Exon and junction microarrays detect widespread mouse strain- and sex-bias expression differences
© Su et al; licensee BioMed Central Ltd. 2008
Received: 26 November 2007
Accepted: 04 June 2008
Published: 04 June 2008
Studies have shown that genetic and sex differences strongly influence gene expression in mice. Given the diversity and complexity of transcripts produced by alternative splicing, we sought to use microarrays to establish the extent of variation found in mouse strains and genders. Here, we surveyed the effect of strain and sex on liver gene and exon expression using male and female mice from three different inbred strains.
71 liver RNA samples from three mouse strains – DBA/2J, C57BL/6J and C3H/HeJ – were profiled using a custom-designed microarray monitoring exon and exon-junction expression of 1,020 genes representing 9,406 exons. Gene expression was calculated via two different methods, using the 3'-most exon probe ("3' gene expression profiling") and using all probes associated with the gene ("whole-transcript gene expression profiling"), while exon expression was determined using exon probes and flanking junction probes that spanned across the neighboring exons ("exon expression profiling"). Widespread strain and sex influences were detected using a two-way Analysis of Variance (ANOVA) regardless of the profiling method used. However, over 90% of the genes identified in 3' gene expression profiling or whole transcript profiling were identified in exon profiling, along with 75% and 38% more genes, respectively, showing evidence of differential isoform expression. Overall, 55% and 32% of genes, respectively, exhibited strain- and sex-bias differential gene or exon expression.
Exon expression profiling identifies significantly more variation than both 3' gene expression profiling and whole-transcript gene expression profiling. A large percentage of genes that are not differentially expressed at the gene level demonstrate exon expression variation suggesting an influence of strain and sex on alternative splicing and a need to profile expression changes at sub-gene resolution.
Variation in mammalian mRNA expression is significantly affected by both strain and sex. In mice, widespread sex differences have been observed in adipose, kidney, liver, muscle, and brain tissue [1–3]. Given the heterogeneity of brain tissues, extensive studies have also been performed in various parts of the brain, highlighting the extent of sexual dimorphism in the hypothalamus, pituitary gland, and the cortex . In mice, the estimate of inter-strain variation in various regions of the brain is as high as 30% .
While alternative splicing has been acknowledged to play an important role in genetic diversity, few large-scale studies have investigated the effects of strain, sex and tissue on exon expression or other alternative splicing mechanisms. Such studies have, for example, identified splicing events involved in cancer and tissue definition [5, 6]. In Drosophila, where sex-specific splicing has long been shown to be involved in sex determination, 22% of alternatively spliced genes demonstrated sexual dimorphism . Further studies on alternative splicing have demonstrated that some single nucleotide polymorphisms (SNPs) are responsible for variations in the ratios of alternative spliced transcripts [8–11]. Given that at least 8 million SNPs exist in the mouse population , we decided to investigate the effects of differing genetic backgrounds and sex on alternative splicing patterns in a mammalian system.
In this study we present a broad survey of the role of naturally occurring genetic variations and sex differences upon gene expression and splicing in liver, a key tissue regulating many disease conditions such as metabolic disorders and cardiovascular disease. We profiled the expression of 9406 exons representing 1020 genes in 71 female and male mouse livers from strains DBA/2J, C57BL/6J, and C3H/HeJ. Genes were selected for involvement in obesity, diabetes, cardiovascular diseases, and bone traits [13–19] and strains were chosen based on their distinct genealogy . We selected a single representative transcript for each gene (see methods for details) and designed a custom microarray with exon and exon-junction probes for each exon, enabling us to investigate expression changes at both the gene and exon levels. In addition, we examined the differences between profiling gene expression using a single probe at the 3' end vs. using multiple probes spaced along the gene.
Our results indicate a degree of concurrence between 3' gene and whole-transcript gene expression profiling: over 64% of the genes identified as significantly differentially expressed using 3' gene expression profiling methods are also identified through whole-transcript gene expression profiling. Moreover, whole-transcript gene expression profiling identified 13% to 20% more differential expression than 3' gene expression profiling. Exon expression profiling, however, identified at least 38% or more genes with at least one differentially expressed exon. 55% and 32% of genes showed differential exon expression by strain and sex, respectively. Finally, by examining gene expression at the sub-gene resolution, we found 205 genes to exhibit differences in exon expression for both strain and sex.
We profiled 9406 exons representing 1020 genes using male and female liver samples from three strains, analyzed the results at three distinct levels: 3' gene expression, whole-transcript gene expression, and exon expression. Microarray data were deposited at GEO under GSE10736.
Sex differences are larger than strain differences
SNPs affect probe intensities
We were able to map 97% of all the probes to the Mouse Genome NCBI Build 36. Using recent genotype data , we identified over 1.5 million SNPs present within C57BL/6J, DBA/2J, and C3H/HeJ mice. Only 3% of all probes overlap a SNP from one of these strains. To test the effects of the presence of SNPs in probes, we performed a two-way factorial ANOVA designed using strain and sex as factors. Of the 3421 probes that were differentially expressed, 194 (6%) had at least one SNP within the probe region. Conversely, only 339 out of 14510 non-differentially expressed probes contained at least one SNP. Hence the enrichment for SNPs within differentially expressed probes is very significant at a Fisher's exact test p-value of 1.3 × 10-21 and an odds ratio of 2.51. Furthermore, junction probes overlapping SNPs have a higher odds ratio (2.83 vs. 2.31) of being differentially expressed compared to exon probes overlapping SNPs (see Additional file 3). Because a change in probe intensity could reflect either a change in alternative splicing or a change in the binding affinity, due to the SNP, we decided to discard probes overlapping SNPs from these strains.
Exon profiling identifies more differences
Number and percentage of differentially expressed genes in each dataset (Bonferroni corrected p-value < 0.01).
3' profiling (n = 941)
Whole-transcript gene profiling (n = 941)
Exons (n = 9055)
Associated Genes (n = 941)
When analyzed at the exon level, we found 19% and 14% of exons are differentially expressed across strain and sex, respectively, using the same Bonferroni-corrected p-value threshold of 0.01. In terms of genes associated with these differentially expressed exons, 55% and 32% of genes have at least one differentially expressed exon across strain and sex, respectively. Overall, 3014 exons (33%), representing 823 genes (87%), showed significant strain- or sex-specific biases in expression. Surprisingly, when we tested each gene for sex-strain interaction effects, we found only 1% to 2% of genes (depending on the datasets used) have significant interaction effects at a Bonferroni-corrected p-value threshold of 0.01, close to the level expected by chance.
We then analyzed the overlap between genes identified as differentially expressed using gene profiling analysis and exon profiling analysis. 95% and 93% of strain-bias and sex-bias genes identified by 3' gene profiling were identified in exon profiling. Similarly, 96% and 93% of strain-bias and sex-bias genes identified via whole-transcript gene profiling were detected via exon profiling. However, 234 and 90 genes containing an exon showing strain-bias and sex-bias effects, respectively, from exon profiling were not detected as differentially expressed by either gene expression profiling method. Thus, 75% and 38% of the genes with differences were identified only by exon profiling.
Variation across strain and sex
and 90 genes showed strain- and sex-bias effects, respectively, for differences in exon expression in genes not identified as differentially expressed using 3' gene expression or whole-transcript gene expression profiling. For example, exon 4 in rhoQ, which encodes ras homolog gene family, member Q protein, showed significantly lower mean log10 ratios in DBA/2J and C57BL/6J while demonstrating elevated expressions in C3H/HeJ relative to the pool of control samples suggesting that different ratios of alternatively spliced isoforms are present among the three strains (Figure 5C). Similarly, exon 21 in pitpnm1 (phosphatidylinositol membrane-associated 1) showed higher expression levels in males relative to females in all three strains (Figure 5D) strongly suggesting that at least two different forms or isoform ratios of pitpnm1 are expressed between males and females.
We found many expression changes associated with sex and thus investigated if the X chromosome was enriched for differentially expressed genes or splicing events. Using a Fisher's exact test, we found no enrichment for differentially expressed genes or differential splicing on the X chromosome (p-value > 0.9, see Additional file 4).
Messenger RNA gene expression variation can be divided into two broad categories: differences in the overall mRNA level (due to transcriptional changes or mRNA stability) and alterations in the ratios of alternative transcripts. Variations in gene expression can be due to a number of different factors, including genetic variation, epigenetic variation, environment variation (which could include, for example, the hormonal differences between males and females) and interactions between these factors. In this paper, we first quantified the contributions to differential expression from two sources of variation – strain and sex – and secondly, provided evidence suggesting that variation in transcript structure contributes significantly to mRNA expression variation.
When we clustered the dataset using hierarchical methods, we found that the mice of the same sex, but different strains, were more similar in terms of gene expression than mice in the same strain but of different sex. Furthermore, after this initial subdivision into males and females, the phylogenetic tree obtained matches that shown in previous SNP-based genealogy studies[19, 20]. Thus, we conclude that (a) the natural variation of gene and exon expression is smaller between mouse populations of similar sex than that of the same strains but differing sexes, and (b) to the extent we have examined, gene and exon expression captures the differences identified by genealogy. Based on genealogy of mouse strains, we believe more differences in gene expression and splicing could have been observed if the strain selection had been even more diverse, such as the inclusion of strains 129S1/Svlm, and SWR/J [19, 21].
If we compare our estimates of strain-bias genes from 3' gene expression profiling to those found in the literature, our estimates of 23% fall within the range that has been documented by others. For example, Nadler et al. (2006) found 57% of genes exhibited strain-biases at the level of gene expression whereas Pavlidis and Noble (2001) and Sandberg et al. (2000) found only 1% to 2% of genes show inter-strain variation [4, 22, 23]. As pointed out by Nadler et al. (2006), the higher estimates are likely due to the inclusion of a larger more diverse set of strains, i.e., 10 strains in Nadler et al. (2006) study vs. 2 strains in Sandberg et al. (2000) [4, 23]. In addition, differences in the tissues examined are also likely to significantly influence variance estimates, since the studies mentioned above used brain tissue while our study was performed using liver. In terms of sex estimates, we find that our estimate, at 17%, is markedly smaller than Yang et al. . This difference likely reflects the dramatic differences in power between the two studies to detect expression differences, given only 11 or 12 animals were profiled from each sex for each strain in or study, versus the more than 150 individuals per sex profiled in the study by Yang et al.
We detected significant variation in exon expression with regards to the genetic background and sex. While the probes used for exon expression profiling may be more susceptible to cross-hybridization and higher background levels given the smaller target regions, the use of cDNA amplification products partially mitigates this effect . Furthermore, by averaging multiple exon and junction probes, we increased the reliability of each measurement and reduce the impact of individual SNPs. However, averaging exon and junction probes makes it more difficult to distinguish different types of splicing events. Nevertheless, through the use of exon expression profiling technologies, we were able to detect 234 and 90 genes with strain- and sex-bias effects, respectively, that were not detected in the 3' gene expression or whole-transcript gene expression profiling analysis. These numbers suggest that many alternative splicing events are differentially expressed but go undetected by current gene expression profiling technologies.
Splicing differences between groups can be attributed to genetic or epigenetic variation. For example, variations in cis-acting regulatory elements, such as SNPs within promoter sequences, splicing enhancers or splicing silencers can alter transcriptional initiation rates and splicing patterns. Structural variations in trans-acting splice regulatory proteins may affect global splicing patterns and nucleotide variation in mRNA transcripts can influence translational efficiency (as shown with apolipoprotein A-II in mice ) and/or mRNA decay rates. Expression and splicing differences observed between different sexes, however, showcase the amount of underlying biological mechanisms that have yet to be elucidated. With the exception of the sex chromosomes, the genome is essentially identical between the males and females of an inbred mice strain, hence the possible mechanisms that give rise to gene expression and/or splicing variation include trans-acting factors on the sex chromosomes (such as SRY or the Sox proteins), epigenetic variations, and/or hormonal differences.
Oligonucleotide probes overlapping SNPs are biased towards differential expression, leading to overestimation of differential expression. A study using probes from the Affymetrix platform recently demonstrated the susceptibility of single probes to SNPs and highlights the impact of natural variation on hybridization based methods . We found similar findings in longer 36 nt and 60 nt probes. For example, we found that 36 nt junction probes overlapping a SNP show higher sensitivity towards differential expression, possibly due to alternative splicing brought about by SNPs within splice sites or due to differences in probe binding affinities due to the SNP.
We have confirmed that gene expression is significantly affected by strain and sex and provided evidence suggesting that this effect extends to alternative splicing which, to our knowledge, had not been shown in mammals. Given that variations in alternative splicing patterns lead to a wide variety of downstream biological effects, our results provides further justification for investigations on alternative splicing variations in genetically segregating populations.
A large degree of strain- and sex-bias variation is observed in mouse liver tissue. Differences are observed both at the overall mRNA levels and in the expression of individual exons. We estimate 55% and 32% of genes demonstrate differential expression between strains and sex, respectively, at the level of gene or exon expression. Exon expression profiling captures the majority of genes identified by 3' gene expression profiling (93%), and identifies many more genes containing differential exon expression – changes invisible to 3' gene expression profiling. In these samples, by profiling only the 3' end of each gene and not exons, more than half the biological information present in the mRNA variation is lost.
To our knowledge this is the first study reporting a broad survey of the strain and sex effects upon individual exon expression. Genetic variation of gene expression has been used by several groups, including ours, in studying the genetic causes of complex disease and the identification of causative genes for such diseases [14, 15, 19, 27, 28]. Recently, Kwan et al. (2007) identified significant association between alternative splicing patterns and cis-regulatory regions in humans demonstrating the heritability of alternative splicing events . These studies along with our finding that changes in exon expression is widespread between mice of difference strains demonstrate the importance of monitoring variations in splice forms and that splicing is affected broadly by natural genetic variations.
Mice background and RNA collection
C57BL/6J, C3H/HeJ, and DBA/2J mice were reared at JAX in Bar Harbor, Maine and shipped to JAX in Sacramento, California at 7 weeks of age. A total of 12 mice for each strain and each sex were placed on a standard chow diet for 12 weeks. Mice were kept in similar environmental conditions to minimize environmental effects. Female mice were not synchronized with respect to estrogen cycle. At 20 weeks of age, all mice were euthanized and liver tissue was collected at necropsy and flash frozen in liquid nitrogen. One liver sample failed quality control, resulting in a total of 71 mouse livers profiled.
From previous studies in our lab and information found in the published studies, we selected 1,312 mouse genes. For each gene, a representative transcript was selected, with priority to longer RefSeq NM transcripts, followed by Genbank mRNAs, and lastly dbEST ESTs. Each transcript was aligned to the Celera mouse genome sequenced to define the exon structure . Probes were selected as in Johnson et al. (2003) , where 36 nt junction probes were placed across exon-exon junctions with 18 nt in each exon, and synthesized on 26 nt stilts (60 nt total) and an optimal 60 nt probe was selected for each exon. Custom-designed exon and junction microarray, containing 25760 probes representing 1312 genes, were transmitted to and printed by Agilent Technologies (California).
Preparation of labeled cDNA, array hybridization, experimental design, and image processing
Hybridization material was generated through a random-priming amplification of poly [A]+ purified RNA using primers with a random sequence at the 3' end and a fixed motif at the 5' end that was optimized to generate strand-specific cDNA copies of full-length mRNA transcripts . Since the region used for exon and junction probe selection is constrained to a smaller region, more probes contain sequence with suboptimal characteristics (e.g. high GC content or higher homology to other genes). The hybridization of cDNA, rather than cRNA as commonly done, partially mitigates this issue due to higher specificity and lower background levels .
Hybridization conditions were as previously described . All 71 samples were hybridized in a two-channel experiment, where one channel was a common reference, generated by pooling all 71 samples in equal mass. Array hybridizations were done in duplicate with fluor reversal to systematically correct for Cy3/Cy5 dye bias. Array images were processed as described to obtain background noise, single channel intensity and associated measurement error estimates . Expression changes between samples and pool were quantified as mean log10 (expression ratio), and associated error.
Gene and exon expression
The expression dataset was first filtered to exclude probes with saturated intensities or those below background levels. Based on previous experiments with the ink-jet microarray platform and Agilent scanner, we flagged probe intensities that fell outside of the linear range, either near saturation or background levels. The filtered dataset contained expression ratios for 1020 genes represented by 9406 exons. We calculated gene expression in two ways; using a single 3' probe to mimic commonly used 3' based microarray profiling and using the mean of all probes measurements associated with the gene. Standard microarray experiment utilizes an oligo-dT based amplification protocol that amplifies only the region immediately 5' of the poly adenylation site. Probes on standard arrays are thus situated near the genes' 3' terminus. As a surrogate for standard 3' gene expression profiling, we selected the exon probe located closest to the 3' end on our custom-design arrays and extracted the mean log10 ratio to the reference pool; forming a "3' gene expression profiling" dataset. We also calculated a "whole-transcript" gene expression dataset using all exon and junction probes along the entire length of the gene. The mean log10 ratios for all probes associated with a given gene were then averaged forming a single measurement for the transcript. For exon expression profiling, we again used the average of the mean log10 ratios of each exon probe and the two flanking junction probes. In the case of the first or last exon, only the exon probe and a single flanking junction probe was used (Figure 1).
Subsequent analysis performed required the removal of probes with SNPs within the probe body. Hence, for the 3' gene expression profiling dataset, if the exon probe closest to the 3' end overlapped SNPs, we discarded the gene. For whole transcript gene expression profiling, we computed the average measurement of all probes with no SNPs and used only exons and flanking junction probes containing no SNPs for exon expression profiling.
Genes and exons were clustered in 1-dimension using agglomerative methods with Pearson correlation being used as a measure of similarity. Correlation values between groups were computed by first averaging the mean log10 expression values from all individuals in each group, and then calculating the Pearson correlation using all complete pairwise values for each gene between all possible group pairs. In terms of differential expression, each dataset was analyzed using the following two-way analysis of variance (ANOVA) model:
Y ijk = μ k + S ik + G jk + ε ijk
where Y ijk is the value for strain i, sex j and gene k; μ is the overall mean; S i , and G j represent the strain effects for strain i and sex effects for sex j respectively; and ε ijk is the error term. The results were then adjusted for multiple hypothesis testing using Bonferroni correction (see Additional files 5, 6, 7). For each main effect, genes and exons with a Bonferroni corrected p-values of less than 0.01 were identified as differentially expressed. Initially, we specified a sex-strain interaction term. However, only 1% to 2% of genes have significant sex-strain interaction effects at a Bonferroni-corrected p-value threshold of 0.01. Since this is either slightly higher or exactly what we would expect by chance alone, we discarded the term in favor of a more precise model.
A set of 1,533,914 SNPs were obtained from the mouse resequencing project by Perlegen-US National Institute of Environmental Health Sciences . These SNPs represented the complete set of polymorphisms between C57BL/6J, DBA/2J and C3H/HeJ where at least one pair was polymorphic for each SNP. No missing data was permitted in the SNP set. Probes with at least one SNP were then identified. Using the same ANOVA model described above, we identified probes that were differentially expressed between strains, again using a Bonferroni-corrected p-value threshold of 0.01. Fisher's exact test was then used to determine if differentially expressed probes were significantly enriched for probes containing SNPs. This analysis was repeated for our 3' gene expression profiling dataset. A similar analysis was performed for the whole-transcript gene expression profiling dataset using all probes associated with the transcript. For exon profiling, because a junction probe often overlaps with both a differentially expressed exon and a non-differentially expressed exon, we associated each exon with the number of SNPs within the exon probe and its flanking junction probe. Results of the above analysis are summarized in Additional files 3, 4 and 8.
We thank the Rosetta Gene Expression Laboratory and the Rosetta Project Management for experimental and technical support.
- Nishida Y, Yoshioka M, St-Amand J: Sexually dimorphic gene expression in the hypothalamus, pituitary gland, and cortex. Genomics. 2005, 85 (6): 679-687. 10.1016/j.ygeno.2005.02.013.PubMedView ArticleGoogle Scholar
- Rinn JL, Rozowsky JS, Laurenzi IJ, Petersen PH, Zou K, Zhong W, Gerstein M, Snyder M: Major molecular differences between mammalian sexes are involved in drug metabolism and renal function. Dev Cell. 2004, 6 (6): 791-800. 10.1016/j.devcel.2004.05.005.PubMedView ArticleGoogle Scholar
- Yang X, Schadt EE, Wang S, Wang H, Arnold AP, Ingram-Drake L, Drake TA, Lusis AJ: Tissue-specific expression and regulation of sexually dimorphic genes in mice. Genome Res. 2006, 16 (8): 995-1004. 10.1101/gr.5217506.PubMedPubMed CentralView ArticleGoogle Scholar
- Nadler JJ, Zou F, Huang H, Moy SS, Lauder J, Crawley JN, Threadgill DW, Wright FA, Magnuson TR: Large-scale gene expression differences across brain regions and inbred strains correlate with a behavioral phenotype. Genetics. 2006, 174 (3): 1229-1236. 10.1534/genetics.106.061481.PubMedPubMed CentralView ArticleGoogle Scholar
- Clark TA, Schweitzer AC, Chen TX, Staples MK, Lu G, Wang H, Williams A, Blume JE: Discovery of tissue-specific exons using comprehensive human exon microarrays. Genome Biol. 2007, 8 (4): R64-10.1186/gb-2007-8-4-r64.PubMedPubMed CentralView ArticleGoogle Scholar
- Gardina PJ, Clark TA, Shimada B, Staples MK, Yang Q, Veitch J, Schweitzer A, Awad T, Sugnet C, Dee S, Davies C, Williams A, Turpaz Y: Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array. BMC Genomics. 2006, 7: 325-10.1186/1471-2164-7-325.PubMedPubMed CentralView ArticleGoogle Scholar
- McIntyre MH, Cohn BA, Ellison PT: Sex dimorphism in digital formulae of children. Am J Phys Anthropol. 2006, 129 (1): 143-150. 10.1002/ajpa.20240.PubMedView ArticleGoogle Scholar
- Blencowe BJ: Alternative splicing: new insights from global analyses. Cell. 2006, 126 (1): 37-47. 10.1016/j.cell.2006.06.023.PubMedView ArticleGoogle Scholar
- Hiller M, Huse K, Szafranski K, Jahn N, Hampe J, Schreiber S, Backofen R, Platzer M: Single-Nucleotide Polymorphisms in NAGNAG Acceptors Are Highly Predictive for Variations of Alternative Splicing. Am J Hum Genet. 2006, 78 (2): 291-302. 10.1086/500151.PubMedPubMed CentralView ArticleGoogle Scholar
- Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, Maeda N, Oyama R, Ravasi T, Lenhard B, Wells C, Kodzius R, Shimokawa K, Bajic VB, Brenner SE, Batalov S, Forrest AR, Zavolan M, Davis MJ, Wilming LG, Aidinis V, Allen JE, Ambesi-Impiombato A, Apweiler R, Aturaliya RN, Bailey TL, Bansal M, Baxter L, Beisel KW, Bersano T, Bono H, Chalk AM, Chiu KP, Choudhary V, Christoffels A, Clutterbuck DR, Crowe ML, Dalla E, Dalrymple BP, de Bono B, Della Gatta G, di Bernardo D, Down T, Engstrom P, Fagiolini M, Faulkner G, Fletcher CF, Fukushima T, Furuno M, Futaki S, Gariboldi M, Georgii-Hemming P, Gingeras TR, Gojobori T, Green RE, Gustincich S, Harbers M, Hayashi Y, Hensch TK, Hirokawa N, Hill D, Huminiecki L, Iacono M, Ikeo K, Iwama A, Ishikawa T, Jakt M, Kanapin A, Katoh M, Kawasawa Y, Kelso J, Kitamura H, Kitano H, Kollias G, Krishnan SP, Kruger A, Kummerfeld SK, Kurochkin IV, Lareau LF, Lazarevic D, Lipovich L, Liu J, Liuni S, McWilliam S, Madan Babu M, Madera M, Marchionni L, Matsuda H, Matsuzawa S, Miki H, Mignone F, Miyake S, Morris K, Mottagui-Tabar S, Mulder N, Nakano N, Nakauchi H, Ng P, Nilsson R, Nishiguchi S, Nishikawa S, Nori F, Ohara O, Okazaki Y, Orlando V, Pang KC, Pavan WJ, Pavesi G, Pesole G, Petrovsky N, Piazza S, Reed J, Reid JF, Ring BZ, Ringwald M, Rost B, Ruan Y, Salzberg SL, Sandelin A, Schneider C, Schonbach C, Sekiguchi K, Semple CA, Seno S, Sessa L, Sheng Y, Shibata Y, Shimada H, Shimada K, Silva D, Sinclair B, Sperling S, Stupka E, Sugiura K, Sultana R, Takenaka Y, Taki K, Tammoja K, Tan SL, Tang S, Taylor MS, Tegner J, Teichmann SA, Ueda HR, van Nimwegen E, Verardo R, Wei CL, Yagi K, Yamanishi H, Zabarovsky E, Zhu S, Zimmer A, Hide W, Bult C, Grimmond SM, Teasdale RD, Liu ET, Brusic V, Quackenbush J, Wahlestedt C, Mattick JS, Hume DA, Kai C, Sasaki D, Tomaru Y, Fukuda S, Kanamori-Katayama M, Suzuki M, Aoki J, Arakawa T, Iida J, Imamura K, Itoh M, Kato T, Kawaji H, Kawagashira N, Kawashima T, Kojima M, Kondo S, Konno H, Nakano K, Ninomiya N, Nishio T, Okada M, Plessy C, Shibata K, Shiraki T, Suzuki S, Tagami M, Waki K, Watahiki A, Okamura-Oho Y, Suzuki H, Kawai J, Hayashizaki Y: The transcriptional landscape of the mammalian genome. Science. 2005, 309 (5740): 1559-1563. 10.1126/science.1112014.PubMedView ArticleGoogle Scholar
- Krawczak M, Thomas NS, Hundrieser B, Mort M, Wittig M, Hampe J, Cooper DN: Single base-pair substitutions in exon-intron junctions of human genes: nature, distribution, and consequences for mRNA splicing. Hum Mutat. 2007, 28 (2): 150-158. 10.1002/humu.20400.PubMedView ArticleGoogle Scholar
- Frazer KA, Eskin E, Kang HM, Bogue MA, Hinds DA, Beilharz EJ, Gupta RV, Montgomery J, Morenzoni MM, Nilsen GB, Pethiyagoda CL, Stuve LL, Johnson FM, Daly MJ, Wade CM, Cox DR: A sequence-based variation map of 8.27 million SNPs in inbred mouse strains. Nature. 2007, 448 (7157): 1050-1053. 10.1038/nature06067.PubMedView ArticleGoogle Scholar
- Lum PY, Chen Y, Zhu J, Lamb J, Melmed S, Wang S, Drake TA, Lusis AJ, Schadt EE: Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes. J Neurochem. 2006Google Scholar
- Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK, Monks S, Reitman M, Zhang C, Lum PY, Leonardson A, Thieringer R, Metzger JM, Yang L, Castle J, Zhu H, Kash SF, Drake TA, Sachs A, Lusis AJ: An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet. 2005, 37 (7): 710-717. 10.1038/ng1589.PubMedPubMed CentralView ArticleGoogle Scholar
- Mehrabian M, Allayee H, Stockton J, Lum PY, Drake TA, Castellani LW, Suh M, Armour C, Edwards S, Lamb J, Lusis AJ, Schadt EE: Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nat Genet. 2005, 37 (11): 1224-1233. 10.1038/ng1619.PubMedView ArticleGoogle Scholar
- Ghazalpour A, Doss S, Sheth SS, Ingram-Drake LA, Schadt EE, Lusis AJ, Drake TA: Genomic analysis of metabolic pathway gene expression in mice. Genome Biol. 2005, 6 (7): R59-10.1186/gb-2005-6-7-r59.PubMedPubMed CentralView ArticleGoogle Scholar
- Drake TA, Schadt EE, Davis RC, Lusis AJ: Integrating genetic and gene expression data to study the metabolic syndrome and diabetes in mice. Am J Ther. 2005, 12 (6): 503-511. 10.1097/01.mjt.0000178775.39149.64.PubMedView ArticleGoogle Scholar
- Davis RC, Schadt EE, Cervino AC, Peterfy M, Lusis AJ: Ultrafine mapping of SNPs from mouse strains C57BL/6J, DBA/2J, and C57BLKS/J for loci contributing to diabetes and atherosclerosis susceptibility. Diabetes. 2005, 54 (4): 1191-1199. 10.2337/diabetes.54.4.1191.PubMedView ArticleGoogle Scholar
- Cervino AC, Li G, Edwards S, Zhu J, Laurie C, Tokiwa G, Lum PY, Wang S, Castellini LW, Lusis AJ, Carlson S, Sachs AB, Schadt EE: Integrating QTL and high-density SNP analyses in mice to identify Insig2 as a susceptibility gene for plasma cholesterol levels. Genomics. 2005, 86 (5): 505-517. 10.1016/j.ygeno.2005.07.010.PubMedView ArticleGoogle Scholar
- Beck JA, Lloyd S, Hafezparast M, Lennon-Pierce M, Eppig JT, Festing MF, Fisher EM: Genealogies of mouse inbred strains. Nat Genet. 2000, 24 (1): 23-25. 10.1038/71641.PubMedView ArticleGoogle Scholar
- Witmer PD, Doheny KF, Adams MK, Boehm CD, Dizon JS, Goldstein JL, Templeton TM, Wheaton AM, Dong PN, Pugh EW, Nussbaum RL, Hunter K, Kelmenson JA, Rowe LB, Brownstein MJ: The development of a highly informative mouse Simple Sequence Length Polymorphism (SSLP) marker set and construction of a mouse family tree using parsimony analysis. Genome Res. 2003, 13 (3): 485-491. 10.1101/gr.717903.PubMedPubMed CentralView ArticleGoogle Scholar
- Pavlidis P, Noble WS: Analysis of strain and regional variation in gene expression in mouse brain. Genome Biol. 2001, 2 (10): RESEARCH0042-10.1186/gb-2001-2-10-research0042.PubMedPubMed CentralView ArticleGoogle Scholar
- Sandberg R, Yasuda R, Pankratz DG, Carter TA, Del Rio JA, Wodicka L, Mayford M, Lockhart DJ, Barlow C: Regional and strain-specific gene expression mapping in the adult mouse brain. Proc Natl Acad Sci U S A. 2000, 97 (20): 11038-11043. 10.1073/pnas.97.20.11038.PubMedPubMed CentralView ArticleGoogle Scholar
- Eklund AC, Turner LR, Chen P, Jensen RV, deFeo G, Kopf-Sill AR, Szallasi Z: Replacing cRNA targets with cDNA reduces microarray cross-hybridization. Nat Biotechnol. 2006, 24 (9): 1071-1073. 10.1038/nbt0906-1071.PubMedView ArticleGoogle Scholar
- Doolittle MH, LeBoeuf RC, Warden CH, Bee LM, Lusis AJ: A polymorphism affecting apolipoprotein A-II translational efficiency determines high density lipoprotein size and composition. J Biol Chem. 1990, 265 (27): 16380-16388.PubMedGoogle Scholar
- Walter NA, McWeeney SK, Peters ST, Belknap JK, Hitzemann R, Buck KJ: SNPs matter: impact on detection of differential expression. Nat Methods. 2007, 4 (9): 679-680. 10.1038/nmeth0907-679.PubMedPubMed CentralView ArticleGoogle Scholar
- Peltz G, Usuka J: From mouse genetics to human therapeutics. Curr Opin Drug Discov Devel. 2005, 8 (2): 253-261.PubMedGoogle Scholar
- Grupe A, Germer S, Usuka J, Aud D, Belknap JK, Klein RF, Ahluwalia MK, Higuchi R, Peltz G: In silico mapping of complex disease-related traits in mice. Science. 2001, 292 (5523): 1915-1918. 10.1126/science.1058889.PubMedView ArticleGoogle Scholar
- Kwan T, Benovoy D, Dias C, Gurd S, Serre D, Zuzan H, Clark TA, Schweitzer A, Staples MK, Wang H, Blume JE, Hudson TJ, Sladek R, Majewski J: Heritability of alternative splicing in the human genome. Genome Res. 2007, 17 (8): 1210-1218. 10.1101/gr.6281007.PubMedPubMed CentralView ArticleGoogle Scholar
- Mural RJ, Adams MD, Myers EW, Smith HO, Miklos GL, Wides R, Halpern A, Li PW, Sutton GG, Nadeau J, Salzberg SL, Holt RA, Kodira CD, Lu F, Chen L, Deng Z, Evangelista CC, Gan W, Heiman TJ, Li J, Li Z, Merkulov GV, Milshina NV, Naik AK, Qi R, Shue BC, Wang A, Wang J, Wang X, Yan X, Ye J, Yooseph S, Zhao Q, Zheng L, Zhu SC, Biddick K, Bolanos R, Delcher AL, Dew IM, Fasulo D, Flanigan MJ, Huson DH, Kravitz SA, Miller JR, Mobarry CM, Reinert K, Remington KA, Zhang Q, Zheng XH, Nusskern DR, Lai Z, Lei Y, Zhong W, Yao A, Guan P, Ji RR, Gu Z, Wang ZY, Zhong F, Xiao C, Chiang CC, Yandell M, Wortman JR, Amanatides PG, Hladun SL, Pratts EC, Johnson JE, Dodson KL, Woodford KJ, Evans CA, Gropman B, Rusch DB, Venter E, Wang M, Smith TJ, Houck JT, Tompkins DE, Haynes C, Jacob D, Chin SH, Allen DR, Dahlke CE, Sanders R, Li K, Liu X, Levitsky AA, Majoros WH, Chen Q, Xia AC, Lopez JR, Donnelly MT, Newman MH, Glodek A, Kraft CL, Nodell M, Ali F, An HJ, Baldwin-Pitts D, Beeson KY, Cai S, Carnes M, Carver A, Caulk PM, Center A, Chen YH, Cheng ML, Coyne MD, Crowder M, Danaher S, Davenport LB, Desilets R, Dietz SM, Doup L, Dullaghan P, Ferriera S, Fosler CR, Gire HC, Gluecksmann A, Gocayne JD, Gray J, Hart B, Haynes J, Hoover J, Howland T, Ibegwam C, Jalali M, Johns D, Kline L, Ma DS, MacCawley S, Magoon A, Mann F, May D, McIntosh TC, Mehta S, Moy L, Moy MC, Murphy BJ, Murphy SD, Nelson KA, Nuri Z, Parker KA, Prudhomme AC, Puri VN, Qureshi H, Raley JC, Reardon MS, Regier MA, Rogers YH, Romblad DL, Schutz J, Scott JL, Scott R, Sitter CD, Smallwood M, Sprague AC, Stewart E, Strong RV, Suh E, Sylvester K, Thomas R, Tint NN, Tsonis C, Wang G, Wang G, Williams MS, Williams SM, Windsor SM, Wolfe K, Wu MM, Zaveri J, Chaturvedi K, Gabrielian AE, Ke Z, Sun J, Subramanian G, Venter JC, Pfannkoch CM, Barnstead M, Stephenson LD: A comparison of whole-genome shotgun-derived mouse chromosome 16 and the human genome. Science. 2002, 296 (5573): 1661-1671. 10.1126/science.1069193.PubMedView ArticleGoogle Scholar
- Johnson JM, Castle J, Garrett-Engele P, Kan Z, Loerch PM, Armour CD, Santos R, Schadt EE, Stoughton R, Shoemaker DD: Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science. 2003, 302 (5653): 2141-2144. 10.1126/science.1090100.PubMedView ArticleGoogle Scholar
- Castle J, Garrett-Engele P, Armour CD, Duenwald SJ, Loerch PM, Meyer MR, Schadt EE, Stoughton R, Parrish ML, Shoemaker DD, Johnson JM: Optimization of oligonucleotide arrays and RNA amplification protocols for analysis of transcript structure and alternative splicing. Genome Biol. 2003, 4 (10): R66-10.1186/gb-2003-4-10-r66.PubMedPubMed CentralView ArticleGoogle Scholar
- Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, Lefkowitz SM, Ziman M, Schelter JM, Meyer MR, Kobayashi S, Davis C, Dai H, He YD, Stephaniants SB, Cavet G, Walker WL, West A, Coffey E, Shoemaker DD, Stoughton R, Blanchard AP, Friend SH, Linsley PS: Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol. 2001, 19 (4): 342-347. 10.1038/86730.PubMedView ArticleGoogle Scholar
- Roberts CJ, Nelson B, Marton MJ, Stoughton R, Meyer MR, Bennett HA, He YD, Dai H, Walker WL, Hughes TR, Tyers M, Boone C, Friend SH: Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. Science. 2000, 287 (5454): 873-880. 10.1126/science.287.5454.873.PubMedView ArticleGoogle Scholar
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