Genetic architecture of regulatory variation in Arabidopsis thaliana

Regulatory networks of coresident subgenomes during rapid fiber cell elongation in upland cotton

Cotton, an intriguing plant species shaped by polyploidization, evolution, and domestication, holds particular interest due to the complex mechanisms governing fiber traits across its two subgenomes. However, the regulatory elements or transcriptional networks between subgenomes during fiber elongation remain to be fully clarified. Here, we analyzed 1462 cotton fiber samples to reconstruct the gene-expression regulatory networks that influence fiber cell elongation. Inter-subgenome expression quantitative trait loci (eQTLs) largely dictate gene transcription, with a notable tendency for the D subgenome to regulate A-subgenome eGenes. This regulation reveals synchronized homoeologous gene expression driven by co-localized eQTLs and divergent patterns that diminish genetic correlations, thus leading to preferential expression in the A and D subgenomes. Hotspot456 emerged as a key regulator of fiber initiation and elongation, and artificial selection of trans-eQTLs in hotspot456 that positively regulate KCS1 has facilitated cell elongation. Experiments designed to clarify the roles of trans-eQTLs in improved fiber breeding confirmed the inhibition of GhTOL9 by a specific trans-eQTL via GhWRKY28, which negatively affects fiber elongation. We propose a model in which the GhWRKY28-GhTOL9 module regulates this process through the ESCRT (endosomal sorting complex required for transport) pathway. This research significantly advances our understanding of cotton's evolutionary and domestication processes and the intricate regulatory mechanisms that underlie significant plant traits.

Pan-transcriptome reveals a large accessory genome contribution to gene expression variation in yeast

Gene expression is an essential step in the translation of genotypes into phenotypes. However, little is known about the transcriptome architecture and the underlying genetic effects at the species level. Here we generated and analyzed the pan-transcriptome of ~1,000 yeast natural isolates across 4,977 core and 1,468 accessory genes. We found that the accessory genome is an underappreciated driver of transcriptome divergence. Global gene expression patterns combined with population structure showed that variation in heritable expression mainly lies within subpopulation-specific signatures, for which accessory genes are overrepresented. Genome-wide association analyses consistently highlighted that accessory genes are associated with proportionally more variants with larger effect sizes, illustrating the critical role of the accessory genome on the transcriptional landscape within and between populations.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

AtSNP_TATAdb: Candidate Molecular Markers of Plant Advantages Related to Single Nucleotide Polymorphisms within Proximal Promoters of Arabidopsis thaliana L

The mainstream of the post-genome target-assisted breeding in crop plant species includes biofortification such as high-throughput phenotyping along with genome-based selection. Therefore, in this work, we used the Web-service Plant_SNP_TATA_Z-tester, which we have previously developed, to run a uniform in silico analysis of the transcriptional alterations of 54,013 protein-coding transcripts from 32,833 Arabidopsis thaliana L. genes caused by 871,707 SNPs located in the proximal promoter region. The analysis identified 54,993 SNPs as significantly decreasing or increasing gene expression through changes in TATA-binding protein affinity to the promoters. The existence of these SNPs in highly conserved proximal promoters may be explained as intraspecific diversity kept by the stabilizing natural selection. To support this, we hand-annotated papers on some of the Arabidopsis genes possessing these SNPs or on their orthologs in other plant species and demonstrated the effects of changes in these gene expressions on plant vital traits. We integrated in silico estimates of the TBP-promoter affinity in the AtSNP_TATAdb knowledge base and showed their significant correlations with independent in vivo experimental data. These correlations appeared to be robust to variations in statistical criteria, genomic environment of TATA box regions, plants species and growing conditions.

Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing

BACKGROUND

Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively.

RESULTS

We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance.

CONCLUSIONS

Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

Sex-specific estimation of cis and trans regulation of gene expression in heads and gonads of Drosophila melanogaster

The regulatory architecture of gene expression is known to differ substantially between sexes in Drosophila, but most studies performed so far used whole-body data and only single crosses, which may have limited their scope to detect patterns that are robust across tissues and biological replicates. Here, we use allele-specific gene expression of parental and reciprocal hybrid crosses between 6 Drosophila melanogaster inbred lines to quantify cis- and trans-regulatory variation in heads and gonads of both sexes separately across 3 replicate crosses. Our results suggest that female and male heads, as well as ovaries, have a similar regulatory architecture. On the other hand, testes display more and substantially different cis-regulatory effects, suggesting that sex differences in the regulatory architecture that have been previously observed may largely derive from testis-specific effects. We also examine the difference in cis-regulatory variation of genes across different levels of sex bias in gonads and heads. Consistent with the idea that intersex correlations constrain expression and can lead to sexual antagonism, we find more cis variation in unbiased and moderately biased genes in heads. In ovaries, reduced cis variation is observed for male-biased genes, suggesting that cis variants acting on these genes in males do not lead to changes in ovary expression. Finally, we examine the dominance patterns of gene expression and find that sex- and tissue-specific patterns of inheritance as well as trans-regulatory variation are highly variable across biological crosses, although these were performed in highly controlled experimental conditions. This highlights the importance of using various genetic backgrounds to infer generalizable patterns.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Discovery of DNA polymorphisms via genome-resequencing and development of molecular markers between two barley cultivars

Genome resequencing uncovers genome-wide DNA polymorphisms that are useful for the development of high-density InDel markers between two barley cultivars. Discovering genomic variations and developing genetic markers are crucial for genetics studies and molecular breeding in cereal crops. Although InDels (insertions and deletions) have become popular because of their abundance and ease of detection, discovery of genome-wide DNA polymorphisms and development of InDel markers in barley have lagged behind other cereal crops such as rice, maize and wheat. In this study, we re-sequenced two barley cultivars, Golden Promise (GP, a classic British spring barley variety) and Hua30 (a Chinese spring barley variety), and mapped clean reads to the reference Morex genome, and identified in total 13,933,145 single nucleotide polymorphisms (SNPs) and 1,240,456 InDels for GP with Morex, 11,297,100 SNPs and 781,687 InDels for Hua30 with Morex, and 13,742,399 SNPs and 1,191,597 InDels for GP with Hua30. We further characterized distinct types, chromosomal distribution patterns, genome location, functional effect, and other features of these DNA polymorphisms. Additionally, we revealed the functional relevance of these identified SNPs/InDels regarding different flowering times between Hua30 and GP within 17 flowering time genes. Furthermore, we developed a series of InDel markers and validated them experimentally in 43 barley core accessions, respectively. Finally, we rebuilt population structure and phylogenetic tree of these 43 barley core accessions. Collectively, all of these genetic resources will facilitate not only the basic research but also applied research in barley.

Cis-regulatory sequences in plants: Their importance, discovery, and future challenges

The identification and characterization of cis-regulatory DNA sequences and how they function to coordinate responses to developmental and environmental cues is of paramount importance to plant biology. Key to these regulatory processes are cis-regulatory modules (CRMs), which include enhancers and silencers. Despite the extraordinary advances in high-quality sequence assemblies and genome annotations, the identification and understanding of CRMs, and how they regulate gene expression, lag significantly behind. This is especially true for their distinguishing characteristics and activity states. Here, we review the current knowledge on CRMs and breakthrough technologies enabling identification, characterization, and validation of CRMs; we compare the genomic distributions of CRMs with respect to their target genes between different plant species, and discuss the role of transposable elements harboring CRMs in the evolution of gene expression. This is an exciting time to study cis-regulomes in plants; however, significant existing challenges need to be overcome to fully understand and appreciate the role of CRMs in plant biology and in crop improvement.

Both Binding Strength and Evolutionary Accessibility Affect the Population Frequency of Transcription Factor Binding Sequences in Arabidopsis thaliana

Mutations in DNA sequences that bind transcription factors and thus modulate gene expression are a source of adaptive variation in gene expression. To understand how transcription factor binding sequences evolve in natural populations of the thale cress Arabidopsis thaliana, we integrated genomic polymorphism data for loci bound by transcription factors with in vitro data on binding affinity for these transcription factors. Specifically, we studied 19 different transcription factors, and the allele frequencies of 8,333 genomic loci bound in vivo by these transcription factors in 1,135 A. thaliana accessions. We find that transcription factor binding sequences show very low genetic diversity, suggesting that they are subject to purifying selection. High frequency alleles of such binding sequences tend to bind transcription factors strongly. Conversely, alleles that are absent from the population tend to bind them weakly. In addition, alleles with high frequencies also tend to be the endpoints of many accessible evolutionary paths leading to these alleles. We show that both high affinity and high evolutionary accessibility contribute to high allele frequency for at least some transcription factors. Although binding sequences with stronger affinity are more frequent, we did not find them to be associated with higher gene expression levels. Epistatic interactions among individual mutations that alter binding affinity are pervasive and can help explain variation in accessibility among binding sequences. In summary, combining in vitro binding affinity data with in vivo binding sequence data can help understand the forces that affect the evolution of transcription factor binding sequences in natural populations.

Association between AMPKα1 gene polymorphisms and gestational diabetes in the Chinese population: A case-control study

AIM

The aim is to examine the association between seven candidate single nucleotide polymorphisms in AMPKα1 and gestational diabetes in Chinese people.

METHOD

We used a matched nested case-control study design, individuals including 334 participants with gestational diabetes and 334 healthy pregnant women. Confirmed 334 gestational diabetes cases and maternal age and district of residence matched controls (1:1) were enrolled. We examined seven candidate single nucleotide polymorphisms in AMPKα1 gene and the risk of gestational diabetes. The associations were estimated in Co-dominant, Dominant, Recessive, and Alleles models. The odds ratios (ORs) and their 95% confidence intervals (95% CI) were estimated by unconditional logistical regression as a measure of the associations between genotypes and gestational diabetes adjusting for maternal age, prepregnancy body mass index (BMI), fetal sex and parity.

RESULT

At the gene level, we found that AMPKα1 was associated with gestational diabetes (p = 0.008). After adjusting the covariates and multiple comparison correction, AMPKα1 (rsc1002424, rs10053664, rs13361707) polymorphisms were associated with the risk of gestational diabetes. In addition, gestational diabetes was related to the AAGGA haplotype comprising rs1002424, rs2570091, rs10053664, rs13361707 and rs3805486 in the haplotype models (p = 0.011).

CONCLUSIONS

This study provides evidence that the AMPKα1 genotypes (rs1002424 G/A, rs10053664 A/G, rs13361707 A/G) and the haplotype (AAGGA) are relevant genetic factors in a Chinese population with gestational diabetes.

From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries

Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.

Parallel and Population-specific Gene Regulatory Evolution in Cold-Adapted Fly Populations

Changes in gene regulation at multiple levels may comprise an important share of the molecular changes underlying adaptive evolution in nature. However, few studies have assayed within- and between-population variation in gene regulatory traits at a transcriptomic scale, and therefore inferences about the characteristics of adaptive regulatory changes have been elusive. Here, we assess quantitative trait differentiation in gene expression levels and alternative splicing (intron usage) between three closely related pairs of natural populations of Drosophila melanogaster from contrasting thermal environments that reflect three separate instances of cold tolerance evolution. The cold-adapted populations were known to show population genetic evidence for parallel evolution at the SNP level, and here we find evidence for parallel expression evolution between them, with stronger parallelism at larval and adult stages than for pupae. We also implement a flexible method to estimate cis- vs trans-encoded contributions to expression or splicing differences at the adult stage. The apparent contributions of cis- vs trans-regulation to adaptive evolution vary substantially among population pairs. While two of three population pairs show a greater enrichment of cis-regulatory differences among adaptation candidates, trans-regulatory differences are more likely to be implicated in parallel expression changes between population pairs. Genes with significant cis-effects are enriched for signals of elevated genetic differentiation between cold- and warm-adapted populations, suggesting that they are potential targets of local adaptation. These findings expand our knowledge of adaptive gene regulatory evolution and our ability to make inferences about this important and widespread process.

Association of CPT1A gene polymorphism with the risk of gestational diabetes mellitus: a case-control study

PURPOSE

Gestational diabetes mellitus (GDM) is a growing public health problem worldwide and its etiology remains unclear. The pathophysiology of GDM is similar to that of type 2 diabetes (T2DM) and insulin resistance (IR) is the main reason for the development of GDM. Carnitine palmitoyltransferase 1A (CPT1A) is a candidate gene for metabolic disorders; however, the association of the CPT1A gene and GDM has not yet been studied. We aimed to explore whether single-nucleotide polymorphisms (SNPs) of the CPT1A gene could influence the risk of GDM.

METHODS

We examined 18 single-nucleotide polymorphisms (SNPs) in the CPT1A gene and the risk of GDM in a nested case-control study of 334 GDM patients and 334 controls. The controls who had no GDM were randomly selected through matching to cases by age and residence.

RESULTS

After adjusting the family history of diabetes, pre-pregnancy body mass index, and multiple comparison correction, the CPT1A rs2846194 and rs2602814 were associated with reduced GDM risk while rs59506005 was associated with elevated GDM risk. Moreover, the GGAC haplotype in the CPT1A gene (rs17399246 rs1016873 rs11228450 rs10896396) was associated with a reduced risk of GDM.

CONCLUSION

Our study provides evidence for an association between genetic polymorphisms in the CPT1A and the risk of GDM.

Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation

Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.

Combined GWAS and eQTL analysis uncovers a genetic regulatory network orchestrating the initiation of secondary cell wall development in cotton

The cotton fibre serves as a valuable experimental system to study cell wall synthesis in plants, but our understanding of the genetic regulation of this process during fibre development remains limited. We performed a genome-wide association study (GWAS) and identified 28 genetic loci associated with fibre quality in allotetraploid cotton. To investigate the regulatory roles of these loci, we sequenced fibre transcriptomes of 251 cotton accessions and identified 15 330 expression quantitative trait loci (eQTL). Analysis of local eQTL and GWAS data prioritised 13 likely causal genes for differential fibre quality in a transcriptome-wide association study (TWAS). Characterisation of distal eQTL revealed unequal genetic regulation patterns between two subgenomes, highlighted by an eQTL hotspot (Hot216) that established a genome-wide genetic network regulating the expression of 962 genes. The primary regulatory role of Hot216, and specifically the gene encoding a KIP-related protein, was found to be the transcriptional regulation of genes responsible for cell wall synthesis, which contributes to fibre length by modulating the developmental transition from rapid cell elongation to secondary cell wall synthesis. This study uncovered the genetic regulation of fibre-cell development and revealed the molecular basis of the temporal modulation of secondary cell wall synthesis during plant cell elongation.

The Evolutionary Forces Shaping Cis- and Trans-Regulation of Gene Expression within a Population of Outcrossing Plants

Understanding the persistence of genetic variation within populations has long been a goal of evolutionary biology. One promising route toward achieving this goal is using population genetic approaches to describe how selection acts on the loci associated with trait variation. Gene expression provides a model trait for addressing the challenge of the maintenance of variation because it can be measured genome-wide without information about how gene expression affects traits. Previous work has shown that loci affecting the expression of nearby genes (local or cis-eQTLs) are under negative selection, but we lack a clear understanding of the selective forces acting on variants that affect the expression of genes in trans. Here, we identify loci that affect gene expression in trans using genomic and transcriptomic data from one population of the obligately outcrossing plant, Capsella grandiflora. The allele frequencies of trans-eQTLs are consistent with stronger negative selection acting on trans-eQTLs than cis-eQTLs, and stronger negative selection acting on trans-eQTLs associated with the expression of multiple genes. However, despite this general pattern, we still observe the presence of a trans-eQTL at intermediate frequency that affects the expression of a large number of genes in the same coexpression module. Overall, our work highlights the different selective pressures shaping variation in cis- and trans-regulation.

The Role of Transcriptional Regulation in Hybrid Vigor

The genetic basis of hybrid vigor in plants remains largely unsolved but strong evidence suggests that variation in transcriptional regulation can explain many aspects of this phenomenon. Natural variation in transcriptional regulation is highly abundant in virtually all species and thus a potential source of heterotic variability. Allele Specific Expression (ASE), which is tightly linked to parent of origin effects and modulated by complex interactions in cis and in trans, is generally considered to play a key role in explaining the differences between hybrids and parental lines. Here we discuss the recent developments in elucidating the role of transcriptional variation in a number of aspects of hybrid vigor, thereby bridging old paradigms and hypotheses with contemporary research in various species.

Genome-Wide Identification of Splicing Quantitative Trait Loci (sQTLs) in Diverse Ecotypes of Arabidopsis thaliana

Alternative splicing (AS) of pre-mRNAs contributes to transcriptome diversity and enables plants to generate different protein isoforms from a single gene and/or fine-tune gene expression during different development stages and environmental changes. Although AS is pervasive, the genetic basis for differential isoform usage in plants is still emerging. In this study, we performed genome-wide analysis in 666 geographically distributed diverse ecotypes of Arabidopsis thaliana to identify genomic regions [splicing quantitative trait loci (sQTLs)] that may regulate differential AS. These ecotypes belong to different microclimatic conditions and are part of the relict and non-relict populations. Although sQTLs were spread across the genome, we observed enrichment for trans-sQTL (trans-sQTLs hotspots) on chromosome one. Furthermore, we identified several sQTL (911) that co-localized with trait-linked single nucleotide polymorphisms (SNP) identified in the Arabidopsis genome-wide association studies (AraGWAS). Many sQTLs were enriched among circadian clock, flowering, and stress-responsive genes, suggesting a role for differential isoform usage in regulating these important processes in diverse ecotypes of Arabidopsis. In conclusion, the current study provides a deep insight into SNPs affecting isoform ratios/genes and facilitates a better mechanistic understanding of trait-associated SNPs in GWAS studies. To the best of our knowledge, this is the first report of sQTL analysis in a large set of Arabidopsis ecotypes and can be used as a reference to perform sQTL analysis in the Brassicaceae family. Since whole genome and transcriptome datasets are available for these diverse ecotypes, it could serve as a powerful resource for the biological interpretation of trait-associated loci, splice isoform ratios, and their phenotypic consequences to help produce more resilient and high yield crop varieties.

GWAS with Heterogeneous Data: Estimating the Fraction of Phenotypic Variation Mediated by Gene Expression Data

Intermediate phenotypes such as gene expression values can be used to elucidate the mechanisms by which genetic variation causes phenotypic variation, but jointly analyzing such heterogeneous data are far from trivial. Here we extend a so-called mediation model to handle the confounding effects of genetic background, and use it to analyze flowering time variation in Arabidopsis thaliana, focusing in particular on the central role played by the key regulator FLOWERING TIME LOCUS C (FLC). FLC polymorphism and FLC expression are both strongly correlated with flowering time variation, but the effect of the former is only partly mediated through the latter. Furthermore, the latter also reflects genetic background effects. We demonstrate that it is possible to partition these effects, shedding light on the complex regulatory network that underlies flowering time variation.

Genetics of trans-regulatory variation in gene expression

Heritable variation in gene expression forms a crucial bridge between genomic variation and the biology of many traits. However, most expression quantitative trait loci (eQTLs) remain unidentified. We mapped eQTLs by transcriptome sequencing in 1012 yeast segregants. The resulting eQTLs accounted for over 70% of the heritability of mRNA levels, allowing comprehensive dissection of regulatory variation. Most genes had multiple eQTLs. Most expression variation arose from trans-acting eQTLs distant from their target genes. Nearly all trans-eQTLs clustered at 102 hotspot locations, some of which influenced the expression of thousands of genes. Fine-mapped hotspot regions were enriched for transcription factor genes. While most genes had a local eQTL, most of these had no detectable effects on the expression of other genes in trans. Hundreds of non-additive genetic interactions accounted for small fractions of expression variation. These results reveal the complexity of genetic influences on transcriptome variation in unprecedented depth and detail.

Co-expression network of transcription factors reveal ethylene-responsive element-binding factor as key regulator of wood phenotype in Eucalyptus tereticornis

Suitability of wood biomass for pulp production is dependent on the cellular architecture and composition of secondary cell wall. Presently, systems genetics approach is being employed to understand the molecular basis of trait variation and co-expression network analysis has enabled holistic understanding of complex trait such as secondary development. Transcription factors (TFs) are reported as key regulators of meristematic growth and wood formation. The hierarchical TF network is a multi-layered system which interacts with downstream structural genes involved in biosynthesis of cellulose, hemicelluloses and lignin. Several TFs have been associated with wood formation in tree species such as Populus, Eucalyptus, Picea and Pinus. However, TF-specific co-expression networks to understand the interaction between these regulators are not reported. In the present study, co-expression network was developed for TFs expressed during wood formation in Eucalyptus tereticornis and ethylene-responsive element-binding factor, EtERF2, was identified as the major hub transcript which co-expressed with other secondary cell wall biogenesis-specific TFs such as EtSND2, EtVND1, EtVND4, EtVND6, EtMYB70, EtGRAS and EtSCL8. This study reveals a probable role of ethylene in determining natural variation in wood properties in Eucalyptus species. Understanding this transcriptional regulation underpinning the complex bio-processing trait of wood biomass will complement the Eucalyptus breeding program through selection of industrially suitable phenotypes by marker-assisted selection.

Similarities between plant traits based on their connection to underlying gene functions

Understanding of phenotypes and their genetic basis is a major focus in current plant biology. Large amounts of phenotype data are being generated, both for macroscopic phenotypes such as size or yield, and for molecular phenotypes such as expression levels and metabolite levels. More insight in the underlying genetic and molecular mechanisms that influence phenotypes will enable a better understanding of how various phenotypes are related to each other. This will be a major step forward in understanding plant biology, with immediate value for plant breeding and academic plant research. Currently the genetic basis of most phenotypes remains however to be discovered, and the relatedness of different traits is unclear. We here present a novel approach to connect phenotypes to underlying biological processes and molecular functions. These connections define similarities between different types of phenotypes. The approach starts by using Quantitative Trait Locus (QTL) data, which are abundantly available for many phenotypes of interest. Overrepresentation analysis of gene functions based on Gene Ontology term enrichment across multiple QTL regions for a given phenotype, be it macroscopic or molecular, results in a small set of biological processes and molecular functions for each phenotype. Subsequently, similarity between different phenotypes can be defined in terms of these gene functions. Using publicly available rice data as example, a close relationship with defined molecular phenotypes is demonstrated for many macroscopic phenotypes. This includes for example a link between 'leaf senescence' and 'aspartic acid', as well as between 'days to maturity' and 'choline'. Relationships between macroscopic and molecular phenotypes may result in more efficient marker-assisted breeding and are likely to direct future research aimed at a better understanding of plant phenotypes.

Gene co-expression network connectivity is an important determinant of selective constraint

While several studies have investigated general properties of the genetic architecture of natural variation in gene expression, few of these have considered natural, outbreeding populations. In parallel, systems biology has established that a general feature of biological networks is that they are scale-free, rendering them buffered against random mutations. To date, few studies have attempted to examine the relationship between the selective processes acting to maintain natural variation of gene expression and the associated co-expression network structure. Here we utilised RNA-Sequencing to assay gene expression in winter buds undergoing bud flush in a natural population of Populus tremula, an outbreeding forest tree species. We performed expression Quantitative Trait Locus (eQTL) mapping and identified 164,290 significant eQTLs associating 6,241 unique genes (eGenes) with 147,419 unique SNPs (eSNPs). We found approximately four times as many local as distant eQTLs, with local eQTLs having significantly higher effect sizes. eQTLs were primarily located in regulatory regions of genes (UTRs or flanking regions), regardless of whether they were local or distant. We used the gene expression data to infer a co-expression network and investigated the relationship between network topology, the genetic architecture of gene expression and signatures of selection. Within the co-expression network, eGenes were underrepresented in network module cores (hubs) and overrepresented in the periphery of the network, with a negative correlation between eQTL effect size and network connectivity. We additionally found that module core genes have experienced stronger selective constraint on coding and non-coding sequence, with connectivity associated with signatures of selection. Our integrated genetics and genomics results suggest that purifying selection is the primary mechanism underlying the genetic architecture of natural variation in gene expression assayed in flushing leaf buds of P. tremula and that connectivity within the co-expression network is linked to the strength of purifying selection.

Uncovering SNP and indel variations of tetraploid cottons by SLAF-seq

Background

Cotton (Gossypium spp.), as the world’s most utilized textile fibre source, is an important, economically valuable crop worldwide. Understanding the genomic variation of tetraploid cotton species is important for exploitation of the excellent characteristics of wild cotton and for improving the diversity of cotton in breeding. However, the discovery of DNA polymorphisms in tetraploid cotton genomes has lagged behind other important crops.

Results

A total of 111,795,823 reads, 467,735 specific length amplified fragment (SLAF) tags and 139,176 high-quality DNA polymorphisms were identified using specific length amplified fragment sequencing (SLAF-seq), including 132,880 SNPs and 6,296 InDels between the reference genome (TM-1) and the five tetraploid cotton species. Intriguingly, gene ontology (GO) enrichment analysis revealed that a number of significant terms were related to reproduction in G. barbadense acc. 3–79. Based on the new data sets, we reconstructed phylogenetic trees that showed a high concordance to the phylogeny of diploid and polyploid cottons. A large amount of interspecific genetic variations were identified, and some of them were validated by the single-strand conformation polymorphism (SSCP) method, which will be applied in introgression genetics and breeding with G. hirsutum cv. Emian22 as the receptor and the other species as donors.

Conclusions

Using SLAF-seq, a large number of DNA polymorphisms were identified. The comprehensive analysis of DNA polymorphisms provided invaluable insights into the different tetraploid cotton species. More importantly, the identification of numerous interspecific genetic variations provides the basis and is very practical for future introgression breeding. The results presented herein provide a valuable genomic resource for new insights into the genetics and breeding of cotton.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3643-4) contains supplementary material, which is available to authorized users.

Regulatory Architecture of Gene Expression Variation in the Threespine Stickleback Gasterosteus aculeatus

Much adaptive evolutionary change is underlain by mutational variation in regions of the genome that regulate gene expression rather than in the coding regions of the genes themselves. An understanding of the role of gene expression variation in facilitating local adaptation will be aided by an understanding of underlying regulatory networks. Here, we characterize the genetic architecture of gene expression variation in the threespine stickleback (Gasterosteus aculeatus), an important model in the study of adaptive evolution. We collected transcriptomic and genomic data from 60 half-sib families using an expression microarray and genotyping-by-sequencing, and located expression quantitative trait loci (eQTL) underlying the variation in gene expression in liver tissue using an interval mapping approach. We identified eQTL for several thousand expression traits. Expression was influenced by polymorphism in both cis- and trans-regulatory regions. Trans-eQTL clustered into hotspots. We did not identify master transcriptional regulators in hotspot locations: rather, the presence of hotspots may be driven by complex interactions between multiple transcription factors. One observed hotspot colocated with a QTL recently found to underlie salinity tolerance in the threespine stickleback. However, most other observed hotspots did not colocate with regions of the genome known to be involved in adaptive divergence between marine and freshwater habitats.

A Comprehensive Analysis of Alternative Splicing in Paleopolyploid Maize

Identifying and characterizing alternative splicing (AS) enables our understanding of the biological role of transcript isoform diversity. This study describes the use of publicly available RNA-Seq data to identify and characterize the global diversity of AS isoforms in maize using the inbred lines B73 and Mo17, and a related species, sorghum. Identification and characterization of AS within maize tissues revealed that genes expressed in seed exhibit the largest differential AS relative to other tissues examined. Additionally, differences in AS between the two genotypes B73 and Mo17 are greatest within genes expressed in seed. We demonstrate that changes in the level of alternatively spliced transcripts (intron retention and exon skipping) do not solely reflect differences in total transcript abundance, and we present evidence that intron retention may act to fine-tune gene expression across seed development stages. Furthermore, we have identified temperature sensitive AS in maize and demonstrate that drought-induced changes in AS involve distinct sets of genes in reproductive and vegetative tissues. Examining our identified AS isoforms within B73 × Mo17 recombinant inbred lines (RILs) identified splicing QTL (sQTL). The 43.3% of cis-sQTL regulated junctions are actually identified as alternatively spliced junctions in our analysis, while 10 Mb windows on each side of 48.2% of trans-sQTLs overlap with splicing related genes. Using sorghum as an out-group enabled direct examination of loss or conservation of AS between homeologous genes representing the two subgenomes of maize. We identify several instances where AS isoforms that are conserved between one maize homeolog and its sorghum ortholog are absent from the second maize homeolog, suggesting that these AS isoforms may have been lost after the maize whole genome duplication event. This comprehensive analysis provides new insights into the complexity of AS in maize.

Investigation of Splicing Quantitative Trait Loci in Arabidopsis thaliana

The alteration of alternative splicing patterns has an effect on the quantification of functional proteins, leading to phenotype variation. The splicing quantitative trait locus (sQTL) is one of the main genetic elements affecting splicing patterns. Here, we report the results of genome-wide sQTLs across 141 strains of Arabidopsis thaliana with publicly available next generation sequencing datasets. As a result, we found 1,694 candidate sQTLs in Arabidopsis thaliana at a false discovery rate of 0.01. Furthermore, among the candidate sQTLs, we found 25 sQTLs that overlapped with the list of previously examined trait-associated single nucleotide polymorphisms (SNPs). In summary, this sQTL analysis provides new insight into genetic elements affecting alternative splicing patterns in Arabidopsis thaliana and the mechanism of previously reported trait-associated SNPs.

A New Mechanism for Mendelian Dominance in Regulatory Genetic Pathways: Competitive Binding by Transcription Factors

We report a new mechanism for allelic dominance in regulatory genetic interactions that we call binding dominance. We investigated a biophysical model of gene regulation, where the fractional occupancy of a transcription factor (TF) on the cis-regulated promoter site it binds to is determined by binding energy (-ΔG) and TF dosage. Transcription and gene expression proceed when the TF is bound to the promoter. In diploids, individuals may be heterozygous at the cis-site, at the TF's coding region, or at the TF's own promoter, which determines allele-specific dosage. We find that when the TF's coding region is heterozygous, TF alleles compete for occupancy at the cis-sites and the tighter-binding TF is dominant in proportion to the difference in binding strength. When the TF's own promoter is heterozygous, the TF produced at the higher dosage is also dominant. Cis-site heterozygotes have additive expression and therefore codominant phenotypes. Binding dominance propagates to affect the expression of downstream loci and it is sensitive in both magnitude and direction to genetic background, but its detectability often attenuates. While binding dominance is inevitable at the molecular level, it is difficult to detect in the phenotype under some biophysical conditions, more so when TF dosage is high and allele-specific binding affinities are similar. A body of empirical research on the biophysics of TF binding demonstrates the plausibility of this mechanism of dominance, but studies of gene expression under competitive binding in heterozygotes in a diversity of genetic backgrounds are needed.

Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues

Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n ≤ 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).

Genetic Regulation of Transcriptional Variation in Natural Arabidopsis thaliana Accessions

An increased knowledge of the genetic regulation of expression in Arabidopsis thaliana is likely to provide important insights about the basis of the plant’s extensive phenotypic variation. Here, we reanalyzed two publicly available datasets with genome-wide data on genetic and transcript variation in large collections of natural A. thaliana accessions. Transcripts from more than half of all genes were detected in the leaves of all accessions, and from nearly all annotated genes in at least one accession. Thousands of genes had high transcript levels in some accessions, but no transcripts at all in others, and this pattern was correlated with the genome-wide genotype. In total, 2669 eQTL were mapped in the largest population, and 717 of them were replicated in the other population. A total of 646 cis-eQTL-regulated genes that lacked detectable transcripts in some accessions was found, and for 159 of these we identified one, or several, common structural variants in the populations that were shown to be likely contributors to the lack of detectable RNA transcripts for these genes. This study thus provides new insights into the overall genetic regulation of global gene expression diversity in the leaf of natural A. thaliana accessions. Further, it also shows that strong cis-acting polymorphisms, many of which are likely to be structural variations, make important contributions to the transcriptional variation in the worldwide A. thaliana population.

Genotypes, Networks, Phenotypes: Moving Toward Plant Systems Genetics

One of the central goals in biology is to understand how and how much of the phenotype of an organism is encoded in its genome. Although many genes that are crucial for organismal processes have been identified, much less is known about the genetic bases underlying quantitative phenotypic differences in natural populations. We discuss the fundamental gap between the large body of knowledge generated over the past decades by experimental genetics in the laboratory and what is needed to understand the genotype-to-phenotype problem on a broader scale. We argue that systems genetics, a combination of systems biology and the study of natural variation using quantitative genetics, will help to address this problem. We present major advances in these two mostly disconnected areas that have increased our understanding of the developmental processes of flowering time control and root growth. We conclude by illustrating and discussing the efforts that have been made toward systems genetics specifically in plants.

Dissection of expression-quantitative trait locus and allele specificity using a haploid/diploid plant system - insights into compensatory evolution of transcriptional regulation within populations

Regulation of gene expression plays a central role in translating genotypic variation into phenotypic variation. Dissection of the genetic basis of expression variation is key to understanding how expression regulation evolves. Such analyses remain challenging in contexts where organisms are outbreeding, highly heterozygous and long-lived such as in the case of conifer trees. We developed an RNA sequencing (RNA-seq)-based approach for both expression-quantitative trait locus (eQTL) mapping and the detection of cis-acting (allele-specific) vs trans-acting (non-allele-specific) eQTLs. This method can be potentially applied to many conifers. We used haploid and diploid meiotic seed tissues of a single self-fertilized white spruce (Picea glauca) individual to dissect eQTLs according to linkage and allele specificity. The genetic architecture of local eQTLs linked to the expressed genes was particularly complex, consisting of cis-acting, trans-acting and, surprisingly, compensatory cis-trans effects. These compensatory effects influence expression in opposite directions and are neutral when combined in homozygotes. Nearly half of local eQTLs were under compensation, indicating that close linkage between compensatory cis-trans factors is common in spruce. Compensated genes were overrepresented in developmental and cell organization functions. Our haploid-diploid eQTL analysis in spruce revealed that compensatory cis-trans eQTLs segregate within populations and evolve in close genetic linkage.

Linking short tandem repeat polymorphisms with cytosine modifications in human lymphoblastoid cell lines

Inter-individual variation in cytosine modifications has been linked to complex traits in humans. Cytosine modification variation is partially controlled by single nucleotide polymorphisms (SNPs), known as modified cytosine quantitative trait loci (mQTL). However, little is known about the role of short tandem repeat polymorphisms (STRPs), a class of structural genetic variants, in regulating cytosine modifications. Utilizing the published data on the International HapMap Project lymphoblastoid cell lines (LCLs), we assessed the relationships between 721 STRPs and the modification levels of 283,540 autosomal CpG sites. Our findings suggest that, in contrast to the predominant cis-acting mode for SNP-based mQTL, STRPs are associated with cytosine modification levels in both cis-acting (local) and trans-acting (distant) modes. In local scans within the ±1 Mb windows of target CpGs, 21, 9, and 21 cis-acting STRP-based mQTL were detected in CEU (Caucasian residents from Utah, USA), YRI (Yoruba people from Ibadan, Nigeria), and the combined samples, respectively. In contrast, 139,420, 76,817, and 121,866 trans-acting STRP-based mQTL were identified in CEU, YRI, and the combined samples, respectively. A substantial proportion of CpG sites detected with local STRP-based mQTL were not associated with SNP-based mQTL, suggesting that STRPs represent an independent class of mQTL. Functionally, genetic variants neighboring CpG-associated STRPs are enriched with genome-wide association study (GWAS) loci for a variety of complex traits and diseases, including cancers, based on the National Human Genome Research Institute (NHGRI) GWAS Catalog. Therefore, elucidating these STRP-based mQTL in addition to SNP-based mQTL can provide novel insights into the genetic architectures of complex traits.

Conifer genomics and adaptation: at the crossroads of genetic diversity and genome function

Conifers have been understudied at the genomic level despite their worldwide ecological and economic importance but the situation is rapidly changing with the development of next generation sequencing (NGS) technologies. With NGS, genomics research has simultaneously gained in speed, magnitude and scope. In just a few years, genomes of 20-24 gigabases have been sequenced for several conifers, with several others expected in the near future. Biological insights have resulted from recent sequencing initiatives as well as genetic mapping, gene expression profiling and gene discovery research over nearly two decades. We review the knowledge arising from conifer genomics research emphasizing genome evolution and the genomic basis of adaptation, and outline emerging questions and knowledge gaps. We discuss future directions in three areas with potential inputs from NGS technologies: the evolutionary impacts of adaptation in conifers based on the adaptation-by-speciation model; the contributions of genetic variability of gene expression in adaptation; and the development of a broader understanding of genetic diversity and its impacts on genome function. These research directions promise to sustain research aimed at addressing the emerging challenges of adaptation that face conifer trees.

Association mapping reveals the role of purifying selection in the maintenance of genomic variation in gene expression

The evolutionary forces that maintain genetic variation in quantitative traits within populations remain poorly understood. One hypothesis suggests that variation is under purifying selection, resulting in an excess of low-frequency variants and a negative correlation between minor allele frequency and selection coefficients. Here, we test these predictions using the genetic loci associated with total expression variation (eQTLs) and allele-specific expression variation (aseQTLs) mapped within a single population of the plant Capsella grandiflora. In addition to finding eQTLs and aseQTLs for a large fraction of genes, we show that alleles at these loci are rarer than expected and exhibit a negative correlation between phenotypic effect size and frequency. Overall, our results show that the distribution of frequencies and effect sizes of the loci responsible for local expression variation within a single outcrossing population are consistent with the effects of purifying selection.

Genetic basis of transcriptome diversity in Drosophila melanogaster

Understanding how DNA sequence variation is translated into variation for complex phenotypes has remained elusive but is essential for predicting adaptive evolution, for selecting agriculturally important animals and crops, and for personalized medicine. Gene expression may provide a link between variation in DNA sequence and organismal phenotypes, and its abundance can be measured efficiently and accurately. Here we quantified genome-wide variation in gene expression in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP), increasing the annotated Drosophila transcriptome by 11%, including thousands of novel transcribed regions (NTRs). We found that 42% of the Drosophila transcriptome is genetically variable in males and females, including the NTRs, and is organized into modules of genetically correlated transcripts. We found that NTRs often were negatively correlated with the expression of protein-coding genes, which we exploited to annotate NTRs functionally. We identified regulatory variants for the mean and variance of gene expression, which have largely independent genetic control. Expression quantitative trait loci (eQTLs) for the mean, but not for the variance, of gene expression were concentrated near genes. Notably, the variance eQTLs often interacted epistatically with local variants in these genes to regulate gene expression. This comprehensive characterization of population-scale diversity of transcriptomes and its genetic basis in the DGRP is critically important for a systems understanding of quantitative trait variation.

Genomic patterns of species diversity and divergence in Eucalyptus

We examined genome-wide patterns of DNA sequence diversity and divergence among six species of the important tree genus Eucalyptus and investigated their relationship with genomic architecture. Using c. 90 range-wide individuals of each Eucalyptus species (E. grandis, E. urophylla, E. globulus, E. nitens, E. dunnii and E. camaldulensis), genetic diversity and divergence were estimated from 2840 polymorphic diversity arrays technology markers covering the 11 chromosomes. Species differentiating markers (SDMs) identified in each of 15 pairwise species comparisons, along with species diversity (HHW ) and divergence (FST ), were projected onto the E. grandis reference genome. Across all species comparisons, SDMs totalled 1.1-5.3% of markers and were widely distributed throughout the genome. Marker divergence (FST and SDMs) and diversity differed among and within chromosomes. Patterns of diversity and divergence were broadly conserved across species and significantly associated with genomic features, including the proximity of markers to genes, the relative number of clusters of tandem duplications, and gene density within or among chromosomes. These results suggest that genomic architecture influences patterns of species diversity and divergence in the genus. This influence is evident across the six species, encompassing diverse phylogenetic lineages, geography and ecology.

Temperature stress mediates decanalization and dominance of gene expression in Drosophila melanogaster

The regulatory architecture of gene expression remains an area of active research. Here, we studied how the interplay of genetic and environmental variation affects gene expression by exposing Drosophila melanogaster strains to four different developmental temperatures. At 18°C we observed almost complete canalization with only very few allelic effects on gene expression. In contrast, at the two temperature extremes, 13°C and 29°C a large number of allelic differences in gene expression were detected due to both cis- and trans-regulatory effects. Allelic differences in gene expression were mainly dominant, but for up to 62% of the genes the dominance swapped between 13 and 29°C. Our results are consistent with stabilizing selection causing buffering of allelic expression variation in non-stressful environments. We propose that decanalization of gene expression in stressful environments is not only central to adaptation, but may also contribute to genetic disorders in human populations.

Prioritization of candidate genes in QTL regions based on associations between traits and biological processes

BACKGROUND

Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait.

RESULTS

The prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait.

CONCLUSIONS

Our approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation.

Dissecting complex traits using the Drosophila Synthetic Population Resource

For most complex traits we have a poor understanding of the positions, phenotypic effects, and population frequencies of the underlying genetic variants contributing to their variation. Recently, several groups have developed multi-parent advanced intercross mapping panels in different model organisms in an attempt to improve our ability to characterize causative genetic variants. These panels are powerful and are particularly well suited to the dissection of phenotypic variation generated by rare alleles and loci segregating multiple functional alleles. We describe studies using one such panel, the Drosophila Synthetic Population Resource (DSPR), and the implications for our understanding of the genetic basis of complex traits. In particular, we note that many loci of large effect appear to be multiallelic. If multiallelism is a general rule, analytical approaches designed to identify multiallelic variants should be a priority for both genome-wide association studies (GWASs) and multi-parental panels.

RNA sequencing reveals the complex regulatory network in the maize kernel

RNA sequencing can simultaneously identify exonic polymorphisms and quantitate gene expression. Here we report RNA sequencing of developing maize kernels from 368 inbred lines producing 25.8 billion reads and 3.6 million single-nucleotide polymorphisms. Both the MaizeSNP50 BeadChip and the Sequenom MassArray iPLEX platforms confirm a subset of high-quality SNPs. Of these SNPs, we have mapped 931,484 to gene regions with a mean density of 40.3 SNPs per gene. The genome-wide association study identifies 16,408 expression quantitative trait loci. A two-step approach defines 95.1% of the eQTLs to a 10-kb region, and 67.7% of them include a single gene. The establishment of relationships between eQTLs and their targets reveals a large-scale gene regulatory network, which include the regulation of 31 zein and 16 key kernel genes. These results contribute to our understanding of kernel development and to the improvement of maize yield and nutritional quality.

Linking the genetic architecture of cytosine modifications with human complex traits

Interindividual variation in cytosine modifications could contribute to heterogeneity in disease risks and other complex traits. We assessed the genetic architecture of cytosine modifications at 283,540 CpG sites in lymphoblastoid cell lines (LCLs) derived from independent samples of European and African descent. Our study suggests that cytosine modification variation was primarily controlled in local by single major modification quantitative trait locus (mQTL) and additional minor loci. Local genetic epistasis was detectable for a small proportion of CpG sites, which were enriched by more than 9-fold for CpG sites mapped to population-specific mQTL. Genetically dependent CpG sites whose modification levels negatively (repressive sites) or positively (facilitative sites) correlated with gene expression levels significantly co-localized with transcription factor binding, with the repressive sites predominantly associated with active promoters whereas the facilitative sites rarely at active promoters. Genetically independent repressive or facilitative sites preferentially modulated gene expression variation by influencing local chromatin accessibility, with the facilitative sites primarily antagonizing H3K27me3 and H3K9me3 deposition. In comparison with expression quantitative trait loci (eQTL), mQTL detected from LCLs were enriched in associations for a broader range of disease categories including chronic inflammatory, autoimmune and psychiatric disorders, suggesting that cytosine modification variation, while possesses a degree of cell linage specificity, is more stably inherited over development than gene expression variation. About 11% of unique single-nucleotide polymorphisms reported in the Genome-Wide Association Study Catalog were annotated, 78% as mQTL and 31% as eQTL in LCLs, which covered 37% of the investigated diseases/traits and provided insights to the biological mechanisms.

Genetic dissection of the Drosophila melanogaster female head transcriptome reveals widespread allelic heterogeneity

Modern genetic mapping is plagued by the “missing heritability” problem, which refers to the discordance between the estimated heritabilities of quantitative traits and the variance accounted for by mapped causative variants. One major potential explanation for the missing heritability is allelic heterogeneity, in which there are multiple causative variants at each causative gene with only a fraction having been identified. The majority of genome-wide association studies (GWAS) implicitly assume that a single SNP can explain all the variance for a causative locus. However, if allelic heterogeneity is prevalent, a substantial amount of genetic variance will remain unexplained. In this paper, we take a haplotype-based mapping approach and quantify the number of alleles segregating at each locus using a large set of 7922 eQTL contributing to regulatory variation in the Drosophila melanogaster female head. Not only does this study provide a comprehensive eQTL map for a major community genetic resource, the Drosophila Synthetic Population Resource, but it also provides a direct test of the allelic heterogeneity hypothesis. We find that 95% of cis-eQTLs and 78% of trans-eQTLs are due to multiple alleles, demonstrating that allelic heterogeneity is widespread in Drosophila eQTL. Allelic heterogeneity likely contributes significantly to the missing heritability problem common in GWAS studies.

For traits with complex genetic inheritance it has generally proven very difficult to identify the majority of the specific causative variants involved. A range of hypotheses have been put forward to explain this so-called “missing heritability”. One idea—allelic heterogeneity, where genes each harbor multiple different causative variants—has received little attention, because it is difficult to detect with most genetic mapping designs. Here we make use of a panel of Drosophila melanogaster lines derived from multiple founders, allowing us to directly test for the presence of multiple alleles at a large set of genetic loci influencing gene expression. We find that the vast majority of loci harbor more than two functional alleles, demonstrating extensive allelic heterogeneity at the level of gene expression and suggesting that such heterogeneity is an important factor determining the genetic basis of complex trait variation in general.

Hypoxic response contributes to altered gene expression and precapillary pulmonary hypertension in patients with sickle cell disease

BACKGROUND

We postulated that the hypoxic response in sickle cell disease (SCD) contributes to altered gene expression and pulmonary hypertension, a complication associated with early mortality.

METHODS AND RESULTS

To identify genes regulated by the hypoxic response and not other effects of chronic anemia, we compared expression variation in peripheral blood mononuclear cells from 13 subjects with SCD with hemoglobin SS genotype and 15 subjects with Chuvash polycythemia (VHL(R200W) homozygotes with constitutive upregulation of hypoxia-inducible factors in the absence of anemia or hypoxia). At a 5% false discovery rate, 1040 genes exhibited >1.15-fold change in both conditions; 297 were upregulated and 743 downregulated including MAPK8 encoding a mitogen-activated protein kinase important for apoptosis, T-cell differentiation, and inflammatory responses. Association mapping with a focus on local regulatory polymorphisms in 61 patients with SCD identified expression quantitative trait loci for 103 of these hypoxia response genes. In a University of Illinois SCD cohort, the A allele of a MAPK8 expression quantitative trait locus, rs10857560, was associated with precapillary pulmonary hypertension defined as mean pulmonary artery pressure ≥25 mm Hg and pulmonary capillary wedge pressure ≤15 mm Hg at right heart catheterization (allele frequency, 0.66; odds ratio, 13.8; n=238). This association was confirmed in an independent Walk-Treatment of Pulmonary Hypertension and Sickle Cell Disease With Sildenafil Therapy cohort (allele frequency, 0.65; odds ratio, 11.3; n=519). The homozygous AA genotype of rs10857560 was associated with decreased MAPK8 expression and present in all 14 of the identified precapillary pulmonary hypertension cases among the combined 757 patients.

CONCLUSIONS

Our study demonstrates a prominent hypoxic transcription component in SCD and a MAPK8 expression quantitative trait locus associated with precapillary pulmonary hypertension.

Genomewide discovery of DNA polymorphisms in rice cultivars with contrasting drought and salinity stress response and their functional relevance

Next-generation sequencing technologies provide opportunities to understand the genetic basis of phenotypic differences, such as abiotic stress response, even in the closely related cultivars via identification of large number of DNA polymorphisms. We performed whole-genome resequencing of three rice cultivars with contrasting responses to drought and salinity stress (sensitive IR64, drought-tolerant Nagina 22 and salinity-tolerant Pokkali). More than 356 million 90-bp paired-end reads were generated, which provided about 85% coverage of the rice genome. Applying stringent parameters, we identified a total of 1 784 583 nonredundant single-nucleotide polymorphisms (SNPs) and 154 275 InDels between reference (Nipponbare) and the three resequenced cultivars. We detected 401 683 and 662 509 SNPs between IR64 and Pokkali, and IR64 and N22 cultivars, respectively. The distribution of DNA polymorphisms was found to be uneven across and within the rice chromosomes. One-fourth of the SNPs and InDels were detected in genic regions, and about 3.5% of the total SNPs resulted in nonsynonymous changes. Large-effect SNPs and InDels, which affect the integrity of the encoded protein, were also identified. Further, we identified DNA polymorphisms present in the differentially expressed genes within the known quantitative trait loci. Among these, a total of 548 SNPs in 232 genes, located in the conserved functional domains, were identified. The data presented in this study provide functional markers and promising target genes for salinity and drought tolerance and present a valuable resource for high-throughput genotyping and molecular breeding for abiotic stress traits in rice.

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

Background

The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling.

Results

We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed.

Conclusions

A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.

Second order optimization for the inference of gene regulatory pathways

With the increasing availability of experimental data on gene interactions, modeling of gene regulatory pathways has gained special attention. Gradient descent algorithms have been widely used for regression and classification applications. Unfortunately, results obtained after training a model by gradient descent are often highly variable. In this paper, we present a new second order learning rule based on the Newton's method for inferring optimal gene regulatory pathways. Unlike the gradient descent method, the proposed optimization rule is independent of the learning parameter. The flow vectors are estimated based on biomass conservation. A set of constraints is formulated incorporating weighting coefficients. The method calculates the maximal expression of the target gene starting from a given initial gene through these weighting coefficients. Our algorithm has been benchmarked and validated on certain types of functions and on some gene regulatory networks, gathered from literature. The proposed method has been found to perform better than the gradient descent learning. Extensive performance comparison with the extreme pathway analysis method has underlined the effectiveness of our proposed methodology.