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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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The genetical theory of natural selection
Fisher, R. A. (1930) · doi:10.5962/bhl.title.27468 · Cited by 17,617
An integrative genomics approach to infer causal associations between gene expression and disease
Schadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., GuhaThakurta, D., Sieberts, S. K., Monks, S., Reitman, M., Zhang, C., et al. (2005) · Nature Genetics · doi:10.1038/ng1589 · Cited by 1,043
Tissue-specific expression and regulation of sexually dimorphic genes in mice
Yang, X., Schadt, E. E., Wang, S., Wang, H., Arnold, A. P., Ingram-Drake, L., Drake, T. A., & Lusis, A. J. (2006) · Genome Research · doi:10.1101/gr.5217506 · Cited by 922
Variation in gene expression within and among natural populations
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The genetics of plant metabolism
Keurentjes, J. J. B., Fu, J., de Vos, C. H. R., Lommen, A., Hall, R. D., Bino, R. J., van der Plas, L. H. W., Jansen, R. C., Vreugdenhil, D., & Koornneef, M. (2006) · Nature Genetics · doi:10.1038/ng1815 · Cited by 499
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Research works printed but not linked: 112
- Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002). The first study to report genetic mapping of global gene expression measured with microarrays. The authors described local and distant linkages and expression QTL hot spots, and showed that the inheritance of transcript levels is usually complex. · DOI
- Sandberg, R. et al. Regional and strain-specific gene expression mapping in the adult mouse brain. Proc. Natl Acad. Sci. USA 97, 11038–11043 (2000). · DOI
- Primig, M. et al. The core meiotic transcriptome in budding yeasts. Nature Genet. 26, 415–423 (2000). · DOI
- Cavalieri, D., Townsend, J. P. & Hartl, D. L. Manifold anomalies in gene expression in a vineyard isolate of Saccharomyces cerevisiae revealed by DNA microarray analysis. Proc. Natl Acad. Sci. USA 97, 12369–12374 (2000). · DOI
- Karp, C. L. et al. Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma. Nature Immunol. 1, 221–226 (2000). · DOI
- Jin, W. et al. The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nature Genet. 29, 389–395 (2001). · DOI
- Jansen, R. C. & Nap, J. P. Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001). A theoretical proposal to combine linkage analysis with expression profiling. · DOI
- Yvert, G. et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nature Genet. 35, 57–64 (2003). This detailed analysis of distant regulatory variation identified two polymorphisms that are responsible for expression QTL hot spots and showed that distant regulatory loci are not enriched for transcription factors. · DOI
- Schadt, E. E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003). This first study of the genetics of global gene expression in a mammal focused on a mouse cross and showed how expression traits can be connected with a physiological phenotype such as obesity. · DOI
- Hubner, N. et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nature Genet. 37, 243–253 (2005). · DOI
- Bystrykh, L. et al. Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'. Nature Genet. 37, 225–232 (2005). · DOI
- Chesler, E. J. et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nature Genet. 37, 233–242 (2005). References 11–13 describe extensive studies of the genetics of global gene expression in recombinant inbred lines of rodents. · DOI · Paper page
- DeCook, R., Lall, S., Nettleton, D. & Howell, S. H. Genetic regulation of gene expression during shoot development in Arabidopsis. Genetics 172, 1155–1164 (2006). · DOI
- Kirst, M. et al. Coordinated genetic regulation of growth and lignin revealed by quantitative trait locus analysis of cDNA microarray data in an interspecific backcross of eucalyptus. Plant Physiol. 135, 2368–2378 (2004). · DOI
- Monks, S. A. et al. Genetic inheritance of gene expression in human cell lines. Am. J. Hum. Genet. 75, 1094–1105 (2004). · DOI
- Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004). References 16 and 17 extended linkage studies of global gene expression to humans by carrying out expression profiling in cell lines from previously genotyped CEPH reference families. · DOI
- Cheung, V. G. et al. Mapping determinants of human gene expression by regional and genome-wide association. Nature 437, 1365–1369 (2005). · DOI
- Stranger, B. E. et al. Genome-wide associations of gene expression variation in humans. PLoS Genet. 1, e78 (2005). References 18 and 19 reported the first population association studies of expression of a subset of human genes measured in cell lines from CEPH individuals previously densely genotyped at over a million SNPs by the International HapMap Project. · DOI
- Mehrabian, M. et al. Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nature Genet. 37, 1224–1233 (2005). · DOI
- Wang, S. et al. Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity. PLoS Genet. 2, e15 (2006). · DOI
- Llinas, M., Bozdech, Z., Wong, E. D., Adai, A. T. & DeRisi, J. L. Comparative whole genome transcriptome analysis of three Plasmodium falciparum strains. Nucleic Acids Res. 34, 1166–1173 (2006). · DOI
- Kendziorski, C. & Wang, P. A review of statistical methods for expression quantitative trait loci mapping. Mamm. Genome 17, 509–517 (2006). · DOI
- King, M. C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975). This classic paper proposed that regulatory mutations that alter gene expression have a key role in biological differences between species. · DOI · Paper page
- Stern, D. L. Evolutionary developmental biology and the problem of variation. Evolution 54, 1079–1091 (2000). · DOI · Paper page
- Wray, G. A. et al. The evolution of transcriptional regulation in eukaryotes. Mol. Biol. Evol. 20, 1377–1419 (2003). · DOI
- Carroll, S. B., Grenier, J. K. & Weatherbee, S. D. From DNA to Diversity: Molecular Genetics and the Evolution of Animal Design (Blackwell Science, London, 2001).
- Beavis, W. D. in Molecular Dissection of Complex Traits (ed. Patterson, A. H.) 145–162 (CRC, New York, 1998).
- Brem, R. B. & Kruglyak, L. The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc. Natl Acad. Sci. USA 102, 1572–1577 (2005). A detailed analysis of complex genetic architectures of expression traits. · DOI · Paper page
- Farrall, M. Quantitative genetic variation: a post-modern view. Hum. Mol. Genet. 13, R1–R7 (2004). · DOI
- Orr, H. A. The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52, 935–949 (1998). · DOI
- Barton, N. H. & Keightley, P. D. Understanding quantitative genetic variation. Nature Rev. Genet. 3, 11–21 (2002). · DOI
- Gibson, G. et al. Extensive sex-specific nonadditivity of gene expression in Drosophila melanogaster. Genetics 167, 1791–1799 (2004). · DOI
- Swanson-Wagner, R. A. et al. All possible modes of gene action are observed in a global comparison of gene expression in a maize F1 hybrid and its inbred parents. Proc. Natl Acad. Sci. USA 103, 6805–6810 (2006). · DOI
- Wayne, M. L., Pan, Y. J., Nuzhdin, S. V. & McIntyre, L. M. Additivity and trans-acting effects on gene expression in male Drosophila simulans. Genetics 168, 1413–1420 (2004). · DOI
- Vuylsteke, M., van Eeuwijk, F., Van Hummelen, P., Kuiper, M. & Zabeau, M. Genetic analysis of variation in gene expression in Arabidopsis thaliana. Genetics 171, 1267–1275 (2005). · DOI
- Brem, R. B., Storey, J. D., Whittle, J. & Kruglyak, L. Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436, 701–703 (2005). · DOI
- Storey, J. D., Akey, J. M. & Kruglyak, L. Multiple locus linkage analysis of genomewide expression in yeast. PLoS Biol. 3, e267 (2005). · DOI
- Carlborg, O. et al. Methodological aspects of the genetic dissection of gene expression. Bioinformatics 21, 2383–2393 (2005). · DOI
- Landry, C. R., Oh, J., Hartl, D. L. & Cavalieri, D. Genome-wide scan reveals that genetic variation for transcriptional plasticity in yeast is biased towards multi-copy and dispensable genes. Gene 366, 343–351 (2006). · DOI
- Chen, W. J. et al. Contribution of transcriptional regulation to natural variations in Arabidopsis. Genome Biol. 6, R32 (2005). · DOI
- Whitehead, A. & Crawford, D. L. Variation in tissue-specific gene expression among natural populations. Genome Biol. 6, R13 (2005). · DOI
- Cotsapas, C. J. et al. Genetic dissection of gene regulation in multiple mouse tissues. Mamm. Genome 17, 490–495 (2006). · DOI
- Benzer, S. Fine structure of a genetic region in Bacteriophage. Proc. Natl Acad. Sci. USA 41, 344–354 (1955). · DOI
- Lewis, E. B. Pseudoallelism and gene evolution. Cold Spring Harb. Symp. Quant. Biol. 16, 159–174 (1951). · DOI
- Pontecorvo, G. Genetic formulation of gene structure and gene action. Adv. Enzymol. Relat. Subj. Biochem. 13, 121–149 (1952).
- Ronald, J., Brem, R. B., Whittle, J. & Kruglyak, L. Local regulatory variation in Saccharomyces cerevisiae. PLoS Genet. 1, e25 (2005). · DOI
- Doss, S., Schadt, E. E., Drake, T. A. & Lusis, A. J. Cis-acting expression quantitative trait loci in mice. Genome Res. 15, 681–691 (2005). References 49 and 50 use classic cis/trans tests to determine the contribution of cis -acting polymorphisms to local regulatory variation. · DOI · Paper page
- Lettice, L. A. et al. Disruption of a long-range cis-acting regulator for Shh causes preaxial polydactyly. Proc. Natl Acad. Sci. USA 99, 7548–7553 (2002). · DOI
- Loots, G. G. et al. Identification of a coordinate regulator of interleukins 4, 13, and 5 by cross-species sequence comparisons. Science 288, 136–140 (2000). · DOI
- Nobrega, M. A., Ovcharenko, I., Afzal, V. & Rubin, E. M. Scanning human gene deserts for long-range enhancers. Science 302, 413 (2003). · DOI · Paper page
- Spilianakis, C. G., Lalioti, M. D., Town, T., Lee, G. R. & Flavell, R. A. Interchromosomal associations between alternatively expressed loci. Nature 435, 637–645 (2005). · DOI
- Hahn, M. W., Stajich, J. E. & Wray, G. A. The effects of selection against spurious transcription factor binding sites. Mol. Biol. Evol. 20, 901–906 (2003). · DOI
- Schadt, E. E. Exploiting naturally occurring DNA variation and molecular profiling data to dissect disease and drug response traits. Curr. Opin. Biotechnol. 16, 647–654 (2005). · DOI
- Yan, H., Yuan, W., Velculescu, V. E., Vogelstein, B. & Kinzler, K. W. Allelic variation in human gene expression. Science 297, 1143 (2002). · DOI
- Cowles, C. R., Hirschhorn, J. N., Altshuler, D. & Lander, E. S. Detection of regulatory variation in mouse genes. Nature Genet. 32, 432–437 (2002). · DOI · Paper page
- Pastinen, T. et al. A survey of genetic and epigenetic variation affecting human gene expression. Physiol. Genomics 16, 184–193 (2004). · DOI
- Knight, J. C., Keating, B. J. & Kwiatkowski, D. P. Allele-specific repression of lymphotoxin-α by activated B cell factor-1. Nature Genet. 36, 394–349 (2004). · DOI
- Damerval, C., Maurice, A., Josse, J. M. & de Vienne, D. Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. Genetics 137, 289–301 (1994). · DOI
- Klose, J. et al. Genetic analysis of the mouse brain proteome. Nature Genet. 30, 385–393 (2002). References 61 and 62 describe classic and modern studies (respectively) of the genetics of protein abundance measured with two-dimensional gels. · DOI
- Lo, H. S. et al. Allelic variation in gene expression is common in the human genome. Genome Res. 13, 1855–1862 (2003). · DOI
- Ronald, J. et al. Simultaneous genotyping, gene-expression measurement, and detection of allele-specific expression with oligonucleotide arrays. Genome Res. 15, 284–291 (2005). · DOI
- Pant, P. V. et al. Analysis of allelic differential expression in human white blood cells. Genome Res. 16, 331–339 (2006). · DOI
- Buckland, P. R. et al. A high proportion of polymorphisms in the promoters of brain expressed genes influences transcriptional activity. Biochim. Biophys. Acta 1690, 238–249 (2004). · DOI
- Hoogendoorn, B. et al. Functional analysis of human promoter polymorphisms. Hum. Mol. Genet. 12, 2249–2254 (2003). · DOI
- Buckland, P. R. et al. A high proportion of chromosome 21 promoter polymorphisms influence transcriptional activity. Gene Expr. 11, 233–239 (2004). · DOI
- Rockman, M. V. & Wray, G. A. Abundant raw material for cis-regulatory evolution in humans. Mol. Biol. Evol. 19, 1991–2004 (2002). · DOI
- Knight, J. C. Allele-specific gene expression uncovered. Trends Genet. 20, 113–116 (2004). · DOI
- Horan, M. et al. Human growth hormone 1 (GH1) gene expression: complex haplotype-dependent influence of polymorphic variation in the proximal promoter and locus control region. Hum. Mutat. 21, 408–423 (2003). · DOI
- Tao, H., Cox, D. R. & Frazer, K. A. Allele-specific KRT1 expression is a complex trait. PLoS Genet. 2, e93 (2006). · DOI
- Perez-Enciso, M. In silico study of transcriptome genetic variation in outbred populations. Genetics 166, 547–554 (2004). · DOI
- Allison, D. B. et al. Multiple phenotype modeling in gene-mapping studies of quantitative traits: power advantages. Am. J. Hum. Genet. 63, 1190–1201 (1998). · DOI
- Jiang, C. & Zeng, Z. B. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111–1127 (1995). · DOI
- Bing, N. & Hoeschele, I. Genetical genomics analysis of a yeast segregant population for transcription network inference. Genetics 170, 533–542 (2005). · DOI
- Kulp, D. C. & Jagalur, M. Causal inference of regulator-target pairs by gene mapping of expression phenotypes. BMC Genomics 7, 125 (2006). · DOI
- Gunderson, K. L. et al. Whole-genome genotyping of haplotype tag single nucleotide polymorphisms. Pharmacogenomics 7, 641–648 (2006). · DOI
- Matsuzaki, H. et al. Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array. Genome Res. 14, 414–425 (2004). · DOI
- Gresham, D. et al. Genome-wide detection of polymorphisms at nucleotide resolution with a single DNA microarray. Science 311, 1932–1936 (2006). · DOI
- Deutschbauer, A. M. & Davis, R. W. Quantitative trait loci mapped to single-nucleotide resolution in yeast. Nature Genet. 37, 1333–1340 (2005). · DOI
- Steinmetz, L. M. et al. Dissecting the architecture of a quantitative trait locus in yeast. Nature 416, 326–330 (2002). · DOI
- Gibson, G. & Weir, B. The quantitative genetics of transcription. Trends Genet. 21, 616–623 (2005). · DOI
- Hamblin, M. T., Thompson, E. E. & Di Rienzo, A. Complex signatures of natural selection at the Duffy blood group locus. Am. J. Hum. Genet. 70, 369–383 (2002). · DOI
- Bersaglieri, T. et al. Genetic signatures of strong recent positive selection at the lactase gene. Am. J. Hum. Genet. 74, 1111–1120 (2004). · DOI
- Crawford, D. L., Segal, J. A. & Barnett, J. L. Evolutionary analysis of TATA-less proximal promoter function. Mol. Biol. Evol. 16, 194–207 (1999). · DOI
- Hahn, M. W., Rockman, M. V., Soranzo, N., Goldstein, D. B. & Wray, G. A. Population genetic and phylogenetic evidence for positive selection on regulatory mutations at the factor VII locus in humans. Genetics 167, 867–877 (2004). · DOI
- Rockman, M. V., Hahn, M. W., Soranzo, N., Goldstein, D. B. & Wray, G. A. Positive selection on a human-specific transcription factor binding site regulating IL4 expression. Curr. Biol. 13, 2118–2123 (2003). · DOI
- Rockman, M. V. et al. Positive selection on MMP3 regulation has shaped heart disease risk. Curr. Biol. 14, 1531–1539 (2004). · DOI
- Rockman, M. V. et al. Ancient and recent positive selection transformed opioid cis-regulation in humans. PLoS Biol. 3, e387 (2005). · DOI
- Bamshad, M. J. et al. A strong signature of balancing selection in the 5′ cis-regulatory region of CCR5. Proc. Natl Acad. Sci. USA 99, 10539–10544 (2002). · DOI
- Emison, E. S. et al. A common sex-dependent mutation in a RET enhancer underlies Hirschsprung disease risk. Nature 434, 857–863 (2005). · DOI
- Takahashi, A., Tsaur, S. C., Coyne, J. A. & Wu, C. I. The nucleotide changes governing cuticular hydrocarbon variation and their evolution in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 98, 3920–3925 (2001). · DOI
- Perlstein, E. O. et al. Revealing complex traits with small molecules and naturally recombinant yeast strains. Chem. Biol. 13, 319–327 (2006). · DOI
- Churchill, G. A. & Doerge, R. W. Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971 (1994). · DOI
- Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003). · DOI · Paper page
- Perez-Enciso, M. Multiple association analysis via simulated annealing (MASSA). Bioinformatics 22, 573–580 (2006). · DOI
- Lan, H. et al. Dimension reduction for mapping mRNA abundance as quantitative traits. Genetics 164, 1607–1614 (2003). · DOI
- Li, H. Q. et al. Inferring gene transcriptional modulatory relations: a genetical genomics approach. Hum. Mol. Genet. 14, 1119–1125 (2005). · DOI
- Haldane, J. B. S. The time of action of genes, and its bearing on some evolutionary problems. Am. Nat. 66, 5–24 (1932). · DOI
- Jacob, F. & Monod, J. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3, 318–356 (1961). · DOI · Paper page
- Schwartz, D. Genetic studies on mutant enzymes in maize. III. Contol of gene action in the synthesis of pH 7.5 esterase. Genetics 47, 1609–1615 (1962). · DOI
- Wallace, B. Genetic diversity, genetic uniformity, and heterosis. Can. J. Genet. Cytol. 5, 239–253 (1963). · DOI
- Zuckerkandl, E. in Classification and Human Evolution (ed. Washburn, S. L.) (Aldine, Chicago, 1963).
- Abraham, I. & Doane, W. W. Genetic regulation of tissue-specific expression of amylase structural genes in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 75, 4446–4450 (1978). · DOI
- Powell, J. R. Population genetics of Drosophila amylase. II. Geographic patterns in D. pseudoobscura. Genetics 92, 613–622 (1979). · DOI
- Matsuo, Y. & Yamazaki, T. Genetic analysis of natural populations of Drosophila melanogaster in Japan. IV. Natural selection on the inducibility, but not on the structural genes, of amylase loci. Genetics 108, 879–896 (1984). · DOI
- Powell, J. R. & Andjelkovic, M. Population genetics of Drosophila amylase. IV. Selection in laboratory populations maintained on different carbohydrates. Genetics 103, 675–89 (1983). · DOI
- Ayala, F. J. & McDonald, J. F. Continuous variation: possible role of regulatory genes. Genetica 52/53, 1–15 (1980). · DOI
- Allendorf, F. W., Knudsen, K. L. & Phelps, S. R. Identification of a gene regulating the tissue expression of a phosphoglucomutase locus in rainbow trout. Genetics 102, 259–268 (1982). · DOI
- Laurie-Ahlberg, C. C., Maroni, G., Bewley, G. C., Lucchesi, J. C. & Weir, B. S. Quantitative genetic variation of enzyme activities in natural populations of Drosophila melanogaster. Proc. Natl Acad. Sci. USA 77, 1073–1077 (1980). · DOI
- Stumpf, M. P., Wiuf, C. & May, R. M. Subnets of scale-free networks are not scale-free: sampling properties of networks. Proc. Natl Acad. Sci. USA 102, 4221–4224 (2005). · DOI
- Gomulkiewicz, R. & Kirkpatrick, M. Quantitative genetics and the evolution of reaction norms. Evolution 46, 390–411 (1992). · DOI
- Ohta, T. Origin of the neutral and nearly neutral theories of evolution. J. Biosci. 28, 371–377 (2003). · DOI
- Wittkopp, P. J., Haerum, B. K. & Clark, A. G. Evolutionary changes in cis and trans gene regulation. Nature 430, 85–88 (2004). · DOI · Paper page
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“Recent advances in the integration of quantitative genetics and expression genomics have provided a global view of gene expression traits and their implications in high-order phenotype variations [1,2,3,4,5,6,7,8].”
In-silico studies of multi drug resistance (MDR) genetic markers of Plasmodium species
Yah, C. S., & Fatumo, S. (2010) · African Journal of Malaria and Tropical Diseases · doi:10.5897/jcbbr.9000010 · Cited by 5
“health services at risk to malaria attack (Rockman and Kruglyak, 2006; Emiliana et al., 2008).”
BackgroundKruglyak, L., & Storey, J. D. (2009) · Nature Biotechnology · doi:10.1038/nbt0609-544 · Cited by 6
Self-citationNon-equilibrium thermodynamics of gene expression and transcriptional regulation
Hernández-Lemus, E. (2009) · doi:10.48550/arxiv.0904.0662 · Cited by 6
“enotypic diversity is gene expression analysis. One hard-to-grasp issue is that the process of gene expression by itself is a complex one, both from the biochemical and thermodynamical points of view [1]. The transcription of messenger RNA (mRNA) for a given gene from a DNA template often is regulated by different gen…”
BackgroundHaley, C., & de Koning, D. (2007) · Trends in Genetics · doi:10.1016/j.tig.2007.06.002 · Cited by 6
Lignin characteristics and ecological interactions of PtCOMT-modified silver birch
Tiimonen, H. (2007) · doi:10.14214/df.51 · Cited by 6
“…analyses such as microarrays have, however, started to extend our knowledge on the regulation of lignin biosynthesis (Kirst et al. 2004) and, more generally, on the potential connections between the DNA sequence and phenotypic variation (global molecular phenotyping) (Rockman & Kruglyak 2006).”
BackgroundWeighted Trigonometric Regression for Suboptimal Designs in Circadian Transcriptome Studies
Gorczyca, M. T., & Sefas, J. (2025) · Statistics in Medicine · doi:10.1002/sim.70201 · Cited by 4
Mitchell, E. D., Kerdaffrec, E., Schmidt, P., Flatt, T., & Kittelmann, S. (2025) · Molecular biology and evolution · doi:10.1093/molbev/msaf209 · Cited by 5
“…implies that a genetic difference between the parental karyotypes (INV/INV and STD/STD) affecting gene expression is in linkage disequilibrium (LD) with the gene whose expression is affected (see Wang et al. 2020; also cf. Rockman and Kruglyak 2006; Wittkopp et al. 2008; Signor and Nuzhdin 2018).”
Broad, Z., Lefevre, J. G., Wilkinson, M. J., Barton, S., Barbier, F., Jung, H., Donovan, D., & Ortiz-Barrientos, D. (2024) · Molecular Ecology · doi:10.1111/mec.17543 · Cited by 5
“Gene expression acts as a pivotal bridge between genotype and phenotype, playing a crucial role in adaptive processes (Rockman 2005; Rockman and Kruglyak 2006).”
Jallet, A., Friedrich, A., & Schacherer, J. (2023) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkad115 · Cited by 6
Insight into genetic regulation of miRNA in mouse brain
Kordas, G., Rudra, P., Hendricks, A. E., Saba, L. M., & Kechris, K. J. (2019) · BMC Genomics · doi:10.1186/s12864-019-6110-6 · Cited by 5
“Local and distal are often referred to as cis or trans, where cis implies variants affecting transcription factor binding sites or other regulatory sequences near a gene, and trans implies variants affecting changes in the structure or function of transcription factors or other regulatory proteins for a more ‘global’…”
BackgroundCommon genetic variants shape broad patterns of within-population variation in gene expression
Josephs, E. B., Lee, Y. W., Wood, C. W., Schoen, D., Wright, S., & Stinchcombe, J. (2019) · bioRxiv · doi:10.1101/763870 · Cited by 5
“Gene expression is a crucial aspect of the genotype to phenotype map and expression studies provide a large set of traits that can be easily measured without prior information about how these traits might relate to fitness (Rockman and Kruglyak 2006).”
BackgroundWeller, C. A., Tilk, S., Rajpurohit, S., & Bergland, A. O. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkab062 · Cited by 5
Cancer-specific expression quantitative loci are affected by expression dysregulation
Sheng, Q., Samuels, D. C., Yu, H., Ness, S. A., Zhao, Y., & Guo, Y. P. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby108 · Cited by 7
Genetic Disorders of Pituitary Development in Patients with Sheehan'S Syndrome
Diri, H. (2016) · Acta Endocrinologica (Bucharest) · doi:10.4183/aeb.2016.413 · Cited by 5
“In other words, increases or decreases in the expression amount of a gene may be results of changes in expressions of other genes or environmental factors (28).”
BackgroundDimensionality and the statistical power of multivariate genome-wide association studiesPreprint
Márquez, E., & Houle, D. (2015) · bioRxiv · doi:10.1101/016592 · Cited by 5
“This is likely to be especially critical in genomic studies like eQTL analyses of transcript abundances [37,38], where the cost of generating and sequencing additional genotypes far”
BackgroundHoushyani, B., van der Krol, A. R., Bino, R. J., & Bouwmeester, H. J. (2014) · BMC Plant Biology · doi:10.1186/1471-2229-14-170 · Cited by 6
“[27,28] and natural polymorphism among Arabidopsis accessions has been already reported to be considerable at the genome [29] as well as transcriptome levels [30].”
BackgroundWang, Q.-J., Qi, W.-W., Wang, Y., Sun, F., Qian, X.-Y., Luo, X.-J., & Yang, J.-S. (2011) · Genetics Research · doi:10.1017/s0016672311000218 · Cited by 5
“Rockman & Kruglyak (2006) define the two types of regulatory sequence variation as local and distant regulatory variation.”
BackgroundIntegrating Genomics into Evolutionary Theory
Wray, G. A. (2010) · The MIT Press eBooks · doi:10.7551/mitpress/9780262513678.003.0005 · Cited by 7
Genetic analysis of variation in transcription factor binding in yeast
Zheng, W., Zhao, H., Mancera, E., Steinmetz, L. M., & Snyder, M. P. (2010) · Nature · doi:10.1038/nature08934 · Cited by 169
Systems genetics: The added value of gene expression
Visscher, P. M., & Goddard, M. E. (2010) · HFSP journal · doi:10.2976/1.3292182 · Cited by 4
“…or association analysis, to detect association within pedigrees or in the population, respectively, (Brem et al. , 2002; Cheung et al. , 2003a, 2003b, 2005; Jansen and Nap, 2001; Monks et al. , 2004; Morley et al. , 2004; Rockman and Kruglyak, 2006; Schadt et al. , 2003; Stranger et al. , 2005).”
BackgroundMeaburn, E. L., Fernandes, C., Craig, I., Plomin, R. J., & Schalkwyk, L. C. (2009) · Twin Research and Human Genetics · doi:10.1375/twin.12.4.372 · Cited by 4
“…DNA variation (quantitative trait loci; QTLs) associated with individual differences in gene expression (expression QTLs; eQTLs; Breitling et al., 2008; Dixon et al., 2007; Emilsson et al., 2008; Goring et al., 2007; Nica & Dermitzakis, 2008; Rockman & Kruglyak, 2006; Stranger et al., 2007).”
Achieving confidence in mechanism for drug discovery and development
Pitluk, Z., & Khalil, I. G. (2007) · Drug Discovery Today · doi:10.1016/j.drudis.2007.10.001 · Cited by 6
eQTL analysis: A bridge from genome to mechanism
Jia, Z., Xu, J., Ma, Y., Wei, S., Sun, C., Chen, X., Kang, J., Chen, H., Zhang, C., Dong, Y., et al. (2025) · Genes & Diseases · doi:10.1016/j.gendis.2025.101850 · Cited by 5
“12,13 Here, we systematically used the data for analysis, identification, analysis tools, and subsequent analysis for researchers.”
A genetic variant in the immune-related gene ERAP1 affects colorectal cancer prognosis
Zou, D., Cai, Y., Jin, M., Zhang, M., Liu, Y., Chen, S., Yang, S., Zhang, H., Zhu, X., Huang, C., et al. (2023) · Chinese Medical Journal · doi:10.1097/cm9.0000000000002845 · Cited by 4
“[13,14] In this study, we confirmed the relationship between CRC survival and eQTLs by conducting a two-stage population study in Chinese population and further experimentally validated the function of CRC survival-associated SNP.”
MethodZhang, J., Sun, M., Zhao, Y., Geng, G., & Hu, Y. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.624766 · Cited by 9
“Traditional QTL methods need to measure the genotype and gene expression level of each individual studied, and then use association analysis (outbred population) or linkage analysis (family or experimental hybrid population) to assess the association between genotype and gene expression level (Rockman and Kruglyak, 20…”
MethodGenetic variants associated mRNA stability in lung
Lǐ, J., Tang, M., Li, Y., Amos, C. I., & Cheng, C. (2022) · BMC Genomics · doi:10.1186/s12864-022-08405-y · Cited by 5
“The most well investigated QTLs are eQTLs, which are associated with the expression level of gene transcripts [2].”
BackgroundLiu, Y., & El-Kassaby, Y. A. (2020) · Heredity · doi:10.1038/s41437-020-00388-4 · Cited by 5
“polymorphism on trans-regulatory factors located far away from the target genes (Rockman and Kruglyak 2006).”
BackgroundInfluentialIdentification of rs11615992 as a novel regulatory SNP for human P2RX7 by allele-specific expression
Peng, T., Zhong, L., Gao, J., Wan, Z.-Y., Fu, W.-P., & Sun, C. (2019) · Zeitschrift für Induktive Abstammungs- und Vererbungslehre · doi:10.1007/s00438-019-01598-0 · Cited by 4
Dale-Kuys, R., Vervalle, J., Roodt-Wilding, R., & Rhode, C. (2017) · Aquaculture International · doi:10.1007/s10499-016-0107-9 · Cited by 4
“…9–35% (R2 values for allelic correlations) of the phenotypic variation between large and small animals, which is consistent with other results obtained by similar studies in both abalone and other species (Rockman and Kruglyak 2006; Liu et al. 2007; Baranski et al. 2008; Slabbert 2010).”
ResultJoosen, R. V. L., Ligterink, W., Hilhorst, H. W. M., & Keurentjes, J. J. B. (2013) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/97816080575661130201 · Cited by 3
Systems genetics: challenges and developing strategies
Li, H., & Zhang, P. (2012) · Biologia · doi:10.2478/s11756-012-0026-9 · Cited by 5
Gene Expression Divergence is Coupled to Evolution of DNA Structure in Coding Regions
Dai, Z., & Dai, X. (2011) · PLoS Comput. Biol. · doi:10.1371/journal.pcbi.1002275 · Cited by 4
“the same species [10,11] and by analysis of hybrid species [12,13].”
BackgroundThe future of mouse genetics in osteoporosis research
Ackert‐Bicknell, C. L., & Rosen, C. J. (2009) · IBMS BoneKEy · doi:10.1138/20090380 · Cited by 4
“Such a QTL is called an “expression QTL” or eQTL (specifically reviewed in (58;59)).”
BackgroundeQTL Analysis: Methods and Protocols
Shi, X.-H. (2020) · Methods in Molecular Biology · doi:10.1007/978-1-0716-0026-9 · Cited by 4
“In doing so, there are far fewer markers to analyze; however, this type of study may suffer by missing common variants not present in the studied cohort and may lack the ability to resolve the location of the eQTL to a high degree [1].”
BackgroundInfluentialA Genomic View of Biotic Stress Resistance
Elmore, J., Perović, D., Ordon, F., Schweizer, P., & Wise, R. (2018) · doi:10.1007/978-3-319-92528-8_14 · Cited by 4
Statistical Methods for Drug Discovery
Kühn, M., Yates, P. D., & Hyde, C. L. (2016) · Statistics for biology and health · doi:10.1007/978-3-319-23558-5_4 · Cited by 6
de la Fuente, A. G. (2013) · Springer Berlin Heidelberg · doi:10.1007/978-3-642-45161-4 · Cited by 4
“Systems Genetics approaches provide a new paradigm of large-scale genome and network analysis (Jansen and Nap 2001; Jansen 2003; Rockman and Kruglyak 2006; Rockman 2008).”
Genomic Methods for Clinical and Translational Pain Research
Wang, D., Kim, H., Wang, X., & Dionne, R. A. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-561-9_2 · Cited by 6
“Thousands of transcripts can be assayed simultaneously, therefore providing data on a large and unbiased set of traits (17).”
BackgroundDiaz, I. A., Zayed, O., de Dios, E. Á., Jiang, C., Bowman, K. D., & Seymour, D. K. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1627531 · Cited by 3
“The genetic architecture of gene regulatory variation in hybrids includes whether genes are cis-or trans-regulated and can provide insight into the mechanistic basis of hybrid phenotypes (Rockman and Kruglyak, 2006).”
Transcriptome-wide root causal inference
Strobl, E. V., & Gamazon, E. R. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013461 · Cited by 3
“…the nearest TSS.), the causal transcriptome-wide association study (cTWAS) [21], the maximally correlated gene within the cis-window (cis-eQTL) [22], colocalization with approximate Bayes factors (ABF) [23], and colocalization with Sum of Single Effects model (SuSiE) [24] without performing…”
Significance of PFKFB4 and gene risk signature in cervical cancer prognosis and progression
Pang, G., Ruan, X., Yao, W., & Cao, M. (2025) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2025.152084 · Cited by 2
A Corrected Score Function Framework for Modelling Circadian Gene Expression
Gorczyca, M. T., McDonald, T. M., & Sefas, J. (2024) · Stat · doi:10.1002/sta4.70007 · Cited by 3
“It is emphasized that Scenarios 2-4 and 6-8 also represent settings where there are individual-specific variations in gene expression (Cheung et al., 2003; Rockman and Kruglyak, 2006; Stranger et al., 2007).”
Trans-eQTL hotspots shape complex traits by modulating cellular states
Renganaath, K., & Albert, F. W. (2023) · bioRxiv · doi:10.1101/2023.11.14.567054 · Cited by 3
“First, more than half of the genes are influenced by one “local” eQTL per gene, which are typically caused by DNA variants in cis-regulatory elements in the gene’s immediate vicinity 27,28 .”
Powerful eQTL mapping through low-coverage RNA sequencing
Schwarz, T., Boltz, T. A., Hou, K., Bot, M. H. M., Duan, C., Loohuis, L. M. O., Boks, M. P., Kahn, R. S., Ophoff, R. A., & Paşaniuc, B. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100103 · Cited by 13
“RNA sequencing (RNA-seq) is the state-of-the-art assay for measuring gene expression in bulk tissue and is therefore the assay of choice for eQTL mapping.(7,8) However, the high cost of RNA-seq often limits the sample size and therefore reduces the discovery power of eQTL studies based on RNA-seq.”
BackgroundShaver, A. O., Garcia, B. M., Gouveia, G., Morse, A. M., Liu, Z.-H., Asef, C. K., Borges, R. M., Leach, F. E., Andersen, E. C., Amster, I., et al. (2022) · bioRxiv · doi:10.3389/fmolb.2022.930204 · Cited by 3
The statistical practice of the GTEx Project: from single to multiple tissues
Liao, X., Chai, X., Shi, X., Chen, L., & Liu, J. (2020) · Quantitative Biology · doi:10.1007/s40484-020-0210-9 · Cited by 3
“Expression quantitative trait loci (eQTLs) are genomic loci that explain all or a fraction of variations in expression levels of mRNAs [9] and eQTL mapping offers a simple but powerful tool for identifying genetic variants which can affect gene expression [10].”
BackgroundYan, K. K., Zhao, H., Wu, J. T. K., & Pang, H. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22341 · Cited by 3
“Consequently, expression quantitative trait loci (eQTLs), the genetic association analysis of these variants and quantitative gene expression, has flourished as one of the research directions in the last decade (Nica & Dermitzakis, 2013; Rockman & Kruglyak, 2006).”
BackgroundMatoba, N., Liang, D., Sun, H.-G., Aygün, N., McAfee, J. C., Davis, J. E., Raffield, L., Qian, H.-J., Piven, J., Li, Y., et al. (2020) · doi:10.1101/2020.01.13.20017319 · Cited by 3
McGirr, J. A., & Martin, C. (2018) · bioRxiv · doi:10.1101/372912 · Cited by 3
“Some studies found evidence for ubiquitous transgressive expression inherited in F1 hybrids (i.e. over- or under-dominance) (Ranz et al. 2004; Rockman and Kruglyak 2006; Roberge et al. 2008), while others found predominately additive patterns (Hughes et al. 2006; Rottscheidt and Harr 2007; Davidson…”
BackgroundDistant regulatory effects of genetic variation in multiple human tissuesPreprint
Jo, B., He, Y. M., Strober, B. J., Parsana, P., Aguet, F., Brown, A. A., Castel, S. E., Gamazon, E. R., Gewirtz, A. D., Gliner, G., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/074419 · Cited by 14
“Trans-eQTLs, or genetic variants that affect gene expression levels of distant target genes, have received much less attention in comparison to cis-eQTLs, in part due to the considerable multiple hypotheses testing burden 9 .”
BackgroundO Trieb de Freud como instinto 2: agressividade e autodestrutividade
Simanke, R. T. (2014) · Science studies · doi:10.1590/s1678-31662014000300003 · Cited by 3
Brynedal, B., Raj, T., Stranger, B. E., Bjornson, R. D., Neale, B. M., Voight, B. F., & Cotsapas, C. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.1728 · Cited by 2
Genetic Diversity, Habitat Fragmentation and Epigenetic Variations
Kimatu, J., Muluvi, G., & Liu, B. (2012) · InTech eBooks · doi:10.5772/36387 · Cited by 3
“However, the correspondences between genes, alleles and environments on the one hand, and phenotypes on the other have been found to be increasingly complex (Rockman and Kruglyak, 2006).”
BackgroundRyten, M., Trabzuni, D., & Hardy, J. (2009) · Briefings in Functional Genomics and Proteomics · doi:10.1093/bfgp/elp028 · Cited by 5
“There is already strong evidence to suggest that mRNA expression is a quantitative trait which can be inherited [7, 8].”
BackgroundSearching for master regulators of transcription in a human gene expression data set
Buil, A., Perera-Lluna, A., Souto, R., Peralta, J., Almasy, L., Vallverdú, M., Caminal, P., & Soria, J. (2007) · BMC Proceedings · doi:10.1186/1753-6561-1-s1-s81 · Cited by 3
“Background One common feature observed in studies of transcript expression is the presence of hot spots, that is, individual loci that affect large number of transcripts [1].”
BackgroundKang, M., & Gao, J. (2019) · Methods in molecular biology · doi:10.1007/978-1-0716-0026-9_11 · Cited by 3
Imaging genetics of seed performance
Joosen, R. V. L. (2013) · doi:10.18174/257214 · Cited by 1
“In addition, cis-regulated genes might encode regulators affecting the expression of downstream target genes in trans (Rockman and Kruglyak 2006).”
BackgroundInfluentialGenes and Pathways Contributing to Obesity
Drake, T. A. (2010) · Progress in molecular biology and translational science · doi:10.1016/b978-0-12-375003-7.00002-9 · Cited by 3
Gadsby, S., Rauch, C., & Wattis, J. (2026) · Mathematical Biosciences · doi:10.1016/j.mbs.2025.109606 · Cited by 2
Genetics and Analysis of Quantitative Traits
(2025) · doi:10.1093/oso/9780192898180.001.0001 · Cited by 2
The Possible Mechanistic Basis of Individual Susceptibility to Spike Protein Injury
Halma, M. T. J., Vottero, P., Thorp, J. A., Peers, T., Tuszyński, J. A., & Marik, P. E. (2025) · Advances in Virology · doi:10.1155/av/7990876 · Cited by 2
“After endocytosis, the modifed mRNA is present in the cytosol to be expressed, which can be highly variable depending on host genetics [42, 43].”
Kadagandla, S., Gunamalai, L., Lee, D., & Kapoor, A. (2025) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.124.005032 · Cited by 3
Everman, E. R., & Macdonald, S. J. (2024) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkae015 · Cited by 6
“See Supplementary Methods for additional details on preparation steps. eQTL mapping (Rockman and Kruglyak 2006) was performed on quantile normalized residual gene expression for each of the six datasets using R/qtl2 (Broman et al. 2019). eQTL mapping treats each gene expression measure as a trait,…”
BackgroundMethodLinking histology and molecular state across human tissuesPreprint
Jones, A., Gundersen, G. W., & Engelhardt, B. E. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.10.495669 · Cited by 2
“QTLs have provided valuable information about gene function and regulation (GTEx Consortium et al., 2017, 2020; Rockman and Kruglyak, 2006), in particular in the analysis and interpretation of the”
BackgroundAn atlas of gene expression variation across the Caenorhabditis elegans speciesPreprint
Zhang, G.-T., Roberto, N. M., Lee, D., Hahnel, S. R., & Andersen, E. C. (2022) · bioRxiv · doi:10.1101/2022.02.06.479320 · Cited by 2
Addressing evolutionary questions with synthetic biologyPreprint
Baier, F., & Schaerli, Y. (2021) · Evolutionary Systems Biology · doi:10.31219/osf.io/6msvq · Cited by 2
Guzella, T., Barreto, V. M., & Carneiro, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007910 · Cited by 2
“Advances in genomics have allowed the analysis of genetic contributions to variation in gene expression, in terms of so-called expression quantitative trait loci (eQTL) [1, 2].”
BackgroundJanissen, R., Eslami-Mossallam, B., Artsimovitch, I., Depken, M., & Dekker, N. (2019) · doi:10.1101/762468 · Cited by 2
Zhao, C., Xie, S.-S., Wu, H., Luan, Y., Hu, S.-Q., Ni, J., Lin, R., Zhao, S.-H., Zhang, D.-X., & Li, X.-Y. (2019) · Scientific Reports · doi:10.1038/s41598-019-42815-5 · Cited by 2
Genome im Fokus: Entwicklung und Anwendungen von CRISPR‐Cas9‐Bildgebungstechnologien
Knight, S. C., Tjian, R., & Doudna, J. A. (2017) · Angewandte Chemie · doi:10.1002/ange.201709201 · Cited by 7
The Gene Regulatory Landscape of Local Adaptation
Gould, B., Chen, Y., & Lowry, D. (2017) · bioRxiv · doi:10.1101/145805 · Cited by 2
A block mixture model to map eQTLs for gene clustering and networking
Wang, N., Gosik, K., Li, R., Lindsay, B. G., & Wu, R. (2016) · Scientific Reports · doi:10.1038/srep21193 · Cited by 2
Albouyeh, R., & Ritland, K. (2008) · Journal of Heredity · doi:10.1093/jhered/esn081 · Cited by 2
“With the advent of microarrays, it is possible to view the transcriptome of an organism as a suite of quantitative traits (Rockman and Kruglyak 2006).”
BackgroundIdentifying Genetic Components of Drug Response in Mice
Cotsapas, C. J. (2008) · Pharmacogenomics · doi:10.2217/14622416.9.9.1323 · Cited by 2
“Whilst this subject has been covered extensively elsewhere [14], we wish to highlight that many studies have now found that gene-expression levels are both heritable and influenced by common genetic variation in humans [13,15], rodents [16,17], yeast [18] and plants [19].”
BackgroundGenetics and molecular biology: micro RNAs are welcome to the lipid field.
Baralle, M., & Baralle, F. (2007) · Current Opinion in Lipidology · doi:10.1097/mol.0b013e32813aee6e · Cited by 2
Performing QTL and eQTL Analyses with the R-Package GenomicTools.
Fischer, D. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0026-9_2 · Cited by 2
RNA sequencing for the study of gene expression regulation
Gonçalves, Â. (2012) · Apollo (University of Cambridge) · doi:10.17863/cam.11726 · Cited by 2
“Phenotypic diversity between organisms is therefore partially explained by DNA sequence variation [126].”
BackgroundApports de la génomique fonctionnelle à la cartographie fine de QTL
Mignon, G. L., Blum, Y., Demeure, O., Bihan-Duval, É. L., Roy, P. L., & Lagarrigue, S. (2010) · INRAE Productions Animales · doi:10.20870/productions-animales.2010.23.4.3313
Systems Genetics and Complex Traits
Carter, G. W., & Dudley, A. M. (2009) · Springer eBooks · doi:10.1007/978-0-387-30440-3_549 · Cited by 2
Song, L., Pan, M., Li, Y., Liu, S., Ma, J., Su, Q., Wu, Y., Yang, Z., Luo, Y., Feng, J., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117790
Trans-regulation of heterochromatin underlies genetic variation in 3D genome contactsPreprint
Fortin, H. J., Struba, A. Z., & Baker, C. L. (2025) · bioRxiv · doi:10.64898/2025.12.10.693515 · Cited by 1
“Cis -regulatory variants affect nearby genes through changes in local CREs, whereas trans -regulatory effects are mediated by diffusible factors, often transcription factors or chromatin regulators, that can act on many CREs throughout the genome [11].”
Accuracy and Scalability of Machine Learning Methods for Genotype-Phenotype Association DataPreprint
Collienne, K., Zhang, L., Sumpter, N., Leask, M., Witbrock, M., & Gavryushkin, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.13.638022
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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