Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

A Method to Quantify Mouse Coat-Color Proportions

  • Songthip Ounpraseuth,

    Affiliation Department of Biostatistics, The University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America

  • Tonya M. Rafferty,

    Affiliation Department of Biochemistry and Molecular Biology, The University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America

  • Rachel E. McDonald-Phillips,

    Current address: Department of Pharmacology and Toxicology, the University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America

    Affiliation Department of Biochemistry and Molecular Biology, The University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America

  • Whitney M. Gammill,

    Current address: University of Arkansas, Fayetteville, Arkansas, United States of America

    Affiliation Department of Biochemistry and Molecular Biology, The University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America

  • Eric R. Siegel,

    Affiliation Department of Biostatistics, The University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America

  • Kristin L. Wheeler,

    Current address: Rhodes College, Memphis, Tennessee, United States of America

    Affiliation Department of Biochemistry and Molecular Biology, The University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America

  • Erik A. Nilsson,

    Current address: Department of Computer and Information Science, Linköping University, Linköping, Sweden

    Affiliation Department of Biochemistry and Molecular Biology, The University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America

  • Craig A. Cooney

    CooneyCraigA@uams.edu

    Affiliations Department of Biochemistry and Molecular Biology, The University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America, Central Arkansas Veterans Healthcare Systems, Little Rock, Arkansas, United States of America

A Method to Quantify Mouse Coat-Color Proportions

  • Songthip Ounpraseuth, 
  • Tonya M. Rafferty, 
  • Rachel E. McDonald-Phillips, 
  • Whitney M. Gammill, 
  • Eric R. Siegel, 
  • Kristin L. Wheeler, 
  • Erik A. Nilsson, 
  • Craig A. Cooney
PLOS
x

Abstract

Coat-color proportions and patterns in mice are used as assays for many processes such as transgene expression, chimerism, and epigenetics. In many studies, coat-color readouts are estimated from subjective scoring of individual mice. Here we show a method by which mouse coat color is quantified as the proportion of coat shown in one or more digital images. We use the yellow-agouti mouse model of epigenetic variegation to demonstrate this method. We apply this method to live mice using a conventional digital camera for data collection. We use a raster graphics editing program to convert agouti regions of the coat to a standard, uniform, brown color and the yellow regions of the coat to a standard, uniform, yellow color. We use a second program to quantify the proportions of these standard colors. This method provides quantification that relates directly to the visual appearance of the live animal. It also provides an objective analysis with a traceable record, and it should allow for precise comparisons of mouse coats and mouse cohorts within and between studies.

Introduction

Animals that exhibit coat-color variation and mosaicism have been used to evaluate a variety of effects in numerous studies [1][9]. In the yellow-agouti mouse model [10], the agouti protein antagonizes the melanocortin-1 receptor to promote the expression of the yellow pigment pheomelanin giving hairs a yellow color [11]. The Avy allele of agouti contains an intracisternal A particle (IAP) element inserted upstream of the agouti coding exons [12]. Over expression of agouti from this allele causes yellow hair. The Avy allele is epigenetically regulated and imprinted leading to only partial penetrance of the allele and to coats on most Avy/a mice that are a combination of yellow and agouti areas (mottling) [2], [10], [12][17].

Epigenetically determined coat color in this model is influenced by prenatal environmental exposure to compounds such as folate, vitamin B12 and methyl donors [2], [15], [16], [18], isoflavone phytoestrogens [19], DNA methyltransferase gene dosage [5] and maternal epigenetics [2], [10], [14]. To measure the influence of treatment factors on coat color, each mouse is classified into a pre-defined category or “phenotype” that constitutes one of several ordinal categories spanning the range of coat colors from “pseudoagouti” (no yellow) to “clear yellow” (no agouti spots). Depending on the laboratory, the number of ordinal categories can be two [10], three [14], four [5], five [16], [18], [19], or six [2], [15]. Although this ordinal-category framework is useful for assaying coat-color differences between treatment groups, the wide variation in the number of ordinal categories among laboratories makes it difficult to compare results from different studies. Even when two laboratories use the same number of ordinal categories, category definitions may differ (compare, for example, Cropley et al. [18] with Dolinoy et al. [19]), thus adding to the difficulty of comparison between studies. Laboratories employing fewer categories have the advantage of fewer definitions, but incur the costs of coarser calibration and attendant lower resolution. Finally, assigning a mouse to a coat-color category requires the grader to estimate the amount of yellow in the mouse's coat; this is currently a largely subjective process.

Here we describe a method to quantify the proportions of yellow and agouti in mouse coats. This method gives a numerical proportion and achieves approximately decile resolution over the population. The data generated by this method are continuous, not discrete, and thus will allow for meaningful use of regression, repeated-measures analysis, and other statistical procedures that go beyond the contingency-table methods currently employed. Most importantly, quantification by this method does not depend appreciably or systematically on the person assessing the mouse, and in that sense is objective. This method thus has the potential to allow for comparisons of experimental data between different studies and research groups.

Materials and Methods

Mice

Mice of the VY strain and heterozygous Avy/a genotype at the agouti locus were used [2], [15]. This VY strain (originally VY/WffC3Hf/Nctr-Avy) was a gift from Dr. George Wolff (National Center for Toxicological Research, Jefferson AR). This colony has been maintained at UAMS for seven years. Most such mice have yellow and agouti mottled coats. Mice were chosen with a range of levels of agouti in their coats to demonstrate the method, although we mainly chose mice between 0 and 50% agouti because, except for the colors used, this coloring process would be equivalent to coloring mice that are 50 to 100% agouti. Coats of these mice vary on the backs, sides, and heads, but not the abdomens. All animal experiments were approved by and conducted in accordance with the Institutional Animal Care and Use Committee of UAMS.

Photography

Mice were photographed with a Nikon Coolpix 5000 digital camera under diffuse illumination from an Elmo model EV-368 illuminator. All images used the same illuminator, camera, camera settings, light blue background (Staples 110lb blue cardstock item number 490891) and were taken by the same photographer (TMR). Each mouse was photographed from the top (T) and from each side (left and right, L and R). Side views were between 30 and 60 degrees from vertical. Sets of photographs were taken on each of three days. Photographs of any one mouse were taken within one week, so that variations in coat appearance due to factors such as weight gain would be minimal. Photographs were taken of twelve mice. Images were saved as jpg files.

Quantification of Mouse Coat Color

A copy of each mouse image was opened in a graphics program, the GNU Image Manipulation Program v.2.2 (GNU Project, http://www.gnu.org/), which is a raster graphics editor. On these pictures, background regions were selected using the Lasso tool (with Antialiasing inactive) and colored red with the Paint Bucket tool. The background, eyes, ears, paws, tail, bald spots and any areas that were not mouse coat were colored red. Agouti regions were selected with the Magic Wand tool, and the threshold was increased or decreased until the selected areas most closely matched the regions of agouti coat. The selected agouti regions were then colored brown with the Paint Bucket tool. The Select Regions By Color tool was then used to select the remainder of the coat (yellow) and color it yellow with the Paint Bucket tool. The Red, Green, Blue (RGB) values used for each color are given in Table 1. The Flatten Image command was used and the new file was saved. Each mouse image was colored at least two times and colorings were matched for correspondence of natural agouti and yellow regions of the mouse image with the agouti and yellow regions of the coloring. Two individuals called raters matched colorings with images. The two raters acted independently of each other, and their results were subsequently evaluated by a third individual for quality-assurance purposes. The brown and yellow proportions were calculated using a program written in the R language (http://cran.r-project.org/) [20]. The following R script computes the brown and yellow proportions for each mouse.

library(“rimage”)

library(base)

x<-read.jpeg(“filename.jpg”)

plot(x)

x1<-as.vector(x[,,1])

x2<-as.vector(x[,,2])

x3<-as.vector(x[,,3])

y<-cbind(x1,x2,x3)

counts<-ifelse(y[,2]< = .05,1,ifelse(y[,2]>.05 & y[,2]< = .3,2,3))

table(counts)

z<-as.matrix(table(counts))

pct<-z[2∶3,]

colors<-c(“brown”,“yellow”)

color_pct<-round(pct/sum(pct)*100,1)

color_pct<-paste(color_pct, “%”, sep = “”)

pie(pct,main = “Mouse Coat Color Proportions”, col = colors, label = color_pct, cex = .8)

legend(1.5,.5,c(“Brown”,“Yellow”),cex = .8,fill = colors)

In the above script the filename.jpg should be replaced with the actual name of the file (e.g. Mouse5colored.jpg) to be analyzed.

Data Analysis

For each of the three days of photographs and for each rater, the mouse's left-side and right-side yellow proportions were averaged together and called “Sides”, while its Top yellow proportion was kept separate. Twelve measurements per mouse (six Top and six Sides) were thus obtained, and the difference between Top and Sides was calculated. Additionally, the Top measurements, and likewise the Sides measurements, were averaged within each mouse across raters, across three days of photographs, and both, and the differences between Top and Sides averages were calculated, in order to study the effect of aggregating a mouse's measurements from different times and raters. Top, Sides, and difference were summarized by rater, time, and average as means and standard deviations (SDs). The Two One-Sided Tests (TOST) procedure [21] was used at 5% alpha to determine whether measurements from Top and Sides were equivalent to within ±10 percentage points. Values from Rater 1 versus Rater 2 were plotted via both standard scatterplot and Bland-Altman plot [22]; for the latter, 95% limits of agreement were calculated using the modification of Bland and Altman [23].

For the continuous mouse coat-color-proportion measurements derived from our proposed method to be useful as a research tool, they must have a high degree of repeatability. Variations in continuous measurements can arise from inconsistent measurement practices as well as differences in (or wearing out of) equipment. We quantified the within- and between-observer variations by re-measuring the same mouse on three different occasions by two different raters.

Because mice were rated by two individuals, measurements of the level of agreement between raters were needed. Many methods-development papers try to measure agreement by calculating the Pearson correlation coefficient between the raters or methods, but this is inadequate [22]. Van Belle [24] notes that the Pearson correlation coefficient captures only precision while ignoring both bias and differences in dynamic range. However, a different measure, the Intraclass Correlation Coefficient [25], incorporates the bias and dynamic-range differences into the calculation; as a result, this measure is widely accepted in the social sciences as the preferred measure of agreement between two raters or two methods.

The intraclass correlation coefficient (ICC) assesses the relative extent to which multiple continuous measurements taken by different individuals, or by the same person on different occasions, are related. In general, the ICC describes the ratio of two varianceswhere σ2b denotes the variation between subjects (i.e., between mice), and σ2w is the pooled variance within subjects. The range of the ICC may be between 0 and 1. A high ICC value would indicate that there is little variation in the coat-color percentages computed for each mouse by the raters.

Shrout and Fleiss [25] describe six ICC notations and their uses for reliability. For our study, we report the ICC(2,1), for which all mice are assessed by the same raters (judges) who are assumed to be a random subset of all possible judges. We used SAS® Version 9.1 (SAS Institute, Carey, NC) along with two user-written macros to calculate the ICCs and their 95% confidence intervals, INTRACC by Robert Hamer (http://www.psych.yorku.ca/lab/sas/intracc.htm), and ICC macro from Douglas Steinley and Phillip Wood (http://ourworld.compuserve.com/homepages/jsuebersax/icc.htm).

Results

We used a group of mottled mice that varied from mainly yellow to mainly agouti to quantify their coat color patterns. Each mouse was photographed at three different angles (left, right and top) on each of three different days. Two individuals, called raters, colored copies of each photograph. In some cases raters had the option of choosing from two or more existing colorings of a photograph or producing their own coloring. Raters' matches of colored pictures were screened for accurate match by a third person. Except for this screening by a third person, raters were independent and did not communicate with each other about mouse coloring. We mainly colored mice between 0 and 50% agouti because, except for the colors used, this coloring process would be equivalent to coloring mice that are 50 to 100% agouti. Example photographs, colorings and quantifications are shown in Figure 1.

thumbnail
Figure 1. Photographs, colorings, and pie charts of mice.

Photographs of mice were processed as described in the text to produce yellow areas of uniform color and agouti areas of uniform color that closely matched yellow and agouti areas on the live mouse. A script was used to generate a pie chart from these areas of uniform color to determine the percentage of yellow and agouti in the mouse. Two examples are shown here.

https://doi.org/10.1371/journal.pone.0005414.g001

An important part of the method is matching the coat color pattern on each mouse to a standardized color version that can be readily quantified using a computer. Because this part of the method relies on visual matching, we had two raters color mice, and we then compared their results.

Photographs of mice were taken from each side at a substantial angle to vertical (“Sides”) as well as along the vertical (“Top”). Figure 2 and Figure 3 show standard scatter plots of percents yellow in the Sides and Top, respectively, of each mouse as determined by rater 1 versus that determined by rater 2. The Pearson Correlation Coefficients between raters were 0.989 (P<0.0001) for Figure 2 and 0.961 (P<0.0001) for Figure 3. The diagonal line in both figures is “the line of equality on which all points would lie if the two [raters] gave exactly the same reading every time” [22]; distance from the line of equality indicates the amount by which the two raters' measurements disagree. A high-resolution view of rater disagreement is shown in the Bland-Altman plots of Figure 4 and Figure 5, for mouse Sides and Top, respectively, in which the difference between raters is plotted against the average between them. For percents yellow in the sides of the mice, the mean difference between raters was 1.92% and the 95% limits of agreement were −7.53% to +11.37%, a spread of around 19 percentage points. For percents yellow in the tops of the mice, the mean difference between raters was −0.04% and the 95% limits of agreement were −16.43% to +16.36%, a spread of almost 33 percentage points.

thumbnail
Figure 2. A plot of the percent yellow in the coats of mice evaluated from their sides.

Two raters matched colorings on 12 mice on three occasions by examination of each mouse from its left and right sides. Vertical and horizontal axes denote the average percent yellow of the mouse's left and right sides. Letters denote the identity of the mouse being rated, while the red diagonal line denotes the line of equality between percentages assigned by the two raters.

https://doi.org/10.1371/journal.pone.0005414.g002

thumbnail
Figure 3. A plot of the percent yellow in the coats of mice evaluated from the top.

Two raters matched colorings on 12 mice on three occasions by examination of each mouse from the top. Vertical and horizontal axes denote the percent yellow assigned by each rater. Letters denote the identity of the mouse being rated, while the red diagonal line denotes the line of equality between percentages assigned by the two raters.

https://doi.org/10.1371/journal.pone.0005414.g003

thumbnail
Figure 4. A Bland-Altman plot of the data shown in Figure 2.

The horizontal and vertical axes respectively show the mean and difference in coat-color percentages assigned by the raters. Letters denote the identity of the mouse being rated. The solid line depicts the mean of the differences between raters, and the area between the dotted lines delineates the 95% limits of agreement.

https://doi.org/10.1371/journal.pone.0005414.g004

thumbnail
Figure 5. A Bland-Altman plot of the data shown in Figure 3.

The horizontal and vertical axes respectively show the mean and difference in coat-color percentages assigned by the raters. Letters denote the identity of the mouse being rated. The solid line depicts the mean of the differences between raters, and the area between the dotted lines delineates the 95% limits of agreement.

https://doi.org/10.1371/journal.pone.0005414.g005

Table 2 shows means and SDs for Top, Sides, and their difference, by rater, time, and both, and after averaging Top and Sides over rater, time, and both. In all cases, the mean of coat color proportion determined from the top was somewhat less than from the sides, ranging from −0.23 to −5.48 percentage points among the different raters and times, and equal to −3.48 percentage points using Top and Sides averaged over both time and rater. However, for all rows of Table 2, the two one-sided 95% confidence limits (TOS CL) on the mean difference between Top and Sides measurements both lie inside equivalence-limit boundaries at +10 and −10 percentage points. This establishes via the TOST procedure that the Top and Sides measurements are equivalent to within ±10 percentage points.

thumbnail
Table 2. Means and SDs of coat percent yellow, as measured from the top and sides at different times by different raters.

https://doi.org/10.1371/journal.pone.0005414.t002

Using coat-color measurements averaged across the three time points, the agreement between raters was high whether evaluated using photos taken from the sides (ICC = 0.994, Table 3) or from the top (ICC = 0.986, Table 3).

thumbnail
Table 3. Inter-rater reliability estimate between the two raters for time-averaged measurement derived from the indicated photographic angle.

https://doi.org/10.1371/journal.pone.0005414.t003

For the method to be reliable, photos of the same mouse should not differ greatly from each other, especially when taken from approximately the same angle. Quantification from colored images was compared for the three days for each rater. The agreement between different days was high for both raters whether evaluated using photos from the sides (ICC = 0.988 and 0.988, Table 4) or photos from the top (ICC = 0.953 and 0.974, Table 4).

thumbnail
Table 4. Intra-rater reliability of each rater measuring the percentage of yellow coat color in each mouse on three different days from the sidesA and from the top.

https://doi.org/10.1371/journal.pone.0005414.t004

Quantification from colored images of the two raters was compared for the three days on which photographs were taken. The agreement between raters was high for pictures taken on different days, whether from the sides (ICC = 0.980 to 0.994, Table 5) or from the top (ICC = 0.956 to 0.966, Table 6).

thumbnail
Table 5. Inter-rater reliability between the two raters on the individual days, evaluated using the average of measurements from photos taken from the left and right sides.

https://doi.org/10.1371/journal.pone.0005414.t005

thumbnail
Table 6. Inter-rater reliability between the two raters on the individual days, evaluated using measurements from photos taken from the top angle only.

https://doi.org/10.1371/journal.pone.0005414.t006

Discussion

We describe a method to quantify the coat color percentage in live yellow-agouti mottled mice using digital photographs saved as jpg files. This method uses available, free, established software, the GNU Image Manipulation Program, and a short script written in the R programming language.

Photographs of the same mouse taken on different days within a one-week period yielded very similar results from one photo session to the next when we took reasonable steps (e.g., using the same diffuse light source, same camera with the same settings, same background material and the same photographer) to insure consistency of conditions among sessions. In general, we found little variation among quantifications of pictures taken on different days, indicating that multiple photographs provide little advantage for quantifying coat colors. Two different raters matched colorings to mice, and obtained, on average, very similar coat-color percentages. Each coloring was evaluated by a third individual who screened colorings for a reasonable match. In a few cases (less than 10% of colorings), raters were asked to recolor images. Fayers and Machin [26] recommended that the ICC should exceed 0.90 for techniques that are to be used to assess individuals in clinical practice. Their recommendation clearly applies to techniques for assessing individual mice in laboratory research. Not only did all our ICCs exceed the 0.90 threshold, but all the lower 95% confidence limits on our ICCs exceeded it, thus establishing that our technique has the inter-rater reliability required by Fayers and Machin [26].

Our results indicate that measuring the coat color of mice from only top-view photographs should be sufficient for most purposes. The proportions yellow obtained from top-view photos averaged about 3.5 percentage points lower than those obtained from side-view photographs. This difference is small compared to the time saved from taking and coloring fewer pictures when only top views are used. Application of a well-established statistical equivalence-testing procedure [21] to the difference between top-view and side-view measurements demonstrated that the two were significantly equivalent to within ±10 percentage points for all combinations of raters, times, and averagings.

Transgenic, knockout and other genetically modified mice are typically made using embryonic stem cells, which are then implanted in an early embryo [7], [8]. Highly chimeric mice are used as an indicator of those most likely to carry a germ-line with the desired modification [7], [8], [27][29]. Similar embryonic stem cell methods have been developed for rats [9], [30]. The method we describe here could provide a quantitative means to select mice and rats with the highest levels of coat color chimerism. In turn, these animals may have the highest levels of germ line chimerism.

Coat color assays provide patterns of epigenetic variation over a wide area of tissue (skin and hair). Further, DNA and other molecular components can be extracted from plucked hairs (including follicles) using hair color as a guide (our unpublished results). Coat color models of epigenetics usually assay the epigenetics of one particular genetic locus and of one tissue (skin). Coat color methods alone do not address the molecular basis of epigenetic regulation. In contrast, molecular methods such as DNA methylation analysis [14][16], [31][35] or chromatin immunoprecipitation [36], [37] can be applied to many loci and tissues. In addition to skin, large patterns of epigenetic mosaicism probably exist in many other tissues (e.g. liver, brain). Except for patterns that can be detected visually or by immunohistochemistry [33], analyses of epigenetic differences in tissue sections at high spatial resolution is likely to be very labor intensive.

Quantification of color in skins removed from mice has been reported [38]. The method used proprietary software to analyze mouse skins after sacrifice but offered few other details. Although the method was used to determine percentage mottling, the vast majority of mice were less than 50% “black” and the highest percent mottling reported in a large mouse population was about 80% “black”. It is unclear how data from this method would relate to live mice or to the visual appearance of mice, many of which are 80 to 100% agouti. Our method provides quantification that relates directly to the visual appearance of the live animal.

Our method has two main advantages over the widely used ordinal-category methods for live mice. First, it is quantitative and provides data that can be analyzed and compared with other measures with greater precision than ordinal-category methods. Second, it provides a record of the mouse image and how the image was colored. This record can be reevaluated as needed.

We use the yellow agouti mouse model of epigenetic variegation to demonstrate this method. However, it should be useful for many types of studies where coat-color mosaicism or coat-color phenotype is important. This method by which the proportion of coat color on a live mouse can be objectively quantified should allow for precise comparisons of mouse coats within and between studies.

Author Contributions

Conceived and designed the experiments: STO CAC. Performed the experiments: TMR REMP WMG KLW. Analyzed the data: STO TMR ERS EAN CAC. Wrote the paper: STO TMR ERS CAC.

References

  1. 1. Wu M, Rinchik EM, Wilkinson E, Johnson DK (1997) Inherited somatic mosaicism caused by an intracisternal A particle insertion in the mouse tyrosinase gene. Proc Natl Acad Sci USA 94: 890–894.M. WuEM RinchikE. WilkinsonDK Johnson1997Inherited somatic mosaicism caused by an intracisternal A particle insertion in the mouse tyrosinase gene.Proc Natl Acad Sci USA94890894
  2. 2. Wolff GL, Kodell RL, Moore SR, Cooney CA (1998) Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice. FASEB J 12: 949–957.GL WolffRL KodellSR MooreCA Cooney1998Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice.FASEB J12949957
  3. 3. Trut LN (1999) Early canid domestication: the farm-fox experiment. Am Sci 87: 160–169.LN Trut1999Early canid domestication: the farm-fox experiment.Am Sci87160169
  4. 4. Hochedlinger K, Blelloch R, Brennan C, Yamada Y, Kim M, et al. (2004) Reprogramming of a melanoma genome by nuclear transplantation. Genes Dev 18: 1875–1885.K. HochedlingerR. BlellochC. BrennanY. YamadaM. Kim2004Reprogramming of a melanoma genome by nuclear transplantation.Genes Dev1818751885
  5. 5. Gaudet F, Rideout WM 3rd, Meissner A, Dausman J, Leonhardt H, et al. (2004) Dnmt1 expression in pre- and postimplantation embryogenesis and the maintenance of IAP silencing. Mol Cell Biol 24: 1640–1648.F. GaudetWM Rideout 3rdA. MeissnerJ. DausmanH. Leonhardt2004Dnmt1 expression in pre- and postimplantation embryogenesis and the maintenance of IAP silencing.Mol Cell Biol2416401648
  6. 6. Grahn RA, Lemesch BM, Millon LV, Matise T, Rogers QR, et al. (2005) Localizing the X-linked orange colour phenotype using feline resource families. Anim Genet 36: 67–70.RA GrahnBM LemeschLV MillonT. MatiseQR Rogers2005Localizing the X-linked orange colour phenotype using feline resource families.Anim Genet366770
  7. 7. Bradley A, Evans M, Kaufman MH, Robertson E (1984) Formation of germ-line chimaeras from embryo-derived teratocarcinoma cell lines. Nature 309: 255–256.A. BradleyM. EvansMH KaufmanE. Robertson1984Formation of germ-line chimaeras from embryo-derived teratocarcinoma cell lines.Nature309255256
  8. 8. Smith AG (2001) Embryo-derived stem cells: of mice and men. Annu Rev Cell Dev Biol 17: 435–462.AG Smith2001Embryo-derived stem cells: of mice and men.Annu Rev Cell Dev Biol17435462
  9. 9. Li P, Tong C, Mehrian-Shai R, Jia L, Wu N, et al. (2008) Germline competent embryonic stem cells derived from rat blastocysts. Cell 135: 1299–1310.P. LiC. TongR. Mehrian-ShaiL. JiaN. Wu2008Germline competent embryonic stem cells derived from rat blastocysts.Cell13512991310
  10. 10. Wolff GL (1978) Influence of maternal phenotype on metabolic differentiation of agouti locus mutants in the mouse. Genetics 88: 529–539.GL Wolff1978Influence of maternal phenotype on metabolic differentiation of agouti locus mutants in the mouse.Genetics88529539
  11. 11. Mynatt RL, Stephens JM (2001) Agouti regulates adipocyte transcription factors. Am J Physiol Cell Physiol 280: C954–961.RL MynattJM Stephens2001Agouti regulates adipocyte transcription factors.Am J Physiol Cell Physiol280C954961
  12. 12. Duhl DM, Vrieling H, Miller KA, Wolff GL, Barsh GS (1994) Neomorphic agouti mutations in obese yellow mice. Nat Genet 8: 59–65.DM DuhlH. VrielingKA MillerGL WolffGS Barsh1994Neomorphic agouti mutations in obese yellow mice.Nat Genet85965
  13. 13. Wolff GL, Roberts DW, Mountjoy KG (1999) Physiological consequences of ectopic agouti gene expression: the yellow obese mouse syndrome. Physiol Genomics 1: 151–163.GL WolffDW RobertsKG Mountjoy1999Physiological consequences of ectopic agouti gene expression: the yellow obese mouse syndrome.Physiol Genomics1151163
  14. 14. Morgan HD, Sutherland HG, Martin DI, Whitelaw E (1999) Epigenetic inheritance at the agouti locus in the mouse. Nat Genet 23: 314–318.HD MorganHG SutherlandDI MartinE. Whitelaw1999Epigenetic inheritance at the agouti locus in the mouse.Nat Genet23314318
  15. 15. Cooney CA, Dave AA, Wolff GL (2002) Maternal methyl supplements in mice affect epigenetic variation and DNA methylation of offspring. J Nutr 132: 2393S–2400S.CA CooneyAA DaveGL Wolff2002Maternal methyl supplements in mice affect epigenetic variation and DNA methylation of offspring.J Nutr1322393S2400S
  16. 16. Waterland RA, Jirtle RL (2003) Transposable elements: targets for early nutritional effects on epigenetic gene regulation. Mol Cell Biol 23: 5293–5300.RA WaterlandRL Jirtle2003Transposable elements: targets for early nutritional effects on epigenetic gene regulation.Mol Cell Biol2352935300
  17. 17. Cooney CA (2006) Maternal Nutrition: Nutrients and Control of Expression. In: Kaput J, Rodriguez RL, editors. Nutrigenomics: Concepts and Technologies. Hoboken: John Wiley and Sons. pp. 219–254.CA Cooney2006Maternal Nutrition: Nutrients and Control of Expression.J. KaputRL RodriguezNutrigenomics: Concepts and TechnologiesHobokenJohn Wiley and Sons219254
  18. 18. Cropley JE, Suter CM, Beckman KB, Martin DI (2006) Germ-line epigenetic modification of the murine Avy allele by nutritional supplementation. Proc Natl Acad Sci USA 103: 17308–17312.JE CropleyCM SuterKB BeckmanDI Martin2006Germ-line epigenetic modification of the murine Avy allele by nutritional supplementation.Proc Natl Acad Sci USA1031730817312
  19. 19. Dolinoy DC, Weidman JR, Waterland RA, Jirtle RL (2006) Maternal genistein alters coat color and protects Avy mouse offspring from obesity by modifying the fetal epigenome. Environ Health Perspect 114: 567–572.DC DolinoyJR WeidmanRA WaterlandRL Jirtle2006Maternal genistein alters coat color and protects Avy mouse offspring from obesity by modifying the fetal epigenome.Environ Health Perspect114567572
  20. 20. R Development Core Team (2008) R: A language and environment for statistical computing. R Development Core Team2008R: A language and environment for statistical computing.R Foundation for Statistical Computing. Vienna, Austria ISBN 3-900051-07-0, URL http://www.R-project.org. R Foundation for Statistical Computing. Vienna, Austria ISBN 3-900051-07-0, URL http://www.R-project.org.
  21. 21. Schuirmann DJ (1987) A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm 15: 657–680.DJ Schuirmann1987A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability.J Pharmacokinet Biopharm15657680
  22. 22. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1: 307–310.JM BlandDG Altman1986Statistical methods for assessing agreement between two methods of clinical measurement.Lancet1307310
  23. 23. Bland JM, Altman DG (2007) Agreement between methods of measurement with multiple observations per individual. J Biopharm Stat 17: 571–582.JM BlandDG Altman2007Agreement between methods of measurement with multiple observations per individual.J Biopharm Stat17571582
  24. 24. Van Belle G (2002) Assess agreement in terms of accuracy, scale differential, and precision. In: Van Belle G, editor. Statistical Rules of Thumb. New York: John Wiley & Sons, Inc. pp. 65–68.G. Van Belle2002Assess agreement in terms of accuracy, scale differential, and precision.G. Van BelleStatistical Rules of ThumbNew YorkJohn Wiley & Sons, Inc6568
  25. 25. Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86: 420–428.PE ShroutJL Fleiss1979Intraclass correlations: uses in assessing rater reliability.Psychol Bull86420428
  26. 26. Fayers PM, Machin D (2000) Quality of Life: Assessment, Analysis, and Interpretation. New York: John Wiley. PM FayersD. Machin2000Quality of Life: Assessment, Analysis, and InterpretationNew YorkJohn Wiley
  27. 27. Schwartzberg PL, Goff SP, Robertson EJ (1989) Germ-line transmission of a c-abl mutation produced by targeted gene disruption in ES cells. Science 246: 799–803.PL SchwartzbergSP GoffEJ Robertson1989Germ-line transmission of a c-abl mutation produced by targeted gene disruption in ES cells.Science246799803
  28. 28. Koller BH, Hagemann LJ, Doetschman T, Hagaman JR, Huang S, et al. (1989) Germ-line transmission of a planned alteration made in a hypoxanthine phosphoribosyltransferase gene by homologous recombination in embryonic stem cells. Proc Natl Acad Sci USA 86: 8927–8931.BH KollerLJ HagemannT. DoetschmanJR HagamanS. Huang1989Germ-line transmission of a planned alteration made in a hypoxanthine phosphoribosyltransferase gene by homologous recombination in embryonic stem cells.Proc Natl Acad Sci USA8689278931
  29. 29. Ware CB, Siverts LA, Nelson AM, Morton JF, Ladiges WC (2003) Utility of a C57BL/6 ES line versus 129 ES lines for targeted mutations in mice. Transgenic Res 12: 743–746.CB WareLA SivertsAM NelsonJF MortonWC Ladiges2003Utility of a C57BL/6 ES line versus 129 ES lines for targeted mutations in mice.Transgenic Res12743746
  30. 30. Buehr M, Meek S, Blair K, Yang J, Ure J, et al. (2008) Capture of authentic embryonic stem cells from rat blastocysts. Cell 135: 1287–1298.M. BuehrS. MeekK. BlairJ. YangJ. Ure2008Capture of authentic embryonic stem cells from rat blastocysts.Cell13512871298
  31. 31. Castilho A, Neves N, Rufini-Castiglione M, Viegas W, Heslop-Harrison JS (1999) 5-Methylcytosine distribution and genome organization in triticale before and after treatment with 5-azacytidine. J Cell Sci 112(Pt 23): 4397–4404.A. CastilhoN. NevesM. Rufini-CastiglioneW. ViegasJS Heslop-Harrison19995-Methylcytosine distribution and genome organization in triticale before and after treatment with 5-azacytidine.J Cell Sci112(Pt 23)43974404
  32. 32. Leakey TI, Zielinski J, Siegfried RN, Siegel ER, Fan CY, et al. (2008) A simple algorithm for quantifying DNA methylation levels on multiple independent CpG sites in bisulfite genomic sequencing electropherograms. Nucleic Acids Res 36: e64.TI LeakeyJ. ZielinskiRN SiegfriedER SiegelCY Fan2008A simple algorithm for quantifying DNA methylation levels on multiple independent CpG sites in bisulfite genomic sequencing electropherograms.Nucleic Acids Res36e64
  33. 33. Koji T, Kondo S, Hishikawa Y, An S, Sato Y (2008) In situ detection of methylated DNA by histo endonuclease-linked detection of methylated DNA sites: a new principle of analysis of DNA methylation. Histochem Cell Biol 130: 917–925.T. KojiS. KondoY. HishikawaS. AnY. Sato2008In situ detection of methylated DNA by histo endonuclease-linked detection of methylated DNA sites: a new principle of analysis of DNA methylation.Histochem Cell Biol130917925
  34. 34. Brunner AL, Johnson DS, Kim SW, Valouev A, Reddy TE, et al. (2009) Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res: Mar 9 Epub. AL BrunnerDS JohnsonSW KimA. ValouevTE Reddy2009Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver.Genome Res: Mar 9 Epub
  35. 35. Yan PS, Potter D, Deatherage DE, Huang TH, Lin S (2009) Differential methylation hybridization: profiling DNA methylation with a high-density CpG island microarray. Methods Mol Biol 507: 89–106.PS YanD. PotterDE DeatherageTH HuangS. Lin2009Differential methylation hybridization: profiling DNA methylation with a high-density CpG island microarray.Methods Mol Biol50789106
  36. 36. Komashko VM, Acevedo LG, Squazzo SL, Iyengar SS, Rabinovich A, et al. (2008) Using ChIP-chip technology to reveal common principles of transcriptional repression in normal and cancer cells. Genome Res 18: 521–532.VM KomashkoLG AcevedoSL SquazzoSS IyengarA. Rabinovich2008Using ChIP-chip technology to reveal common principles of transcriptional repression in normal and cancer cells.Genome Res18521532
  37. 37. Visel A, Blow MJ, Li Z, Zhang T, Akiyama JA, et al. (2009) ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature 457: 854–858.A. ViselMJ BlowZ. LiT. ZhangJA Akiyama2009ChIP-seq accurately predicts tissue-specific activity of enhancers.Nature457854858
  38. 38. Badger TM, Ronis MJ, Wolff G, Stanley S, Ferguson M, et al. (2008) Soy protein isolate reduces hepatosteatosis in yellow Avy/a mice without altering coat color phenotype. Exp Biol Med (Maywood) 233: 1242–1254.TM BadgerMJ RonisG. WolffS. StanleyM. Ferguson2008Soy protein isolate reduces hepatosteatosis in yellow Avy/a mice without altering coat color phenotype.Exp Biol Med (Maywood)23312421254