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Heritability of sudden death syndrome and its associated correlationsto ascites and body weight in broilers

H.K. MOGHADAM, I. MCMILLAN, J.R. CHAMBERS1, R.J. JULIAN2

AND C.C. TRANCHANT

3

Department of Animal and Poultry Science, University of Guelph, 1Agriculture and Agri-Food Canada,2Department of Pathobiology, University of Guelph, Guelph and 3School of Food Science and Nutrition,Universite de Moncton, Moncton, New Brunswick, Canada

Abstract 1. Genetic parameters for the sudden death syndrome (SDS) were estimated in meat-typechickens. Data were collected over 11 generations of selection for body weight within two distinctbreeds (Cornish and White Rock).2. The animal model was used exclusively with linear methods (LM) to estimate genetic parameters.

Heritability (h2

) of SDS on the liability scale was 0.300

.002 and 0

.250

.002 in the Cornish and WhiteRock breeds, respectively.

3. A positive genetic correlation (rg) with ascites (AS) was determined ($0.30.006). However, it wasnot possible to estimate the rg of SDS with body weight because of the low prevalence of the defecttrait studied (1.8% in the Cornish and 1.5% in the White Rock).4. Heritability of SDS calculated using male records only was 0.450.009 and 0.350.009, and rg withbody weight was 0.30 0.010 and 0.27 0.009, in the Cornish and White Rock breeds, respectively.5. In conclusion, the heart defect investigated was heritable with a positive genetic correlation withAS and body weight.

INTRODUCTION

Sudden death syndrome (SDS), also known asflip-over or acute death syndrome, is a diseaseof young and fast-growing broiler chickens whichdie suddenly and occasionally on their back, withabout 60 to 80% of those affected being males(Bowes et al., 1988). It has been suggested thatSDS is a metabolic disease causing ventricularfibrillation, with the mortality peak ranging from1 to 3 weeks of age, usually coinciding with the

age at which the rate of feed conversion isgreatest ( Julian, 1988). This topic has been fullydiscussed by Olkowski and Classen (1995) andJulian (1996).

A variety of factors such as nutrition, gene-tics and environmental factors can affect theincidence of SDS (Riddell, 1991), causing mostof the modern broiler chicken strains to besusceptible. Different strains exhibit differentdegrees of susceptibility to SDS (Riddell, 1993).Gardiner et al. (1988) showed that the incidence

of SDS increased appreciably as the flock bodyweight increased, an indicator of the existenceof a positive phenotypic correlation (rp) withbody weight, which leads to the possibility ofa high genetic correlation (rg). However, theestimation of genetic parameters of SDS, includ-ing heritability (h2), has been difficult to imple-ment. These difficulties are attributable in part tothe relatively low prevalence of this disorder andto its threshold nature.

One approach for estimating the genetic

parameters for threshold traits is linear methods(LM). An attractive feature of LM is that theanimal effect can be readily incorporated into themodel. Although, according to Gianola (1982)and Hoschele (1986), LM predictions may notbe free of bias because of the non-additivitypresent in the observed scale, other studieshave demonstrated little practical differencebetween them and those estimated using non-linear methods (Schaeffer and Wilton, 1976;Berger and Freeman, 1978; Thompson et al.,

Correspondence to: Dr H.K. Moghadam, Department of Zoology, University of Guelph, Guelph, Ontario, Canada N1G 2W1. Tel: 1-519-824-4120 ext8596. E-mail: hkhalegh@uoguelph.ca

Accepted for publication 9th September 2004.

British Poultry Science Volume 46, Number 1 (February 2005), pp. 5457

ISSN 00071668(print)/ISSN 14661799(online) 2005 British Poultry Science LtdDOI: 10.1080/00071660400023862

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1981; Boettcher et al., 1998), even when theincidence of one category is low (Mercer andHill, 1984; Moghadam et al., 2001).

Moghadam et al . (2001) have previouslyreported h2 estimates for ascites (AS) in broilers.Using the same data-set, the objectives of the

present study were to estimate the h2

of SDS onthe liability scale and its genetic correlation withAS and body weight in two breeds of meat-typechickens, namely, Cornish and White Rock.

MATERIALS AND METHODS

Data collection

Data were obtained from broiler strains ofchickens reared between 1981 and 1992 at theAnimal Research Center, Agriculture Canada,Ottawa, Canada. Cornish (sire population) and

White Rock (dam population) breeds wereevaluated during one generation of randommating and 11 generations of selection. Progenywere obtained from parents that were randomlymated with the exclusion of either full or half-sibmating.

In the first 5 generations, the sire populationconsisted of an unselected strain (control)and three selected strains with two replicates.In the initial generation, parents were selectedat random, whereas in subsequent generationsall the strains were selected for high 28-d

body weight. The dam population also consistedof a control strain and a selected strain withtwo replicates. In generation 5, the parents fromthe selected replicate strains (in both sire anddam populations) were crossed to combinethe replicate strains selected for the same trait.Subsequently only one strain was kept for eachprogramme of selection. Additional detailscan be found in the papers by Chambers et al.(1984), Wang et al. (1991) and Moghadam et al.(2001).

Dead chickens were necropsied and a diag-nosis assigned. Those with evidence of heartfailure were classified as either acute or chronic.Those with acute heart failure were classified ascases of SDS (or flip-over disease). Symptomsleading to this diagnosis included: contractedheart muscle; ruptured heart tissue; heart attack(but not congested); flip-over attached to reportfrom the barn (chicken found dead lying on itsback); heart tissues appeared healthy. Thesechickens had died by 65 d of age. Chickens thatdied of chronic heart failure were classifiedas cases of AS (pulmonary hypertension, rightventricular failure). Symptoms leading to this

diagnosis included: congestive heart failure;enlarged right ventricle; heart tissues wereoften turgid or flaccid; heart was not constricted;ascetic fluid accumulated in the peritoneal

cavity. These chickens had usually died by 105 dof age.

Statistical analyses

Genetic estimations were based on the analyses

of binary data in the LM. The PEST software(Groeneveld and Kovac, 1990) was used tore-code edited data. The restricted maximumlikelihood (REML) method was then applied toestimate rg and h

2 on the observed scale by usingthe VCE4 software (Groeneveld, 1998). Bothtwo- and three-trait animal models (AM) with thefollowing general equation were considered:

Y XZu e

where Y is the vector of observations, X is theincidence matrix for the fixed effects (sex, strain,generation), Z is the incidence matrix of randomeffects (animal), is the vector of unknownparameters for fixed effects, and u is the vector ofunknown parameters for random effects. Valuesof h2 estimated on the observed scale weretransformed to the values on the liability scaleby using the following equation:

h2l h2o p1 p

z2

where h2l is the estimation of h2 on the liability

scale, h2o is the h2 on the observed scale, p is theproportion of affected individuals, and z is theheight (in standard deviation units) of the normalcurve at the threshold point and is equal to ip,where i is the mean deviation from the popula-tion mean of animals with values exceeding thethreshold (Falconer, 1965).

RESULTS AND DISCUSSION

Phenotypic variance was calculated from abinomial function of the prevalence. The pre-valence of the defect trait in both populationswas relatively low, ranging from 1.5 t o 1.8%(Table 1) in the White Rock and Cornish breeds,respectively. A three-trait animal model was usedto minimise the bias attributable to the effects ofselection on 28-d body weight. Many of theelements in the right-hand side of the mixedmodel equations were either 1 or 0. Because thenumbers of sires, dams, and progeny tested perstrain were relatively large, a high accuracy wasexpected.

Difficulties were encountered in trying to

estimate the genetic correlations of SDS withbody weight. This was probably attributable to acombination of (i) low incidence of the ailmentstudied and (ii) missing values of body weight

GENETICS OF SUDDEN DEATH SYNDROME 55

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not change substantially with age (Havensteinet al., 1994), this may be used as a criterion forfamily selection.

The positive genetic correlation between

SDS and AS may make it possible to reducetheir incidence by selecting against one of themor to consider them both as a single trait.The defects also had an unfavourable positive(moderate) genetic correlation with live weight.Problems remain, however, because SDS and ASare positively correlated with live weight, mean-ing that selection programmes for body weightare likely to increase, or at best maintain, thecurrent incidence of these abnormalities.

ACKNOWLEDGEMENTS

This research was supported by Poultry IndustryCouncil and Ontario Ministry of AgricultureResearch grants. The authors are grateful forhelpful comments provided by Dr VincentDucrocq and the anonymous reviewer.

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Table 3. Estimations of genetic parameters for sudden death syndrome (SDS), ascites (AS) and body weight(BW) in Cornish and White Rock breeds using male records only 1

Breed SDS AS BW2

(rg)3

BW(rp)

4

Cornish SDS 0.45 0.009 0.35 0.070 0.300.010 0.010AS 0.41 0.009 0.220.010 0.015

BW 0.450.009 WhiteRock

SDS 0.35 0.009 0.33 0.010 0.270.009 0.030AS 0.22 0.017 0.350.007 0.017BW 0.300.002

1Estimated values of h2 (liability scale) on diagonals, and genetic and phenotypic correlations off-diagonal.2BWbody weight at d 28.3rg genetic correlation.4rpphenotypic correlation.

GENETICS OF SUDDEN DEATH SYNDROME 57