locus (eQTL) mapping in humans. Pedigrees or families of related individuals can also be used (not shown). B | The molecular quantity of interest is measured in each individual in the study panel. The figure illustrates the results for two individuals that differ in the expression of a certain gene. To map the loci involved (marked by the star), this molecular variation is compared to genetic variation among the individuals (Box 1). Many of the steps along the gene expression cascade can be studied in this way, including DNA methylation (Me), histone modifications, transcription factor (TF) binding, active transcription, mRNA levels (resulting in eQTLs), translation and protein levels (resulting in protein QTLs (pQTLs)). In the example, the altered protein level due to the genetic polymorphism influences disease risk, an organismal trait. C | eQTLs can be classified according to their location (local or distant to the gene they influence) and according to their mode of action (cis or trans). MRCA, most recent common ancestor.

  2. Key insights into the causal relationship between eQTLs and organismal traits provided by recent studies in yeast.
    Figure 2: Key insights into the causal relationship between eQTLs and organismal traits provided by recent studies in yeast.

    A | Fitness consequences of experimentally altered expression of the LCB2 gene are shown135. Note that even low levels of upregulation or downregulation can have drastically different consequences on fitness, depending on the baseline expression level and the environment. B | Genetic effects on gene expression caused by four well-characterized single-nucleotide polymorphisms (SNPs) that had been originally identified through their effects on yeast sporulation efficiency137. The genes differ from each other in the degree to which their expression is influenced by the four SNPs and in the relative importance of epistatic effects compared with additive effects. For comparison, the effects of the four SNPs on sporulation efficiency are shown in the bar furthest to the right. The impact of these SNPs on the organismal trait (sporulation) cannot be simply explained by their effect on the expression of any one gene. C | Gagneur et al.136 studied the possible flows of causality from DNA variation between two yeast strains (Y and S) to gene expression and yeast growth rate (part Ca). The correspondence of expression quantitative trait locus (eQTL) hot spots (black peaks) and growth QTLs (coloured bars) varies substantially among different environments (part Cb). Part A reproduced from Rest, J. S. et al. Nonlinear fitness consequences of variation in expression level of a eukaryotic gene. Mol. Biol. Evol. (2013) 30(2), 448456, by permission of Oxford University Press. Parts B and C from Ref. 137 and Ref. 136, respectively.

  3. An example of a full chain of causality in humans.
    Figure 3: An example of a full chain of causality in humans.

    a | The minor allele of a non-coding single-nucleotide polymorphism (SNP) in the 3′ untranslated region (3′UTR) of the CELSR2 (cadherin, EGF LAG seven-pass G-type receptor 2) gene creates a transcription factor binding site for CCAAT/enhancer-binding protein (C/EBP), to which the major allele does not bind100. Binding of C/EBP at this site leads to increased expression of the sortilin 1 (SORT1) gene in liver cells. b | In mice, overexpression of Sort1 in the liver reduces low-density lipoprotein cholesterol (LDL-C) levels (calculated using fractions 10–26). c | Small interfering RNA (siRNA)-mediated knockdown of Sort1 increases LDL-C levels in mice. As LDL-C, in turn, is a known risk factor for myocardial infarction, this work provides a complete causal path from a non-coding variant to altered risk for a major human disease. SORT1 is separated from the causal SNP by two additional genes, and the causal effect on LDL-C is not mediated through CELSR2, although the causal SNP is in the 3′UTR of this gene. Chr1, chromosome 1; MYBPHL, myosin-binding protein H-like; PSMA5, proteasome (prosome, macropain) subunit, alpha type, 5; PSRC1, proline/serine-rich coiled-coil 1; SARS, seryl-tRNA synthetase; SYPL2, synaptophysin-like 2. Figure adapted from Ref. 100, Nature Publishing Group.

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Affiliations

  1. Departments of Human Genetics and Biological Chemistry, University of California, Los Angeles, California 90095, USA.

    • Frank W. Albert &
    • Leonid Kruglyak
  2. Gonda Center 5309, 695 Charles E. Young Drive South, Los Angeles, California 90095, USA.

    • Frank W. Albert &
    • Leonid Kruglyak
  3. Howard Hughes Medical Institute, University of California, Los Angeles, California 90095, USA.

    • Leonid Kruglyak

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  • Frank W. Albert

    Frank W. Albert is a postdoctoral researcher at the University of California, Los Angeles, USA. He received his Ph.D. in biology from the University of Leipzig, Germany, after conducting graduate research on the genetics of behaviour and animal domestication at the Max Planck Institute for Evolutionary Anthropology in Leipzig. His current research is focused on the genetic basis and evolution of variation in gene expression and complex traits.

  • Leonid Kruglyak

    Leonid Kruglyak holds appointments in the Departments of Human Genetics and Biological Chemistry at the David Geffen School of Medicine at the University of California, Los Angeles (UCLA), USA. He is also a founding member of the UCLA Computational Biosciences Institute and serves as an Investigator of the Howard Hughes Medical Institute. He received his Ph.D. degree in physics from the University of California, Berkeley. Along with contributions to human genomic medicine, he pioneered genetic studies of gene expression variation in yeast. His research interests focus on understanding the genetic basis of complex phenotypes. Leonid Kruglyak's homepage.

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