This report specifies an information model of machine-tool-performance tests in the EXPRESS  language This report specifies an information model of machine-tool-performance tests in the EXPRESS modeling language . It is bThe information model specifies the test procedure, the test conditions, the equipment, the measurement set-up, and the test results. The model can be used to describe the properties and results of a performance test at a level close to the raw measurement data. The information elements also enable the user to re-create the set-up, equipment settings, and measurement procedure. The model captures key information on the large variety of possible test set-ups and measurement procedures, which is essential for the interpretation of the test results. A subset of the specification can be used to summarize the test, focusing on performance parameters that are estimated from the measurement results.The information model addresses machine tool properties that are verified by performance tests. It complements machine-tool-specification data that is not tested, e.g., the machine configuration, the workspace, weight and size of the machine, tool holder standard, auxiliary devices, etc. .The information model is intended to serve as the starting point for a future, standardized representation. The model is expected to change and grow based on further review and future implementation experience.This report is structured as follows: In the remainder of Sec. 1 the problem statement and scope of the information model are defined; modeling language and implementation methods are identified. In Sec. 2 the information model is presented. In Sec. 3 three sample data files are provided. In Sec. 4 software tools that support the implementation of EXPRESS information models are briefed. Section 5 presents the conclusion of this report. An appendix is provided for describing a set of EXPRESS keywords in Sec. 6. The final section lists the references used in this report.Today’s manufacturing industry greatly relies on computer technology to support activities throughout a product’s life cycle. Efficient and distributed access to the performance data of machine tools is important in manufacturing. The results of performance tests are used for machine acceptance, predictive maintenance, error compensation, and to evaluate the capability of a machine to manufacture parts to specified tolerances. A critical enabler to the efficient interchange and storage of performance data is a unified information model for the results and properties of performance tests.Currently, there is no agreed-upon mechanism for representing the properties and results of machine-tool-performance tests . There ePositioning accuracy and repeatability of linear and angular positioning axes.Geometric errors of linear and angular positioning axes.Spindle axis of rotation.Machine thermal tests: environmental temperature variation error (ETVE), spindle, axis, and composite.Critical alignments: parallelism and squareness of machine axes.Circular contouring tests.Diagonal displacement tests.Subsystem repeatability .Compliance and hysteresis.This specification supports the majority of instrumented, machine-tool-performance tests defined in the American ,5 and inDate and time of the test.Identification of the machine tool on which the test was performed.Indication as to why the test was performed.The operator who performed the test.The machine status and environmental conditions during the test.The standard in which the test is defined.The equipment and software used to perform the test.The measurement set-up and operating parameters.The raw measurement data.The calculated performance parameters.For these tests the following information is described:The information model presented in this report is in the EXPRESS language. The EXPRESS modeling language  was devedata transfer via a working form, which is a structured, in-memory representation of data.data transfer via an exchange file, which is a file with a predefined structure or format.data transfer using a database management system .An information model provides a sharable, stable, and organized structure of information requirements. It is developed to be independent from implementations using different data format structures. Implementation independence allows users to select their implementation methods. The selection of an implementation method is heavily dependent on the target environment where the application system resides, but the information contained in the model is not dependent on the target environment if the modeling is done properly. Currently, the implementation methods used by the manufacturing community include:Part 21 file, as an implementation method for actual EXPRESS models  OF UNIQUE TEST_DEF; END_ENTITY;ENTITY TEST_DEF; ID: OPTIONAL STRING; TEST_CLASS: TEST_CLASS_DEF; DATE: DATE_DEF; TIME: OPTIONAL TIME_DEF; WHY: OPTIONAL WHY_DEF; MACHINE: MACHINE_DEF; CONDITIONS: OPTIONAL CONDITIONS_DEF; OPERATOR: OPTIONAL STRING; STANDARD: OPTIONAL STANDARD_DEF; EQUIPMENT: EQUIPMENT_DEF; SETUP: SETUP_DEF; RUN_DATA: OPTIONAL LIST [1:?] OF RUN_DATA_DEF; RESULT: OPTIONAL LIST [1:?] OF RESULT_DEF; COMMENT: OPTIONAL TEXT;END_ENTITY;ENTITY DATE_DEF; YYYY: INTEGER; MM: INTEGER; DD: INTEGER;  WHERE  WR1: (YYYY = 1900);  WR2: (1 <= MM) AND (MM <= 12);  WR3: (1 <= DD) AND (DD <= 31);END_ENTITY;ENTITY TIME_DEF; HH: INTEGER; MM: INTEGER; SS: INTEGER;  WHERE  WR1: (0 <= HH) AND (HH <= 24);  WR2: (0 <= MM) AND (MM <= 59);  WR3: (0 <= SS) AND (SS <= 59);  WR4: ((HH = 24) AND ((MM = 0) AND (SS = 0)));END_ENTITY;ENTITY MACHINE_DEF; ID: OPTIONAL STRING; MANUFACTURER: STRING; MACHINE_MODEL: STRING; SERIAL_NUMBER: STRING; LOCATION: OPTIONAL STRING;END_ENTITY;ENTITY CONDITIONS_DEF; CLAMPED_AXES: OPTIONAL LIST [1:?] OF AXIS_DEF; COMPENSATION: OPTIONAL BOOLEAN; COMPENSATION_ID: OPTIONAL STRING; COOLANT: OPTIONAL BOOLEAN; DRIVE_STATUS: OPTIONAL DRIVE_STATUS_DEF; TEMP_ENVIRONMENT: OPTIONAL REAL; WARMUP: OPTIONAL BOOLEAN; WARMUP_DESCRIPTION: OPTIONAL STRING;END_ENTITY;ENTITY STANDARD_DEF; ORGANIZATION: STRING; STANDARD_NUMBER: STRING; NAME: OPTIONAL STRING; YEAR: INTEGER; TEST_NAME: OPTIONAL STRING; SECTION_NUMBER: OPTIONAL STRING; SECTION_NAME: OPTIONAL STRING; STATUS: OPTIONAL STANDARD_STATUS_DEF;  WHERE  WR1: (YEAR>1900);END_ENTITY;ENTITY EQUIPMENT_DEF SUPERTYPE OF );  ID: OPTIONAL STRING;  COMPONENT: OPTIONAL LIST [1:?] OF COMPONENT_DEF;  SOFTWARE: OPTIONAL SOFTWARE_DEF;  RESOLUTION: OPTIONAL REAL;  SAMPLE_RATE_RAW: OPTIONAL REAL;END_ENTITY;ENTITY COMPONENT_DEF; ID: OPTIONAL STRING; DESCRIPTION: OPTIONAL STRING; MANUFACTURER: STRING; COMPONENT_MODEL: STRING; SERIAL_NUMBER: STRING; CALIBRATION_DATE: OPTIONAL DATE_DEF; CALIBRATION_EXP_DATE: OPTIONAL DATE_DEF; CERTIFICATE_NUMBER: OPTIONAL STRING; CALIBRATION_ORGANIZATION: OPTIONAL STRING;END_ENTITY;ENTITY SOFTWARE_DEF; ID: OPTIONAL STRING; MANUFACTURER: STRING; NAME: STRING; VERSION_NUMBER: STRING;END_ENTITY;ENTITY SETUP_DEF SUPERTYPE OF );END_ENTITY;ENTITY RUN_DATA_DEF SUPERTYPE OF );END_ENTITY;ENTITY RESULT_DEF; STANDARD: STANDARD_DEF; MEASURAND: OPTIONAL MEASURAND_POINT_DEF; PARAMETER: LIST [1:?] OF PARAMETER_DEF;END_ENTITY;ENTITY PARAMETER_DEF; NAME: STRING; VAL: REAL; APPROACH_DIRECTION: OPTIONAL APPROACH_DIRECTION_DEF;END_ENTITY;ENTITY EQUIPMENT_CIRCULAR_DEF SUPERTYPE OF ) SUBTYPE OF (EQUIPMENT_DEF);  EQUIPMENT_CLASS: EQUIPMENT_CLASS_CIRCULAR_DEF;  ABSOLUTE: BOOLEAN;  FILTER_LS_CENTER: BOOLEAN;  FILTER_LS_RADIUS: BOOLEAN;  TEMP_REFERENCE_COMP: BOOLEAN;  TEMP_REFERENCE_SENSOR: OPTIONAL LIST [1:?] OF TEMP_SENSOR_DEF;  TEMP_REFERENCE_COEFFICIENT: REAL;END_ENTITY;ENTITY BALL_BAR_DEF SUBTYPE OF (EQUIPMENT_CIRCULAR_DEF);  CALIBRATOR: OPTIONAL BOOLEAN;END_ENTITY;ENTITY DISK_DEF SUBTYPE OF (EQUIPMENT_CIRCULAR_DEF);  MACHINE_PROBE: BOOLEAN;  INNER_CIRCLE: OPTIONAL BOOLEAN;END_ENTITY;ENTITY GRID_ENCODER_DEF SUBTYPE OF (EQUIPMENT_CIRCULAR_DEF);END_ENTITY;ENTITY SETUP_CIRCULAR_DEF SUPERTYPE OF )SUBTYPE OF (SETUP_DEF);  ID: OPTIONAL STRING;  MEAS_MODE: MEAS_MODE_DEF;  PLANE: PLANE_DEF;  ROTARY_AXIS: OPTIONAL AXIS_DEF;  CENTER: MACHINE_POSITION_DEF;  TOOL_VECTOR: TOOL_VECTOR_DEF;  SPINDLE_NUMBER: OPTIONAL INTEGER;  TURRET_NUMBER: OPTIONAL INTEGER;  RADIUS_MACHINE: REAL;  RADIUS_REFERENCE: REAL;  INCLINATION: REAL;  FEEDRATE: REAL;  OVERSHOOT: OPTIONAL REAL;  TEMP_MATERIAL_COMP: BOOLEAN;  TEMP_MATERIAL_SENSOR: OPTIONAL LIST [1:?] OF TEMP_SENSOR_DEF;  TEMP_MATERIAL_COEFFICIENT: OPTIONAL REAL;  SAMPLES_AVERAGED: OPTIONAL INTEGER;  POINT_MODE: POINT_MODE_CIRCULAR_DEF;  INTERPOLATION: OPTIONAL INTERPOLATION_DEF;  NC_PROGRAM_ID: OPTIONAL STRING;  ALIGNMENT_METHOD: OPTIONAL ALIGNMENT_METHOD_DEF;  ALIGNMENT_WHEN: OPTIONAL ALIGNMENT_WHEN_DEF;  DATUM_WHEN: OPTIONAL DATUM_WHEN_DEF;  PREVIOUS_TEST_ID: OPTIONAL STRING;   WHERE   WR1: (TEMP_MATERIAL_COMP) AND (EXISTS(TEMP_MATERIAL_COEFFICIENT));END_ENTITY;ENTITY PLANE_DEF; X: AXIS_DEF; Y: AXIS_DEF;END_ENTITY;ENTITY SETUP_CIRCULAR_STATIC_DEF SUBTYPE OF (SETUP_CIRCULAR_DEF);  SETUP_STATIC: SETUP_STATIC_DEF;  APPROACH_MODE: APPROACH_MODE_CIRCULAR_DEF;END_ENTITY;ENTITY SETUP_CIRCULAR_DYNAMIC_DEF SUBTYPE OF (SETUP_CIRCULAR_DEF);  SETUP_DYNAMIC: SETUP_DYNAMIC_DEF;  CAM_SOFTWARE: OPTIONAL SOFTWARE_DEF;  NC_CIRCULARITY: OPTIONAL REAL;END_ENTITY;ENTITY RUN_DATA_CIRCULAR_DEF SUBTYPE OF (RUN_DATA_DEF);  APPROACH_DIRECTION: APPROACH_DIRECTION_DEF;  LS_CENTER_OFFSET_X: OPTIONAL REAL;  LS_CENTER_OFFSET_Y: OPTIONAL REAL;  LS_RADIUS_ERROR: OPTIONAL REAL;  TEMP_REFERENCE: OPTIONAL LIST [1:?] OF TEMP_DATA_DEF;  TEMP_MATERIAL: OPTIONAL LIST [1:?] OF TEMP_DATA_DEF;  POINTS: LIST[1:?] OF REAL;   WHERE   WR1: ((EQUIPMENT_CIRCULAR_DEF.FILTER_LS_CENTER) AND    (EXISTS (LS_CENTER_OFFSET_X) AND    EXISTS (LS_CENTER_OFFSET_Y)));   WR2: ((EQUIPMENT_CIRCULAR_DEF.FILTER_LS_RADIUS) AND    (EXISTS (LS_RADIUS_ERROR)));END_ENTITY;ENTITY EQUIPMENT_LINE_DEF SUBTYPE OF (EQUIPMENT_DEF);  EQUIPMENT_CLASS: EQUIPMENT_CLASS_LINE_DEF;  FILTER_LS_SLOPE: OPTIONAL BOOLEAN;  FILTER_LS_CENTER: OPTIONAL BOOLEAN;  FILTER_OFFSET: BOOLEAN;  TEMP_REFERENCE_COMP: OPTIONAL BOOLEAN;  TEMP_REFERENCE_COEFFICIENT: OPTIONAL REAL;  TEMP_REFERENCE_SENSOR: OPTIONAL LIST [1:?] OF TEMP_SENSOR_DEF;  LASER_INTERFEROMETER: OPTIONAL LASER_INTERFEROMETER_DEF;  TARGET_SHAPE: OPTIONAL TARGET_SHAPE_DEF;  TARGET_DIAMETER: OPTIONAL REAL;END_ENTITY;ENTITY LASER_INTERFEROMETER_DEF; AIR_HUMIDITY_COMP: OPTIONAL BOOLEAN; AIR_PRESSURE_COMP: OPTIONAL BOOLEAN; DEADPATH_COMP: OPTIONAL BOOLEAN; VOL_COMP_METHOD: OPTIONAL VOL_COMP_METHOD_DEF;END_ENTITY;ENTITY SETUP_LINE_DEF SUPERTYPE OF ) SUBTYPE OF (SETUP_DEF);  ID: OPTIONAL STRING;  MEAS_MODE: MEAS_MODE_DEF;  MEASURAND: MEASURAND_LINE_DEF;  MEAS_METHOD: MEAS_METHOD_DEF;  AXIS: OPTIONAL AXIS_DEF;  SENSITIVE_DIRECTION: OPTIONAL SENSITIVE_DIRECTION_DEF;  START_POINT: MACHINE_POSITION_DEF;  END_POINT: OPTIONAL MACHINE_POSITION_DEF;  TOOL_VECTOR: TOOL_VECTOR_DEF;  SPINDLE_NUMBER: OPTIONAL INTEGER;  TURRET_NUMBER: OPTIONAL INTEGER;  FEEDRATE: REAL;  DEADPATH: OPTIONAL REAL;  OVERSHOOT: OPTIONAL REAL;  WARMUP_MOVES: OPTIONAL INTEGER;  WARMUP_RUNS: OPTIONAL INTEGER;  TEMP_MATERIAL_COMP: OPTIONAL BOOLEAN;  TEMP_MATERIAL_SENSOR: OPTIONAL LIST [1:?] OF TEMP_SENSOR_DEF;  TEMP_MATERIAL_COEFFICIENT: OPTIONAL REAL;  TEMP_ADDITIONAL_SENSOR: OPTIONAL LIST [1:?] OF TEMP_SENSOR_DEF;  SAMPLES_AVERAGED: OPTIONAL INTEGER;  ALIGNMENT: OPTIONAL ALIGNMENT_DEF;  DIFFERENTIAL_MEAS_DIR : OPTIONAL DIFFERENTIAL_MEAS_DIR_DEF;  SENSOR_OFFSET : OPTIONAL REAL;  REVERSAL: OPTIONAL BOOLEAN;   WHERE   WR1: (((TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.DIAGONAL_ACCELERATION) OR    (TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.DIAGONAL_ANGULAR) OR    (TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.DIAGONAL_POSITION) OR    (TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.DIAGONAL_STRAIGHT) OR    (TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.DIAGONAL_VELOCITY)) AND    (EXISTS(END_POINT)));   WR2: (((TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.DIAGONAL_POSITION) OR    (TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.AXIS_POSITION) OR    (TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.AXIS_REPEAT) OR    (TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.AXIS_REVERSAL) OR    (TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.AXIS_PERIODIC) OR    (TEST_DEF.TEST_CLASS = TEST_CLASS_DEF.THERMAL_AXIS)) AND    (EXISTS(TEMP_MATERIAL_COMP)));   WR3: ((TEMP_MATERIAL_COMP) AND    ((EXISTS(TEMP_MATERIAL_SENSOR) AND    EXISTS(TEMP_MATERIAL_COEFFICIENT))));   WR4: ((MEAS_METHOD = MEAS_METHOD_DEF.DIFFERENTIAL) AND    (EXISTS(DIFFERENTIAL_MEAS_DIR)));END_ENTITY;ENTITY SETUP_LINE_STATIC_DEF SUBTYPE OF (SETUP_LINE_DEF);  SETUP_STATIC: SETUP_STATIC_DEF;END_ENTITY;ENTITY SETUP_LINE_DYNAMIC_DEF SUBTYPE OF (SETUP_LINE_DEF);  SETUP_DYNAMIC: SETUP_DYNAMIC_DEF;END_ENTITY;ENTITY ALIGNMENT_DEF; AXIS_SECOND: AXIS_DEF; TARGETS_SECOND: OPTIONAL LIST[1:?] OF REAL; TARGET_START_SECOND: OPTIONAL REAL; TARGET_END_SECOND: OPTIONAL REAL; MACHINING_TIME: OPTIONAL REAL; SOAK_OUT_TIME: OPTIONAL REAL;  WHERE  WR1: (((SETUP_LINE_DEF.MEAS_MODE = MEAS_MODE_DEF.STATIC) AND    (SETUP_LINE_DEF.MEAS_METHOD = MEAS_METHOD_DEF.SQUARE)) AND    (EXISTS (TARGETS_SECOND)));   WR2: (((SETUP_LINE_DEF.MEAS_MODE = MEAS_MODE_DEF.DYNAMIC) AND    (SETUP_LINE_DEF.MEAS_METHOD = MEAS_METHOD_DEF.SQUARE)) AND    (EXISTS (TARGET_START_SECOND) AND EXISTS (TARGET_END_SECOND)));   WR3: (((SETUP_LINE_DEF.MEAS_METHOD = MEAS_METHOD_DEF.PAST_CENTER) OR    (SETUP_LINE_DEF.MEAS_METHOD = MEAS_METHOD_DEF.REVERSE_PART)) AND    (EXISTS (MACHINING_TIME) AND EXISTS (SOAK_OUT_TIME)));END_ENTITY;ENTITY RUN_DATA_LINE_DEF SUBTYPE OF (RUN_DATA_DEF);   APPROACH_DIRECTION: APPROACH_DIRECTION_DEF;   LEG: OPTIONAL LEG_DEF;   LS_OFFSET: OPTIONAL REAL;   LS_CENTER_OFFSET_X: OPTIONAL REAL;   LS_CENTER_OFFSET_Y: OPTIONAL REAL;   LS_SLOPE: OPTIONAL REAL;   TEMP_REFERENCE: OPTIONAL LIST [1:?] OF TEMP_DATA_DEF;   TEMP_MATERIAL: OPTIONAL LIST [1:?] OF TEMP_DATA_DEF;   TEMP_ADDITIONAL: OPTIONAL LIST [1:?] OF TEMP_DATA_DEF;   AIR_HUMIDITY: OPTIONAL LIST [1:?] OF REAL;   AIR_PRESSURE: OPTIONAL LIST [1:?] OF REAL;   ELAPSED_TIME: OPTIONAL REAL;   POINTS: OPTIONAL LIST[1:?] OF REAL;    WHERE    WR1: ((EQUIPMENT_LINE_DEF.FILTER_OFFSET) AND EXISTS(LS_OFFSET));    WR2: ((EQUIPMENT_LINE_DEF.FILTER_LS_CENTER) AND     (EXISTS(LS_CENTER_OFFSET_X) AND EXISTS(LS_CENTER_OFFSET_Y)));    WR3: ((EQUIPMENT_LINE_DEF.FILTER_LS_SLOPE) AND EXISTS(LS_SLOPE));    WR4: (((EQUIPMENT_LINE_DEF.EQUIPMENT_CLASS =     EQUIPMENT_CLASS_LINE_DEF.LASER_INTERFEROMETER) OR     (EQUIPMENT_LINE_DEF.EQUIPMENT_CLASS =     EQUIPMENT_CLASS_LINE_DEF.ND_LASER)) AND     (EXISTS(AIR_HUMIDITY)) AND (EXISTS(AIR_PRESSURE)));END_ENTITY;ENTITY EQUIPMENT_POINT_DEF SUBTYPE OF (EQUIPMENT_DEF);  EQUIPMENT_CLASS: OPTIONAL EQUIPMENT_CLASS_POINT_DEF;  CUT_OFF: OPTIONAL REAL;  SYNCHRONIZATION: OPTIONAL SYNCHRONIZATION_DEF;  TEMP_REFERENCE_COEFFICIENT: OPTIONAL REAL;  FILTER_LS_CENTER: OPTIONAL BOOLEAN;  TARGET_SHAPE : OPTIONAL TARGET_SHAPE_DEF;  TARGET_DIAMETER : OPTIONAL REAL;  DUAL_SENSOR : OPTIONAL BOOLEAN;END_ENTITY;ENTITY SETUP_POINT_DEF SUBTYPE OF (SETUP_DEF);  ID: OPTIONAL STRING;  MEAS_MODE: OPTIONAL MEAS_MODE_DEF;  MEASURAND: LIST [1:?] OF MEASURAND_POINT_DEF;  SENSOR_OFFSET: OPTIONAL REAL;  MACHINE_POSITION: MACHINE_POSITION_DEF;  TOOL_VECTOR: TOOL_VECTOR_DEF;  SPINDLE_NUMBER: OPTIONAL INTEGER;  TURRET_NUMBER: OPTIONAL INTEGER;  TEMP_ADDITIONAL_SENSOR: OPTIONAL LIST [1:?] OF TEMP_SENSOR_DEF;  SAMPLE_RATE: REAL;  SAMPLES_AVERAGED: OPTIONAL INTEGER;  SENSITIVE_DIRECTION: OPTIONAL SENSITIVE_DIRECTION_DEF;  NUMBER_OF_REVOLUTIONS: OPTIONAL INTEGER;  AXIS: OPTIONAL AXIS_DEF;  SPINDLE_SPEED: OPTIONAL LIST[1:?] OF REAL;  DURATION: OPTIONAL LIST[1:?] OF REAL;  TOOL_LENGTH_LONG: OPTIONAL REAL;  FEEDRATE: OPTIONAL REAL;  APPROACH_POINT: OPTIONAL MACHINE_POSITION_DEF;  SETUP_STATIC: OPTIONAL SETUP_STATIC_DEF;END_ENTITY;ENTITY RUN_DATA_POINT_DEF SUBTYPE OF (RUN_DATA_DEF);TEMP  _ADDITIONAL: OPTIONAL LIST [1:?] OF TEMP_DATA_DEF;  LS_CENTER_OFFSET_X: OPTIONAL REAL;  LS_CENTER_OFFSET_Y: OPTIONAL REAL;  POINTS: LIST[1:?] OF REAL;   WHERE   WR1: ((EQUIPMENT_POINT_DEF.FILTER_LS_CENTER) AND    (EXISTS(LS_CENTER_OFFSET_X) AND EXISTS (LS_CENTER_OFFSET_Y)));END_ENTITY;ENTITY EQUIPMENT_COMPLIANCE_DEF SUBTYPE OF (EQUIPMENT_DEF);  EQUIPMENT_CLASS: OPTIONAL EQUIPMENT_CLASS_COMPLIANCE_DEF;  LOAD_MEASUREMENT: OPTIONAL LOAD_MEASUREMENT_DEF;  LOAD_RESOLUTION: OPTIONAL REAL;END_ENTITY;ENTITY SETUP_COMPLIANCE_DEF SUBTYPE OF (SETUP_DEF);  ID: OPTIONAL STRING;  AXIS: AXIS_DEF;  EXTERNAL_LOAD: BOOLEAN;  AXIS_LOAD: OPTIONAL AXIS_DEF;  MEAS_DIR: OPTIONAL MEAS_DIR_DEF;  RADIUS: OPTIONAL REAL;  MACHINE_POSITION: MACHINE_POSITION_DEF;  TOOL_VECTOR: TOOL_VECTOR_DEF;  SPINDLE_NUMBER: OPTIONAL INTEGER;  TURRET_NUMBER: OPTIONAL INTEGER;  SAMPLES_AVERAGED : OPTIONAL INTEGER;END_ENTITY;ENTITY RUN_DATA_COMPLIANCE_DEF SUBTYPE OF (RUN_DATA_DEF);  POINTS: LIST[1:?] OF REAL;END_ENTITY;ENTITY AXIS_POSITION_DEF; AXIS: AXIS_DEF; POSITION: REAL;END_ENTITY;ENTITY MACHINE_POSITION_DEF; POSITIONS: LIST [1:?] OF AXIS_POSITION_DEF;END_ENTITY;ENTITY SETUP_DYNAMIC_DEF; TARGET_START: REAL; TARGET_END: REAL; TRIGGER_MODE: OPTIONAL TRIGGER_MODE_DEF; SAMPLE_RATE: OPTIONAL REAL; INFEED_MODE: OPTIONAL INFEED_MODE_DEF; INFEED_DISTANCE: OPTIONAL REAL; INFEED_RADIUS: OPTIONAL REAL; INFEED_ANGLE: OPTIONAL REAL;  WHERE  WR1: ((INFEED_MODE = INFEED_MODE_DEF.LINEAR) AND   (EXISTS (INFEED_DISTANCE)));  WR2: ((INFEED_MODE = INFEED_MODE_DEF.CIRCULAR) AND   (EXISTS (INFEED_RADIUS) AND EXISTS (INFEED_ANGLE)));END_ENTITY;ENTITY SETUP_STATIC_DEF; TARGETS: OPTIONAL LIST [1:?] OF REAL; REPETITIONS: OPTIONAL INTEGER; TRIGGER_MODE: OPTIONAL TRIGGER_MODE_DEF; TRIGGER_DWELL: OPTIONAL REAL; TRIGGER_WIDTH: OPTIONAL REAL; TRIGGER_STABILITY: OPTIONAL REAL;END_ENTITY;ENTITY TEMP_SENSOR_DEF; NAME: OPTIONAL STRING; LOCATION: STRING; CHANNEL: OPTIONAL STRING; SERIAL_NUMBER: OPTIONAL STRING;END_ENTITY;ENTITY TEMP_DATA_DEF; NAME: OPTIONAL STRING; DATA: LIST [1:?] OF REAL;END_ENTITY;ENTITY TOOL_VECTOR_DEF; X: OPTIONAL REAL; Y: OPTIONAL REAL; Z: REAL;END_ENTITY;END_SCHEMA;—END MACHINE_TOOL_PERFORMANCE_TESTS (* *)The types are formally defined in this subsection and they are presented in alphabetical order.*)TYPE ALIGNMENT_METHOD_DEF = ENUMERATION OF  ;END_TYPE;TYPE ALIGNMENT_WHEN_DEF = ENUMERATION OF  ;END_TYPE;TYPE APPROACH_DIRECTION_DEF = ENUMERATION OF  ;END_TYPE;TYPE APPROACH_MODE_CIRCULAR_DEF = ENUMERATION OF  ;END_TYPE;TYPE AXIS_DEF= STRING;END_TYPE;TYPE DATUM_WHEN_DEF = ENUMERATION OF ;END_TYPE;TYPE DIFFERENTIAL_MEAS_DIR_DEF = ENUMERATION OF ;END_TYPE;TYPE DRIVE_STATUS_DEF = ENUMERATION OF  ;END_TYPE;TYPE EQUIPMENT_CLASS_COMPLIANCE_DEF = ENUMERATION OF  ;END_TYPE;TYPE EQUIPMENT_CLASS_CIRCULAR_DEF = ENUMERATION OF  ;END_TYPE;TYPE EQUIPMENT_CLASS_LINE_DEF = ENUMERATION OF  ;END_TYPE;TYPE EQUIPMENT_CLASS_POINT_DEF = ENUMERATION OF  ;END_TYPE;TYPE INFEED_MODE_DEF= ENUMERATION OF  ;END_TYPE;TYPE INTERPOLATION_DEF = ENUMERATION OF  ;END_TYPE;TYPE LEG_DEF = ENUMERATION OF  ;END_TYPE;TYPE LOAD_MEASUREMENT_DEF = ENUMERATION OF  ;END_TYPE;TYPE MEAS_DIR_DEF = ENUMERATION OF  ;END_TYPE;TYPE MEAS_METHOD_DEF = ENUMERATION OF  ;END_TYPE;TYPE MEAS_MODE_DEF = ENUMERATION OF  ;END_TYPE;TYPE MEASURAND_LINE_DEF = ENUMERATION OF  ;END_TYPE;TYPE MEASURAND_POINT_DEF = ENUMERATION OF  ;END_TYPE;TYPE POINT_MODE_CIRCULAR_DEF= ENUMERATION OF  ;END_TYPE;TYPE SENSITIVE_DIRECTION_DEF = ENUMERATION OF  ;END_TYPE;TYPE STANDARD_STATUS_DEF = ENUMERATION OF  ;END_TYPE;TYPE SYNCHRONIZATION_DEF = ENUMERATION OF  ;END_TYPE;TYPE TARGET_SHAPE_DEF = ENUMERATION OF  ;END_TYPE;TYPE TEST_CLASS_DEF = ENUMERATION OF  ;END_TYPE;TYPE TEXT = STRING;END_TYPE;TYPE TRIGGER_MODE_DEF = ENUMERATION OF  ;END_TYPE;TYPE VOL_COMP_METHOD_DEF = ENUMERATION OF  ;END_TYPE;TYPE WHY_DEF = ENUMERATION OF  ;END_TYPE; , followed by a unique entity name, which is an unsigned integer of 1 or more digits. When a value is not provided for an optional attribute, the attribute value is encoded as the dollar sign ($). Both forward and backward references are permitted. A comment is encoded as a solidus asterisk (/*) followed by any number of characters, and terminated by an asterisk solidus (*/).The following is a sample of an exchange structure based on the ISO 10303-21, Clear Text Encoding Of the Exchange Structure . Each Pa/*The exchange file is generated based on the ISO 10303-21: 1994(E).The file has been presented in a line-oriented or record-oriented manner in order to aid readability.Unnecessary spaces have been added to aid readability.Note that an ordinary Part 21 file is not aligned in this manner, but is a continuous stream of characters.*//*The following gives the short names for the schema of MACHINE_TOOL_PERFORMANCE_TESTS.Entity name                   Short nameBALL_BAR_DEF                BALL_BARCOMPONENT_DEF              COMPONENTCONDITIONS_DEF             CONDITIONSDATE_DEF                  DATEMACHINE_DEF               MACHINEPLANE_DEF                 PLANEMACHINE_POSITION_DEF         MACHINE_POSITIONRESULT_DEF                 RESULTRUN_DATA_CIRCULAR_DEF       RUN_DATA_CIRCULARSETUP_CIRCULAR_DYNAMIC_DEF   SETUP_CIRCULAR_DYNAMICSETUP_DYNAMIC_DEF          SETUP_DYNAMICSOFTWARE_DEF              SOFTWARESTANDARD_DEF              STANDARDTEMP_SENSOR_DEF            TEMP_SENSORTEST_DEF                  TESTTIME_DEF                  TIMETOOL_VECTOR_DEF            TOOL_VECTOR*/ISO-10303-21;HEADER;FILE_DESCRIPTION ((‘THIS FILE CONTAINS A SAMPLE CIRCULAR TEST’), ‘2’);FILE_NAME ,(‘NIST/MEL/MSG’), ‘PREPROCESSOR_VERSION NONE’,‘ORIGINATING SYSTEM RELEASE 1.0’, ‘APPROVED BY TINA LEE’);FILE_SCHEMA ((‘MACHINE_TOOL_PERFORMANCE_TESTS’));ENDSEC;DATA;#1=DATE;#2=TIME;#3=MACHINE;#4=CONDITIONS;#5=STANDARD;#6=COMPONENT;#7=COMPONENT;#8=SOFTWARE;#9=BALL_BAR, #8,0.1,$, .BALL_BAR.,.T.,.F.,.F.,.F.,$,0.5,.T.);#10=PLANE,);#11=MACHINE_POSITION,,));#12=TOOL_VECTOR;#13=SETUP_DYNAMIC;#14=SETUP_CIRCULAR_DYNAMIC),11.5,$,.R.,.LINEAR.,$,.KINEMATIC., $,$,$,#13,$,0.5);#15=RUN_DATA_CIRCULAR)), );#16=RUN_DATA_CIRCULAR)), );#17=RESULT, ));#18=RESULT, ));#19=RESULT, ));#20=STANDARD;#21=RESULT, ,));#22=TEST,,‘THIS IS AN EXAMPLE’);ENDSEC;END-ISO-10303-21;XML, an extensible markup language, is a universal format for structured documents and data on the Web . The lanless-than symbol (<), the name of the element, and a greater-than symbol (>). Start-tags can also include attributes. An XML end-tag consists of the string “</”, the same element name as in the start-tag, and a greater-than symbol (>).The following is an XML document sample. This XML document is well-formed, which means that the tags are properly constructed. This XML document, however, does not contain a document type definition<?xml version =”1.0”?><TEST> <ID> BB0699A.RTB</ID> <TEST_CLASS>CIRCULAR</TEST_CLASS> <DATE><YYYY>1999</YYYY><MM>06</MM><DD>22</DD></DATE> <TIME><HH>10</HH><MM>06</MM><SS>00</SS></TIME> <WHY>PERIODIC</WHY> <MACHINE>   <ID>2434</ID>   <MANUFACTURER>XYZ</MANUFACTURER>   <MACHINE_MODEL>ABC</MACHINE_MODEL>   <SERIAL_NUMBER>123</SERIAL_NUMBER>   <LOCATION>SHOPS</LOCATION> </MACHINE> <CONDITIONS>   <COMPENSATION>YES</COMPENSATION>   <TEMP_ENVIRONMENT>22.5</TEMP_ENVIRONMENT> </CONDITIONS> <OPERATOR> JOHN DOE</OPERATOR> <STANDARD>  <ORGANIZATION>ASME</ORGANIZATION>  <STANDARD_NUMBER>B5.57</STANDARD_NUMBER>  <YEAR>1997</YEAR> </STANDARD> <EQUIPMENT>  <ID>BALL BAR BOX 123</ID>  <COMPONENT>   <DESCRIPTION>BALLBAR</DESCRIPTION>   <MANUFACTURER>XYZ</MANUFACTURER>   <COMPONENT_MODEL>ABC1</COMPONENT_MODEL>   <SERIAL_NUMBER>123</SERIAL_NUMBER> </COMPONENT> <COMPONENT>  <DESCRIPTION>CALIBRATOR</DESCRIPTION>  <MANUFACTURER>XYZ</MANUFACTURER>  <COMPONENT_MODEL>ABC2</COMPONENT_MODEL> <SERIAL_NUMBER>456</SERIAL_NUMBER> </COMPONENT> <SOFTWARE>  <MANUFACTURER>XYZ</MANUFACTURER>  <NAME>ABC3</NAME>  <VERSION_NUMBER>3.0</VERSION_NUMBER> </SOFTWARE> <RESOLUTION>0.1</RESOLUTION> <EQUIPMENT_CLASS>BALL_BAR</EQUIPMENT_CLASS> <ABSOLUTE>YES</ABSOLUTE> <FILTER_LS_CENTER>NO</FILTER_LS_CENTER> <FILTER_LS_RADIUS>NO</FILTER_LS_RADIUS> <TEMP_REFERENCE_COMP>NO</TEMP_REFERENCE_COMP> <TEMP_REFERENCE_COEFFICIENT>0.5</TEMP_REFERENCE_COEFFICIENT> <CALIBRATOR>YES</CALIBRATOR> </EQUIPMENT> <SETUP>  <ID>2434</ID>  <MEAS_MODE>DYNAMIC</MEAS_MODE>  <PLANE><X>X</X><Y>Y</Y></PLANE>  <CENTER>  <AXIS_POSITION>   <AXIS>X</AXIS><POSITION>400</POSITION>  </AXIS_POSITION>  <AXIS_POSITION>   <AXIS>Y</AXIS><POSITION>350</POSITION>  </AXIS_POSITION>  <AXIS_POSITION>   <AXIS>Z</AXIS><POSITION>100</POSITION>  </AXIS_POSITION>  </CENTER>  <TOOL_VECTOR><X>0</X><Y>0</Y><Z>-100</Z></TOOL_VECTOR>  <SPINDLE_NUMBER>1</SPINDLE_NUMBER>  <RADIUS_MACHINE>150</RADIUS_MACHINE>  <RADIUS_REFERENCE>150</RADIUS_REFERENCE>  <INCLINATION>0</INCLINATION>  <FEEDRATE>1500</FEED_RATE>  <OVERSHOOT>180</OVER_SHOOT>  <TEMP_MATERIAL_COMP>YES</TEMP_MATERIAL_COMP>  <TEMP_MATERIAL_SENSOR>   <LOCATION>TABLE</LOCATION></TEMP_MATERIAL_SENSOR>  <TEMP_MATERIAL_COEFFICIENT>11.5</TEMP_MATERIAL_COEFFICIENT>  <POINT_MODE>R</POINT_MODE>  <INTERPOLATION>LINEAR</INTERPOLATION>  <ALIGNMENT_METHOD>KINEMATIC</ALIGNMENT_METHOD>  <SETUP_DYNAMIC>    <TARGET_START>0</TARGET_START><TARGET_END>360</TARGET_END>    <SAMPLE_RATE>125</SAMPLE_RATE></SETUP_DYNAMIC>  <NC_CIRCULARITY>0.5</NC_CIRCULARITY> </SETUP> <RUN_DATA>  <APPROACH_DIRECTION>POSITIVE</APPROACH_DIRECTION>  <LS_CENTER_OFFSET_X>5</LS_CENTER_OFFSET_X>  <LS_CENTER_OFFSET_Y>22</LS_CENTER_OFFEST_Y>  <LS_RADIUS_ERROR>122</LS_RADIUS_ERROR>  <TEMP_MATERIAL><DATA>22.4</DATA></TEMP_MATERIAL>  <POINTS>1.5 0.5 0.6 0.2 0.4 ….. </POINTS> </RUN_DATA> <RUN_DATA>  <APPROACH_DIRECTION>NEGATIVE</APPROACH_DIRECTION>  <LS_CENTER_OFFSET_X>8</LS_CENTER_OFFSET_X>>  <LS_CENTER_OFFSET_Y>24</LS_CENTER_OFFSET_Y>  <LS_RADIUS_ERROR>112</LS_RADIUS_ERROR>  <TEMP_MATERIAL><DATA>22.4</DATA></TEMP_MATERIAL>  <POINTS>0.5 0.5 0.6 0.2 0.4 ….. </POINTS> </RUN_DATA> <RESULT>  <STANDARD>   <ORGANIZATION>ASME</ORGANIZATION>   <STANDARD_NUMBER>B5.57</STANDARD_NUMBER><YEAR>1997</YEAR>  </STANDARD>  <PARAMETER>   <NAME>LS_RADIUS_ERROR</NAME><VAL>122</VAL>   <APPROACH_DIRECTION>POSITIVE</APPROACH_DIRECTION>  </PARAMETER>  <PARAMETER>   <NAME>CIRCULARITY</NAME><VAL>11</VAL>   <APPROACH_DIRECTION>POSITIVE</APPROACH_DIRECTION>  </PARAMETER> </RESULT> <RESULT>  <STANDARD>   <ORGANIZATION>ASME</ORGANIZATION>   <STANDARD_NUMBER>B5.57</STANDARD_NUMBER><YEAR>1997</YEAR>  </STANDARD>  <PARAMETER>   <NAME>LS_RADIUS_ERROR</NAME><VAL>112</VAL>   <APPROACH_DIRECTION>NEGATIVE</APPROACH_DIRECTION>  </PARAMETER>  <PARAMETER>   <NAME>CIRCULARITY</NAME><VAL>14</VAL>   <APPROACH_DIRECTION>NEGATIVE</APPROACH_DIRECTION>  </PARAMETER> </RESULT> <RESULT>  <STANDARD>   <ORGANIZATION>ASME</ORGANIZATION>   <STANDARD_NUMBER>B5.57</STANDARD_NUMBER><YEAR>1997</YEAR>  </STANDARD>  <PARAMETER>   <NAME>LS_RADIUS_ERROR</NAME><VAL>117</VAL>   <APPROACH_DIRECTION>BIDIRECTIONAL</APPROACH_DIRECTION>  </PARAMETER>  <PARAMETER>   <NAME>CIRCULARITY</NAME><VAL>22</VAL>   <APPROACH_DIRECTION>BIDIRECTIONAL</APPROACH_DIRECTION>  </PARAMETER> </RESULT> <RESULT>  <STANDARD>   <ORGANIZATION>ISO</ORGANIZATION>   <STANDARD_NUMBER>230-4</STANDARD_NUMBER><YEAR>1996</YEAR>  </STANDARD>  <PARAMETER>   <NAME>LS_RADIUS_ERROR</NAME><VAL>117</VAL>   <APPROACH_DIRECTION>BIDIRECTIONAL</APPROACH_DIRECTION>  </PARAMETER>  <PARAMETER>   <NAME>CIRCULARITY</NAME><VAL>22</VAL>   <APPROACH_DIRECTION>BIDIRECTIONAL</APPROACH_DIRECTION>  </PARAMETER>  <PARAMETER>   <NAME>HYSTERESIS</NAME><VAL>12</VAL>   <APPROACH_DIRECTION>BIDIRECTIONAL</APPROACH_DIRECTION>  </PARAMETER> </RESULT> <COMMENT>THIS IS AN EXAMPLE</COMMENT></TEST>Database technology has evolved rapidly. The evolution has moved from simple files to the use of network and hierarchical database management systems, and to today’s relational systems and object-oriented systems. Evolving technology has made data sharing a realistic alternative. Moreover, today’s generation of powerful, inexpensive workstation computers enables users to design software that maintains and distributes data quickly and inexpensively. Relational database management systems are generally desirable for data transfer for the manufacturing community.All information in a relational database is represented explicitly as values in tables. The Structured Query Language (SQL)  was deveThe following is a sample of relational tables for the EQUIPMENT entity. These tables have been manually mapped from the respective portion of the EXPRESS information model. Our intention for this subsection is to demonstrate the relational database implementation of the EXPRESS model, the development of the SQL statements that map the complete MACHINE_TOOL_PERFORMANCE_TESTS schema will be the topic for another report. In the SQL statements below, the OID_MAPPING table contains identity information for every EXPRESS object in the database and “OID” stands for Object Identifier.fedex, one of the applications in the NIST EXPRESS Toolkit [fedex is the tool that reports syntactic and semantic errors in EXPRESS schemas.The information model, MACHINE_TOOL_PERFORMANCE_TESTS, presented in Sec. 2 specifies the information necessary to represent the properties and results of machine-tool-performance tests. The model has been successfully parsed using  Toolkit . The NISThe MACHINE_TOOL_PERFORMANCE_TESTS information model is independent of any implementation method. Several commercial and non-commercial software tools exist to support the implementation of EXPRESS information models. A document describing software tools and services for EXPRESS was published by P. R. Wilson  and is aThis report describes the approach being taken by NIST in developing a neutral format for exchanging machine-tool-performance data. An information model of machine-tool-performance tests in EXPRESS has been developed. The implementations of the information model using the STEP exchange structure, XML, and SQL have been demonstrated. The information model will continue to evolve based on experience and feedback from others involved in this effort. Our objective is to promote the information model to an official standard. Broader participation in this effort will help the standardization work proceed more quickly and will also enhance the system performance and user satisfaction.


The broad-sense heritability of hair color was estimated between 73% and 99% and the genetic component included non-additive genetic variance. Assortative mating for hair color was significant, except for red and black hair color. From GCTA analyses, at most 24.6% of the additive genetic variance in hair color was explained by 1000G well-imputed SNPs. Genome-wide association analysis for each hair color showed that SNPs in the MC1R region were significantly associated with red, brown and black hair, and also with light versus dark hair color. Five other known genes  gave genome-wide significant hits for blond, brown and light versus dark hair color. We did not find and replicate any new loci for hair color.Hair color is one of the most visible and heritable traits in humans. Here, we estimated heritability by structural equation modeling , and performed a genome wide association (GWA) analysis (N = 7091) and a GCTA study (N = 3340) on hair color within a large cohort of twins, their parents and siblings from the Netherlands Twin Register (NTR). Self-reported hair color was analyzed as five binary phenotypes, namely “blond  Hair color is a genetic and physiologically complex phenotype, which represents one of the most visible variations within humans and between populations . It inflPigmentation of skin and hair is important for protection against sunlight . From a Hair pigmentation is a highly heritable trait. In Europeans, genetic factors explain a large part 61%–92%) of the variation in natural hair color, while the rest of the variation is due to environmental influences and measurement error ,9,10. Pr2% of theMC1R) are known to be associated with a switch from eumelanin to pheomelanin production, resulting in a red or yellow coat color in animal models [Studying the genetic background of a human pigmentation trait like hair color is useful to understand human evolution and biology and may have important applications to melanoma treatment and to forensics. Loss-of-function mutations at the melanocortin 1 receptor  . In addiHair color data were available for 25,201 NTR participants, clustered in a total of 7862 families, with ages ranging between 14 and 80 years old. Each participant completed one or more longitudinal surveys that included questions about age, sex, and natural hair color. The self-reported hair color was obtained from a question with five answer possibilities: “fair/blond”, “light brown”, “red/auburn”, “dark brown”, and “black”. Written informed consent was obtained from all participants. The Medical Ethics Committee of the VU University Medical Center approved the study protocols.For the twin-family based heritability analyses, we selected families comprising at most 6 members . The subjects included in these families were the twins , a maximum of two siblings (N = 2008), and the biological father and mother (N = 2774) . Of the Buccal or blood DNA samples  were collected for multiple NTR projects. DNA extraction and purification of these samples were performed at various points in time, following several manufacturer specific protocols to obtain the best quality and concentration prior to SNP platform genotyping . Genotypp < 0.00001. Samples were excluded from the data if their expected sex did not match their genotyped sex, if the genotype missing rate was above 10% or if the Plink F inbreeding value was either >0.10 or <−0.10.Quality control was done within and between platforms and subsets prior to imputation. For each platform, the individual SNP markers were lifted over to build 37 (HG19) of the Human reference genome, using the LiftOver tool. SNPs that were not mapped at all, SNPs that had ambiguous locations, and SNPs that did not have matching  were removed. Subsequently, the data were strand aligned with the 1000 Genomes GIANT phase1 release v3 20,101,123 SNPs INDELS SVS ALL panel. SNPs from each platform were removed if they still had mismatching alleles with this imputation reference set, if the allele frequencies differed more than 0.20 with the reference. From each platform, SNPs with a Minor Allele Frequency (MAF) <0.01 were removed, as well as SNPs that were out of Hardy–Weinberg Equilibrium (HWE) with After these steps, the data of the individual arrays were merged into a single dataset using PLINK 1.07 . Within 2 metric is 0.38 .Phasing of all samples and imputing cross-missing platform SNPs was done with MACH 1 ,20. The 2 < 0.30. HWE was calculated on the genotype probability counts for the full sample, and SNPs were removed if the p-value < 0.00001. This left 7,981,681 SNPs prior to the statistical analyses.After imputation, SNPs were filtered based on the Mendelian error rate in families. The Mendelian error rate was calculated on the best guess genotypes in families (trios and sib-pairs with parents) using first GTOOL to calculate best guess genotypes and then PLINK 1.07 to analyze the data. SNPs were removed if the Mendelian error rate >2%, if the imputed allele frequency differed more than 0.15 from the 1000G reference allele frequency, if MAF < 0.005 and if Rversus the other hair colors. In the following analysis, we found a much lower heritability for the light brown and dark brown classification than for other classifications. By combining light and dark brown hair color into a single brown category, we obtained a more sensible heritability estimate as compared to the other colors. Assuming this discrepancy is due to confusion among participants concerning the distinction light versus dark brown, we decided to proceed with a simple category “brown”. An additional binary variable, denoted “light versus dark”, was created representing blond and red hair versus brown (light and dark) and black. We studied this binary variable to detect the genes involved in the switch from eumelanin to pheomelanin. Hence, in total we analyzed the following 5 binary classifications, namely “blond versus non-blond”, “red versus non red”, “brown versus non-brown”, “black versus non-black”, and “light versus dark”.First, we created binary variables for each hair color, representing a given hair color i.e., a point on the liability scale. For instance, a threshold of zero on the standard normal liability scale is associated with a proportion of 0.50 . The more extreme the threshold is, the greater or smaller the proportion .To obtain an indication of family clustering, we calculated (tetrachoric) correlations among family members for binary hair color variables in OPENMX . We adopversus light dichotomy [i.e., the MZ correlation was higher than twice the DZ correlation). The twin correlations for the red and black hair color suggested the possible presence of common environmental influences (C). We focused on the model including additive genetic effects (A), which represent the sum of the additive effects of all loci relevant to the trait, genetic dominance effects (D), which represent interactions between alleles at the same locus, and unique (or unshared) environmental effects (E), which are not shared by family members. Note that the unique environmental factors may include genetic causes, such as personal genetic mutations, and also measurement errors. In the analyses of red and black hair, we also considered the model including C instead of D. We accommodated the phenotypic assortative mating by deriving the expected correlation between the family members taking into account phenotypic assortment , whether sex effects in genetic architecture were absent, and whether dominance effects  were absent. Given the power that our large sample size confers to detect minor effects, we tested the specific effects mentioned using a stringent alpha of 0.0001 [OPENMX was then used to estimate the heritability of hair color by fitting a genetic model to the liabilities of the parents and offspring (twins and sibs). The total liability variance was decomposed into genetic and environmental variance components . We starichotomy   by including these PCs in the ADE model as covariates on the threshold.Since hair color displays a geographical gradient in the Netherlands , we refitted the full twin model in the subsample of genotyped individuals  while correcting for the gradient using their data on three Dutch genotype principal components (PCs). These PCs were calculated from the genotype data with the EIGENSOFT software . First, 2 quality metric of 0.80 and MAF > 0.01. In order to avoid explained variance and artificially increased GRM differences due to differing platforms and subsamples, additional SNP Quality Control (QC) included an evaluation of the SNP platform effects. We tested the effect of different platforms and removed SNPs showing platform effects. This was done by defining individuals on a specific platform as cases and the remaining individuals as controls. Allelic association was then calculated and SNPs were removed if the specific platform allele frequencies were significantly different from the remaining platforms with p-value < 0.00001. The selected 5,987,253 SNPs were transformed to best guess Plink binary format, and subsets were made for each of the 22 chromosomes. The GRM for all NTR samples was then calculated per chromosome and subsequently the 22 matrixes were merged into a single autosomal GRM using GCTA. Ethnic outliers based on the PCs were excluded and 3340 unrelated individuals with hair color data were selected using the standard GRM cut-off filter of 0.025.The proportion of variance of the binary hair color traits that can be explained by the measured and imputed SNPs was estimated in GCTA  using the Restricted maximum likelihood (REML) analysis procedure under a case-control design, where we report the proportion of genetic variance explained on the underlying liability scale untransformed for prevalence . Sex andversus dark hair color. For red and black hair color a MAF > 0.05 was selected to account for the lower prevalence of these hair colors. For imputation quality we filtered on the Plink information criterion, which is similar to R2: the variance of the mean posterior genotype probabilities divided by the maximum expected variance given full HWE and complete known genotypes. In all hair colors we filtered with 0.40 < Plink Info < 1.02. In total 6,473,680 SNPs survived this QC leading to a mean original imputation R2 of 0.77 (0.22) for MAF 0.01–0.05 and a mean R2 of 0.97 (0.07) for MAF ≥ 0.05 for the selected SNPs.The GWA analyses were run for all binary hair color variables. The input SNPs used were all 7,981,681 that passed the initial imputation QC, however post GWAS analyses we additionally filtered the SNPs, depending on the cases sample size for MAF and imputation quality in the sub-sample of individuals with a hair color phenotype. Re-filtering on MAF > 0.01 was done after the GWAS analyses for blond, brown, and light −8 to be statistically significant [Since, hair color is affected by population stratification; we used the three Dutch ancestry PCs as covariates  and exclnificant .versus dark hair colors are presented in versus dark hair colors ranges from 39% to 53%, but the prevalence of red and black hair colors is appreciably lower .The prevalences and the familial tetrachoric correlations based on N = 20,142 (clustered in 7497 families) for blond, brown, red, black and light The MZ correlations are consistently high, ranging from 0.93 to 0.99. The full sib correlations, including the DZ twins, are lower (range 0.14–0.86) and that also goes for the parent-offspring correlations (range 0.19–0.73). Overall, the pattern of correlations is consistent with the expected large genetic contribution to hair color variation. In all but the red and black hair colors, the correlations suggest the presence of additive genetic and dominance effects, as the MZ correlations (range 0.93–0.99) are appreciably higher than the full sibs  and the parents and offspring (range 0.33–0.58). The correlations among first-degree relatives for red and black hair color tend to be larger than for the other hair colors. However, given the lower prevalence of these hair colors, these correlations are subject to larger standard errors. The spousal correlations suggest weak assortative mating with respect to hair colors (except black hair where the correlation is −0.179 and red hair where the correlation is 0.528). We note that the spousal correlations may be due to direct phenotypic assortment for hair color, or may be related to the geographic population structure of the Netherlands, as was established previously in our data [versus dark hair colors. Furthermore, the results unambiguously show that no effects could be dropped, i.e., age effects, phenotypic assortment, sex differences on the genetic architecture, and non-additive genetic effects are present . The results pertaining to the red and black hair colors are mixed. In the ACE model, as fitted to the red hair color, there is no sex difference , but all other effects are present . With respect to the black hair color, we find a simple AE with age effects, but no assortative mating , no C  and no sex differences (p = 0.56). The absence of specific effects in the red and black hair colors may be due to low power as a consequence of the low prevalence of these hair colors.The results of fitting all models are shown in p-values are <1.0 × 10−9).MC1R gene is located) for red hair, on chromosome 15 (where the HERC2 gene is located) for brown and light versus dark, and on chromosome 6 (where the RPS6KA2 gene is located) for black hair color. We also studied the explained liability given by the top SNPs that are already known hair color loci as reported in earlier studies. These SNPs explain between 0.5% and 6.9% of the hair color liability in our sample.The GCTA analyses in unrelated individuals show that depending on the hair color, at most 24.6% of the hair color liability can be explained by the autosomal SNPs . All SNP2 > 0.80, leading to a large reduction of all SNP variants present within the GCTA matrix, and the coverage of rare alleles and less well imputed SNPs might therefore not be optimal. Also the LD tagging of SNPs might not be good enough to detect all hair color variants. Finally, for red and black hair color the results may be difficult to interpret due to the lack of power.The estimated genetic liability explained by common SNPs is low (<30%), given the fact that the heritability of hair color as a trait is very high (>70%). There are several possible explanations for this: GCTA is less appropriate for binary data than for quantitative phenotypes; there are not many genes related to hair color; the distribution of effect sizes of the genes and a combination of assortative mating plus possible common environment and dominance for this trait are not accounted for by the modeling assumptions of GCTA. In addition, SNPs were filtered by MAF > 0.01 and Rversus dark). Although standard errors do not show significant differences between the estimates, it indicates there are still unknown variants that determine blond and brown hair color, which are captured by the PCs, or there are variants that determine traits related to hair color (population stratification).When adding the three Dutch PCs as covariates in the model, the estimates of the hair color liability explained by the known hair color loci do not change much . Howeverversus dark hair color are shown in Figures S1–S10. As shown with GCTA and with the twin heritability modeling, the PC’s are significantly related to hair color in the Netherlands along the three Dutch major axes of genetic variation . Using the PCs, we corrected for this population stratification, with post correction GWAS λs ranging between 1.004 and 1.027. However, as a consequence, we have likely also reduced the significance of SNPs, which are truly associated with hair color.In total, genotype and hair color data were available for 7091 subjects from the NTR. We performed five case-control GWAS with logistic regression for all SNPs including age, sex, the three Dutch PCs and genotype platform as covariates. Familial structure was taken into account in the analysis using Plink and selecting a single monozygotic twin. The resulting Q–Q and Manhattan plots for all colors and light HERC2 (15q11.2-13), along with neighboring gene OCA2, are known as the most essential genes for determining human pigmentation traits including eye, hair and skin color [HERC2 q11.2-13,in color . These gSLC24A5, SLC45A2 and SLC24A4 gene. Interactions between HERC2 and SLC24A4 play a role in determining blue eye color, but also light hair color and less tanning ability [SLC24A4 (14q32.12) encodes the sodium/potassium/calcium exchanger 4 protein (NCKX4). Alternative splicing of this gene results in multiple transcript variants. Variants in SLC24A4 have been previously associated with eye and hair color, skin sensitivity to sun and coetaneous malignant melanoma [SLC24A4, rs8014907 was significantly associated with all hair colors, except red. The SLC45A2 gene, which is in the same family as SLC24A4, encodes a transporter protein that mediates melanin synthesis. The protein of SLC45A2 is expressed in a high percentage of melanoma cell lines. Mutations in this gene are a cause of oculocutaneous albinism type 4, and polymorphisms in this gene are associated with variations in skin and hair color [p-value = 9.1 × 10−5 for the gene. SLC24A5 was found to be involved in skin pigmentation in European populations [SLC24A5 gene, causing a frame shift and premature termination, was identified in a man with Indian ancestry [SLC24A5 gene the lowest p-value is 0.7 and the question is whether we have people with this particular insertion present within our Dutch population.The solute carrier (SLC) gene family group is a large family gene group that consists of 458 genes in 52 families. Three loci have been found to be associated with human pigmentation:  ability ,33. SLC2melanoma . We confir color ,36. Multancestry . Homozygancestry . HoweverMC1R (16q24.3) is an intron-less gene of the size less than 1 kb. Non-synonymous variants in MC1R are present in approximately 50% in the European population [MC1R is associated with multiple hair colors, except blond.pulation . Its mulpulation . A subsepulation ,41. We rKITLG (12q22) is known to regulate the number of melanocytes during development, melanin distribution and hyper/hypo pigmentation. Sequence variation is thought to affect expression of KITLG , which results in the blond hair color. In European populations rs12821256 T/C SNP is found to be associated with blond hair color [versus dark hair color. Recently, a functional study showed that this SNP alters a transcription factor binding site for lymphoid enhancer-binding factor 1 (LEF1), reducing LEF1 responsiveness and enhancer activity in cultured human keratinocytes [ir color ,43, and ir color . Our stuinocytes .p-value = 3.7 × 10−18) [p < 1.3 × 10−5).IRF4 (6p25.3) is associated with hair color and skin pigmentation . There i× 10−18) . In our ASIP haplotype’, rs1015362G and rs4911414T was shown to be associated with red hair color, freckling, and skin sensitivity to sun, in addition to burning and freckling that reached genome wide significance  in the Icelandic and Dutch populations [ASIP locus. Minimum p-values for these genes were above 0.35 for the hair colors. Note that red hair color in our sample is not so frequent, so this could be related to detection power.Genetic variants in 3-prime-untranslated region of the ASIP result in skin/hair/eye pigmentation variation. ‘The ulations . In our TPCN2 (11q13.3.) was found to be significantly associated with blond versus brown hair color in Icelandic Europeans [versus dark hair colors (p < 4.7 × 10−10).uropeans . In our TYRP1 gene are known skin/hair/eye pigmentation variation locus. A suggestive association for blond versus brown hair was observed for rs1408799 in Iceland and Dutch populations, and functional data suggest that the TYRP1 gene encodes a melanosomal enzyme with a role in the eumelanin pathway [p-values in our study are about 10−4 implying a potential, but weak association with hair color.The variants of  pathway . The p-vversus dark hair color and MAF > 0.05 for red and black hair color. Initially we had some associations for black and red hair color when also filtering on MAF > 0.01. However, with this threshold of filtering the number of cases having the minor allele(s) is extremely small, which leads to inflated statistics. Subsequently, these were not confirmed as positive results as none of the red hair color findings (black hair color unavailable) were replicated by the Decode study, and permutation analyses showed that the findings were also likely under the hypothesis of no association . Both additive and non-additive genetic models, as well as positive assortative mating and population stratification should be taken into consideration when conducting genetic studies of these traits in the Dutch population. In the GWA analysis we could confirm previously known associated variants in the


Myh14, as a negative regulator of ISO-induced left ventricular mass hypertrophy in an in vivo mouse model and demonstrated the up-regulation of immediate early gene Myc, fetal gene Nppb, and fibrosis gene Lgals3 in ISO-treated Myh14 deficient hearts compared to controls.We aimed to understand the genetic control of cardiac remodeling using an isoproterenol-induced heart failure model in mice, which allowed control of confounding factors in an experimental setting. We characterized the changes in cardiac structure and function in response to chronic isoproterenol infusion using echocardiography in a panel of 104 inbred mouse strains. We showed that cardiac structure and function, whether under normal or stress conditions, has a strong genetic component, with heritability estimates of left ventricular mass between 61% and 81%. Association analyses of cardiac remodeling traits, corrected for population structure, body size and heart rate, revealed 17 genome-wide significant loci, including several loci containing previously implicated genes. Cardiac tissue gene expression profiling, expression quantitative trait loci, expression-phenotype correlation, and coding sequence variation analyses were performed to prioritize candidate genes and to generate hypotheses for downstream mechanistic studies. Using this approach, we have validated a novel gene,  Myh14 affects heart failure by altering the mechanical responses of heart muscles to isoproterenol-induced stress. Follow-up studies of this gene and additional candidate genes and loci should reveal potential mechanisms by which genetic variations contribute to heart failure in the general human population. Such insights may lead to improved diagnosis and tailor treatment based on the genetic makeup of individuals in the population.Heart failure is the most common cause of morbidity and mortality in the aging population. Previous large-scale human genome-wide association studies have yielded only a handful of genetic loci contributing to heart failure-related traits. Using a panel of diverse inbred mouse strains, treated with a β-adrenergic agonist isoproterenol to mimic the heart failure state, we sought to uncover the contribution of common genetic variation in heart failure. We found that heart failure has a strong genetic component. We successfully identified 17 genome-wide significant loci associated with indices of heart failure. We showed that genetic variation in a novel gene  Heart failure (HF) is a major health issue, affecting 5.7 million people in the United States . DespiteSuch molecular, cellular and extracellular changes, manifested clinically as changes in size, shape and function of the heart, is also known as cardiac remodeling and is one of the most important clinical determinants of HF progression. In addition, β-adrenergic receptor blockers and angiotensin-converting enzyme (ACE) inhibitors, HF therapeutic agents that provide morbidity and mortality benefits, reverse ventricular dilation and systolic dysfunction, further supporting the importance of clinical cardiac remodeling as HF therapeutic targets ,5. UnderThe unbiased genome-wide association study (GWAS) design is well suited to detect the effects of genetic variation on complex traits such as HF . HoweverThe laboratory mouse, with its fully sequenced and annotated genome, targeted germ line modification and easy accessibility, provides a means of overcoming some of the challenges in human studies and is complementary. Particularly informative have been reverse genetic studies of candidate genes and pathways utilizing engineered mouse models. Studies of natural variation of mice and rats, some involving sensitized models, have also resulted in the identification of novel pathways contributing to HF and other common cardiovascular traits ,13,14,15Recently, with the development of high-density genotyping and sequencing in rodents, relatively high-resolution association mapping approaches, analogous to human GWAS, have become feasible . Our groin vitro and in vivo models, we validate a novel gene Myh14 as causative in ISO-induced cardiac remodeling not previously reported in our heart weight analyses . All animal experiments were conducted following guidelines established and approved by the University of California, Los Angeles Institutional Animal Care and Use Committee.Using i.p. ketamine as a surgical anesthetic agent, ALZET Model 1004 minipumps  were implanted intra-peritoneally to administered ISO, a non-selective β-adrenergic agonist that has been used widely to mimic the HF state in experimental animals ,25, at aTo ensure adequate sedation while minimizing the effects of inhaled isoflurane on loading conditions, HR, cardiac structure and function, we minimized induction and maintenance doses of isoflurane at or below 1.25% and 1%, respectively, while closely monitoring for HR < 475 bpm as a sign for deep sedation and adjusting isoflurane dosage as needed .A single operator (JJW), who followed a standard operating protocol detailed below, performed all of the echocardiograms using the Vevo 770 ultrasound system : A parasternal long-axis B-mode image was obtained. The maximal long-axis of the LV was positioned perpendicular to the ultrasound beam. A 90° rotation of the ultrasound probe at the papillary muscle level was performed to obtain a parasternal short-axis view of the LV. A M-mode image to document LV dimensions was captured and saved for analysis. At a later time, saved images were analyzed using the Vevo 770 cardiac analysis package by a single observer (JJW) who was blinded to mouse strains.A baseline echocardiogram was performed on all the mice. In ISO-treated mice, serial echocardiograms were performed at 1, 2, and 3 weeks of treatment. In control mice, a second echocardiogram was performed in 70 mouse strains at week 3 as an internal control. The reproducibility of our echocardiographic measurements was assessed using the Bland-Altman plots, which demonstrated acceptable agreement between week 0 and week 3 control measurements . IVSd anFrozen LV tissues were homogenized in QIAzol Lysis Reagent prior to RNA isolation using RNeasy columns . RNA quality was assessed using the Bioanalyzer RNA kits . RIN ≥ 7.0 was considered acceptable.Expression profiling of pooled biological replicates was performed using Illumina MouseRef-8 v2.0 Expression BeadChip arrays . To minimize artifacts due to single nucleotide polymorphisms (SNPs), we excluded probes that aligned to sequences containing known SNPs. Background correction and quantile normalization of the image data was performed using the neqc method from the R package limma . HierarcUsing the R package limma, moderated t-statistics and the associated p values were calculated. Multiple testing was corrected by controlling for false discovery rate using the Benjamini-Hochberg procedure . Probes 2) explained was computed from the formula:The standard R program was used to performed t-test. The R package vioplot was used to plot violin plots. The R package corrplot was used to plot correlations among traits. Consistent with time series data, serial measurements of the same trait were generally correlated to each other with adjacent time points being the most correlated. Of note, LVM was a calculated measure based in part on IVSd and LVIDd; therefore, it is not surprising that LVM was correlated with IVSd and LVIDd at each time point. The R package FactoMineR was used to compute principal component analysis and display variables factor map and individuals factor map. The R package WGCNA was used to calculate biweight midcorrelation (bicor), which is a median based correlation measure that is more robust than the Pearson correlation but often more powerful than the Spearman correlation. The proportion of variance  reflects all the genetic contributions to a population's phenotypic variance including additive, dominant, and epistatic effects, while narrow-sense heritability (h2) represents the proportion of phenotypic variance that is due to additive genetic effects alone. In inbred model organisms, repeatability expresses the proportion of variation in a trait that is due to differences between individual strains and not due to differences within individual strains. Under the assumption that all differences between genotypes are genetic and not due to genotype-environment correlation, repeatability is equal to broad-sense heritability; otherwise it only provides an upper-bound for broad-sense heritability. Using the R package heritability , an anal−6. The significance threshold for cis-eQTL and trans-eQTL were 3.63 x 10−3 and 6.1 x 10−5, respectively. Given that the studied traits could be collapsed to roughly a handful of PCs, the significance threshold for clinical trait analyses was kept at the nominal p-value of 4.1 x 10−6, without Bonferroni adjustment for multiple testing.The threshold for genome-wide significance was determined by simulation and permutation of strain genotypes, as previously described . The sigWe used the Factored Spectrally Transformed Linear Mixed Models (FaST-LMM) to test for association while accounting for population structure and genetic relatedness among strains as previously described , which p−5 that were no more than 2 Mb apart between nearest consecutive pairs. Lead SNPs without any neighboring SNPs at p-value < 1 x 10−5 were excluded. LD between lead SNPs and flanking SNPs were calculated and visualized by plotting regional plots using a custom-built of LocusZoom [Linkage disequilibrium (LD) is the non-random association of alleles generally near one another at a locus. SNPs in strong LD tend to have association p-values that are similar in strength. Genomic boundaries around lead association SNPs were chosen based on flanking SNPs with p-values < 1 x 10ocusZoom  based on2 > 0.8) of the lead SNPs were examined for coding sequence and splice region variations using the Wellcome Trust Mouse Genomes Project (MGP) sequencing database [Genes within LD  for an additional 24 hours prior to bright field imaging on day 4.Neonatal rat ventricular myocytes (NRVMs) were isolated and cultured at the UCLA NRVM core facility using 2–4 day old rats. Myocytes and fibroblasts were separated using Percoll density gradient. NRVMs were plated at 375,000 cells/well in DMEM + 10% FBS + 100 U/mL penicillin + 100 μg/mL streptomycin (p/s) on Day 1. On the morning of Day 2, plated NRVMs were transfected with 30 pmol per well of siRNA  using Lipofectamine 2000 (Life Technologies) and media changed to DMEM + 1% ITS  without p/s for Myh14 siRNA using Lipofectamine RNAimax overnight. Cells were then treated the next day with 20 μM ISO or 50 μM PE for 24 hours and then viability was assayed using MTT.MTT Assay was performed using the Vybrant MTT Cell Proliferation Assay Kit (Life Technologies V13154). Briefly, NRVMs were plated in 96 well plates at a density of 10,000 cells per well and were transfected with either scrambled control or Myh14-/- mice generated on a 129/Sv background followed by backcrossing to strain C57BL/6 for more than 7 generations were obtained from the Adelstein lab [Myh14-/- male x C57BL/6J female) to generate Myh14+/- mice. Myh14+/- heterozygous crosses generated wild-type (WT), heterozygous (HET) and homozygous (KO) littermates at normal mendelian ratios .Expression array data are deposited in the Gene Expression Omnibus 19 (GEO) online database We characterized 104 classical and RI mouse strains by obtaining echocardiographic measurements under the baseline condition and in response to chronic ISO administration for 3 weeks . Global −16), validating our echocardiographic measures externally.To minimize inter-operator and inter-observer variation, all echocardiograms were performed by a single operator and interpreted by the same observer who was blinded to strain name and treatment assignment. Inhaled isoflurane was titrated to achieve adequate sedation, while maintaining target HR above 475 bpm. The Bland-Altman plot, which shows the agreement between two quantitative methods, demonstrated acceptable differences between ISO-treated LVM  and LV weight  estimates . LVM by We observed striking variation in cardiac structure and function among the HMDP strains both under baseline condition and in response to chronic ISO administration . ImportaWe performed correlation analysis across the HMDP to further understand the phenotype data observed. Not surprisingly, baseline body weight (BBW), a surrogate for body size, was significantly correlated with LVM and LVIDd but not with FS , consistBBW- and HR-adjusted partial correlations among pairs of echocardiographic and LV weight traits showed that ISO treatment uncoupled many measures of cardiac structure and function from baseline . ISO-treTo describe each strain based on a composite of correlated traits, we used principal component (PC) analysis to transform ISO-induced changes into a set of linearly uncorrelated variables. The first three PCs account for 35%, 28% and 11% of the inter-strain variance observed, with the remaining PCs each accounting for < 10% of the variance observed. The variables factor map showed that the first three PCs captured phenotypes such as LVIDd dilation and FS decrease; LVM hypertrophy; and early IVSd increase . The ind2) reflects all the genetic contributions to a population's phenotypic variance including additive, dominant, and epistatic effects, while narrow-sense heritability (h2) represents the proportion of phenotypic variance that is due to additive genetic effects alone. In inbred model organisms, where the environment is controlled and multiple animals of identical genetic background are available, between-strain and within-strain variation can be used to estimate H2, which reflects all genetic contribution to phenotypic variance. The majority of the traits we examined were highly heritable (H2 > 0.4)  . H2 esti and 81% , which i9 to 57% . Our resHuman heart failure is characterized by chronic sympathetic activation to compensate for progressive cardiac function loss. Chronically, high levels of catecholamines may lead to calcium overload, initiating a cascade of alterations at the cellular level to result in cardiomyocyte death and progressive deterioration in cardiac structure and function. To protect itself from harmful effects of chronic adrenergic stimulation, a protective mechanism, called desensitization, reduced membrane availability of the beta-adrenergic receptors, leading to diminished downstream signaling. ISO is a non-selective β-adrenergic agonist that, when administered chronically in mice, mimics the state of chronic sympathetic activation in human heart failure .Adrb1, adjusted p-value = 2.56 x 10−5) and the inhibitory G-protein Gi increased by 16% , reminiscent of diminished β-adrenergic responsiveness secondary to chronic β-adrenergic overdrive in cardiomyopathic hearts , possibly reflecting down-regulation of cardiac structural proteins or increased fibrotic tissue. Finally, one of the most significantly up-regulated genes Lgals3 was increased by 3.5-fold . Lgals3, or galectin-3, is an emerging clinical marker of cardiac fibrosis, adverse LV remodeling, and mortality in HF patients [Consistent with molecular changes of cardiac remodeling in humans, ISO resulted in gene expression changes downstream of β-adrenergic receptor signaling across the HMDP mice. β1-adrenoceptor expression decreased by 17% , angiogenesis, polysaccharide binding, actin cytoskeleton, vacuole, response to wounding, chemokine signaling pathway, and epidermal growth factor (EGF)-like calcium-binding, prenylation and growth factor binding . The dowAssociation mapping at each ISO-treated time point was performed to define cQTLs for BBW- and HR-adjusted echocardiographic changes in measures from baseline. In addition, association mapping was performed for each gene transcripts under baseline and ISO-treated conditions, to define local expression quantitative trait loci (cis-eQTL) near each cQTL. Because genetic information flows from DNA to transcript to clinical phenotypes, a transcript with an eQTL that coincides with cQTL of a trait of interest may be hypothesized to be causal for the trait. Using a custom pipeline that overlays cQTL, cis-eQTL, expression-phenotype correlation, and structural variation, candidate genes for each cQTL were prioritized. The significantly associated loci and candidate genes for echocardiographic measures are provided in Fam46a on chromosome 9 has previously been shown in a multi-ethnic GWAS meta-analysis to be associated with systolic and diastolic blood pressure  [Lrp12 and Zfpm2 on chromosome 15 has previously been shown in a small human GWAS to be associated with sudden cardiac arrest due to ventricular tachycardia and ventricular fibrillation in patients with coronary artery disease  [A total of 17 genome-wide significant cQTL were identified, including 4 for IVSd, 5 for LVIDd, 4 for LVM and 4 for FS, many of which were less than 1 Mb in size . As expectively) . One of  x 10−6) .−7) conferred a median week 3 LVM of 117 mg and 135 mg, respectively. SNP rs27794497, located in an intergenic region between Tmem38b and Zfp462, and surrounding SNPs in LD (r2 > 0.8) spanned a region containing 4 genes  and was significantly correlated with LVM hypertrophy at week 3  conferred a median LVM increase of 43 mg and 28 mg, respectively. SNP rs40560913, located in the intron of Izumo2, and surrounding SNPs in LD (r2 > 0.8) spanned only one other gene Myh14 . The available qPCR results was not adequate to map for eQTL. As Myh14 was represented on microarray platforms used in prior HMDP studies, we queried our cis-eQTL database and found that Myh14 has cis-eQTL signals across multiple tissues at the same locus and its expression in adipose and liver was negatively correlated with body mass normalized heart weight at week 8 in the HMDP obesity study  followed by phenylephrine and ISO stimulation in vitro. Myh14 knock-down demonstrated > 70% efficiency (Myh14 siRNA treated cells displayed poor attachment to the tissue culture plates (Myh14 siRNA treated NRVMs failed to undergo characteristic cellular hypertrophy under phenylephrine (PE) and ISO stimulation  and homozygous (Myh14-/-) littermates were not phenotypically different and no disproportionate spontaneous sudden death was observed in Myh14 deficient mice (oldest Myh14-/- mouse 12 months to date). qPCR analysis showed a complete loss of Myh14 expression in Myh14-/- hearts  in females than in males, which is important for our association mapping study design. Under the constraints of cost for animal procurement and maintenance, we chose to study females only. We believe that at least some of the findings observed in females will likely recapitulate in males, since gender differences in mortality in humans appear to be predominantly due to factors, such as age, co-morbidities and treatment approaches , rather Our study established an echocardiographic reference range for 100+ inbred mouse strains. To minimize the variability to handling among different mouse strains, inhaled isoflurane, one of the most commonly used agent for prolonged anesthesia in mice, was administered during echocardiography. Based on recent studies on sedated mice, FS of 33% and 32% in 129X1/SvJ male and C57BL/6J female mice were similar to our strain averages of 33% and 34%, respectively ,51.Inhaled anesthetics, including isoflurane, are known cardiac depressants, especially upon deep sedation. In a study involving C57BL/6 male mice, mean arterial pressure (MAP), HR, and body temperature at an isoflurane concentrations of 1.5%  remained comparable to the conscious state for up to 90 minutes post-induction . In anotIn spite of ongoing inotropic stimulation by ISO throughout the 3-week treatment time course, some strains displayed the same or decreased FS at later ISO time points compared to baseline, consistent with a decrease in contractile reserve—an observation in early HF stages of decreased cardiac function augmentation to direct inotropic stimulation—marked by reduced beta-adrenergic receptors along with altered sarcoplasmic reticulum Ca2+-adenosine triphosphatase 2a (SERCA2a) and phospholamban . Our narOur association analyses provided direct evidence that common genetic variation control ISO-induced cardiac remodeling. The following considerations are relevant to the interpretation of the results. Equivalent dosages of ISO were given to each strain to control the degree of cardiac insult. Although we could not control for ISO metabolism, we believe its contribution to the results was minor. Linkage analysis using only the BXD RI strains resulted in a significant reduction in resolution . On the In our previously published manuscript, we identified a number of heart weight and fibrosis loci but none for ISO-induced LV weight hypertrophy . A numbeIn a mapping study, the spread rather than the absolute values of the data drives the association analysis. Therefore, in spite of the positive bias in the upper ranges, LVM and LV weights yielded similar association results Figs  and 7D, Our association analysis not only highlighted a number of genomic regions and genes for HF susceptibility in mice, it also provides independent supportive evidence for syntenic regions in humans and is complementary to existing and future human HF genome-wide association and family-based linkage studies. For example, a candidate causal genomic region or gene for HF or inherited cardiomyopathy in humans not reaching statistical significance may be recovered based on additional supportive evidence from mice. Next, based on the central dogma that the flow of genomic information starts from the DNA, to the RNA then to the phenotype of interest, the cardiac transcriptome data allow us to generate hypotheses to test causal inference. For instance, when the transcript level of a gene in an association locus is associated with genetic variation and correlates with phenotype, we may hypothesize that the gene could be causally related to the phenotype, thereby facilitating prioritization of candidate genes in an association locus . Our datKlf4 and Myh14 to be negative regulators of cardiac hypertrophy under ISO stress. Our results showed that the degree of LVM hypertrophy was inversely correlated with the expression of Klf4  [Klf4 in mice resulted in a slightly increased heart weight and increased ANF levels compared to controls. When subjected to pressure overload, these mice developed increased pathologic cardiac hypertrophy, fibrosis, apoptosis and mortality, compared to control banded mice [In vitro studies confirmed that KLF4 binds to the ANF and GATA4 promoters and acts as a repressor of cardiomyocyte hypertrophy [Our study implicated  of Klf4 . Since t (iPSCs) , has recded mice . In vitrertrophy .Myh14 was the only gene expressed in the heart that was located at the chromosome 7 locus affecting cardiac hypertrophy in our study. Myh14, myosin heavy polypeptide 14, encodes the heavy chain of the molecular motor nonmuscle myosin II-C (NMIIC), which is a member of the nonmuscle myosin II motor protein family that plays an integral role in mechanotransduction, converging external and cell-generated forces by interacting with cytoskeletal actin. When bred in a background of non-muscle myosin II-B (NMIIB) hypomorphic mice expressing only 12% of wild-type amounts of NMIIB protein, Myh14-/- mice developed marked cardiac myocyte hypertrophy, interstitial fibrosis and diffuse N-cadherin and β-catenin patterns at the intercalated discs, where NMIIB and NMIIC colocalized [Klf4 and Myh14 as negative regulators of stress-induced cardiac hypertrophy in vivo, which is also supported by our results.ocalized . Collectin vivo study, we showed that Myh14 deficient mice exhibited increased ISO-induced LVID dilation, LVM hypertrophy, cardiac fibrosis, and hypertrophic markers Myc and Nppb. On the other hand, In vitro siRNA knockdown of Myh14 in NRVMs failed to elicit a hypertrophic response when stimulated with ISO but rather displayed poor attachment to the tissue culture plates and poor survival. As a non-muscle myosin, Myh14 is ubiquitously expressed and could act through non-cardiomyocytes to modulate cardiomyocyte growth and hypertrophy. We focused on Myh14 inside cardiomyocytes due to a previous study demonstrating the location of Myh14 in the intercalated disc and its implicated role at the cell-cell junction [Myh14 deficiency may be quite different in vitro versus in vivo. These seemingly discrepant results raise important questions regarding the mechanisms through which Myh14 deficiency leads to cardiomyocyte death in the absence of mechanotransduction stress in vitro and cardiac hypertrophy on an organ level in vivo.In our junction . Since NMyh14 deficiency in LVM hypertrophy, we queried the existing HMDP cardiac transcriptome database available via http://systems.genetics.ucla.edu/ [Myh14 expression also controls Foxo1 in trans  and that the expression of Foxo1 was significantly positively correlated with Myh14 , which implicated Foxo1, a forkhead family transcription factor, as a potential mediator of Myh14. In addition, Myh14 expression was correlated with Cdk11b  and Cdk11b was controlled by the Myh14 locus , which implicated Cdk11b, a member of the cyclin-dependent kinase family known to be a negative regulator of normal cell cycle progression, as a potential mediator of Myh14.To further explore mechanisms mediating cla.edu/ . We founMyh14 deficient heart demonstrated disrupted assembly of intercalated discs involving β-catenin, which is a dual function protein that provides cell-cell connections at the adherens junctions in the intercalated disc, to facilitate mechanical and electrical coupling between adjacent cardiomyocytes, and regulates gene transcription as a transcriptional coactivator, raising the possibility that disordered β-catenin localization in the intercalated disk due to Myh14 deficiency could have direct consequences on β-catenin signaling. In fact, altered intercalated disc architecture in cardiac muscle hypertrophy and HF has previously been associated with altered β-catenin abundance [Myh14 is important in the maintenance of normal cardiac structure under ISO stress and that the loss of Myh14 exacerbates the hypertrophic response to ISO. Further understanding of the crosstalk between Myh14, β-catenin, and Foxo1, will likely reveal important mechanisms regarding how Myh14 deficiency leads to cardiac hypertrophy under ISO stress.Moreover, we showed that ISO-treated bundance , subcellbundance , and canbundance . Our expCardiac remodeling is one of the most important prognostic determinants of clinical HF. Our study results provide an important resource to the HF research community and highlight the strength of a systems approach to studying HF. The integration of high throughput molecular phenotypes, such as genomic and transcriptomic data, provides a means to identify novel candidate causal genes and a powerful alternative to human studies to understand the complex interactions underpinning phenotypic variation of cardiac remodeling. Similar to human GWAS, many of our lead SNPs lie in noncoding regions. Future directions will include sequence-based computational approaches to systematically prioritize functional regulatory variants. Future insights in how common genetic variations in a population modify HF progression will further shed light on genetic risk profiling, gene-gene and gene-environment interactions as well as the design of personalized therapies for HF patients.S1 Fig(A) Baseline and week 3 echocardiographic measures in control mice were significantly correlated. (B) Bland-Altman plots demonstrated agreement between baseline and week 3 echocardiographic measures in control mice. IVSd represents the interventricular septal wall thickness during diastole. LVIDd represents the left ventricular internal dimension during diastole. LVM represents left ventricular mass. FS represents fractional shortening. Each data point represents a mouse in the control group.(PDF)Click here for additional data file.S2 FigThe manhattan plots for the change in week 1 IVSd show that association p-values by EMMA and FaST-LMM analyses were similar.(PDF)Click here for additional data file.S3 Fig(A) LVM at baseline versus control LV. (B) LVM versus LV after ISO for 3 weeks. (C) The change in LVM from baseline to week 3 of ISO versus the difference in the strain-averaged LV weights between control and ISO. Bland-Altman plots (bottom row) showing the agreement between LV and LVM are provided below. (D) Comparison of LV and LVM phenotypic spectra between control and isoproterenol hearts at week 3.Each data point represents a mouse strain.(PDF)Click here for additional data file.S4 Fig(A) Black bars represent measurements under the baseline condition. White bars represent measurements after 3 weeks of continuous ISO infusion. Data presented in alphabetical order of strain names. Error bars represent the standard errors of the means. (B) Gray bars represent the differences in measurements between baseline and week 3 of continuous ISO infusion in ranked order.(PDF)Click here for additional data file.S5 FigThe baseline LVM and week 3 LVM for each of the C57BL/6, DBA/2, and BXD RI strains (top). The change in LVM at week 3 for C57BL/6, DBA/2, and BXD RI strains (bottom). Error bars represent the standard error of the means.(PDF)Click here for additional data file.S6 Fig(A) Relationships between echocardiographic measures and baseline body weight. BBW represents baseline body weight. (B) Relationships between echocardiographic measures and heart rate at corresponding time points. HR represents heart rate. (C) Relationships between HR across time points. Each data point represents a mouse strain.(PDF)Click here for additional data file.S7 FigLine repeatability and marker-based h2 estimates for echo measures IVSd, LVIDd, LVM, and FS at baseline, week 1, week 2, and week 3 of ISO are compared.(PDF)Click here for additional data file.S8 FigKlf4 expression (ILMN_1221264) around SNP rs27794497 (purple).A. Regional plot for LVM hypertrophy at week 1. B. Regional plot for LVM hypertrophy at week 2. C. Regional plot for ISO-treated LV hypertrophy at week 3. D. Regional plot for the ISO-treated (PDF)Click here for additional data file.S9 FigKlf4 are as follows: Control ILMN_1241903 r = -0.28 p-value = 0.01, ISO ILMN_1241903 r = -0.21 p-value = 0.07; Control ILMN_1221264 r = -0.23 p-value = 0.04, ISO ILMN_1221264 r = -0.21 and p-value = 0.07.Color circles represent SNP genotype at rs27811538. Bicor correlation and p-values are provided above. Pearson correlation between LVM and (PDF)Click here for additional data file.S10 FigThe genome-wide and chromosome 9 manhattan plots for the change in week 1 IVSd analyzed using the classical inbred strains only (A) and all of the HMDP strains (C). The genome-wide and chromosome 9 linkage maps for the change in week 1 IVSd analyzed using the BXD recombinant inbred strains only (B).(PDF)Click here for additional data file.S11 FigThe genome-wide and chromosome 7 manhattan plots for the change in week 3 LVM analyzed using the classical inbred strains only (A) and all of the HMDP strains (C). The genome-wide and chromosome 7 linkage maps for the change in week 3 LVM analyzed using the BXD recombinant inbred strains only (B).(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 TableA. Significantly up- or down-regulated cardiac genes by isoproterenol treatment. B. DAVID gene ontology analysis of up-regulated cardiac genes. C. DAVID gene ontology analysis of down-regulated cardiac genes. D. Summary of DAVID gene ontology analysis of cardiac genes correlated to echocardiographic measures. DAVID gene ontology analysis was performed based on top 1000 correlated cardiac genes for each echocardiographic measures either at baseline or at wee 3 of isoproterenol. Gene clusters exceeding nominal p-value 0.05 are shown.(XLSX)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 TableA. Summary of genetic loci by trait. Weight traits such as total heart (TH), left ventricle (LV) and right ventricle (RV) weights are denoted by the prefix wt. Echo traits are denoted by the prefix w1 for week 1, w2 for week 2, and w3 for week 3 followed by echo for measurement or delta for the difference compared to baseline at the corresponding time point. Genomic coordinates were listed in mm9. B. Genes within the association loci were queried using a custom computational pipeline to examine the presence of differential expression with isoproterenol, cis-eQTL, correlation to trait of interest and sequence or structural variations.(XLSX)Click here for additional data file.S7 TableSelect SNPs from our analysis that exceed the association p-value threshold of < 0.001.(XLSX)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 Table(PDF)Click here for additional data file.S10 Table(PDF)Click here for additional data file.