B.         Hybrid Mouse Diversity Panel: Second set

1.         Mice (B. Bennett)
2.         Plasma lipids, insulin & glucose (L. Castellani)
3.         Tissues, weights (B. Bennett)
4.         Blood cell parameters (B. Bennett)
5.         Liver metabolites, Metabolon (B. Bennett)
6.         Heart expression arrays (C. Rau)
7.         Heart calcium (B. Bennett)
8.         Plasma apolipoprotein, BMS (L. Castellani)
9.         Body composition, NMR (B. Bennett)
10.        Plasma polyamines (B. Bennett)
11.        Aorta expression arrays (A. Erbilgin)
12.        Association analysis (B. Bennett)
13.        Sphingosine-1-phosphate levels (R. LeBeouf)



1.         Mice (B. Bennett)
All 100 strains were ordered in duplicate on 7/7/2008 and experiments began at 9/16/2008 and ended 3/4/2009.  These data were then added to the database and denoted with a population =2.  There is one batch of mice that were not housed correctly and they are denoted with a beddingHousingIssues=1.  Mice injected with thioglycolate are also noted with a ’1’ in the  database. For further details please see the protocols for HMDP :: Original Panel.


2.         Plasma lipids, insulin & glucose assays (L. Castellani)

General Overview: The assays that we routinely perform are total cholesterol, HDL cholesterol, unesterified cholesterol (UC), unesterified (free) fatty acids (FFA), triglycerides (TG) and glucose. Total plasma cholesterol is assayed after treating the samples with cholesterol esterase to hydrolyze cholesterol esters and then performing the cholesterol assay, which will now encompass all of the cholesterol that was in both the cholesterol ester and unesterfied cholesterol pools. Hence, this is the “total” cholesterol assay. Unesterified cholesterol is assayed by performing the cholesterol assay without treating the plasma sample with cholesterol esterase, so that only the free or unesterified cholesterol is determined. This value can then be subtracted from the total cholesterol to calculate the esterfied or cholesterol esters (CE). The HDL cholesterol is determined by performing the total cholesterol assay on the supernatant after precipitation of the apoB containing lipoproteins with Heparin/Manganese Chloride. The triglyceride assay is a typical assay that actually determines the mass of glycerol released after the hydrolysis of fatty acids from triglycerides. However, we also do a triglyceride (glycerol) “blank” assay to measure the endogenous levels of glycerol present in the mouse plasma prior to hydrolysis of triglycerides. This endogenous “blank” value is then subtracted from the total glycerol determined after the hydrolysis of triglycerides in order to correct for the endogenous plasma free glycerol concentrations, which are considerably higher in mice than in humans. Most of the pipetting is performed using a Beckman Biomek 2000 Automated Workstation with the assays read in a 96 well format using a Molecular Devices Spectramax-Plus microplate reader.

Quality Control:
Internal quality control: All assays are run in triplicate determinations on a 96 well plate. We also run standards on each plate as well as control samples with known analyte concentrations on each plate in order to validate the accuracy of the assay. This allows us to run 26 unknown samples (in triplicate) on each plate, with the remainder of the wells used by the standards, control samples, and sample blanks, all of which are also run in triplicate.

External quality control: We participate in the Centers for Disease Control and Prevention Lipid Standarization program. Our laboratory ID # is LSP-251. Each quarter we receive 12 test samples from the CDC, and one of the CDC test samples is included with an actual run of our unknown samples each week. The values we obtain from the test samples are submitted to the CDC at the end of each quarter, and we are notified if we have met their criteria for accuracy and precision. We have passed each quarter for the past 18 years.

Supplies/Reagents needed:

A.  0.9 % sodium chloride solution
B.  0.65ml minitubes (Phenix catalog# M-931B)
C. 96 well round bottom plates (USA Scientific catalog #5665-0101)
D.  96 well flat bottom plates (USA Scientific catalog #5665-    5101)
E.   Reagents for triglyceride and triglyceride blank assays

Triglyceride Assay Reagents: Triglyceride reagent (Sigma catalog # F6428 triglyceride [free glycerol] reagent) reconstituted as per manufacturers recommendations and 8000U lipase (EMD Biosciences catalog #437707). Reagent for triglyceride ‘blank’ assay is made exactly the same way but the lipase is omitted.

F.  Glucose assay reagent (Fisher catalog # SB1070-125)
G.  Glucose standard (Sigma catalog # G6152)
H.  Triglyceride standard (Sigma catalog #G556-100ml)
I.    Cholesterol standard (Fisher catalog # SB1012-030)
J.  FFA assay reagents (reconstituted per manufacturers recommendations)
NEFA Color reagent A (Wako catalog# 999-34691)
NEFA Solvent A: (Wako catalog# 995-34791)
NEFA Color reagent B: (Wako catalog# 991-34891)
NEFA Solvent B: (Wako catalog# 995-35191)
NEFA Standard solution (1mEq/L) (Wako catalog # 276-76491)

K.  Control samples
Control 1- SER-T-FY-1 Level 1 human control serum (Stanbio cat # G427-86)
Control 2 is SER-T-FY-2 Level 2 human control serum (Stanbio cat # G428-86).
L.  Reagents for cholesterol assays

Cholesterol Assay Reagent:
4-amino antipyrine (Aldrich catalog # A3,930-0), KCl (Fisher catalog # P217-500), sulfonic acid (Research Organics catalog # 6062H-3), sodium cholate (USB catalog # 13630), pipes (Sigma catalog # P-3768), Triton X-100 (Sigma catalog # T-6878), horseradish peroxidase (Amresco catalog # 0417), cholesterol oxidase (EMD Biosciences catalog # 228250) and cholesterol esterase (EMD Biosciences catalog # 228180) [Omit the cholesterol esterase from the reagent for ‘UC’ assay]

Heparin/MnCl2 for precipitation reagents for HDL assay     
MnCl2 (Fisher catalog # M87-100), Heparin solution (EMD Biosciences catalog # 375095)

Preparation of Plasma Samples, Standards, Controls, and Blanks:

General Overview: The enzymatic colorimetric assays are read on a 96 well plate in a Molecular Devices SpectraMax plus plate reader. This allows the analysis of 26 samples per plate when run in triplicate determinations, with the remainder of the wells used for the 3 standards, 2 control plasmas, and 1 assay blank, all of which are also run in triplicate. Therefore, a total of 32 minitubes should be labeled for each assay being run (26 for the samples, 3 for standards, 2 for control plasmas and 1 for the assay “blank”). After the tubes are labeled proceed with the preparation of the plasma samples, standards, controls and blanks for each assay as described below.

Initial Dilution of Plasma Samples: The frozen mouse plasma samples, which should have at least 100ul per tube, are thawed on ice. Once thawed they are vortexed and centrifuged in a table top microcentrifuge to recover all of the sample at the bottom of the tube. Depending on the strains and diets, the plasma is diluted from 4 to 16 fold with 0.9% NaCl, since very lipemic samples can occur with genetically modified mouse strains on a high fat/high cholesterol diet. For assays of common inbred strains on a normal low fat chow diet, we take 75 ul of plasma and add 225 ul of 0.9% saline for a 4 fold dilution. This will give enough total diluted sample to run all of the assays in triplicate. Obviously, for lipemic samples that will be diluted more, a smaller volume of plasma is sufficient to run all assays. The goal of the dilutions is have the lipid concentrations high enough to give reliable OD readings significantly above background, without exceeding or approaching the maximal OD reading of the plate reader. Thus, different batches of samples have to be diluted differently to meet these requirements.

Preparing Standards for each assay: Standards for the various assays are initially prepared at the concentrations listed below for each assay. Each standard is then diluted with 0.9% NaCl to the same fold dilution as the unknown plasma samples, with the following exceptions. The most concentrated cholesterol standard (standard 4; 400 mg/dl) is diluted only half as much as the unknown plasma samples, and none of the FFA standards undergo further dilution after they are prepared.

Triglyceride (glycerol) standards: Standard 1- 4.81 mg/dl glycerol (equivalent to 46.25mg/dl TG), Std 2- 9.62 mg/dl glycerol (equivalent to 92.5mg/dl TG), Standard 3- 19.24 mg/dl glycerol (equivalent to 185mg/dl TG) and Standard 4- 38.48 mg/dl glycerol (equivalent to 370mg/dl TG).

Cholesterol Standards: Standards 5 and 4 are prepared by taking the cholesterol standard (Fisher catalog # SB1012-030; 200mg/dl) directly as provided. Standards 3 through 1 are then prepared by serial twofold dilutions from standard 4. Thus, the actual initial concentrations of your standards are Standard 1- 25 mg/dl, Standard 2- 50 mg/dl, Standard 3- 100 mg/dl, Standard 4- 200 mg/dl, and Standard 5- also 200 mg/dl. When performing the assay, standards 4 through 1 are diluted with 0.9% NaCl to the same fold dilution as the unknown samples, while Std 5 undergoes only half the dilution of the unknowns.

FFA (Nonesterified fatty acid) standards: The FFA concentration in the standard solution we order (Wako catalog # 276-76491) is 28.25 mg/dl. Standard 1 is the stock solution diluted 1.5 x of your sample dilution and is labeled 18.83 mg/dl. Standard 2 is the stock at the same dilution as your samples and is labeled 28.25 mg/dl. Standard 3 is the stock diluted 0.5x your sample dilution and is labeled 56.5 mg/dl.

Glucose standards: The glucose (Sigma catalog #G6152) standards are made at the following conentrations. Standard 1; 100 mg/dl, Standard 2; 200 mg/dl, and standard 3; 400 mg/dl. Once the three standards have been prepared, they are each then diluted further with 0.9% NaCl to the same extent as the unknown samples.

Preparing Control Samples: We run two control samples, a low value (Control 1) and a high value (Control 2), for each assay on each 96-well plate. The controls are purchased from Stanbio (Boerne, Tx, USA). Control 1 is SER-T-FY-1 Level 1 human control serum (cat # G427-86) and Control 2 is SER-T-FY-2 Level 2 human control serum (cat # G428-86). The exact concentration of each analyte varies slightly by lot# and the specific values for a lot are included on the lot specification sheet with each shipment. The values for each analyte for each control are generally in the following ranges:
Glucose- Control 1; 95mg/dl  Control 2; 300mg/dl
Cholesterol- Control 1; 95mg/dl  Control 2; 300mg/dl
Free fatty acids- Control 1; 9mg/dl  Control 2; 40mg/dl
HDL chol- Control 1; 95mg/dl  Control 2; 300mg/dl

Preparing Assay blanks: Sample “blanks” are prepared for each assay by adding 75 ul of saline, rather than plasma, to make the initial dilutions from which aliquots are taken for all of the assays. This “blank” is then run exactly the same way as the unknown plasma samples for all of the assays (except HDL, see below), and the “blank” OD reading (which should be essentially zero) is subtracted from all other values. In addition to the saline “blank” described above, the HDL assay also includes a heparin-MnCl2 blank, since there is a heparin-MnCl2 precipitation step in the HDL assay. In the case of the heparin-MnCl2 blank, you do not actually have a precipitate (since it has saline instead of plasma), but take 30uls of the “supernatant” just as you would for the samples which contain plasma. This “blank” from the precipitation is used to subtract from the HDL cholesterol values obtained for the unknown and control samples, while the saline “blank”, prepared as described above, is used for the standards on the HDL assay.

Setting up for the Individual Assays:

Total cholesterol, unesterified cholesterol, and HDL cholesterol assays:

Additional Step for HDL Assay Only: The cholesterol assays for total cholesterol and unesterified cholesterol are done directly on aliquots of the diluted plasma samples. For the analysis of HDL cholesterol, prior to running the cholesterol assay the HDL has to be isolated from the other lipoproteins by precipitation. The apoB containing lipoproteins are precipitated from 100ul of the diluted plasma in the 96 well U-bottom plates using heparin-MnCl2. The plates are then centrifuged for 30min at 40c at 2500rpm, in a Beckman TJ-6 (or comparable) centrifuge.  30ul of the supernatants are then taken for the cholesterol assay. Since the plasma sample is diluted further in the heparin-MnCl2 precipitation step, the HDL cholesterol values have to be multiplied by 1.2.

Cholesterol Assay: The total cholesterol assay is done on 20ul of the diluted plasma sample, the UC assay on 25ul of the diluted plasma sample, and the HDL assay on 30ul of the supernatant after heparin-MnCl2 precipitation. The samples, controls and standards are added to 0.65 ml minitubes to which 600 ul of the cholesterol reagent is added. The reagent with esterase is used for the total cholesterol and HDL assays, and the reagent without esterase is used for the UC assay. The samples are then incubated at 37degrees C in a water bath. After the incubation 170ul aliquots in triplicates are loaded into the 96 well flat bottom plates. Read the plates at 515nm, subtracting the “blank” values from all readings.. Because the total cholesterol is higher than unesterified or HDL cholesterol, use standards 100 mg/dl, 200 mg/dl, and 400 mg/dl. For the HDL assays, use standards 50 mg/dl, 100 mg/dl, and 200 mg/dl. For the UC assay use standards 25 mg/dl, 50 mg/dl, and 100 mg/dl.

Triglyceride and Triglyceride blank assays: For the triglyceride and triglyceride blank assays aliquot 30ul of the diluted sample, controls, and standards into 0.65ml minitubes. Use Glycerol standards 46.25 mg/dl, 92.5 mg/dl, and 185 mg/dl for the triglyceride blank assay and glycerol standards 92.5 mg/dl, 185 mg/dl, and 370 mg/dl for the triglyceride assay. Add 600uls of the triglyceride assay reagent with lipase to each tube for the triglyceride assays, and add 600uls of the triglyceride reagent without lipase to each tube for the triglycride blank assay. Incubate the tubes for 10 min at 37 degrees C in a water bath.  After the incubation load 170ul in triplicates into 96 well flat bottom plates. Read the plates at 540nm. After running the triglyceride and triglyceride blank assays for each sample, the value of the triglyceride blank is subtracted from the triglyceride value to correct for endogenous levels of free glycerol in the plasma.

FFA assay: For the free fatty acid assay aliquot 30ul of the diluted plasma sample, controls, and standards into 0.65ml minitubes. Add 400uls of reagent A and incubate for 5 min at 37 degrees C in a water bath. Then add 200uls of reagent B and incubate for 5 min at 37 degrees C in a water bath.  After incubation load 170ul in triplicates into 96 well flat bottom plates. Read the plates at 550nm and subtract “blank” values from the reading.

Glucose assay:
For the glucose assay, aliquot 15ul of the diluted plasma, controls, and standards into 0.65ml minitubes. Add 600uls of the glucose reagent directly as supplied by the manufacturer, (Fisher cat # SB1070-125 manufactured by Stanbio) to each tube and incubate for 5 min at 37 degrees C in a water bath. After the incubation load 170ul in triplicates into 96 well flat bottom plates. Read the plates at 505nm. Subtract the assay “blank” from the values.

Insulin assay:
Insulin levels in plasma were measured using the ALPCO Mouse Ultrasensitive Insulin ELISA according to manufacturer’s instructions as described : http://www.alpco.com/products/Insulin_Ultrasensitive_Mouse_ELISA.aspx


3.         Tissues, weights (B. Bennett)

Weight of following tissues taken: gonadal, femoral, mesenteric fat pads, heart and on a subset of the
NMR analysis.


4.         Blood cell parameters (B. Bennett)

Complete Blood counts including circulating leukocytes and Red blood cell parameters were performed using HemaTrue Hematology Analyzer (Heska Corp, Loveland, CO, USA) according to the manufacturer’s specifications.


5.         Liver metabolites, Metabolon (B. Bennett)

mViewTM REPORT Global metabolic profiles across multiple mouse strains, classic inbred and recombinant inbred, as well as mutant lines. Metabolon, Inc. • 800 Capitola Drive, Suite 1, Durham, NC 27713 www.metabolon.com.

STUDY DESCRIPTION AND RESULTS I. Purpose of Experiment The goal of this study was to obtain global metabolic profiling data from mouse liver samples relating to a multi-center genome wide association study [Bennett et al. 2010. Genome Research. 20:281-290] and additional samples.

II. Experimental design Global biochemical profiles were obtained for a total of 164 mouse liver samples from the Hybrid Mouse Diversity Panel (HMDP) comprised of both classic inbred and recombinant inbred strains, as well as additional samples. Samples were grouped in the provided client metadata into 9 groups by either the classic inbred strain, the recombinant cross, or the designations “misc” or “multi-lab” which consisted of multiple subgroups. Sample Group Number of Samples Base 8 B6 3 AXB 17 BXA 14 BXD 30 BXH 11 CXB 28 misc 29 multi-lab 24

III. Summary of Procedure Metabolon received 164 samples on 23 June 2010 and a complete sample manifest on 08 July 2010. Following receipt, samples were inventoried, and immediately stored at -80oC. At the time of analysis samples were extracted and prepared for analysis using Metabolon’s standard solvent extraction method. The extracted samples were split into equal parts for analysis on the GC/MS and LC/MS/MS platforms. Also included were several technical replicate samples created from a homogeneous pool containing a small amount of all study samples (“Client Matrix”). General platform methods are described in APPENDIX A.

Data Quality: Instrument and Process Variability
QC Sample Measurement Median RSD Internal Standards Instrument Variability 5 % Endogenous Biochemicals Total Process Variability 9 % Instrument variability was determined by calculating the median relative standard deviation (RSD) for the internal standards that were added to each sample prior to injection into the mass spectrometers. Overall process variability was determined by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of the Client Matrix samples, which are technical replicates of pooled client samples. Values for instrument and process variability meet Metabolon’s acceptance criteria as shown in the table above.

V. Metabolite Summary The mView product specification includes all detectable compounds of known identity (named biochemicals). The present dataset comprises a total of 342 named biochemicals that can be assigned to biochemical pathways. Information related to biochemical pathways, chemical properties, and public databases are included in the electronic files that accompany this report. We have also included in the electronic deliverables, a file with data for each biochemical displayed as boxplots grouped by client-provided metadata parameter, “Strain Group”, like that shown in the example figure below.

Appendix A: Metabolon Platform Sample Accessioning: Each sample received was accessioned into the Metabolon LIMS system and was assigned by the LIMS a unique identifier, which was associated with the original source identifier only. This identifier was used to track all sample handling, tasks, results etc. The samples (and all derived aliquots) were bar-coded and tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by the LIMS when a new task was created; the relationship of these samples was also tracked. All samples were maintained at -80 ºC until processed.

Sample Preparation: The sample preparation process was carried out using the automated MicroLab STAR® system from Hamilton Company. Recovery standards were added prior to the first step in the extraction process for QC purposes. Sample preparation was conducted using a proprietary series of organic and aqueous extractions to remove the protein fraction while allowing maximum recovery of small molecules. (See METHODS AND RESULTS section for study-specific methods.) The resulting extract was divided into two fractions; one for analysis by LC and one for analysis by GC. Samples were placed briefly on a TurboVap® (Zymark) to remove the organic solvent. Each sample was then frozen and dried under vacuum. Samples were then prepared for the appropriate instrument, either LC/MS or GC/MS.

QA/QC: For QA/QC purposes, a number of additional samples are included with each day’s analysis. Furthermore, a selection of QC compounds is added to every sample, including those under test. These compounds are carefully chosen so as not to interfere with the measurement of the endogenous compounds. Tables 1 and 2 describe the QC samples and compounds. These QC samples are primarily used to evaluate the process control for each study as well as aiding in the data curation.

Table 1: Description of Metabolon QC Samples Type Description Purpose MTRX Large pool of human plasma maintained by Metabolon that has been characterized extensively. Assure that all aspects of Metabolon process are operating within specifications. CMTRX Pool created by taking a small aliquot from every customer sample. Assess the effect of a non-plasma matrix on the Metabolon process and distinguish biological variability from process variability. PRCS Aliquot of ultra-pure water Process Blank used to assess the contribution to compound signals from the process. SOLV Aliquot of solvents used in extraction. Solvent blank used to segregate contamination sources in the extraction.

Table 2: Metabolon QC Standards Type Description Purpose DS Derivatization Standard Assess variability of derivatization for GC/MS samples. IS Internal Standard Assess variability and performance of instrument. RS Recovery Standard Assess variability and verify performance of extraction and instrumentation.

Liquid chromatography/Mass Spectrometry (LC/MS, LC/MS2): The LC/MS portion of the platform was based on a Waters ACQUITY UPLC and a Thermo-Finnigan LTQ mass spectrometer, which consisted of an electrospray ionization (ESI) source and linear ion-trap (LIT) mass analyzer. The sample extract was split into two aliquots, dried, then reconstituted in acidic or basic LC-compatible solvents, each of which contained 11 or more injection standards at fixed concentrations. One aliquot was analyzed using acidic positive ion optimized conditions and the other using basic negative ion optimized conditions in two independent injections using separate dedicated columns. Extracts reconstituted in acidic conditions were gradient eluted using water and methanol both containing 0.1% Formic acid, while the basic extracts, which also used water/methanol, contained 6.5mM Ammonium Bicarbonate. The MS analysis alternated between MS and data-dependent MS2 scans using dynamic exclusion.

Gas chromatography/Mass Spectrometry (GC/MS): The samples destined for GC/MS analysis were re-dried under vacuum desiccation for a minimum of 24 hours prior to being derivatized under dried nitrogen using bistrimethyl-silyl-triflouroacetamide (BSTFA). The GC column was 5% phenyl and the temperature ramp is from 40° to 300° C in a 16 minute period. Samples were analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer using electron impact ionization. The instrument was tuned and calibrated for mass resolution and mass accuracy on a daily basis. The information output from the raw data files was automatically extracted as discussed below.

Accurate Mass Determination and MS/MS fragmentation (LC/MS), (LC/MS/MS): The LC/MS portion of the platform was based on a Waters ACQUITY UPLC and a Thermo-Finnigan LTQ-FT mass spectrometer, which had a linear ion-trap (LIT) front end and a Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer backend. For ions with counts greater than 2 million, an accurate mass measurement could be performed. Accurate mass measurements could be made on the parent ion as well as fragments. The typical mass error was less than 5 ppm. Ions with less than two million counts require a greater amount of effort to characterize. Fragmentation spectra (MS/MS) were typically generated in data dependent manner, but if necessary, targeted MS/MS could be employed, such as in the case of lower level signals.

Bioinformatics: The informatics system consisted of four major components, the Laboratory Information Management System (LIMS), the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization tools for use by data analysts. The hardware and software foundations for these informatics components were the LAN backbone, and a database server running Oracle Enterprise Edition.

LIMS: The purpose of the Metabolon LIMS system was to enable fully auditable laboratory automation through a secure, easy to use, and highly specialized system. The scope of the Metabolon LIMS system encompasses sample accessioning, sample preparation and instrumental analysis and reporting and advanced data analysis. All of the subsequent software systems are grounded in the LIMS data structures. It has been modified to leverage and interface with the in-house information extraction and data visualization systems, as well as third party instrumentation and data analysis software.

Data Extraction and Quality Assurance: The data extraction of the raw mass spec data files yielded information that could loaded into a relational database and manipulated without resorting to BLOB manipulation. Once in the database the information was examined and appropriate QC limits were imposed. Peaks were identified using Metabolon’s proprietary peak integration software, and component parts were stored in a separate and specifically designed complex data structure.

Compound identification: Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Identification of known chemical entities was based on comparison to metabolomic library entries of purified standards. As of this writing, more than 1000 commercially available purified standard compounds had been acquired registered into LIMS for distribution to both the LC and GC platforms for determination of their analytical characteristics. The combination of chromatographic properties and mass spectra gave an indication of a match to the specific compound or an isobaric entity. Additional entities could be identified by virtue of their recurrent nature (both chromatographic and mass spectral). These compounds have the potential to be identified by future acquisition of a matching purified standard or by classical structural analysis.

Curation: A variety of curation procedures were carried out to ensure that a high quality data set was made available for statistical analysis and data interpretation. The QC and curation processes were designed to ensure accurate and consistent identification of true chemical entities, and to remove those representing system artifacts, mis-assignments, and background noise. Metabolon data analysts use proprietary visualization and interpretation software to confirm the consistency of peak identification among the various samples. Library matches for each compound were checked for each sample and corrected if necessary.

Normalization: For studies spanning multiple days, a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound was corrected in run-day blocks by registering the medians to equal one (1.00) and normalizing each data point proportionately (termed the “block correction”; Figure 1). For studies that did not require more than one day of analysis, no normalization is necessary, other than for purposes of data visualization. Figure 1: Visualization of Data Normalization

Statistical Calculation: For many studies, two types of statistical analysis are usually performed: (1) significance tests and (2) classification analysis. (1) For pair-wise comparisons we typically perform Welch’s t-tests and/or Wilcoxon’s rank sum tests. For other statistical designs we may perform various ANOVA procedures (e.g., repeated measures ANOVA). (2) For classification we mainly use random forest analyses. Random forests give an estimate of how well we can classify individuals in a new data set into each group, in contrast to a t-test, which tests whether the unknown means for two populations are different or not. Random forests create a set of classification trees based on continual sampling of the experimental units and compounds. Then each observation is classified based on the majority votes from all the classification trees. Statistical analyses are performed with the program “R” http://cran.r-project.org/.


6.         Heart expression arrays (C. Rau)

A portion of heart tissue was isolated from one male mouse each of 99 strains and RNA extracted via the Trizol method following homogenization of the sample.  RNA integrity was confirmed using the Agilent 2100 Bioanalyzer.  Microarray expression profiles were generated using the Affymetrix MOE430A Chip.  Data was normalized using RMA after removal of bad probes from the probesets.


7.         Heart calcium (B. Bennett)

A total of one hundred and two strains were used in this experiment, of which most were replicated at least twice (often three times for most BXD, BXA/AXB, BXH strains).  Hearts were selected for the experiment from mice of progenitor strains DBA, B6, C3H, A/J, and their recombinant inbred strains BXD, AXB/ BXA, BXH.  These hearts were then lyophilized overnight in a speed-vac apparatus until they were completely dry.  The lyophilized heart samples were weighed and later incubated at room temperature in 200 microliters of 0.6 N HCl.  After six days of incubation, the supernatant was collected to be used in a calcium assay.

  For the calcium assay, calcium standards of 10, 5, 2.5, 1.25, 0.625, and 0.3125 mg/dL were prepared and 4 microliters of each were placed on a 96 well plate in repetitions of three for analysis.  All samples of heart supernatant were also placed on the 96 well plate (4 microliters each) in duplicates.  Calcium sensitive reagent was prepared from a Teco Diagnostics calcium assay kit.  The principle reaction by which the kit works is the reaction of calcium with cresolphthalein complexone in 8-hydroxyquinoline to form a colored complex (purple color) that absorbs light at 570 nm.  The intensity of the color is proportional to the calcium concentration.  200 microliters of this reagent was added to each of the samples on the plates.  Data was normalized by plating supernatant samples from DBA and C57 strains on every plate assayed.  DBA and C57 strains were selected for normalization due to the high myocardial calcium content of DBA and the relatively low calcium content of C57.  The plates were then read by a spectrophotometer at 570 nm.  Absorption data was collected and converted to calcium content quantities using a standard curve. 

This calcium content data was used in combination with previously collected heart weight data to make a table, using Microsoft Excel, giving the average weight, average calcium content, and calcium content per unit weight for the various mouse strain heart samples.  Some strains were analyzed in duplicates and triplicates based on availability of heart samples from multiple mice of each strain.  Data values were normalized across plates using values for standard 10 and DBA_7.  A pivot table was created using Excel to plot average calcium content per unit weight (normalized) by strain.  This data was analyzed using R and a histogram was plotted of the normalized calcium to weight ratio.  In order to obtain a normal distribution of data, the normalized calcium to weight ratios were log transformed (base 2) and the new values were plotted on a histogram.  Both the raw normalized values and log transformed values were plotted by strain in mean deviation plots using R.


8.         Plasma apolipoprotein, BMS (L. Castellani)

Plasma Lipoprotein Profiles Obtained by Gel Filtration Chromatography: Plasma lipoproteins were fractionated from 400ul of plasma by gel fitration chromatography using two Superose 6 columns (Superose 6 HR10/10; Amersham Pharmacia Biotech) connected in series to a Pharmacia FPLC system. The elution solvent composition was 8.999g NaCl, 0.3722g sodium EDTA, and 0.2 g sodium azide per liter at pH 8.2.
Fractions of 0.5 ml were collected at a flow rate of 0.5 ml/min. The first 20 fractions were allowed to go to waste and the next 60 0.5ml-fractions were collected. The lipoprotein profiles were then obtained by determining the cholesterol and triglyceride concentrations in a 150ul aliquot of each fraction and plotting the values.

The original protocol from which we adapted this method was from the laboratory of G. Schonfeld (1, 2).

Jiao, S., Cole, T. G., Kitchens, R. T., Pfleger, B., and Schonfeld, G. (1990) J. Lipid Res. 31, 467-477.
2. Cole, 4 G.,, Kitchens, R. T., Daugherty, A., and Schonfeld, G. (1988) Pharmacia FPLC Biocommunique 4, 4-6.


9.       Body composition, NMR (B. Bennett)

Animals were measured for total body fat mass and lean mass by nuclear magnetic resonance (NMR) using the Bruker Minispec with software from Echo Medical Systems (Houston, TX).  All F2 mice were measured at 16 weeks of age on the day prior to euthanasia. 


10.       Plasma polyamines (B. Bennett)

Quantification of TMAO, TMA and creatinine in mouse plasma and urine was performed using stable isotope dilution HPLC with on line electrospray ionization tandem mass spectrometry on an API 365 triple quadrupole mass spectrometer (Applied Biosystems,Foster, CA) interfaced with a Cohesive HPLC (Franklin, MA) equipped with phenyl column (4.6 × 2505mm, 5 μm Rexchrom Phenyl; Regis, Morton Grove, IL) and the separation was performed as reported previously (Wang et al., 2011). TMAO, TMA and creatinine were monitored in positive MRM MS mode using characteristic precursor–product ion transitions: m/z 76  58, m/z 6044 and m/z 11486, respectively. The internal standards TMAO-trimethyl-d9 (d9-TMAO),TMA-d9 (d9-TMA), and creatinine methyl-d3were added to plasma or urine samples before sample processing, and were similarly monitored in MRM mode at m/z 85  68, m/z 6949 and m/z 11789,  respectively. Various concentrations of TMAO, TMA and creatinine standards and a fixed amount of internal standards were spiked into control plasma or urine to prepare the calibration curves for quantification of plasma or urine TMAO, TMA and creatinine.


12.       Aorta expression arrays (A. Erbilgin)

In development.


13.       Association analysis (B. Bennett)

Genome-wide association mapping accounting for population structure:  We applied the following linear mixed model to account for the population structure and genetic relatedness among strains in the genome-wide association mapping. (Kang, Zaitlen et al. 2008)

where μ represents mean, x represents SNP effect, u represents random effects due to genetic relatedness with Var (u) = σg2K and Var (e) = σe2, where K represents IBS (identity-by-state) matrix across all genotypes. A restricted maximum likelihood (REML) estimate of σg2 and σe2 are computed using EMMA (Efficient Mixed Model Association), and the association mapping is performed based on the estimated variance component with a standard F test to test β≠0. We defined an eQTL as local if the peak association signal was within a 10Mb sliding window of the physical location of the gene(s). We then calculated the average distance between these local eQTL and the transcription start site of the corresponding gene(s) transcription start site.

Estimation of power and mapping resolution: We evaluated the statistical power of the HMDP through simulation studies, with various parameters including the variance explained by SNP, variance explained by genetic background, and variance explained by random errors, and the number of repeated measurements per strain. For the comparison of power with single RI set or classical inbred (CI) only studies, we selected a subset of the simulated phenotypes for each RI or CI set and evaluated the power in the same way. Since there are 8 possibilities of SNPs being polymorphic among three sets of RI strains, the putative causal SNPs are categorized into 8 classes and power is evaluated for each class. The significance threshold per each RI set is determined separately using parametric bootstrapping described below.  See Supplemental Methods for comparison of BXD RI set to the full HMDP and simulations.

Genome-wide Significance Threshold: Genome-wide significance threshold in genome-wide association mapping is determined by the family-wise error rate (FWER) as the probability of observing one or more false positives across all SNPs per phenotype. We ran 100 different sets of permutation tests and parametric bootstrapping of size 1000, and observed that the mean and standard error of the genome-wide significance threshold at family-wise error rate (FWER) of 0.05 were 3.9 x 10-6 ± 0.3 x 10-6, and 4.0 x 10-6 ± 0.3 x 10-6, respectively. This is approximately an order of magnitude larger than the significance threshold obtained by Bonferroni correction (4.6 x 10-7). We also performed parametric bootstrapping under simulated the genetic background effect from population structure using EMMA. With 50% and 100% of variance explained by genetic background, the thresholds were determined to be 1.6 x 10-6 ± 0.2 x10-6 and 1.7 x 10-6 ± 0.2 x 10-6. The reduction in the significance threshold compared to no genetic background effect is due to the fact that inter-SNP correlation due to long-range LD reduces when conditioning on the population structure.  A detailed explanation of these analyses is provided in the Supplemental Methods.


14.       Sphingosine-1-phosphate levels (R. LeBeouf)

In development.