Genetic and phenotypic relationships between physiological traits and performance test traits in sheep

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Genetic and phenotypic relationships between

physiological traits and performance test traits in sheep

Nd Cameron, E Cienfuegos-Rivas

To cite this version:

Original

article

Genetic

and

_phenotypic

_{relationships}

between

_{physiological}

traits

and

performance

test

traits

in

_sheep

ND

_Cameron,

E

_{Cienfuegos-Rivas}

AFRC Roslin Institute

_(Edinburgh),

Roslin, Midlothian,

EH25

_9PS,

UK

(Received

3 November

_{1992 ; accepted}

12 November

₁₉₉₃₎

Summary - Genetic and

_{phenotypic relationships}

between

_{physiological}

traits and

perfor-mance test traits were estimated with data from lines of Texel-Oxford

_{sheep divergently}

selected for carcass lean _content, for the identification of

_{physiological predictors}

of

_genetic

merit for carcass lean content. At the end of the

performance

test, blood

samples

were taken from 151

animals,

the progeny of 31

sires,

when fed and when fasted for 30 and 54 h.

Heritability

estimates for traits associated with

protein metabolism, creatinine,

urea and insulin-like

growth

factor-1

_(IGF-1),

were

higher

with ad-libitu!n

feeding

than heritabilities of traits associated with

lipid

and energy

metabolism, β-hydroxybutyrate,

non-esterified

fatty acids, triglyceride

and

glucose.

Several

_{physiological}

traits,

!3-hydroxybutyrate,

cre-atinine and insulin-like

_growth

factor-1

_(IGF-1),

were

_moderately

correlated with the per-formance test traits of

_liveweight

and muscle

_depth,

but

_only

urea and insulin-like

_growth

factor-1 were

_{significantly}

correlated with backfat

_depth.

Correlations between

physiolog-ical traits and

_predicted

carcass

_composition

were

_estimated,

as animals were

_required

for

_breeding

or were allocated to another

_experiment.

Based on

_{phenotypic correlations,}

β-hydroxybutyrate

and IGF-1 may be useful indicators of merit for

predicted

carcass lean

weight,

with urea

_being

an indicator of

_predicted

carcass lean content. Measurement of

physiological

traits when animals were fasted _may not be

_required

for the

_prediction

of

genetic

merit

_using

_{physiological}

traits, as

_genetic

correlations between

_feeding

and

_fasting

were

high

in absolute value for all

physiological traits,

_except

glucose.

sheep

/

genetic parameter

/

physiological trait

_/

carcass _composition

Résumé - Relations _génétiques et _{phénotypiques} entre des caractères _{physiologiques}

et des _performances chez le mouton. Les relations

_génétiques

et

_{phénotypiques}

entre des caractères

_{physiologiques}

et des

_performances

ont été estimées sur des données _provenant de

_lignées

ovines

_Texel-Oxford

sélectionnées de manière

_divergente

pour la teneur en

mai-gre de la carcasse, en vue

d’identifier

des

prédicteurs physiologiques

de la valeur

génétique

pour la teneur en

_maigre

de la carcasse. Au terme du contrôle individuel

_(associant

des mesures de

poids vif

et

_{d’épaisseurs}

de gras et de muscle mesurées par

ultra-sons),

des

échantillons de sang ont été

prélevés

sur 151 _animaux, descendant de 31

pères,

et étant

somatomédine

_{(IGF-1) -}

sont

_plus

élevées que les héritabilités de caractères associés au métabolisme

_{énergétique}

et

_lipidique,

tels que

/!-hydroxybutyrate,

acides gras non

_{estérifiés,}

triglycérides

et

_glucose.

Plusieurs caractères

_{physiologiques, /3-hydroxybutyrate,}

créatinine

et

IGF-1,

sont modérément corrélés avec les

performances

individuelles de

poids vif

et

d’épaisseur

de gras. Les corrélations entre les caractères

_{physiologiques}

et la

_composition

prédite

de la carcasse ont été

_{estimées, puisque}

les animaux étaient soit mis à la

reproduc-tion soit destinés à une autre

_expérience.

Sur la base des corrélations

phénotypiques,

le

{3-hydroxybutyrate

et l’IGF-1 peuvent être considérés comme des indicateurs intéressants de la teneur en

_maigre

de la carcasse. La mesure de caractères

physiologiques après

mise à

jeun

n’est pas

requise

pour

prédire

la valeur

génétique, puisque

les corrélations

génétiques

entre les conditions de

jeûne

et d’alimentation normale sont élevées en valeur absolue _pour

tous les caractères

physiologiques

à

_{l’exception}

du

_glucose.

mouton

_/

_{paramètre génétique}

_/

caractère _{physiologique}

_/

_composition de la carcasse

INTRODUCTION

Genetic

_improvement

of lean

_growth

rate in

_sheep

_breeding

_programmes

_requires

predictors

of

_genetic

merit for carcass traits. Selection criteria have included whole

animal

measurements,

such as

liveweight

with ultrasonic backfat and muscle

depths

(Bennet

et

_{al, 1988;}

Cameron and

_{Bracken, 1992;}

Kadim et

_{al, 1989;}

Simm et

_al,

1990).

Physiological

traits _may

_provide

additional information for estimation of

genetic

merit for lean

_growth

_(Land,

_1981;

Blair et

_al,

_1990).

Several studies have examined differences in

_{physiological}

traits between par-ticular

_genotypes,

to

_identify

traits as indicators of

_genetic

merit. For

_example,

lower urea concentrations have been

_reported

in selection lines of

_high

_genetic

merit for

_protein

_deposition

_(lean

meat or

wool)

or secretion

(milk)

than in lines of low

genetic

merit in

_sheep

_(Bremmers

et

_al,

_1988;

_Cameron,

_1992;

Carter et

_al,

_1989;

Clark et

al,

1989;

Van Maanen et

_al,

_1989),

in

_pigs

_(Mersmann

et

_al,

₁₉₈₄₎

and in

dairy

cattle

_(Tilakaratne

et

_{al, 1980;}

_Sejrsen

et

_{al, 1984;}

Sinnett-Smith et

_al,

_1987).

Phenotypic

and

_genetic

_parameters

for

_{physiological}

traits and

_phenotypic

correlations with

_performance

test traits of

sheep

in 2

_divergently

selected lines for

carcass lean content were estimated. There _may be

_genetic

variation in

_sensitivity

or

_{responsiveness}

to homeostatic factors in the control of metabolism

_(Bauman

and

_Currie,

_1980),

such that the correlation between a

_{physiological}

trait and the selection

_objective

may be

higher

under one

feeding

_regime

than another.

Therefore,

physiological

traits were measured under fed and under fasted conditions. The

study

was not able to estimate

_{genetic relationships}

between

_{physiological}

traits and

_production

_traits,

as carcass information was not

_available,

because animals

MATERIALS AND METHODS Animals

A

_divergent

selection

_experiment

was started in 1985 with a Texel-Oxford flock

and the selection criterion was

designed

to

_{change body}

_composition

without a

corresponding

change

in

_liveweight

at _{20 weeks of age. A} detailed

_description

of the establishment of the selection

lines,

the ram

performance

test and the selection

procedures

is

_{given by}

Cameron and Bracken

_(1992).

Selection was based on ram

performance only

and, by 1988,

the selection line difference in the selection criterion

was 0.62 sd with a

_generation

interval of 1.85 _yr.

In

_1988,

1989 and

_1990,

a total of 151

_performance

tested rams were measured

for several

_{physiological}

traits,

with

32,

25 and 23 rams from the lean line in the 3 _yr and

_34,

17 and 20 rams from the fat line. On average, there were 5 sire families

each _year in both selection lines and the rams were _progeny of 31 sires. There were

physiological

measurements on the sires of 85 _rams, as selected rams were mated at 6 _{months of age.} After

_weaning

at 8 _{weeks of age,} rams were

_penned

and fed to

_appetite,

twice a

_day,

until the end of test at 20 weeks of age.

Early

weaning

was

_practised

to lessen maternal effects on ram

_performance

test and a

_high

_energy

(12 MJ/kg DM)

and

_high

_protein

_{(180 g/kg}

DM crude

_protein)

ration was fed to reduce nutritional constraints on the rams’

_genetic

_ability

for

_protein

and

_lipid

deposition

on test. At the end of the

_test,

_liveweight

_(WT),

ultrasonic subcutaneous fat

_depth

_(FD)

and

_longissimus

dorsi muscle

_depth

_(MD)

were recorded. Carcass

lean

_weight

_(kg)

and content

_(g/kg)

were

_predicted

from

_performance

test

_traits,

using regression equations

derived

_by

Cameron and Bracken

₍₁₉₉₂₎

for the Texel-Oxford

_flock,

as carcass information was not available on

_performance

tested rams

in the current

_study.

_Regression

_equations

from the

_study

of Cameron and Bracken

(1992)

were:

Carcass lean

_weight

_(kg)

= _constant + 0.11 WT - 0.13 FD ₊ 0.13 MD Carcass lean content

_(g/kg)

= constant - 0.32 WT - 13.7 FD + 4.0 MD

and the

_proportional

reductions in variance of carcass

_lean,

for animals in the

Cameron and Bracken

₍₁₉₉₂₎

_study,

due to the

_{regression equations}

were 0.57 and

0.49,

respectively.

Measurement of

_{physiological}

traits

The

_day

after ultrasonic

_scanning,

animals were fed as normal at 0800 h and then fasted for 54 h. Blood

_samples

were taken on each of 3 d at 1430

_h,

at which time

measurements were most

_repeatable

_{(Cameron, 1992).}

At each

_{sampling period,}

5-ml blood

_samples

were collected

by jugular

venipuncture, using vacutainers,

and after

_overnight

_storage

to allow

_clotting,

serum was

_{separated by centrifugation.}

The

_{physiological}

traits measured were

_{,Q-hydroxybutyrate}

_(BHB)

and

_glucose

(GLUC),

as indicators of _energy

_balance,

non-esterified

_fatty

acids

(NEFA)

and

triglyceride

(TRIG),

as intermediaries of

_lipid

_metabolism,

creatinine

_(CREA)

and

urea

(UREA),

as indicators of

_protein

_degradation,

and insulin-like

_growth

factor-1

Serum IGF concentrations were determined

_{by radioimmunoassay}

_(Armstrong

et

_al,

₁₉₉₀₎

_following

extraction

_{procedures of Daughaday}

et al

₍₁₉₈₀₎

_(DProc)

and

Enright

et al

₍₁₉₈₉₎

_(EProc)

for the

samples

taken in 1988 and in 1989 and

1990,

respectively.

Enright

et al

₍₁₉₈₉₎

_reported

that their

_procedure

was more efficient in

separating

IGF from its

_{binding protein complex}

than DProc and that serum IGF

concentrations

_using

DProc would be overestimated

_by

_{radioactivity associating}

with

_binding

_{protein complex.}

A subset of 25

_samples

from

_1988,

which

_uniformly

covered a wide range of IGF

_values,

were

_re-assayed

_using

EProc to determine an

adjustment

for the 1988 values to be

_compatible

with the 1989 and 1990

values,

as not all 1988

samples

were available for _re-assay

_using

EProc. The mean and

variance for DProc

_samples

was

_larger

than for EProc

_samples,

as

_expected,

and the

correlation coefficient of 0.88

_(se

_0.21)

indicated that a linear

adjustment

of DProc

samples

was

_appropriate.

The

_regression

coefficient was

_equal

to 0.505

_{(se 0.020),}

with the

_regression

forced

_through

the

_origin,

which was consistent with EProc

being

42%

more efficient than

DProc,

as

reported by Enright

et al

_(1989).

IGF

values from

DProc

in 1988 were

_{adjusted accordingly,}

and the within-animal sd of

adjusted

1988 IGF values

_(0.065)

was similar to those for 1989 and 1990 IGF values

(0.056

and

_0.053).

Statistical

_analyses

Power transformations of the data were examined

_{using methodology suggested}

by

Solomon

_(1985),

as between-animal variation in the

_{physiological}

_response to

fasting

may have resulted in the data not

_satisfying

the

_assumptions

of a linear

mixed model. The method identified a _power transformation which maximised the

log

likelihood under the

_{joint hypotheses}

of

_(i)

independence

of mean and

_variance,

(ii)

normality,

and

_(iii)

_additivity

under the

_proposed

model.

For a

_{given physiological}

_trait,

_x, the transformed

trait,

y, was

_equal

_to

_{y =}

(x

A

-

1) / À

for A 54

0 and _y =

_log!

for A = 0. An

_approximate

_log

likelihood

_equal

to:

was

_used,

where MSE and MSS were the residual and between-sire mean _squares,

respectively,

from the

_analysis

of variance of transformed

_data,

with fixed effects included in the

model;

N and s were the total numbers of animals and

_sires,

and

_{(A -}

_1)E

_{log x}

was the Jacobian of the transformation. The A

value,

which maximised the

_{log likelihood, A}

_max

_,

was determined

by

differentiation of the

quadratic function,

describing

the

_log

likelihood in terms of A.

For each transformed

_{physiological}

_trait,

fixed

_effects,

_genetic

and

_phenotypic

(co)variances

for the 3

_{sampling days}

were estimated

by

residual maximum

like-lihood

_(REML)

_(Patterson

and

_Thompson,

₁₉₇₁₎

_{analyses, using}

the multivariate REML program of

Meyer

(1985).

An individual animal model was

fitted,

which

included the fixed effects of selection

_line,

_year of

_measurement,

_{dam age}

_(1,

_2,

3 or

4 yr of

_age)

and birth

type

(single

and

_twin).

The

_performance

test was on a fixed

age

basis,

8-20 weeks of age, such that

weight

at the start of test was not included in the model as a covariate.

_Divergence

between selection lines in the first year of the

_study

was accounted for

_by

inclusion of selection line in the model.

_Meyer

and

account for selection in

_generations

0 and

1,

as records from those

_generations

were not used in the

_analyses.

The rate of _{convergence in} the REML

_analyses

was

_improved

_{by scaling}

the

_traits,

so that

phenotypic

variances of all traits were of the same order of

magnitude.

Iterations were assumed to have

_converged

when the maximum

_proportional

difference between iterations in

_genetic

variance

_components

on the canonical scale

was less than

10-

5

.

Sampling

errors of

_genetic

_parameters

were determined from

variances of the variance

_components

_(Meyer,

_1985).

Inclusion of

_performance

test

_traits,

on which selection had been

practised,

and

_{incorporation}

of

pedigree

information in the multivariate

_analysis

of each

_{physiological trait,}

accounted for the continued selection

_(Sorensen

and

Kennedy, 1986; Kennedy,

1990).

However,

in

the multivariate

_analyses

of

_{physiological}

traits and

_predicted

carcass lean

_weight

and

content,

the 3

_performance

test traits were not

included,

as the

predicted

carcass traits were linear functions of

performance

test traits.

RESULTS

Physiological

traits

Within-sampling day

means and residual sd after

_fitting

fixed

_effects,

for each

physiological

trait are

_presented

in table I. Between

feeding

and

_fasting

for 54

h,

BHB,

NEFA and TRIG

_increased,

while GLUC and IGF decreased. UREA was

highest

30 h after

_fasting.

Standard deviations of NEFA were

_{substantially higher}

with

_fasting

than with

feeding

but variation in GLUC decreased with

fasting

(table I).

Although

there

were differences in residual

_variances,

for the

_{remaining traits,}

the differences were

’1ransformation of traits

The

_A

_max

_values,

which maximised the

_{within-sampling day log likelihoods,}

are

given

in table II. There was considerable

_{between-sampling day}

variation in

A

max

values for

BHB,

NEFA and CREA but the

A

max

values for other traits were

relatively

constant between

_{sampling days.}

Within-sampling

day

differences between the maximum

_log

likelihood and the

log

likelihoods with a

_log,

_square root or no transformation of each trait were

examined to determine if a

simple

transformation could be

applied

(table II),

as

Solomon

₍₁₉₈₅₎

_suggested

that the use of

_{simple A}

values would be

_practicable.

One

transformation for each trait on all

_{sampling days}

was sufficient for

_TRIG,

_GLUC,

UREA and IGF. A different transformation on d 1

_compared

to d 2 and d 3 was

required

for NEFA and CREA. For

_BHB,

a

_log

transformation was used for d 1

and d

_3,

but a _square root transformation on d 2.

After

_{transformation,}

the distribution of each trait was

normal,

in terms of skewness and kurtosis coefficients. For each transformed

_trait,

skewness and kurtosis coefficients were not

_{statistically significantly}

different from 0 and

_3,

_{respectively,}

the values for a normal distribution. In

_general,

the relative reduction in skewness

coefficients,

due to

_{transformations,}

was

_greater

than the

_change

in kurtosis

coefficients. For

example,

the skewness and kurtosis coefficients for BHB with

feeding changed

from 1.26 to 0.27 and 5.03 to

_3.36,

_{respectively.}

Fixed effects

Effects of selection

_line,

dam _age and birth

_type

on transformed

_{physiological}

traits,

with

_feeding

of

animals,

and

_performance

test traits are

_given

in table III.

Mean values

_(phenotypic

_sd)

for the

_performance

test traits of

_liveweight

were

48.8

_kg

_(5.9),

with 25.5 mm

_(2.2)

and 4.2 mm

_(1.0)

for ultrasonic muscle and

TRIG and

_UREA,

but the lean line had lower backfat

_depths,

even

_though

there was no selection line difference in

_liveweight.

_Progeny

of older dams had

_higher

concentrations of

_CREA,

UREA and

IGF,

with smaller differences of

_{opposite sign}

for twin-born animals.

_Single-born

_progeny from older ewes were

_heavier,

with

greater

ultrasonic muscle and backfat

depths.

Phenotypic

and

_genetic

_parameter

estimates for

_{physiological}

traits

Within-sampling

day

heritability

estimates for

_{physiological}

traits are

_given

in

table IV. Under normal

_{feeding, heritability}

estimates for traits associated with

protein

metabolism,

CREA,

UREA and

_IGF,

were

_higher

than for GLUC and traits

associated with

_lipid

_{metabolism, BHB,}

NEFA and TRIG.

_{Within-trait,}

between-sampling day phenotypic

and

_genetic

correlations are

_presented

in table IV. The

positive

phenotypic

correlations between

_feeding

and

_fasting

were

_higher

for traits

associated with

protein

metabolism than those involved in

_lipid

metabolism.

Genetic correlations between

_feeding

and

_fasting

were

_high

in absolute value for

all

_{physiological}

_traits,

except

GLUC. Correlations for NEFA and TRIG between the 2

_{fasting days}

were lower in

_magnitude

than between

_feeding

and

_fasting,

and

conversely

for GLUC. The

_negative

correlation between

_feeding

and

fasting

for

NEFA was consistent with selection line differences on each

sampling day

(Cameron,

1992),

as

_{during feeding}

the lean line had lower NEFA concentrations than the fat

line,

but on

_fasting

the lean line had

_higher

values.

Phenotypic

correlations between

_{physiological}

and

_performance

traits

Phenotypic

correlations between certain

_{physiological}

and

_performance

test traits

are

_given

in table V. Correlations between

_performance

test traits with

_NEFA,

BHB,

CREA and IGF were

moderately

correlated with

liveweight

and muscle

depth,

but

_only

UREA and IGF were

_{significantly}

correlated with backfat

_depth.

On

fasting,

correlations for BHB and CREA with

_liveweight

and muscle

_depth

were

smaller

_compared

to normal

_feeding,

while UREA became

_negatively

correlated with no

change

in the IGF correlations.

Phenotypic

correlations between

BHB,

CREA,

UREA and IGF with

_predicted

carcass lean

weight

were similar to the correlations with

_liveweight

_(table

_V),

but

_only

UREA was

_{significantly}

correlated

with

_predicted

carcass lean

content,

due to the

_negative

correlation with backfat

depth.

DISCUSSION

Heritability

estimates for

_{physiological}

traits involved in

_{protein metabolism,}

CREA,

UREA and

_IGF,

indicate that there is substantial

_genetic

variation in these traits. There is little

_comparable

information on

heritability

estimates for

physiological

traits associated with

_growth

in

_sheep

_(Woolliams

et

_al,

_1984),

as

the

_majority

of studies have examined the role of

_{gonadotrophins}

as

predictors

of

_genetic

merit for

_reproductive

traits

_(Land

et

al,

1988).

Correlated responses in

_{physiological}

traits to selection for

_growth

_and/or

fat

_deposition

have been

studied,

but heritabilities have not been

_previously

estimated.

_Similarly,

there are

few estimates of correlations between

performance

test traits and

_{physiological}

traits. The association between muscle

_depth

and CREA was

_reported

_by

Hanset

and Michaux

₍₁₉₈₆₎

for

cattle,

with CREA

_suggested

as an indicator of carcass

lean mass. A

_negative

relationship

between UREA and

_{protein deposition}

has been

reported

in several studies

_(Bremmers

et

_al,

_1988;

Carter et

_al,

_1989;

Clark et

_al,

1989;

Van Maanen et

_al,

_1989;

_Cameron,

_1992),

which is consistent to an

_extent,

Based on

phenotypic

correlations,

UREA _may be a useful

predictor

of carcass

lean content with BHB and IGF as

_predictors

of carcass lean

_weight.

Measurement

of

_{physiological}

traits when animals were fasted _may not be

_required

for the

prediction

of

_genetic

merit

_using

_{physiological}

_traits,

as

_genetic

correlations between

feeding

and

_fasting

were

high

in absolute value for all

physiological

_traits,

_except

GLUC.

_Further,

the

_phenotypic

correlations between

_{physiological}

and

_performance

test traits were of

greater

magnitude

with normal

_feeding,

which also

suggests

that

fasting

would not be necessary when

measuring

physiological

traits as

_predictors

of

genetic

merit. Precise estimates of the

_genetic

correlations between the

_{physiological}

traits and carcass

_composition

are

required

for calculation of

_appropriate

selection

criteria,

to combine

_performance

test traits with

_{physiological}

traits. Genetic correlations were estimated in this

_study,

but standard errors of the estimates were

sufficiently large,

such that the correlations were of limited value.

Although

the

_performance

test

_incorporated

_early

_weaning,

in an

_attempt

to

reduce maternal

_effects,

there were still effects of dam _age and birth

_type

on the

performance

test and

_{physiological}

traits.

_However,

the effects of dam age and birth

_type

on the

_performance

of

_intensively

tested rams were

_markedly

less than

on

extensively

tested rams,

suggesting

that the intensive

performance

test had

certainly

reduced maternal effects. For

example,

in a

_genotype

with environment

study

in 1991 the

_proportional

difference in backfat

_depth

due to dam _age

(1-yr-old versus older

dams)

was 0.36 for 53 ram lambs tested _{at grass} with their

dams,

relative to the mean backfat

_depth

of 1.8 _mm, while for 44 rams on the intensive

performance

test the

_{corresponding}

values were 0.15 and 2.5 mm.

Similarly,

the

proportional

differences between

_single-

and twin-born lambs were 0.49 and 0.12. Estimation of an animal’s

_genetic

merit for

_protein

and

_{lipid deposition}

based on

performance

test and

_{physiological}

traits would be biased

_by

the effects of dam age and birth

_type,

so

_they

were included in the model to remove their effects on the measured traits. The

_positive

correlation for

_liveweight

with

_CREA,

UREA and

IGF,

with animals on normal

_feeding,

_may

_{partially explain}

the effect of dam _age

and birth

type

on these

traits,

since

_single

lambs were heavier than twin lambs as were _progeny of older dams. Morel et al

₍₁₉₉₁₎

also

_reported

an effect of dam _age

and birth

type

on IGF.

Physiological predictors

of

_genetic

merit for carcass content _may be identified

by

examining

correlated _responses to selection for carcass lean or

_{by selecting}

on a

potentially

useful

physiological

trait and

_measuring

the correlated _response in carcass content. Hill

₍₁₉₈₅₎

considered the former

_approach,

as used in the

_present

study,

preferable

to the latter. Blair et al

₍₁₉₉₀₎

established

_divergent

selection lines for IGF in

_sheep

and Morel et al

₍₁₉₉₁₎

_reported

a realised

_heritability

of 0.27 for

IGF. The IGF

_heritability

estimate from the current

_study,

_0.53,

_may be

_higher

than the Morel et al

₍₁₉₉₁₎

estimate for at least 3 reasons. Data in the Morel et

al

₍₁₉₉₁₎

_study

may not have been

_normally

distributed,

which _may have resulted

in a lower

_heritability

estimate than from transformed data

(see

below).

Animals

in the Morel et al

₍₁₉₉₁₎

_study

were

performance

tested on _grass, rather than on a

high protein

and energy ration as in the

_present

_study,

which _may have constrained

expression

of

genetic

variation in serum IGF concentration. Animals in the

_present

study

were blood

sampled

at 20 weeks of age, while those in the Morel et al

(1991)

a

_positive

association between _age at

_sampling

and

_IGF,

which

_suggests

that it is

important

to standardise the test

procedure

for the identification of

_{physiological}

predictors

of

_genetic

merit for carcass

_composition.

Between-breed differences in

_adipose

tissue metabolism have

_provided

infor-mation on the metabolic basis for

_genetic

variation in fat

deposition

(Sinnett-Smith and

_Woolliams,

_1988),

but each breed had a

_unique

combination of

an-abolic and catabolic processes.

Therefore,

if

physiological

traits associated with

lipid

metabolism are identified as

_predictors

of

_{genetic merit,}

based on a

within-breed difference between

_divergent

selection

_lines,

then

_they

_may be breed

_specific.

Heritabilities for traits associated with

_{protein metabolism,}

under normal

_feeding,

were

_higher

than for traits associated with

lipid metabolism,

in the current

_study.

Similar results have been

_reported

in

_pigs

_(Lingaas

et

_al,

_1992),

_although

not in

cattle

_(Bittante

et

_{al, 1987;}

_Bishop

et

_al,

_1992).

_Therefore,

the

_relatively

_greater

genetic

variation in

_components

of

_protein

metabolism

_suggests

that

_study

of

regu-latory

factors for

_protein

metabolism _may

_identify

_predictors

of

_genetic

merit for

carcass lean content in

_sheep.

For

_example,

Cameron

₍₁₉₉₂₎

_suggested

_that,

when

fasted,

animals from the fat selection line made

_relatively

more use of

_products

from

_protein

catabolism as

_glucose

_precursors, than those of the lean line.

For each

_{physiological}

_trait,

a series of _power transformations was used to

_identify

a transformation which maximised the

_log

likelihood with a mixed model. The

transformations were

justified

on statistical _reasons,

although

they

_may have an

underlying

genetic

or

_biological

basis.

_Log

transformations were

_required

for traits

associated with fat

_metabolism,

but not for CREA under normal

_{feeding. Lingaas}

et al

₍₁₉₉₂₎

reported

that

_log

transformations were _necessary for NEFA and

TRIG,

but not for CREA measurements in

_pigs.

In

_dairy

_cattle,

_log

transformations of

NEFA,

TRIG and UREA were

required,

but not for BHB and GLUC

_(Woolliams

et

_al,

_1992).

_Heritability

estimates for traits associated with fat metabolism were

low,

with transformed

_data,

but the results of Ibe and Hill

₍₁₉₈₈₎

_suggest

that

heritability

estimates with untransformed data would be even lower. Hill et al

₍₁₉₈₃₎

reported higher heritability

estimates for

_log

milk

_yield

than for milk

_yield,

which

was attributed to increased

_homogeneity

of

_phenotypic

variance between herds.

Similarly,

the

_genetic

_{(co)variances}

for

_{physiological}

traits with

_production

traits may be

underestimated,

if the data have not been transformed to ensure

_normality

and

_homogeneity

of within-sire variances.

ACKNOWLEDGMENTS

RS Nisbet carried out all the assays except for insulin-like

_{growth factor-1,}

which was

assayed by

K

_Angus.

J Bracken

managed

the

_{sheep performance}

test and blood

sampled

the

sheep.

The constructive comments of referees were

_appreciated.

The

_Ministry

of

Agriculture,

Fisheries and Food

provided funding

for this research

_project.

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