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Genetics of global gene expression
Abstract
A new field of genetic analysis of global gene expression has emerged in recent years, driven by the realization that traditional techniques of linkage and association analysis can be applied to thousands of transcript levels measured by microarrays. Genetic dissection of transcript abundance has shed light on the architecture of quantitative traits, provided a new approach for connecting DNA sequence variation with phenotypic variation, and improved our understanding of transcriptional regulation and regulatory variation.
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“Previous eQTL studies in genetically diverse populations suggest that the most significant eQTL tend to be local (Rockman and Kruglyak 2006; Pickrell et al. 2010; Aylor et al. 2011; Lappalainen et al. 2013).”
From RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations (2014) · cites this paper as Rockman and Kruglyak 2006
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BackgroundLigterink, W., Joosen, R. V. L., & Hilhorst, H. W. M. (2012) · Seed Science Research · doi:10.1017/s0960258511000328 · Cited by 19
“Therefore, in the absence of allele-specific expression data (see elsewhere in this review), it is better to use the terms ‘local’ and ‘distant’ eQTL (Rockman and Kruglyak, 2006) (Fig.”
BackgroundQuantitative genetics: past and present
Narain, P. (2010) · Molecular breeding · doi:10.1007/s11032-010-9406-4 · Cited by 16
“These have led to some general principles of genetic mapping of genome-wide gene expression as reviewed by Rockman and Kruglyak (2006).”
BackgroundDissecting complex phenotypes using the genomics of twins
Tan, Q., Kyvik, K. O., Kruse, T. A., & Christensen, K. (2010) · Functional & Integrative Genomics · doi:10.1007/s10142-010-0160-9 · Cited by 16
“The eQTLs can be classified into cis (local) or trans (distant) acting eQTLs according to the location of the transcription site and that of the eQTL or eSNP significantly correlated with the transcriptional activity (Rockman and Kruglyak 2006).”
BackgroundDeciphering the molecular basis of venous thromboembolism: where are we and where should we go?
Morange, P., & Trégouët, D. (2009) · British Journal of Haematology · doi:10.1111/j.1365-2141.2009.07975.x · Cited by 18
“Rockman, M.V. & Kruglyak, L. (2006) Genetics of global gene expression.”
Novel Approaches for Identifying the Molecular Background of Schizophrenia
Golov, A. K., Kondratyev, N. V., Kostyuk, G. P., & Golimbet, A. V. E. (2020) · Cells · doi:10.3390/cells9010246 · Cited by 15
“Another approach, which appears to be more productive, utilises the results of genome-wide searches for quantitative trait loci (QTL) that are associated with changes in gene expression (eQTL) or the ratio of RNA isoforms (isoQTL) in the neuronal tissues of healthy people [74,75].”
BackgroundeQTL Networks Reveal Complex Genetic Architecture in the Immature Soybean Seed
Bolon, Y., Hyten, D., Orf, J., Vance, C., & Muehlbauer, G. (2014) · doi:10.3835/plantgenome2013.08.0027 · Cited by 15
“Because these eQTL have yet to be demonstrated to act in cis or trans, they may be more appropriately termed local or distant regulators (Rockman and Kruglyak, 2006), respectively, but the more familiar terms cis-acting and trans-acting are used for clarity.”
Chen, Y., van Zon, S., Adams, A. T., Schmidt‐Arras, D., Laurence, A. D. J., & Uhlig, H. H. (2023) · Journal of Clinical Immunology · doi:10.1007/s10875-023-01603-7 · Cited by 13
“…genetic variation contributes to polygenic disease susceptibility via protein-coding variants (that modify protein function or stability) or more often intra-or intergenic variants in regulatory regions that modify mRNA expression (expression or protein quantitative loci in polygenic diseases) [1].”
BackgroundMulti-omics analysis identifies drivers of protein phosphorylation
Zhang, T., Keele, G. R., Gyuricza, I. G., Vincent, M. J., Brunton, C., Bell, T. A., Hock, P., Shaw, G. D., Munger, S. C., de Villena, F. P., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02892-2 · Cited by 28
“In addition to clinical phenotypes, QTL mapping can be applied to molecular traits such as gene expression [17-22], chromatin accessibility [23], and protein abundance [24, 25].”
BackgroundBACH1 Binding Links the Genetic Risk for Severe Periodontitis with ST8SIA1
Chopra, A., Mueller, R., Weiner, J. M., Rosowski, J., Dommisch, H., Grohmann, E., & Schäefer, A. S. (2021) · Journal of Dental Research · doi:10.1177/00220345211017510 · Cited by 13
“The different alleles of a SNP may cause variation in expression levels of messenger RNAs (mRNAs), if they are located in a gene regulatory noncoding chromatin element (Rockman and Kruglyak 2006).”
Qu, W., Gurdziel, K., Piqué-Regi, R., & Ruden, D. M. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00395 · Cited by 20
Maroilley, T., Lemonnier, G., Lecardonnel, J., Esquerré, D., Ramayo‐Caldas, Y., Mercat, M.-J., Rogel-Gaillard, C., & Estellé, J. (2017) · BMC Genomics · doi:10.1186/s12864-017-4354-6 · Cited by 54
“Variation in transcript abundance has been studied as an inherited quantitative trait [5] and association mapping of loci for expression traits has been performed in several species for a wide range of tissues [6].”
BackgroundulfasQTL: an ultra-fast method of composite splicing QTL analysis
Yang, Q., Hu, Y., Li, J., & Zhang, X. (2017) · BMC Genomics · doi:10.1186/s12864-016-3258-1 · Cited by 11
Integrative Analysis of Low- and High-Resolution eQTL
Loguercio, S., Overall, R. W., Michaelson, J. J., Wiltshire, T., Pletcher, M. T., Miller, B. H., Walker, J. R., Kempermann, G., Su, A. I., & Beyer, A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013920 · Cited by 14
“In general, the presence of eQTL hotspots suggests either the presence of a master regulator gene which controls the expression of groups of genes together or the presence of several tightly linked genes within the hotspot, each of which regulates the expression of a subset of genes mapping to this locus [31,33,34].”
BackgroundGenetic networks for the functional study of genomes
Pisabarro, A. G., Pérez, G., Lavín, J. L., & Ramı́rez, L. (2008) · Briefings in Functional Genomics and Proteomics · doi:10.1093/bfgp/eln026 · Cited by 14
“The mapping of the QTLs responsible of gene expression variability (eQTLs, [10, 11]) permits to identify genome regions acting as master regulators of the expression of many genes, and to establish networks of co-regulated genes [9, 12, 13].”
BackgroundInfluentialNonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms
Gjuvsland, A., Hayes, B. J., Meuwissen, T., Plahte, E., & Omholt, S. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-32 · Cited by 14
“Although frequently considered to be the phenotype level closest to DNA sequence variation [11], transcript abundance does actually reflect the balance between production and decay rates.”
Delivering systems pharmacogenomics towards precision medicine through mathematics
Wang, Y., Wang, N., Wang, J., Wang, Z., & Wu, R.-L. (2013) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2013.03.002 · Cited by 13
“Coupling with DNA polymorphism information, these models are capable of mapping quantitative trait loci (QTLs) that control transcriptional (eQTL), proteomic (pQTL), and metabolomic (mQTL) expressions 20 and their interaction networks among these different types of QTLs.”
MethodInference of modules associated to eQTLs
Kreimer, A., Litvin, O., Hao, K., Molony, C. M., Pe’er, D., & Pe'er, I. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks269 · Cited by 14
“Local gene expression level is formally considered as a quantitative trait that is directly modified by allelic variation in regulatory elements (3,4).”
BackgroundGenetics, Genomics and Metabolomics
Fernie, A. R., & Keurentjes, J. J. B. (2011) · Wiley eBooks · doi:10.1002/9781444339956.ch8 · Cited by 12
Insights from GWAS into the quantitative genetics of transcription in humans
Kim, J., & Gibson, G. C. (2010) · Genetics Research · doi:10.1017/s001667231000056x · Cited by 11
“This gave rise to the concept of local eQTL, namely expression quantitative trait loci that map to the location of the target gene (Rockman & Kruglyak, 2006).”
BackgroundGenetics and the general physician: insights, applications and future challenges
Knight, J. C. (2009) · QJM · doi:10.1093/qjmed/hcp115 · Cited by 12
“Gene expression varies between individuals and has been successfully mapped as a quantitative trait in model organisms and more recently in humans.(82,83) Genome-wide mapping of disease association and of global gene expression offers a complimentary and synergistic approach, which was elegantly demonstrated by a rece…”
BackgroundCardoso, F. F., Rosa, G., Steibel, J., Ernst, C., Bates, R., & Tempelman, R. (2008) · Genetics · doi:10.1534/genetics.108.090969 · Cited by 13
“lation, when mapped near the genomic location of the gene encoding the transcript, or ‘‘distant’’ regulation, when mapped elsewhere in the genome (Rockman and Kruglyak 2006).”
BackgroundThe positives, protocols, and perils of genome‐wide association
Neale, B. M., & Purcell, S. (2008) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.30747 · Cited by 44
Valcu, C., Lalanne, C., Müller-Starck, G., Plomion, C., & Schlink, K. (2008) · Journal of Heredity · doi:10.1093/jhered/esn007 · Cited by 13
“, polymorphism of protein amounts) is the result of a complex network of regulatory mechanisms subject themselves to genetic variation and interaction with the environment (Thiellement et al. 2002; Rockman and Kruglyak 2006).”
BackgroundDeleterious Mutations and the Rare Allele Burden on Rice Gene Expression
Lye, Z. N., Choi, J. Y., & Purugganan, M. D. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac193 · Cited by 17
“Variants in the coding sequence could also influence expression if a gene has auto-regulatory functionality or by triggering changes in feedback loops controlling that gene’s expression (Rockman and Kruglyak 2006).”
BackgroundIn silico Analysis of Polymorphisms in microRNAs Deregulated in Alzheimer Disease
Moraghebi, M., Maleki, R., Ahmadi, M., Negahi, A. A., Abbasi, H., & Mousavi, P. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.631852 · Cited by 10
“Thus, as we have expected, all of this miRNA, except one, was not found in the eQTL database (Rockman and Kruglyak, 2006; West et al., 2007; Majewski and Pastinen, 2011).”
BackgroundConnelly, C. F., Skelly, D. A., Dunham, M. J., & Akey, J. M. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst073 · Cited by 13
“Noncoding genetic variation makes a significant contribution to phenotypic diversity and disease susceptibility by modulating gene expression (Rockman and Kruglyak 2006; Skelly et al. 2009).”
BackgroundCheng, W. D., Zhang, X., Wu, Y., Yin, X., Li, J., Heckerman, D. E., & Wang, W. W. (2012) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2382936.2382996 · Cited by 10
“INTRODUCTION Thanks to the advanced high-throughput technologies for profiling gene expressions and assaying genetic variations, genome-wide study of expression quantitative trait loci (eQTL) has been widely applied to dissect genetic basis of gene expression and molecular mechanisms underlying complex traits [5, 40,…”
BackgroundCarroll, R. K., Beres, S. B., Sitkiewicz, I., Peterson, L., Matsunami, R., Engler, D., Flores, A. R., Sumby, P., & Musser, J. (2011) · Epidemics · doi:10.1016/j.epidem.2011.07.001 · Cited by 12
SNP discovery, expression and cis-regulatory variation in the UGT2B genes
Sun, C., Southard, C., Huo, D., Hernandez, R. D., Witonsky, D., Olopade, O. I., & Rienzo, A. D. (2011) · The Pharmacogenomics Journal · doi:10.1038/tpj.2011.2 · Cited by 10
Vuylsteke, M., & van Eeuwijk, F. (2008) · Euphytica · doi:10.1007/s10681-007-9452-0 · Cited by 12
“2005), and to detect gene expression quantitative trait loci (eQTL) (Rockman and Kruglyak 2006).”
BackgroundInferring Gene Regulatory Networks from Genetical Genomics Data
Liu, B., Hoeschele, I., & de la Fuente, A. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-685-3.ch004 · Cited by 4
Zhang, K., Wang, J., Ding, F., Shi, R., Wang, W., Zhang, G., & Li, L. (2021) · Genes · doi:10.3390/genes12071040 · Cited by 13
“However, distant cis and local trans effects have also been reported [28].”
BackgroundSpirito, G., Mangoni, D., Sanges, R., & Gustincich, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3113-x · Cited by 13
“These kinds of analyses are performed by regressing gene expression levels for individual samples against locus-specific genotypes for matched samples [42, 43], and they are usually referred to as expression Quantitative Trait Loci (eQTL).”
MethodKringel, D., Kaunisto, M. A., Kalso, E. A., & Lötsch, J. (2019) · Pain · doi:10.1097/j.pain.0000000000001616 · Cited by 13
“This variant seems to be an expression quantitative trait locus (eQTL), defined as genomic loci that explain all or a fraction of variation in expression levels of mRNAs.(118) Specifically, rs613355 is listed as located in a ciseQTL locus for OPRM1,(44) a locus that maps to the approximate location of their gene-of-or…”
BackgroundQu, W., Gurdziel, K., Piqué-Regi, R., & Ruden, D. M. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00145 · Cited by 13
“This trans-sQTL hotspot is similar to what has been observed in many eQTL studies, where genomic loci that are linked with abnormally high numbers of eQTLs are called trans-eQTL hotspots (Joo et al., 2014; King et al., 2014) or trans-eQTL bands (Rockman and Kruglyak, 2006).”
ResultDecomposing genomic variance using information from GWA , GWE and eQTL analysis
Ehsani, A., Janss, L., Pomp, D., & Sørensen, P. B. (2015) · Animal Genetics · doi:10.1111/age.12396 · Cited by 9
Read, T. D., Richmond, P., & Dowell, R. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005746 · Cited by 11
Allelic-specific expression in relation to Bombyx mori resistance to Bt toxin.
Chen, Y.-Z., Li, M.-W., Islam, I., You, L., Wang, Y.-Q., Li, Z.-Q., Ling, L., Zeng, B., Xu, J., Huang, Y.-P., et al. (2014) · Insect Biochemistry and Molecular Biology · doi:10.1016/j.ibmb.2014.07.007 · Cited by 11
“The trans-acting mechanism is manifested because of effects of other genes that influence its expression, which involves a complex gene regulatory network (Rockman and Kruglyak, 2006).”
BackgroundYang, H., Lin, C., Chen, C., & Chen, J. J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-319 · Cited by 10
“Most of the available studies have discussed eQTL mapping in a simple scenario in which they only modeled the relationship between single gene expression and a single SNP at a time [20,24,26,28,31,39-45].”
BackgroundYang, H., Wang, P., Lin, C., Chen, C.-H., & Chen, C. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-346 · Cited by 11
“Regarding the relationship between SNP and GE markers, the regulation of GE may be unrelated to DNA sequences, as with epigenetic mechanisms [25], or it may be associated with SNPs, as with expression quantitative trait locus (eQTL) [22,26-28]; nevertheless, even in the case of an eQTL, only a limited proportion of GE…”
BackgroundModeling expression quantitative trait loci in data combining ethnic populations
Hsiao, C.-L., Lian, I., Hsieh, A., & Fann, C. S. J. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-111 · Cited by 11
“Diversified factors underlying the complexity of gene regulation could contribute to this phenomenon, such as polygenic regulation, environmental input and possibly interactions among loci [13,25].”
BackgroundMethodTowards a comprehensive picture of the genetic landscape of complex traits
Wang, Z., Wang, Y., Wang, N., Wang, J., Wang, Z., Vallejos, C. E., & Wu, R. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbs049 · Cited by 11
“Classic correlation methods or multivariate statistics have been used to correlate gene expression data with proteomic and metabolomic data [47, 48], but they fail to capture the true quantitative variation and relationship between activity of thousands of gene–protein couples or protein–metabolite couples in a cellul…”
MethodPowerful eQTL mapping through low coverage RNA sequencing
Schwarz, T., Boltz, T., Hou, K., Bot, M., Duan, C., Loohuis, L. M., Boks, M., Kahn, R. S., Ophoff, R., & Pasaniuc, B. (2021) · doi:10.1101/2021.08.08.455466 · Cited by 10
Que, E. S., James, K. L., Coffey, A. R., Smallwood, T., Albright, J., Huda, M. N., Pomp, D., Sethupathy, P., & Bennett, B. J. (2021) · Genetics · doi:10.1093/genetics/iyab068 · Cited by 12
Local adaptation contributes to gene expression divergence in maize
Blanc, J., Kremling, K. A. G., Buckler, E., & Josephs, E. B. (2020) · bioRxiv · doi:10.1093/g3journal/jkab004 · Cited by 10
Hartanto, M., Joosen, R. V. L., Snoek, B. L., Willems, L. A. J., Sterken, M. G., de Ridder, D., Hilhorst, H. W. M., Ligterink, W., & Nijveen, H. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401477 · Cited by 10
Schröder, J., Schüller, V., May, A., Gerges, C., Anders, M., Becker, J., Hess, T., Kreuser, N., Thieme, R., Ludwig, K. U., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0227072 · Cited by 9
“In eQTL studies, the alleles or genotypes of genetic variants are correlated with the quantitative expression level of transcripts [10], thereby identifying genetic variants that influence the expression level of a gene.”
BackgroundGenome structure predicts modular transcriptome responses to genetic and environmental conditions
Mark, S., Weiss, J., Sharma, E., Liu, T.-A., Wang, W., Claycomb, J. M., & Cutter, A. (2019) · bioRxiv · doi:10.1111/mec.15185 · Cited by 10
eQTL networks unveil enriched mRNA master integrators downstream of complex disease-associated SNPs
Li, H., Pouladi, N., Achour, I., Gardeux, V., Lǐ, J., Li, Q., Zhang, H. H., Martínez, F. D., Garcia, J. G. N., & Lussier, Y. A. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.10.010 · Cited by 12
“We concentrate specifically on expression Quantitative Trait Loci association (eQTL) [28] networks and investigate whether mRNAs related to complex disease SNPs are more central than the remaining mRNAs in the network of eQTL associations.”
Over-expression of EPS15 is a favorable prognostic factor in breast cancer
Dai, X., Liu, Z., & Zhang, S. (2015) · Molecular BioSystems · doi:10.1039/c5mb00219b · Cited by 9
Jiang, G., Chakraborty, A., Wang, Z. P., Boustani, M. A., Liu, Y., Skaar, T. C., & Li, L. (2013) · BioMed Research International · doi:10.1155/2013/493019 · Cited by 8
“The potential of genomewide eQTL identification has been shown originally in the yeast Saccharomyces cerevisiae [9] and then in humans, animals, and plants [10, 11].”
Variants in exons and in transcription factors affect gene expression in trans
Kreimer, A., & Pe’er, I. (2013) · Genome biology · doi:10.1186/gb-2013-14-7-r71 · Cited by 13
“Formally, tr nscription level is considered as a quantitative trait that is altered by allelic v ariation with thousands of SNPs reported as associated with changes in gene expression [10-13].”
BackgroundProspects for advancing defense to cereal rusts through genetical genomics
Ballini, E., Lauter, N., & Wise, R. (2013) · Frontiers in Plant Science · doi:10.3389/fpls.2013.00117 · Cited by 10
Introduction to Systems Biology for Animal Scientists
Tuggle, C. K., Towfic, F., & Honavar, V. (2011) · Wiley eBooks · doi:10.1002/9780470963012.ch1 · Cited by 10
Genome Analysis: The State of Knowledge of Barley Genes
Druka, A., Sato, K., & Muehlbauer, G. J. (2010) · Wiley eBooks · doi:10.1002/9780470958636.ch4 · Cited by 8
Trans-eQTL hotspots shape complex traits by modulating cellular states
Renganaath, K., & Albert, F. W. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100873 · Cited by 11
“27,28 Second, the majority of the eQTLs (92%; 33,529) were located far from the genes they influence, typically on a different chromosome.”
Bioinformatics Strategies for the Analysis and Integration of Large-Scale Multiomics Data
Tesi, N., van der Lee, S. J., Hulsman, M., Holstege, H. H., & Reinders, M. J. T. (2023) · The Journals of Gerontology Series A · doi:10.1093/gerona/glad005 · Cited by 7
Miculan, M., Nelissen, H., Hassen, M. B., Marroni, F., Inzé, D., Pè, M. E., & Dell’Acqua, M. (2021) · The Plant Journal · doi:10.1111/tpj.15364 · Cited by 23
“Still, trans-regulation may occur via downstream effects of cis-regulatory elements, like in the case of transcription factors (Rockman and Kru- glyak, 2006).”
BackgroundTakahashi, Y., Maynard, K. R., Tippani, M., Jaffe, A. E., Martinowich, K., Kleinman, J. E., Weinberger, D. R., & Hyde, T. M. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01046-9 · Cited by 13
Ludl, A.-A., & Michoel, T. (2020) · Molecular Omics · doi:10.1039/d0mo00140f · Cited by 9
“5B) are called transcriptional “hotspots”, and finding the causal genes underlying a hotspot is an important problem in quantitative genetics [26].”
BackgroundFrochaux, M., Sleiman, M. S. B., Gardeux, V., Dainese, R., Hollis, B., Litovchenko, M., Braman, V. S., Andreani, T., Osman, D., & Deplancke, B. (2020) · Genome biology · doi:10.1186/s13059-019-1912-z · Cited by 13
Homeostatic interplay between FoxO proteins and ER proteostasis in cancer and other diseases
González-Quiroz, M., Urra, H., Limia, C. M., & Hetz, C. A. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.01.011 · Cited by 11
Whole Transcriptome Profiling: An RNA‐Seq Primer and Implications for Pharmacogenomics Research
Sá, A. C. C., Sadée, W., & Johnson, J. A. (2017) · Clinical and Translational Science · doi:10.1111/cts.12511 · Cited by 14
“studied over the years and are known to be widespread over human populations [35, 36].”
BackgroundAdrenal cortex expression quantitative trait loci in a German Holstein × Charolais cross
Brand, B., Scheinhardt, M. O., Friedrich, J., Zimmer, D., Reinsch, N., Ponsuksili, S., Schwerin, M., & Ziegler, A. (2016) · BMC Genetics · doi:10.1186/s12863-016-0442-x · Cited by 11
“…known to influence economically important traits in cattle is no evidence that the adrenocortical transcript abundance is causal, but similar effects might be present in other organs and tissues [29] providing insight into molecular mechanisms that contribute to the phenotypic variation in cattle.”
InfluentialOstberg, C., Chase, D., & Hauser, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141373 · Cited by 9
“Furthermore, transcription regulation is highly polygenic [6] and demonstrates complex patterns of inheritance that contribute to gene expression variation in hybrids [5].”
Partitioning Transcript Variation inDrosophila: Abundance, Isoforms, and Alleles
Yang, Y., Graze, R. M., Walts, B., López, C. M., Baker, H. V., Wayne, M. L., Nuzhdin, S. V., & McIntyre, L. M. (2011) · G3 Genes Genomes Genetics · doi:10.1534/g3.111.000596 · Cited by 8
“Allele-specific association studies advance these analyses and increase scientific knowledge of the regulatory process (Rockman and Kruglyak 2006; Serre et al. 2008; Stamatoyannopoulos 2004).”
BackgroundLi, X., Peng, T., Gao, J., Feng, J.-G., Wu, D.-D., Yang, T., Zhong, L., Fu, W.-P., & Sun, C. (2019) · American Journal of Physiology - Lung cellular and Molecular Physiology · doi:10.1152/ajplung.00275.2018 · Cited by 8
Marchetti-Bowick, M., Yu, Y.-L., Wu, W., & Xing, E. (2019) · Annals of Applied Statistics · doi:10.1214/18-aoas1186 · Cited by 8
“…two sub-problems of particular interest are (a) understanding how various genetic loci regulate gene expression, a problem known as eQTL mapping (Rockman and Kruglyak, 2006), and (b) determining which genes have a direct influence on the expression of other genes, a problem known as gene…”
BackgroundGenome-wide haplotype association analysis of primary biliary cholangitis risk in Japanese
Im, C., Sapkota, Y., Moon, W., Kawashima, M., Nakamura, M., Tokunaga, K., & Yasui, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-26112-1 · Cited by 8
“6 ((rs9268831 = T) or (rs9269190 = T)) or (rs9270652 = C) rs9268831 32427748 intergenic HLA-DRA (dist = 14922), HLA-DRB5 (dist = 57406) 5 (1) 31 (19) 71 (16) GM18951 (POL2) 1 Whole Blood (HLA-DQA1, HLADQA2, HLA-DQB1, HLA-DQB1-AS1, HLA-DQB2, HLADRB1, HLA-DRB6, HLA-DRB9); Lymphoblastoid (HLA-DQA2, HLA-DQB2, HLADRB6, HLA…”
Whole genome sequencing and its applications in medical genetics
Wu, J., Wu, M., Chen, T., & Jiang, R. (2016) · Quantitative Biology · doi:10.1007/s40484-016-0067-0 · Cited by 13
“Name Molecular trait Other techniques Ref. eQTL Gene expression RNA-seq or microarray Rockman [108] dsQTL Open chromatin DNaseI-seq Degner [109] sQTL RNA splicing RNA-seq Monlong [110] rtQTL DNA replication timing FACS-sorting Amnon [111] haQTL Histone acetylation ChIP-seq Rosario [112] metQTL DNA methylation Bisulfit…”
MethodChen, X., McClelland, M., Jia, Z., Rahmatpanah, F. B., Sawyers, A., Trent, J. M., Duggan, D. J., & Mercola, D. (2015) · Oncotarget · doi:10.18632/oncotarget.2763 · Cited by 7
“To make the first step towards this goal, we applied eQTL (Expression Quantitative Trait Loci) analysis to expression data from tumor-adjacent stroma, to define associations between gene activity and risk variants [33–36].”
MethodGuan, L., Yang, Q., Gu, M., Chen, L., & Zhang, X. (2014) · Quantitative Biology · doi:10.1007/s40484-014-0031-9 · Cited by 9
“Expression quantitative trait locus (eQTL) analysis studies the association of genomic variations with gene expression variations, and is a key to decode the molecular network that regulates the expression of genes [1–6].”
BackgroundEfficiently Identifying Significant Associations in Genome-wide Association Studies
Kostem, E., & Eskin, E. (2013) · Journal of Computational Biology · doi:10.1089/cmb.2013.0087 · Cited by 4
“In these studies, the goal is to identify regions of the genome harboring genetic variation that affect gene expression levels or expression quantitative trait loci (eQTL) (Bochner, 2003; Rockman and Kruglyak, 2006; Cookson et al., 2009).”
mRNA Levels in Control Rat Liver Display Strain-Specific, Hereditary, and AHR-Dependent Components
Boutros, P. C., Moffat, I. D., Okey, A. B., & Pohjanvirta, R. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018337 · Cited by 8
“To identify genes with evidence for interacting loci [15] we focused on those ProbeSets with statistically significant (padjusted,0.”
BackgroundApplication of Computational Tools for Identification of miRNA and Their Target SNPs
C, G., Ip, D.-K., & Sethumadhavan, R. (2008) · Journal of Proteomics & Bioinformatics · doi:10.4172/jpb.1000044 · Cited by 8
“…past decades, studies have analyzed and unveiled the genetic variants in the human genome, such as single-nucleotide polymorphisms (SNPs) (Stranger et al., 2007) which contribute to gene expression variation, and eventually to phenotypic variation in human populations (Rockman and Kruglyak 2006).”
BackgroundMethylation quantitative trait loci are largely consistent across disease states in Crohn’s disease
Venkateswaran, S., Somineni, R. P., Kilaru, V., Katrinli, Ş., Prince, J., Okou, D. T., Hyams, J. S., Denson, L. A., Kellermayer, R., Gibson, G. C., et al. (2022) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkac041 · Cited by 8
“In the past decade, studies have focused on demonstrating the potential of these variants to influence specific traits or diseases through transcriptomic regulation [2-4].”
BackgroundViewpoint: Toward the Genetic Architecture of Disease Severity in Inflammatory Bowel Diseases
Liefferinckx, C., & Franchimont, D. P. (2018) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izy109 · Cited by 8
Kel, I., Chang, Z., Galluccio, N., Romeo, M., Beretta, S., Diomede, L., Mezzelani, A., Milanesi, L., Dieterich, C., & Merelli, I. (2016) · Molecular BioSystems · doi:10.1039/c6mb00453a · Cited by 6
Non-Equilibrium Thermodynamics of Gene Expression and Transcriptional Regulation
Lemus, E. H. (2009) · Journal of Non-Equilibrium Thermodynamics · doi:10.1515/jnetdy.2009.019 · Cited by 11
“One hard-to-grasp issue is that the process of gene expression in itself is a complex one, both from the biochemical and thermodynamical points of view [1].”
BackgroundGenomics of Biotic Interactions in the Triticeae
Wise, R., Lauter, N., Szabo, L., & Schweizer, P. (2009) · doi:10.1007/978-0-387-77489-3_19 · Cited by 7
“Transcript abundance of each single gene is treated as a quantitative trait and its regulation is genetically interrogated using QTL mapping approaches (Chen and Kendziorski 2007; Rockman and Kruglyak 2006).”
BackgroundMethodPredicting Fitness-Related Traits Using Gene Expression and Machine Learning
Henry, G. A., & Stinchcombe, J. (2024) · bioRxiv · doi:10.1093/gbe/evae275 · Cited by 6
“…of tissue yields a less biased sampling of phenotypic traits compared with an observer’s opinions on what might be an important trait (Rockman and Kruglyak 2006; Josephs 2021), although investigators must still decide on growth or rearing environments, as well as the tissues and…”
Metadata
- Authors
- Rockman, M. V., & Kruglyak, L.
- Affiliations
- Princeton University (US)
- Corresponding author
- Kruglyak, Leonid
- Venue
- Nature Reviews Genetics
- Published
- 2006-11
- Volume
- 7
- Issue
- 11
- Pages
- 862-872
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- OpenAlex
- W2040912377
- Cited by
- 666
- References
- 0
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 18 · 2023: 19 · 2022: 24 · 2021: 50 · 2020: 27 · 2019: 35 · 2018: 26 · 2017: 29 · 2016: 36 · 2015: 33 · 2014: 41 · 2013: 39 · 2012: 41
- FWCI
- 4.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- James S. McDonnell Foundation
- Jane Coffin Childs Memorial Fund for Medical Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- global gene expression, mRNA levels, quantitative traits, transcriptional regulation, regulatory variation, DNA sequence variation, phenotypic variability, gene expression microarrays, linkage analysis, association analysis
- MeSH terms
- Amino Acid Sequence, Animals, Fungal Proteins, Genetic Techniques, Humans, Molecular Sequence Data, Transcription, Genetic, Genetic Variation, Yeasts, Gene Expression, Sequence Homology, Amino Acid, Quantitative Trait Loci
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