# This data structure provides information about each QTL

# analysis. Each analysis is specified by the following 3 parameters:

#

#   pheno     phenotype or trait to map

#   cov       covariates included in regression model

#   outliers  data points to remove according to their residuals

#             after removing linear effects of covariates.

#

# See comments below for additional details on the QTL analyses.

analyses <- list(

# MUSCLE AND BONE TRAITS

# ----------------------

# For all five muscle weights (TA, EDL, soleus, plantaris and

# gastrocnemius), we map QTLs conditioning on tibia length

# ("tibia"). For tibia length, we map QTLs conditioned on body weight.

#

# Tibia length explains 12-18% of variance in the muscle weights. The

# rationale for including tibia length as a covariate is bone length

# may somehow regulate muscle weight as well, and we would like to

# isolate the genetic factors that directly regulate development of

# the muscle tissues.

#  

# For bone-mineral density (BMD), we created a binary trait that

# signals "abnormal" BMD. We do not include any covariates when

# mapping QTLs for these traits. Note that body weight is also

# uncorrelated with BMD.

# 

# For all muscle and bone traits, we include a binary indicator for

# round SW16 as a covariate because the mice from this round showed

# substantial deviation in these traits compared to the rest of the

# mice.

TA      = list(pheno="TA",cov=c("SW16","tibia"),

               outliers=function (x) x < (-18)),

EDL     = list(pheno="EDL",cov=c("SW16","tibia"),

               outliers=function (x) x < (-5) | x > 4),

soleus  = list(pheno="soleus",cov=c("SW16","tibia"),

               outliers=function (x) x < (-4) | x > 4),

plant   = list(pheno="plantaris",cov=c("SW16","tibia"),

               outliers=function (x) x < (-9) | x > 8),

gastroc = list(pheno="gastroc",cov=c("SW16","tibia"),

               outliers=function (x) x < (-40) | x > 50),

tibia   = list(pheno="tibia",cov=c("SW6","SW16","sacweight"),

               outliers=function (x) x < (-1.5)),

BMD     = list(pheno="BMD",cov="SW16",outliers=function (x) x > 0.14),

abBMD   = list(pheno="abBMD",cov="SW16",outliers=NULL),

# OTHER PHYSIOLOGICAL TRAITS

# --------------------------

# Body weights bw1, bw2 and bw3 were measured on subsequent days of

# the methamphetamine sensitivity tests, and are highly correlated

# with each other (r^2 = 98%), so it is only necessary to map QTLs for

# one of them. The body weight measurements after sacrifice

# ("sacweight") show a considerable departure in Round SW17, so we

# include a binary indicator for this round as a covariate for

# sacweight. We include age as a covariate for the "bw0" body weight

# because it was measured while the mouse was still growing.

#

# Fasting glucose levels are explained partially by body weight (PVE =

# 6%), so we include body weight as a covariate for fasting glucose

# levels. Rounds SW1 and SW11 showed a considerable departure in

# fasting glucose levels from the other rounds, so we included binary

# indicators for these two rounds as covariates for fasting glucose

# levels.

bw0    = list(pheno="bw0",cov="glucoseage",

              outliers=function (x) x < (-8.5) | x > 8.5),

bw1    = list(pheno="bw1",cov=c("methage","SW17"),

              outliers=function (x) x < (-9) | x > 10),

ppiwt  = list(pheno="PPIweight",cov="SW17",outliers=NULL),

sacwt  = list(pheno="sacweight",cov="SW17",outliers=NULL),

fastgl = list(pheno="fastglucose",cov=c("SW1","SW11","bw0"),

              outliers=function (x) x < (-60) | x > 60),

tail   = list(pheno="taillength",outliers = NULL,

              cov=c("bw0","glucoseage","SW3","SW4","SW19","SW20","SW22",

                    "SW24")),

testis = list(pheno="testisweight",cov="sacweight",

              outliers=function (x) x < (-0.075)),

# FEAR CONDITIONING TRAITS

# ------------------------

# For all fear conditioning traits, the cage used for testing appears

# to have an effect on the phenotype, so we include binary indicators

# for cage as covariates for all FC phenotypes. Further, the FC

# phenotype measurements in Round SW17 show a noticeably different

# distribution in the FC phenotypes from the other rounds, so we

# include a binary indicator for round SW17 as a covariate in all FC

# traits.

#

# These analyses control for proportion of freezing on day 1 during

# exposure to the tone ("AvToneD1"). AvToneD1 explains 11-25% of the

# variance in the Day 2 and Day 3 freezing measures. Note that here we

# can map QTLs for freezing to the altered context on Day 3

# ("AvAltContextD3") as a quantitative trait after conditioning on

# AvToneD1 because the distribution for this trait is no longer quite

# so bimodal, and looks fairly "normal". So there is no need to map

# QTLs for the binary version of this trait.

#

# PreTrainD1 is a very ugly trait with massive box effects and a lot

# of low values, which might have to be removed as outliers. It is

# quite likely that these outliers represent the "deaf" mice that

# might be skewing the whole results. These outliers are present in

# every box, so not a box-specific effect.

d2ctxt     = list(pheno="AvContextD2",outliers=NULL,

                  cov=c("AvToneD1","FCbox1","FCbox2","FCbox3","SW17")),

d3altc     = list(pheno="AvAltContextD3",outliers=function (x) x > 1,

                  cov=c("AvToneD1","FCbox1","FCbox2","FCbox3","SW17")),

d3tone     = list(pheno="AvToneD3",outliers=NULL,

                  cov=c("AvToneD1","FCbox1","FCbox2","FCbox3","SW17")),

pretraind1 = list(pheno="PreTrainD1",outliers=NULL,

               cov=c("FCbox1","FCbox2","FCbox3","SW10","SW16","SW17","SW20")),

d1avtone   = list(pheno="AvToneD1",outliers=NULL,

               cov=c("FCbox1","FCbox2","FCbox3","SW10","SW7","SW14","SW20")),

# METHAMPHETAMINE SENSITIVITY, LOCOMOTOR ACTIVITY AND ANXIETY-LIKE BEHAVIOR 

# -------------------------------------------------------------------------

# We checked all the cages used in these tests to see whether the

# phenotypes measured using any given cage departed noticeably from

# the other cages. Cage #7 consistently has a large effect.

d1dist0to15 = list(pheno="D1totaldist0to15",cov="methcage7",

                   outliers=function (x) x < (-2000) | x > 2200),

d2dist0to15 = list(pheno="D2totaldist0to15",cov="methcage7",

                   outliers=function (x) x < (-2000) | x > 2500),

d3dist0to15 = list(pheno="D3totaldist0to15",cov="methcage7",

                   outliers=function (x) x > 8500),

d1dist0to30 = list(pheno="D1totaldist0to30",cov="methcage7",

                   outliers=function (x) x < (-3500) | x > 4000),

d2dist0to30 = list(pheno="D2totaldist0to30",cov="methcage7",

                   outliers=function (x) x > 4500),

d3dist0to30 = list(pheno="D3totaldist0to30",cov="methcage7",

                   outliers=function (x) x > 20000),

d1totdist5  = list(pheno="D1TOTDIST5",cov="methcage7",

                   outliers=function (x) x < (-1000) | x > 1000),

d1totdist10 = list(pheno="D1TOTDIST10",cov="methcage7",

                   outliers=function (x) x < (-750) | x > 750),

d1totdist15 = list(pheno="D1TOTDIST15",cov="methcage7",

                   outliers=function (x) x < (-750) | x > 750),

d1totdist20 = list(pheno="D1TOTDIST20",cov="methcage7",

                   outliers=function (x) x < (-750) | x > 750),

d1totdist25 = list(pheno="D1TOTDIST25",cov="methcage7",

                   outliers=function (x) x < (-750) | x > 750),

d1totdist30 = list(pheno="D1TOTDIST30",cov="methcage7",

                   outliers=function (x) x > 700),

d2totdist5  = list(pheno="D2TOTDIST5",cov="methcage7",

                   outliers=function (x) x < (-1250) | x > 1250),

d2totdist10 = list(pheno="D2TOTDIST10",cov="methcage7",

                   outliers=function (x) x > 1000),

d2totdist15 = list(pheno="D2TOTDIST15",cov="methcage7",

                   outliers=function (x) x > 850),

d2totdist20 = list(pheno="D2TOTDIST20",cov="methcage7",

                   outliers=function (x) x > 1000),

d2totdist25 = list(pheno="D2TOTDIST25",cov="methcage7",

                   outliers=NULL),

d2totdist30 = list(pheno="D2TOTDIST30",cov="methcage7",

                   outliers=function (x) x > 900),

d3totdist5  = list(pheno="D3TOTDIST5",cov="methcage7",

                   outliers=function (x) x > 2000),

d3totdist10 = list(pheno="D3TOTDIST10",cov="methcage7",

                   outliers=function (x) x > 4000),

d3totdist15 = list(pheno="D3TOTDIST15",cov="methcage7",

                   outliers=function (x) x > 4000),

d3totdist20 = list(pheno="D3TOTDIST20",cov="methcage7",

                   outliers=function (x) x > 4500),

d3totdist25 = list(pheno="D3TOTDIST25",cov="methcage7",

                   outliers=function (x) x > 4500),

d3totdist30 = list(pheno="D3TOTDIST30",cov="methcage7",

                   outliers=function (x) x > 3750),

D1ctrtime0to15 = list(pheno="D1ctrtime0to15",cov="methcage7",

                      outliers=function (x) x < (-0.5)),

D2ctrtime0to15 = list(pheno="D2ctrtime0to15",cov="methcage7",

                      outliers=function (x) x < (-0.75)),

D3ctrtime0to15 = list(pheno="D3ctrtime0to15",cov="methcage7",

                      outliers=function (x) x < (-0.75)),

D1ctrtime0to30 = list(pheno="D1ctrtime0to30",cov="methcage7",

                      outliers=function (x) x < (-0.6)),

D2ctrtime0to30 = list(pheno="D2ctrtime0to30",cov="methcage7",

                      outliers=function (x) x < (-0.75)),

D3ctrtime0to30 = list(pheno="D3ctrtime0to30",cov="methcage7",

                      outliers=function (x) x < (-0.85)),

D1hact0to15 = list(pheno="D1hact0to15",cov="methcage7",outliers=NULL),

D2hact0to15 = list(pheno="D2hact0to15",cov="methcage7",outliers=NULL),

D3hact0to15 = list(pheno="D3hact0to15",cov="methcage7",outliers=NULL),

D1hact0to30 = list(pheno="D1hact0to30",cov="methcage7",outliers=NULL),

D2hact0to30 = list(pheno="D2hact0to30",cov="methcage7",outliers=NULL),

D3hact0to30 = list(pheno="D3hact0to30",cov="methcage7",outliers=NULL),

D1vact0to15 = list(pheno="D1vact0to15",cov=c("methcage7","methcage8",

                   "methcage9","methcage10","methcage11","methcage12"),

                   outliers=function (x) x < (-0.85) | x > 0.85),

D2vact0to15 = list(pheno="D2vact0to15",cov=c("methcage7","methcage8",

                   "methcage9","methcage10","methcage11","methcage12"),

                   outliers=function (x) x < (-1) | x > 1),

D3vact0to15 = list(pheno="D3vact0to15",cov=c("methcage7","methcage8",

                   "methcage9","methcage10","methcage11","methcage12"),

                   outliers=function (x) x < (-1.25) | x > 1.25),

D1vact0to30 = list(pheno="D1vact0to30",cov=c("methcage7","methcage8",

                   "methcage9","methcage10","methcage11","methcage12"),

                   outliers=function (x) x < (-1) | x > 1),

D2vact0to30 = list(pheno="D2vact0to30",cov=c("methcage7","methcage8",

                   "methcage9","methcage10","methcage11","methcage12"),

                   outliers=function (x) x < (-1) | x > 1),

D3vact0to30 = list(pheno="D3vact0to30",cov=c("methcage7","methcage8",

                   "methcage9","methcage10","methcage11","methcage12"),

                   outliers=function (x) x > 1.5),

# PREPULSE INHIBITION (PPI) PHENOTYPES

# ------------------------------------

# All boxes appear to have some effect on some of the PPI phenotypes,

# with Box #3 having a particularly large effect on some phenotypes,

# so we include all PPI box indicators as covariates in analysis of the

# PPI phenotypes.

#

# We also map QTLs for habituation to pulses by analyzing the startle

# response during the fourth block of pulse-alone trials against the

# startle response during the first block of pulse-alone trials.

pp3PPIavg  = list(pheno="pp3PPIavg",outliers=function (x) x < (-0.9),

                   cov=c("PPIbox1","PPIbox2","PPIbox3","PPIbox4")),

pp6PPIavg  = list(pheno="pp6PPIavg",outliers=function (x) x < (-1.1),

                  cov=c("PPIbox1","PPIbox2","PPIbox3","PPIbox4")),

pp12PPIavg = list(pheno="pp12PPIavg",outliers=function (x) x < (-1),

                  cov=c("PPIbox1","PPIbox2","PPIbox3","PPIbox4")),

PPIavg     = list(pheno="PPIavg",outliers=function (x) x < (-1),

                  cov=c("PPIbox1","PPIbox2","PPIbox3","PPIbox4")),

PPIstartle = list(pheno="startle",outliers=function (x) x > 250,

                  cov=c("PPIbox1","PPIbox2","PPIbox3","PPIbox4")),

PPIhabit   = list(pheno="p120b4",outliers=function (x) x < (-140) | x > (140),

                  cov=c("p120b1","PPIbox1","PPIbox2","PPIbox3","PPIbox4")))