Ritsert C. Jansen

Systematic identification of trans eQTLs as putative drivers of known disease associations

Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3′ UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.

Identification of the SAAT Gene Involved in Strawberry Flavor Biogenesis by Use of DNA Microarrays

Fruit flavor is a result of a complex mixture of numerous compounds. The formation of these compounds is closely correlated with the metabolic changes occurring during fruit maturation. Here, we describe the use of DNA microarrays and appropriate statistical analyses to dissect a complex developmental process. In doing so, we have identified a novel strawberry alcohol acyltransferase (SAAT) gene that plays a crucial role in flavor biogenesis in ripening fruit. Volatile esters are quantitatively and qualitatively the most important compounds providing fruity odors. Biochemical evidence for involvement of the SAAT gene in formation of fruity esters is provided by characterizing the recombinant protein expressed in Escherichia coli. The SAAT enzyme showed maximum activity with aliphatic medium-chain alcohols, whose corresponding esters are major components of strawberry volatiles. The enzyme was capable of utilizing short- and medium-chain, branched, and aromatic acyl-CoA molecules as cosubstrates. The results suggest that the formation of volatile esters in fruit is subject to the availability of acyl-CoA molecules and alcohol substrates and is dictated by the temporal expression pattern of the SAAT gene(s) and substrate specificity of the SAAT enzyme(s).

The genetics of plant metabolism

Variation for metabolite composition and content is often observed in plants. However, it is poorly understood to what extent this variation has a genetic basis. Here, we describe the genetic analysis of natural variation in the metabolite composition in Arabidopsis thaliana. Instead of focusing on specific metabolites, we have applied empirical untargeted metabolomics using liquid chromatography–time of flight mass spectrometry (LC-QTOF MS). This uncovered many qualitative and quantitative differences in metabolite accumulation between A. thaliana accessions. Only 13.4% of the mass peaks were detected in all 14 accessions analyzed. Quantitative trait locus (QTL) analysis of more than 2,000 mass peaks, detected in a recombinant inbred line (RIL) population derived from the two most divergent accessions, enabled the identification of QTLs for about 75% of the mass signals. More than one-third of the signals were not detected in either parent, indicating the large potential for modification of metabolic composition through classical breeding.

Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'.

We combined large-scale mRNA expression analysis and gene mapping to identify genes and loci that control hematopoietic stem cell (HSC) function. We measured mRNA expression levels in purified HSCs isolated from a panel of densely genotyped recombinant inbred mouse strains. We mapped quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts. By comparing the physical transcript position with the location of the controlling QTL, we identified polymorphic cis-acting stem cell genes. We also identified multiple trans-acting control loci that modify expression of large numbers of genes. These groups of coregulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of candidate genes involved with HSC turnover. We compared expression QTLs in HSCs and brain from the same mice and identified both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of coregulated transcripts.

The nature and identification of quantitative trait loci: a community’s view

This white paper by eighty members of the Complex Trait Consortium presents a communitys view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci

Accessions of a plant species can show considerable genetic differences that are analyzed effectively by using recombinant inbred line (RIL) populations. Here we describe the results of genome-wide expression variation analysis in an RIL population of Arabidopsis thaliana. For many genes, variation in expression could be explained by expression quantitative trait loci (eQTLs). The nature and consequences of this variation are discussed based on additional genetic parameters, such as heritability and transgression and by examining the genomic position of eQTLs versus gene position, polymorphism frequency, and gene ontology. Furthermore, we developed an approach for genetic regulatory network construction by combining eQTL mapping and regulator candidate gene selection. The power of our method was shown in a case study of genes associated with flowering time, a well studied regulatory network in Arabidopsis. Results that revealed clusters of coregulated genes and their most likely regulators were in agreement with published data, and unknown relationships could be predicted.

Trans-eQTLs Reveal That Independent Genetic Variants Associated with a Complex Phenotype Converge on Intermediate Genes, with a Major Role for the HLA

For many complex traits, genetic variants have been found associated. However, it is still mostly unclear through which downstream mechanism these variants cause these phenotypes. Knowledge of these intermediate steps is crucial to understand pathogenesis, while also providing leads for potential pharmacological intervention. Here we relied upon natural human genetic variation to identify effects of these variants on trans-gene expression (expression quantitative trait locus mapping, eQTL) in whole peripheral blood from 1,469 unrelated individuals. We looked at 1,167 published trait- or disease-associated SNPs and observed trans-eQTL effects on 113 different genes, of which we replicated 46 in monocytes of 1,490 different individuals and 18 in a smaller dataset that comprised subcutaneous adipose, visceral adipose, liver tissue, and muscle tissue. HLA single-nucleotide polymorphisms (SNPs) were 10-fold enriched for trans-eQTLs: 48% of the trans-acting SNPs map within the HLA, including ulcerative colitis susceptibility variants that affect plausible candidate genes AOAH and TRBV18 in trans. We identified 18 pairs of unlinked SNPs associated with the same phenotype and affecting expression of the same trans-gene (21 times more than expected, P<10−16). This was particularly pronounced for mean platelet volume (MPV): Two independent SNPs significantly affect the well-known blood coagulation genes GP9 and F13A1 but also C19orf33, SAMD14, VCL, and GNG11. Several of these SNPs have a substantially higher effect on the downstream trans-genes than on the eventual phenotypes, supporting the concept that the effects of these SNPs on expression seems to be much less multifactorial. Therefore, these trans-eQTLs could well represent some of the intermediate genes that connect genetic variants with their eventual complex phenotypic outcomes.

Genotype-by-environment interaction in genetic mapping of multiple quantitative trait loci

The interval mapping method is widely used for the genetic mapping of quantitative trait loci (QTLs), though true resolution of quantitative variation into QTLs is hampered with this method. Separation of QTLs is troublesome, because single-QTL is models are fitted. Further, genotype-by-environment interaction, which is of great importance in many quantitative traits, can only be approached by separately analyzing the data collected in multiple environments. Here, we demonstrate for the first time a novel analytic approach (MQM mapping) that accommodates both the mapping of multiple QTLs and genotype-by-environment interaction. MQM mapping is compared to interval mapping in the mapping of QTLs for flowering time in Arabidopsis thaliana under various photoperiod and vernalization conditions.

System-wide molecular evidence for phenotypic buffering in Arabidopsis

We profiled 162 lines of Arabidopsis for variation in transcript, protein and metabolite abundance using mRNA microarrays, two-dimensional polyacrylamide gel electrophoresis, gas chromatography time-of-flight mass spectrometry, liquid chromatography quadrupole time-of-flight mass spectrometry, and proton nuclear magnetic resonance. We added all publicly available phenotypic data from the same lines and mapped quantitative trait loci (QTL) for 40,580 molecular and 139 phenotypic traits. We found six QTL hot spots with major, system-wide effects, suggesting there are six breakpoints in a system otherwise buffered against many of the 500,000 SNPs.

Mapping the Epigenetic Basis of Complex Traits

Quantifying the impact of heritable epigenetic variation on complex traits is an emerging challenge in population genetics. Here, we analyze a population of isogenic Arabidopsis lines that segregate experimentally induced DNA methylation changes at hundreds of regions across the genome. We demonstrate that several of these differentially methylated regions (DMRs) act as bona fide epigenetic quantitative trait loci (QTLepi), accounting for 60 to 90% of the heritability for two complex traits, flowering time and primary root length. These QTLepi are reproducible and can be subjected to artificial selection. Many of the experimentally induced DMRs are also variable in natural populations of this species and may thus provide an epigenetic basis for Darwinian evolution independently of DNA sequence changes. Genetic mapping reveals epigenetic changes associated with flowering time and root length. [Also see Perspective by Schmitz] Plant Epigenetics Quantitative trait loci (QTLs) are genetic regions associated with phenotypic traits that help to determine the underlying genetics controlling the magnitude of a specific trait. Cortijo et al. (p. 1145, published online 6 February; see the Perspective by Schmitz) identified epigenetic QTLs associated with differences in methylation marks (epiQTLs) controlling flowering time and root length in the model plant Arabidopsis. These epiQTLs were mapped in genetically identical lines that differ only in their methylation marks. A small number of QTLs were able to explain up to 90% of the heritable variation in these traits. Thus, in plants, the heritability of some complex traits can be determined by epigenetic variation.