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Evidence on the genetic control of LH release in response to GnRH from crosses between selected lines of sheep
Livestock Production Science, 37 (1993) 153-167
153
Elsevier Science Publishers B.V., Amsterdam
Evidence on the genetic control of LH release in response to GnRH from crosses between selected lines of sheep C.S. Haley~, G.J. Leea, R. W e b b a a n d
S.A. K n o t t b
aAFRCRoslin Institute (Edinburgh)*Roslin, U.K. blnstitute of Cell, Animal and Population Biology, Universityof Edinburgh, Edinburgh, U.K. (Accepted 20 April 1993 )
ABSTRACT The purpose of this study was to determine the genetic basis of response to divergent selection for the amount of luteinizinghormone (LH) released in ten-week-old lambs after an injection of gonadotrophin releasing hormone (GnRH). Data from 518 animals in the selection lines, the Ft cross between the lines and both backcrosses were analysed by residual maximum likelihood to estimate crossbreeding genetic effects. There was no evidence for sex-linked effects, maternal effects, sex-limited effects or dominance of the autosomal direct effects. Thus selection responses have been achieved by changes at additive autosomal loci which have the same effect in both sexes. Maximum likelihood segregation analysis was used to look for a major gene affecting LH release. An apparent effect of a major gene with a similar allele frequency in both selection lines was detected, but this result was attributed to residual non-normalitywhich had not been removed by the transform applied. There was no evidence that a single major gene explained the difference between the selection lines, but the study was not sufficiently powerful to rule out contributions from one or more genes of smaller, but still appreciable, effect. Key words: Sheep; Reproduction; Genetics; Crossbreeding; Major gene
INTRODUCTION
Land ( 1973 ) suggested that traits which could be measured in young males might provide indirect indicators of the merit of the genes they carried for reproductive traits. If this is the case, then identification of, and selection upon, the appropriate traits in males could increase the rates of response to genetic selection for traits such as litter size in females (Walkley and Smith, 1980). To date, much of the effort to identify indirect indicators has focused Correspondence to: C.S. Haley, AFRC Roslin Institute (Edinburgh), Roslin, Midlothian, EH25 9PS, U.K. *Formerly AFRC Institute of Animal Physiology and Genetics Research, Edinburgh Research Station.
0301-6226/93/$06.00 © 1993 Elsevier Science Publishers B.V. All rights reserved.
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on testis size and on the gonadotrophins, luteinizing hormone (LH) and follicle stimulating hormone (FSH) (for a review see Land et al., 1988). One potential indirect indicator trait that we have studied in young rams is the amount of LH released in response to an injection of gonadotrophin releasing hormone ( G n R H ) . Divergent lines selected for the amount of LH released by 10-week-old rams in response to G n R H were developed from a Finnish Landrace-Dorset Horn synthetic line (Haley et al., 1989). After eight male generations the geometric mean of three measurements of LH released in response to G n R H in I 0-week-old rams in the line selected for high response (the high line) was more than 5-fold that in the low line, and the heritability of this mean response trait was estimated to be 0.44. The correlated responses to selection for LH release in males at 20 weeks of age and in females at 10 and 20 weeks of age suggested that the genetic correlations between these traits and LH release in 10-week-old males were close to unity. That is, the same genes mediated LH release in males and females at 10 and 20 weeks of age (Haley et al., 1989). The purpose of this study was to determine the genetic basis of the responses to selection for LH release after G n R H injection. Three aspects were of interest. Firstly, the action of the genes involved (i.e. the relative influence of the genotypes of the dam and of the lamb itself and whether the genes were additive or dominant or sex-limited in their effect). Secondly, whether there was any evidence of sex-linked, as opposed to autosomally linked, inheritance of the genes. Thirdly, whether there was evidence of involvement of a single major gene in the response to selection. This last question is of particular interest because if a major gene is implicated, our accumulating knowledge of the physiological and molecular basis of the line differences might eventually allow the identification and isolation of the gene involved. This in turn would allow the detailed study of a gene mediating hormonal responses. Two factors hint at the possible involvement of a major gene in the selection response. Firstly, the relatively high heritability for the trait and secondly, the apparent asymmetry of response to selection observed in the high and low lines (Fig. 1 ). One of several possible causes of asymmetry of response to selection may be the segregation of a major gene in the base population (Falconer, 1989), although in the absence of a control line, this asymmetry could be caused by a trend in environmental influences. To achieve these aims, data were collected on LH release after G n R H injection on animals in the high and low selection lines and in reciprocal F, crosses and backcrosses. Analysis of the mean of the crosses allows inferences to be drawn about the action of the genes involved in the selection response and whether they are on the autosomes or sex chromosomes. If selection has resuited in the fixation of the alternative alleles of a major gene in the two lines, then this would be expected to segregate in the backcross generations, inflating the variance within these generations. We analysed these data using max-
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GENETIC CONTROL OF LH RELEASE IN SHEEP
1.
J
---i--
1.4,
High line
Mean log LH release
Low line |
J
1.7,
1.C
0.~ 1978
i
1980
1982
1984
1986
Year
Fig. 1. Response of mean log~o LH release to selection in the high and low lines. Data from Haley et al. (1989).
imum likelihood segregation analysis (Elston and Stewart, 1971, 1973 ), which combines information from all sources and should provide the most powerful test for the presence of a major gene. In order to perform these segregation analyses it was necessary to extend methods we have used previously (Knott et al., 1992) to allow the analysis of data from line crosses. MATERIAL AND METHODS
Animals and trial design The development of the high and low selection lines has been detailed by Haley et al. (1989). The experiment described here was commenced in 1988, after two years of relaxed selection, with the mating of animals to produce both reciprocal F I crosses between the lines (i.e. high males × low females and low males×high females). The F1 animals were born in 1989 and only the males were retained and recorded. F1 males of the two types were randomly chosen and mated to females from the high and low lines to produce four types ofbackcross. Backcross animals were born in 1990 and in 1992. In each year from 1989 to 1992 some pure high and low line animals were also produced. There were thus data from eight genotypes, viz: the high and low lines, two reciprocal FI crosses and four types of backcross. Traits recorded and hormone assays LH release in response to G n R H was measured at 10 weeks ___3 days of age. Lambs were given an injection of 5 #g G n R H (Hoechst, Frankfurt, Ger-
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many) directly into the jugular vein. Jugular blood samples were taken 30, 50 and 70 min after G n R H injection and assayed for LH (ng/ml) using a modified double-antibody radio-immunoassay (Martensz et al., 1976; Webb et al., 1985 ). The date of birth of the lambs and the size of the litter into which they were born were also recorded.
Statistical analyses In order to remove a positive relationship between the mean and the variance, all analyses of LH release were performed after loglo transformation of the data. The trait analysed was the mean of the three log~o transformed LH concentrations for each animal (recorded at 30, 50 and 70 min after GnRH injection). This was the trait upon which the selection to create the lines had been performed and the antilogarithm of this value is the geometric mean of the three LH concentrations in ng/ml. Estimation of genotypic and fixed effects was conducted by residual maxim u m likelihood (Patterson and Thompson, 1971 ), using the REML option within GENSTAT (GENSTAT 5 Committee, 1987). In these REML analyses the random effects of sire and dam (nested within sire ) of the lamb were fitted. Fixed effects included in the analyses were genotype of the lamb (i.e. which of eight cross types), sex and its interaction with the genotype of the lamb, year of birth of the lamb and size of litter at birth. A linear covariate was included for the day of birth (relative to January 1 ) of the lamb. The variance within each sex for each of the eight lamb cross types was estimated using the same REML model applied to each cross and sex separately, with the variance estimated as the sum of the sire, dam within sire and residual components. In order to investigate the gene action underlying the mean differences between genotypes, crossbreeding parameters were estimated as an alternative to genotypic means. The mid-parental value (#, i.e. the mean of the two selection lines) was estimated and the crossbreeding parameters were estimated as deviations from this mid-parental value. These were: direct (in the lamb) autosomal additive (or) and dominance (~) effects, maternal (from the dam of a lamb) autosomal additive effects (cem), and sex-linked (i.e. X linked) additive (OtSmin the male and asf in the female) and dominance effects (~sf in the female). Females are homogametic for the sex chromosomes whereas the males are heterogametic, therefore the expression of sex-linked genes is likely to differ between the sexes and dominance is only possible in females. The expression of autosomal genes may also differ between sexes (i.e. sexlimitation) and this can be incorporated in the model by allowing an interaction between sex and ot and J. When interactions between sex and the autosomal maternal effects (otm) are included in the model, however, the additive sex-linked effect in the male (cesta) becomes confounded with c~m and so it was assumed that maternal effects were the same for both sexes of lamb.
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GENETIC CONTROL OF LH RELEASE IN SHEEP TABLE l Linear model used for the estimation of crossbreeding parameters Cross1
Genetic effects 2 a
High line Low line H×L L×H HL×H LH×H HL×L LH×L
(H ) (L) (HL) (LH) (HL(H)) (LH(H)) (HL(L)) (LH(L))
1 1 1 1 1 1 1 1
6 1
-1 0 0 0.5 0.5 -0.5 -0.5
0 0 1 1 0.5 0.5 0.5 0.5
am
OgSm
1 -1 -1
1 -1 -1
1 1 1 -1 -1
OdSf
1 -1 0 0 0 1
1 1 1 -1 -I
-1 0
t~Sf 0 0 1 1 1 0 0 1
l Genotype of the sire of the cross is shown first. 2Abbreviations:/t, mean; ct, additive direct effect; ~, d o m i n a n c e direct effect; a m , additive maternal effect; aSm, additive sex-linked effect in males; cesf, additive sex-linked effect in females; 6% dominance sex-linked effect in females.
Crossbreeding parameters were estimated by replacing the fixed effect of each genotype by linear functions using the model shown in Table 1.
Segregation analyses In the analysis of crossbreeding parameters only mean differences between crosses are considered, but the presence of a major gene would be expected to contribute to differences between the crosses in variance, skewness, kurtosis, etc. Maximum likelihood segregation analysis allows the contributions of a single gene, as well as background genetic and environmental effects, to the cross differences to be modelled. Elston and Stewart (1973) describe the likelihoods required for populations derived from crosses between inbred parental lines. In these data, however, many loci, including any major gene, may still be segregating within the parental lines (i.e. the high and low selection lines). Likelihoods have also been given for general outbreeding populations, for example, Knott et at. (1992). These two approaches are combined here in order to obtain a likelihood suitable for data from crosses between outbred lines. The development of the likelihood took account of the results of the crossbreeding analyses, i.e. any major gene was assumed to be autosomally linked and not sex-limited in expression. In the general likelihood the postulated major gene was assumed to be segregating in Hardy-Weinberg equilibrium in the two parental populations, with the frequencies of the high scoring allele being pu and PL in the high and low selected lines, respectively. Using these allele frequencies and assuming random mating, the expected frequencies of the three genotypes in each cross type could be obtained. The relevant ex-
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pected frequencies for a given cross type were used as prior probabilities for each major genotype for each individual, i.e. family structure was not taken into account. In addition to line differences due to a major gene, allowance was made for there to be differences due to polygenic loci and these were modelled in the same way as ot and fl were in the crossbreeding analyses. A common family random component was fitted for groups of full-sib offspring to allow for any common environmental effects and the effects of background genetic variation. This component was assumed to be normally distributed with mean zero and variance a 2. An individual residual random effect was also fitted. Fixed effects and covariates were the same as those described for the REML crossbreeding analyses. The likelihood of the data for this model can therefore be written as follows: +oo
L = l i=1 -[
(2ntrE)l/Eexp
[
-~ exp
FL
"1-I
l 1/2 ~.freq~(g) (2/tO'2w)
U/ J=l g=l
'l
(1)
(Yo - & - ac - P c - ui - x o f ) . , dui 2ffw
Where: N is the number of full-sib families; n,- is the number of sibs in the ith family; u~ is the random effect of the ith family, u ~ N ( 0 , a ~ ); c is the cross type of the individual; freqc (g) is the expected frequency of major genotype g in cross type c; Yo is the phenotype of the jth sib in the ith family; ~g is the effect of the gth major genotype; o~ and fl are, respectively, the additive and dominance effects of the background genes; xij is a design vector for fixed effects and the covariate for individual ij; f is a vector of estimates of fixed effects and the partial regression coefficient; a2 w is the residual variance. To analyse the loglo transformed LH concentrations the likelihood was implemented in a FORTRAN program, with integration carried out numerically using Hermite polynomials to provide suitable weights and abscissae (e.g. Knott and Haley, 1992 ). The likelihood was maximised using the quasi-Newton routine E04JAF (Numerical Algorithms Group, 1990). Estimates and likelihoods from three alternative analyses were compared. Firstly, assuming the absence of any major gene and hence fixing its effect at zero. Secondly, assuming the presence of a major gene which was fixed for alternative alleles in the high and low line and hence fixing PH= 1 and PL = 0. Thirdly, assuming the presence of a major gene which may be segregating within the high and low lines and hence estimating all parameters simultaneously. The significance of any improvement in the likelihood resulting from increasing the complexity of the model can be judged from the test statistic obtained as twice the difference between the maximised log~ likelihoods of the more complex and simpler models. Under the null hypothesis this test statistic is expected
GENETIC CONTROL OF LH RELEASEIN SHEEP
159
to follow a chi-squared distribution with degrees of freedom equal to the number of parameters which are fixed in the simpler model but estimated in the more complex model (Wilks, 1938 ). RESULTS
Genotypic means and crossbreeding parameters There were a total of 518 LH response records and the distribution of these over genotypes and sexes is shown in Table 2. The estimates of sire, dam within sire and residual variance components from the model fitted to all genotypes and sexes simultaneously were 0.0015, 0.0092, and 0.0342, respectively. Thus the estimate of the phenotypic variance was 0.0449 (a phenotypic standard deviation of 0.212 ). The means estimated from this model for each genotype and sex of lamb are given in Table 2. In both sexes the difference between the high and low lines in mean log~o LH response is about 0.65, equivalent to about three standard deviations between the lines. The within genotype and sex phenotypic variances estimated by fitting the REML crossbreeding model to each combination of genotype and sex separately are also given in Table 2. It can be seen that the variances within the high and low lines are similar on the loglo scale. The distribution of records of mean loglo LH release within each of the five basic generations (high and low selection lines, Fl cross and the backcrosses to the high and low lines) is shown in Fig. 2. The data have been adjusted by removal of the fixed effects of sex, year of birth, size of birth litter and date of birth. Estimates of crossbreeding parameters are shown in Table 3 for three models: the full model of all estimable crossbreeding parameters, the model TABLE2 Number of observations and estimated means and total variance of mean log~o LH response for each sex in each cross type Cross ~
High line Low line HXL L×H HLXH LHXH HL×L LH×L
Males
(H) (L) (HL) (LH) (HL(H)) (LH ( H ) ) (HL(L)) (LH(L))
Females
No. of records
mean
variance
No. of records
mean
variance
91 73 17 20 32 24 32 28
1.766 1.117 1.526 1.449 1.637 1.627 1.260 1.215
0.0363 0.0575 0.0715 0.0381 0.0268 0.0157 0.1065 0.0514
57 52 0 0 26 22 27 19
1.496 0.857 1.321 1.270 1.038 0.951
0.0374 0.0405 0.0553 0.0711 0.0591 0.0340
~Genotype of the sire of the cross is shown first.
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C.S. HALEYET AL.
Highline4 0 " l j No.of 30" records 20"
~
10" 0
0.3
0.9
'°1
No. of 20 records 10i
0.3
0.9
1.5
F1 2 0 ~ l ~ j ~ l ~ No. of 10" records 0 0.3 0.9 1.5
.x,o Ot
2.1
2.1
uul
No. of 2 0 L ~ ~ L records 10
4°L 0
2.1
I,
F1 x Higg 0
0
1.5
0.3
0.9
~ 1.5
2.1
30 Lowline No. of 20" records 10" 0
0.3
0.9 1.5 2.1 Adjustedmeanlog LH.release
Fig. 2. Distribution of mean log~o LH release in the five basic generations (high and low selection lines, F1 cross and backcrosses to high and low lines). The raw data have been adjusted by removal of the effects of sex, year of birth, size of birth litter and date of birth within year with the residuals being given around the mean of the appropriate generation.
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GENETIC CONTROL OF LH RELEASE IN SHEEP
TABLE3 E s t i m a t e d crossbreeding p a r a m e t e r s for m e a n loglo L H response f r o m R E M L analyses Effect ~
Full m o d e l
Males /t a 6 am aSm asf ~sf Sex difference P h e n o t y p i c variance
Females
1.454+0.027 1.187+0.030 0.330+0.038 0.414+0.079 0.029+0.040 -0.026+0.084 - 0 . 0 3 1 -+0.0512 0.036 + 0.056 . . - 0 . 0 6 1 + 0.047 -0.015+0.045 0.270 + 0.023 0.0447
Sex-limited direct effect m o d e l
Direct effect model
Males
Both sexes
Females
1.453+0.027 1.177+0.028 0.336_+0.017 0.315+0.021 0.028_+0.040 -0.036_+0.072 _3 _ . . 0.276 + 0.020 0.0446
1.315+0.025 0.328_+0.014 0.018+0.038 _ 0.275 + 0.020 0.0446
1Abbreviations: #, m e a n ; a , additive direct effect; & d o m i n a n c e direct effect; a m , additive m a t e r n a l effect; asm, additive sex-linked effect in males; asf, additive sex-linked effect in females; 6sf, d o m i n a n c e sex-linked effect in females. 2 C o m m o n p a r a m e t e r e s t i m a t e d for the two sexes. 3 p a r a m e t e r n o t estimated.
of sex-limited direct effects and the model of direct effects which are not sexlimited. None of the more complicated models were a significant improvement over the model which included only direct effects which were not sexlimited and the only estimates of genetic effects which are significantly greater than zero are those for 04 the additive direct effect. There is thus no evidence for dominance at the autosomal genes or for sex-linked genes or maternal effects contributing to the line differences. The mean difference between sexes is significant, but despite this the estimate of a differs little between the sexes, i.e. there is little evidence that the genes controlling the line difference are sex-limited on the scale on which the data were analysed.
Segregation analyses The results of the segregation analyses are given in Table 4. No major gene was included in the first model, which hence assumed all genotype differences were polygenic in origin. This model is very similar to that with direct effects which are not sex-limited (the results of which are given in Table 3 ) with the exception that the segregation analysis is a m a x i m u m likelihood full-sib family model rather than a restricted m a x i m u m likelihood sire and dam within sire model. Inspection of Tables 3 and 4 shows that estimates of crossbreeding parameters from the two models were very similar. A major gene was included in the second model but it was assumed that it was fixed for alternative alleles in the high and low selection lines and so the allele frequencies were fixed and not estimated in this analysis. The estimates
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TABLE4
Estimated parameters from segregation analyses Effect
Model Polygenic effect only
Polygenic effect + fixed major gene
Polygenic effects Additive effect (i.e. c~) Dominance effect (i.e. 8)
0.328 0.019
0.238 -0.070
0.312 0.023
0.281 0.018
Major genie effects Additive effect Dominance effect
_1 -
0.090 0.090
0.360 0.094
0.187 0.076
1.0 0.0
0.900 0.844
0.762 0.479
0.273 0.0234 32.64
0.276 0.0225 9.32
Frequencies of increasing allele High line (Pn) Low line (PL) Sex difference Phenotypic variance Test statistic2
0.273 0.0433 -
0.274 0.0416 1.96
Polygenic effect + segregating major gene
Polygenic effect + segregating major gene 3
'Parameter not estimated. 2Twice improvement in log~-likelihood compared to previous model. 3Analysis of data omitting seven most deviant records.
which maximised the likelihood included a dominant major gene responsible for around one third of the difference between the high and low lines. Comparison of the log-likelihoods, however, indicates that this model was not a significant improvement over the model with no major gene. The third model included a major gene for which allele frequencies in each of the high and low lines were estimated. Comparison of the log-likelihoods shows that this model is a significant improvement over the previous two models. The estimated effect of the major gene is relatively large, at around 2.5 phenotypic standard deviations between the two homozygotes, and dominance is in the direction of the high allele. High estimates of the frequency of the allele which increases LH release were obtained for both the high and low selection lines, being marginally higher for the low line. These results appear inconsistent with the history of selection in the lines, which would be expected to result in the allele frequency for a gene of such large effect to be at opposite extreme frequencies in the two lines. Inspection of the data (Fig. 2 ) reveals that there is a tendency for there to be one or two animals with very low values within each cross type. The major gene model would explain these
GENETIC CONTROL OF LH RELEASE IN SHEEP
163
few low values as, in a population with a dominant increasing allele at high frequency, there would be a few extremely low scoring animals which were homozygous for the decreasing allele. DISCUSSION
The crossbreeding analyses show that the responses to selection for mean log~o LH release in the high and low lines can be explained by a very simple genetic model, with the line difference controlled by autosomal genes of additive effect. Despite a relatively large difference between the means of the sexes, the difference between the lines was very similar in the two sexes and there was no evidence for the actions of the genes being sex-limited. This latter conclusion is consistent with that of Haley et al. (1989) that the genetic correlation between the trait in males and females was close to unity. Our segregation analyses provided no evidence for a major gene being involved in the response to selection. The apparent major gene effect detected did not explain the difference between the selection lines as the estimated allele frequency was not markedly different between the lines. Additionally, the estimates of the polygenic components in the model containing the major gene effect were very similar to those in the model from which it was absent. It seems unlikely that the apparent major gene effect was real, as selection would have been expected to change the allele frequencies of a gene of such large effect to opposite extremes in the two selection lines. Our analysis does not suggest that this has occurred and in fact the estimated frequency of the increasing allele was higher in the low selection line, the opposite of expectation. The detection of a putative major gene effect may be associated with the presence within some of the genotypic classes of a few animals with low values for log~o LH release. It is possible that such records were due to accidental events, such as the mis-identification of samples or animals. Alternatively, such cases may have been due to occasional sporadic environmental events affecting individual animals, such as disease. In order to explore the effect these few extreme records have on the results of the analysis, we re-analysed the data after removal of animals with values for log~o LH release which were more than three within-line standard deviations from the mean of a cross. This resulted in the removal of a total of seven animals: one with a low record and one with a high record from the high line and animals with low records from the low line (one animal), the F~ crosses (one animal), the backcrosses to the high line (one animal) and the backcrosses to the low line (two animals). The results of the analysis of the reduced data with a model in which the major gene was allowed to segregate within the selection lines are shown in Table 4. A comparison of the log-likelihoods of this model with one in which the major gene is assumed fixed for alternative alleles in the two selection lines still suggests a significant effect of a segregating major gene. Compared to the complete data, however, the test
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statistic and the estimate of the additive effect of the major gene were greatly reduced and the estimates of allele frequencies within the selection lines have been changed. In view of the effects of a few extreme animals on the results of the analyses and the inconsistency of the results with expectation, we are reluctant to accept this effect as due to a major gene without further supporting evidence. One concern about the conclusion that a major gene was not involved in the response to selection is whether the trial was sufficiently powerful to det e c t any major gene. The best case for detection would have been if a single gene completely explained the responses to selection. It is unlikely that such a gene would have been completely fixed by selection during the course of experiment, but this would probably not greatly influence the power of its detection. This is because most of the evidence for the presence of a major gene is likely to come from the inflation it produces in the variance of the backcross generations. For a gene of additive effect, this inflation of the variance will be the same if a given line difference is produced by a gene fixed for alternative alleles in the two lines or by a gene of (necessarily) larger effect which is at less extreme frequencies in the two lines. If the gene were fixed for alternative alleles, our crossbreeding analyses suggest that it would be autosomal with an additive effect of three phenotypic standard deviations on log lo LH release between the two homozygotes. We have used simulation in order to investigate the power to detect such a gene. The same data structure was simulated as was analysed (i.e. same number of animals of each cross type, same sex difference, same family and residual variance, etc. ) with a single gene fixed for alternative alleles explaining the whole of the difference between the high and low lines. Segregation analysis detected a significant effect of a major gene in 94 of 100 simulated replicates. When data were simulated with the major gene explaining only half of the line difference (the rest being due to genes of small effect), however, it was detected in only 13 of 100 replicates. Thus we can conclude that the experiment was likely to detect a single gene explaining most or all of the line difference, but a larger trial would have been needed to detect genes of smaller effect. The data were analysed on the log~0 scale in this paper and that of Haley et al. (1989). This transform was necessary to remove the strong positive relationship between the mean and the variance which is present on the nominal scale. The success of this transform is evident from the similarity of the variance within the high and low lines (Table 2). It could be argued that this transformation may obscure evidence for the presence of a major gene. It is certainly the case that a major gene fixed for one allele in the low line but segregating in the high line would lead to a positive relationship between the mean and the variance and the log~o transformation could remove this evidence. On the other hand, any relationship between mean and variance due to a scale effect is likely to cause spurious detection of a major gene (Elston,
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1984). Furthermore, due to the selection applied to the lines, any gene of large effect is likely to be fixed or at opposite extreme frequencies for alternative alleles in the high and low lines. Such a gene would not cause differences in variance between the high and low lines, but its segregation in the backcrosses would inflate their variances. In this situation, the loglo transform would not remove all evidence of a major gene, as the variance in the backcrosses would remain above those in the high and low lines and the F1 after transformation. Thus the use of transformation to equalise the variance in the high and low lines seems reasonable, as it should reduce the chance of detecting a spurious major gene without removing all evidence for a gene fixed for alternative alleles in the two lines. In summary, selection responses in LH release after a GnRH challenge have been achieved by changes at additive autosomal loci which have the same effect in both sexes. A single major gene does not seem to explain the response to selection, but the effects of several genes of large effect can not be ruled out at this stage. This conclusion is consistent with the results of detailed studies of the high and low selection lines (e.g. Evans, et al., 1991 a,b; McNeilly et al., 1993 ), which indicate a number of differences at the physiological and molecular level. Therefore the observed asymmetry in the selection response (Fig. 1 ) may simply be due to an environmental trend. Continuing physiological studies should identify potential candidate loci, changes at which may have contributed to the selection responses. In this case, as the genetic map of the ovine is developed, allowing markers close to any chosen locus to be identified, looking for evidence of co-segregation of markers close to candidate loci with loglo LH release is likely to provide a more powerful test for an effect of these loci, and thus for major genetic effects, than does segregation analysis (Knott and Haley, 1992 ). ACKNOWLEDGEMENTS
We are very grateful to staff at Blythbank farm, particularly John Bracken and Phil Davies, for their care of the animals and to staffat the Large Animal Unit, particularly Marjorie Ritchie, and to Clair Warren and Gerry Baxter for their excellent technical assistance. This work was supported by the Ministry of Agriculture, Fisheries and Food and the Agricultural and Food Research Council.
REFERENCES Elston, R.C. 1984. The genetic analysis of quantitative trait differences between two homozygous lines. Genetics, 108: 733-744. Elston, R.C. and Stewart, J. 1971. A general model for the genetic analysis of pedigree data. Human Hered., 21: 523-542.
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Elston, R.C. and Stewart, J. 1973. The analysis of quantitative traits for simple genetic models from parental, F~ and backcross data. Genetics, 73:695-711. Evans, N.P., Land, R.B., McNeilly, J.R. and Webb, R. 1991a. Role of gonadal negative feedback on the gonadotrophin responses to gonadotrophin-releasing hormone (GnRH) in ram lambs from two lines of sheep selected for their luteinizing hormone response to GnRH. J. Reprod. Felt., 93: 549-558. Evans, N.P., McNeilly, J.R., Springbett, A.J. and Webb, R. 199 lb. Alterations in pituitary gland sensitivity in ram lambs to physiological doses of gonadotrophin-releasing hormone (GnRH), after divergent selection based on the luteinizing hormone response to pharmacological GnRH challenge. J. Reprod. Fert., 93: 559-567. Falconer, D.S. 1989. Introduction to Quantitative Genetics (Third Edition). Longman, Harlow, U.K. GENSTAT 5 COMMITTEE, 1989. Genstat 5 Reference Manual, Clarendon Press, Oxford, U.K. Haley, C.S., Lee, G.J., Fordyce, M., Baxter, G., Land, R.B. and Webb, R. 1989. Study of LH response to GnRH in the young male as a criterion of genetic merit for female reproduction in sheep. J. Reprod. Fert., 86:119-133. Hill, W.G. and Knott, S.A. 1990. Detection of genes of large effect. In: K. Hammond and D. Gianola, (eds), Advances in Statistical Methods for Genetic Improvement of Livestock, Springer-Verlag, Berlin, Germany. pp. 477-495. Knott S.A. and Haley, C.S. 1992. Maximum likelihood mapping of quantitative trait loci using full-sib families. Genetics, 132:1211-1222. Knott, S.A., Haley, C.S. and Thompson, R. 1992. Methods of segregation analysis for animal breeding data: a comparison of power. Heredity, 68:299-311. Land, R.B. 1973. The expression of female sex-limited characters in the male. Nature, 241: 208209. Land, R.B., Bodin, U Driancourt, M.A., Haley, C.S. and McNeilly, J.R. 1988. Physiological prediction of genetic merit for female reproduction in the sheep. Proc. 3rd World Congr. Sheep Beef Cattle Breed., Vol. 2:611-622. Martensz, N.D., Baird, D.T., Scaramuzzi, R.J. and Van Look, P.F.A. 1976. Androstenedione and the control ofluteinizing hormone in the ewe during anoestrus. J. Endocrinol., 69: 22i7237. McNeilly, J.R., Evans, N.P., Bramley, T.A., Brown, P., Clark, A.J. and Webb, R. 1993. The relationship between selection for pituitary responsiveness to gonadotrophin releasing hormone in sheep and differences in gonadotrophin subunit mRNAs. J. Reprod. Fert., 97:311315. Numerical Algorithms Group, 1990. The NAG Fortran Library Manual - Mark 14. NAG Ltd., Oxford, U.K. Patterson, H.D. and Thompson, R. 1971. The recovery of inter-block information when block sizes are unequal. Biometrika, 58: 545-554. Walkley, J.R.W. and Smith, C. 1980. The use of physiological traits in genetic selection for litter size in sheep. J. Reprod. Fert., 59: 83-88. Webb, R., Baxter, G. Preece, R.D., Land, R.B. and Springbett, A.J. 1985. Control ofgonadotrophin release in Scottish Blackface and Finnish Landrace ewes during seasonal anoestrus. J. Reprod. Fert., 73: 369-378. Wilks, S.S. 1938. The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat., 9: 60-62.
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RESUME Haley, C.S., Lee, C.J., Webb, R. et Knott, S.A., 1993. Indications sur le d6terminisme g6n6tique de la s6cr6tion de LH en r6ponse h l'injection de GnRH fournies par les croisements entre lign6es ovines s61ectionn6es. Livest. Prod. Sci., 37:15 3-167 (en anglais). L'objectif de cette 6tude 6tait de d6terminer la base g6n6tique de la r6ponse ~ la s61ection divergente sur la quantit6 d'hormone luteinisante (LH) s6cr6t6e par des agneaux de 10 semaines apr~s une injection de GnRH. Des donn6es provenant de 518 animaux des lign6es s61ectionn6esl, des F~ et des backcrosses ont 6t6 analys6es par le maximum de vraisemblance r6siduelle pour estimer les effets g6n6tiques du croisement. On n'a pas trouv6 d'indication d'effets li6s au sexe, d'effets maternels, d'effets d6pendant du sexe ou de dominance pour les effets directs autosomaux. Les r6ponses h la s61ection ont ainsi 6t6 obtenues par des variations ~ des loci autosomaux additifs qui ont le m6me effet dans les deux sexes. L'analyse de s6gr6gation par le maximum de vraisemblance a 6t6 utilis6e pour rechercher un g~ne majeur affectant la s6cr6tion de LH. On a d6tect6 apparemment un g~ne majeur de m~mes fr6quences all61iques dans les lign6es s61ectionn6es mais ce r6sultat s'expliquait par la non normalit6 des donn6es, car il ne se maintenait pas aprbs transformation de cell-ci. Ii n'y a eu aucune preuve qu'un g~ne majeur simple explique la diff6rence entre lign6es s61ectionn6es mais l'6tude n'a pas 6t6 suffisamment puissante pour exclure la contribution de g~nes d'effets plus petits mais appr6ciables. KURZFASSUNG Haley, C.S., Lee, G.J., Webb, R. and Knott, S., 1993. Erkentnisse fiber die genetische Steuerung der LH Ausschiittung durch GnRH bei Kreuzungen von selektierten Schaflinien. Livest. Prod. Sci., 37:153-167 (aufenglisch). Ziel dieser Studie war es, die genetischen Grundlagen des Selektionserfolges bei divergierender Selektion auf die von zehn Wochen alten L~immern ausgeschfittete Menge luteinisierenden Hormons (LH) nach einer Verabreichung yon Gonadotropin Releasing Hormon (GnRH) zu bestimmen. Daten von 518 Tieren in den selektierten Linien, der F1-Kreuzung dieser Linien und beiden Rfickkreuzungen wurden mit der Residual Maximum Likelihood Methode analysiert, um Kreuzungsparameter zu sch~itzen. Weder geschlechtsgekoppelte Effekte, noch maternale, geschlechtsspezifische oder DOminanzeffekte der autosomalen direkten Effekte Liel3en sich nachweisen. Folglich wurde der Selektionserfolg durch Vedinderungen von additiven, autosomalen Loci herbeigef'tihrt, die dieselbe Wirkung in beiden Geschlechtern besitzen. Eine Maximum Likelihood Segregationsanalyse wurde durchgeftihrt, um das Vorhandensein eines major genes ftir die LH-Ausschfittung zu prfifen. Die Analyse zeigte das Vorhandensein eines major genes mit ahnlichen Allelfrequenzen in beiden Selektionslinien an. Dieses Ergebnis wurde j edoch auf die Nichtnormalitiit der Residuen zurufickgeftihrt, die durch die verwendete Transformation nicht beseitigt werden konnte. Es gab keine Hinweise darauf, dab ein einzelnes major gene die Differenz zwischen den beiden Selektionslinien erkl~iren konnte. Die M~ichtigkeit des Testverfahrens reichte jedoch nicht aus, die Wirkung eines oder mehrerer Gene mit kleineren, jedoch immer noch bedeutenden Effekten nachzuweisen.
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Elsevier Science Publishers B.V., Amsterdam
Evidence on the genetic control of LH release in response to GnRH from crosses between selected lines of sheep C.S. Haley~, G.J. Leea, R. W e b b a a n d
S.A. K n o t t b
aAFRCRoslin Institute (Edinburgh)*Roslin, U.K. blnstitute of Cell, Animal and Population Biology, Universityof Edinburgh, Edinburgh, U.K. (Accepted 20 April 1993 )
ABSTRACT The purpose of this study was to determine the genetic basis of response to divergent selection for the amount of luteinizinghormone (LH) released in ten-week-old lambs after an injection of gonadotrophin releasing hormone (GnRH). Data from 518 animals in the selection lines, the Ft cross between the lines and both backcrosses were analysed by residual maximum likelihood to estimate crossbreeding genetic effects. There was no evidence for sex-linked effects, maternal effects, sex-limited effects or dominance of the autosomal direct effects. Thus selection responses have been achieved by changes at additive autosomal loci which have the same effect in both sexes. Maximum likelihood segregation analysis was used to look for a major gene affecting LH release. An apparent effect of a major gene with a similar allele frequency in both selection lines was detected, but this result was attributed to residual non-normalitywhich had not been removed by the transform applied. There was no evidence that a single major gene explained the difference between the selection lines, but the study was not sufficiently powerful to rule out contributions from one or more genes of smaller, but still appreciable, effect. Key words: Sheep; Reproduction; Genetics; Crossbreeding; Major gene
INTRODUCTION
Land ( 1973 ) suggested that traits which could be measured in young males might provide indirect indicators of the merit of the genes they carried for reproductive traits. If this is the case, then identification of, and selection upon, the appropriate traits in males could increase the rates of response to genetic selection for traits such as litter size in females (Walkley and Smith, 1980). To date, much of the effort to identify indirect indicators has focused Correspondence to: C.S. Haley, AFRC Roslin Institute (Edinburgh), Roslin, Midlothian, EH25 9PS, U.K. *Formerly AFRC Institute of Animal Physiology and Genetics Research, Edinburgh Research Station.
0301-6226/93/$06.00 © 1993 Elsevier Science Publishers B.V. All rights reserved.
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on testis size and on the gonadotrophins, luteinizing hormone (LH) and follicle stimulating hormone (FSH) (for a review see Land et al., 1988). One potential indirect indicator trait that we have studied in young rams is the amount of LH released in response to an injection of gonadotrophin releasing hormone ( G n R H ) . Divergent lines selected for the amount of LH released by 10-week-old rams in response to G n R H were developed from a Finnish Landrace-Dorset Horn synthetic line (Haley et al., 1989). After eight male generations the geometric mean of three measurements of LH released in response to G n R H in I 0-week-old rams in the line selected for high response (the high line) was more than 5-fold that in the low line, and the heritability of this mean response trait was estimated to be 0.44. The correlated responses to selection for LH release in males at 20 weeks of age and in females at 10 and 20 weeks of age suggested that the genetic correlations between these traits and LH release in 10-week-old males were close to unity. That is, the same genes mediated LH release in males and females at 10 and 20 weeks of age (Haley et al., 1989). The purpose of this study was to determine the genetic basis of the responses to selection for LH release after G n R H injection. Three aspects were of interest. Firstly, the action of the genes involved (i.e. the relative influence of the genotypes of the dam and of the lamb itself and whether the genes were additive or dominant or sex-limited in their effect). Secondly, whether there was any evidence of sex-linked, as opposed to autosomally linked, inheritance of the genes. Thirdly, whether there was evidence of involvement of a single major gene in the response to selection. This last question is of particular interest because if a major gene is implicated, our accumulating knowledge of the physiological and molecular basis of the line differences might eventually allow the identification and isolation of the gene involved. This in turn would allow the detailed study of a gene mediating hormonal responses. Two factors hint at the possible involvement of a major gene in the selection response. Firstly, the relatively high heritability for the trait and secondly, the apparent asymmetry of response to selection observed in the high and low lines (Fig. 1 ). One of several possible causes of asymmetry of response to selection may be the segregation of a major gene in the base population (Falconer, 1989), although in the absence of a control line, this asymmetry could be caused by a trend in environmental influences. To achieve these aims, data were collected on LH release after G n R H injection on animals in the high and low selection lines and in reciprocal F, crosses and backcrosses. Analysis of the mean of the crosses allows inferences to be drawn about the action of the genes involved in the selection response and whether they are on the autosomes or sex chromosomes. If selection has resuited in the fixation of the alternative alleles of a major gene in the two lines, then this would be expected to segregate in the backcross generations, inflating the variance within these generations. We analysed these data using max-
155
GENETIC CONTROL OF LH RELEASE IN SHEEP
1.
J
---i--
1.4,
High line
Mean log LH release
Low line |
J
1.7,
1.C
0.~ 1978
i
1980
1982
1984
1986
Year
Fig. 1. Response of mean log~o LH release to selection in the high and low lines. Data from Haley et al. (1989).
imum likelihood segregation analysis (Elston and Stewart, 1971, 1973 ), which combines information from all sources and should provide the most powerful test for the presence of a major gene. In order to perform these segregation analyses it was necessary to extend methods we have used previously (Knott et al., 1992) to allow the analysis of data from line crosses. MATERIAL AND METHODS
Animals and trial design The development of the high and low selection lines has been detailed by Haley et al. (1989). The experiment described here was commenced in 1988, after two years of relaxed selection, with the mating of animals to produce both reciprocal F I crosses between the lines (i.e. high males × low females and low males×high females). The F1 animals were born in 1989 and only the males were retained and recorded. F1 males of the two types were randomly chosen and mated to females from the high and low lines to produce four types ofbackcross. Backcross animals were born in 1990 and in 1992. In each year from 1989 to 1992 some pure high and low line animals were also produced. There were thus data from eight genotypes, viz: the high and low lines, two reciprocal FI crosses and four types of backcross. Traits recorded and hormone assays LH release in response to G n R H was measured at 10 weeks ___3 days of age. Lambs were given an injection of 5 #g G n R H (Hoechst, Frankfurt, Ger-
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many) directly into the jugular vein. Jugular blood samples were taken 30, 50 and 70 min after G n R H injection and assayed for LH (ng/ml) using a modified double-antibody radio-immunoassay (Martensz et al., 1976; Webb et al., 1985 ). The date of birth of the lambs and the size of the litter into which they were born were also recorded.
Statistical analyses In order to remove a positive relationship between the mean and the variance, all analyses of LH release were performed after loglo transformation of the data. The trait analysed was the mean of the three log~o transformed LH concentrations for each animal (recorded at 30, 50 and 70 min after GnRH injection). This was the trait upon which the selection to create the lines had been performed and the antilogarithm of this value is the geometric mean of the three LH concentrations in ng/ml. Estimation of genotypic and fixed effects was conducted by residual maxim u m likelihood (Patterson and Thompson, 1971 ), using the REML option within GENSTAT (GENSTAT 5 Committee, 1987). In these REML analyses the random effects of sire and dam (nested within sire ) of the lamb were fitted. Fixed effects included in the analyses were genotype of the lamb (i.e. which of eight cross types), sex and its interaction with the genotype of the lamb, year of birth of the lamb and size of litter at birth. A linear covariate was included for the day of birth (relative to January 1 ) of the lamb. The variance within each sex for each of the eight lamb cross types was estimated using the same REML model applied to each cross and sex separately, with the variance estimated as the sum of the sire, dam within sire and residual components. In order to investigate the gene action underlying the mean differences between genotypes, crossbreeding parameters were estimated as an alternative to genotypic means. The mid-parental value (#, i.e. the mean of the two selection lines) was estimated and the crossbreeding parameters were estimated as deviations from this mid-parental value. These were: direct (in the lamb) autosomal additive (or) and dominance (~) effects, maternal (from the dam of a lamb) autosomal additive effects (cem), and sex-linked (i.e. X linked) additive (OtSmin the male and asf in the female) and dominance effects (~sf in the female). Females are homogametic for the sex chromosomes whereas the males are heterogametic, therefore the expression of sex-linked genes is likely to differ between the sexes and dominance is only possible in females. The expression of autosomal genes may also differ between sexes (i.e. sexlimitation) and this can be incorporated in the model by allowing an interaction between sex and ot and J. When interactions between sex and the autosomal maternal effects (otm) are included in the model, however, the additive sex-linked effect in the male (cesta) becomes confounded with c~m and so it was assumed that maternal effects were the same for both sexes of lamb.
157
GENETIC CONTROL OF LH RELEASE IN SHEEP TABLE l Linear model used for the estimation of crossbreeding parameters Cross1
Genetic effects 2 a
High line Low line H×L L×H HL×H LH×H HL×L LH×L
(H ) (L) (HL) (LH) (HL(H)) (LH(H)) (HL(L)) (LH(L))
1 1 1 1 1 1 1 1
6 1
-1 0 0 0.5 0.5 -0.5 -0.5
0 0 1 1 0.5 0.5 0.5 0.5
am
OgSm
1 -1 -1
1 -1 -1
1 1 1 -1 -1
OdSf
1 -1 0 0 0 1
1 1 1 -1 -I
-1 0
t~Sf 0 0 1 1 1 0 0 1
l Genotype of the sire of the cross is shown first. 2Abbreviations:/t, mean; ct, additive direct effect; ~, d o m i n a n c e direct effect; a m , additive maternal effect; aSm, additive sex-linked effect in males; cesf, additive sex-linked effect in females; 6% dominance sex-linked effect in females.
Crossbreeding parameters were estimated by replacing the fixed effect of each genotype by linear functions using the model shown in Table 1.
Segregation analyses In the analysis of crossbreeding parameters only mean differences between crosses are considered, but the presence of a major gene would be expected to contribute to differences between the crosses in variance, skewness, kurtosis, etc. Maximum likelihood segregation analysis allows the contributions of a single gene, as well as background genetic and environmental effects, to the cross differences to be modelled. Elston and Stewart (1973) describe the likelihoods required for populations derived from crosses between inbred parental lines. In these data, however, many loci, including any major gene, may still be segregating within the parental lines (i.e. the high and low selection lines). Likelihoods have also been given for general outbreeding populations, for example, Knott et at. (1992). These two approaches are combined here in order to obtain a likelihood suitable for data from crosses between outbred lines. The development of the likelihood took account of the results of the crossbreeding analyses, i.e. any major gene was assumed to be autosomally linked and not sex-limited in expression. In the general likelihood the postulated major gene was assumed to be segregating in Hardy-Weinberg equilibrium in the two parental populations, with the frequencies of the high scoring allele being pu and PL in the high and low selected lines, respectively. Using these allele frequencies and assuming random mating, the expected frequencies of the three genotypes in each cross type could be obtained. The relevant ex-
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C.S. HALEY ET AL.
pected frequencies for a given cross type were used as prior probabilities for each major genotype for each individual, i.e. family structure was not taken into account. In addition to line differences due to a major gene, allowance was made for there to be differences due to polygenic loci and these were modelled in the same way as ot and fl were in the crossbreeding analyses. A common family random component was fitted for groups of full-sib offspring to allow for any common environmental effects and the effects of background genetic variation. This component was assumed to be normally distributed with mean zero and variance a 2. An individual residual random effect was also fitted. Fixed effects and covariates were the same as those described for the REML crossbreeding analyses. The likelihood of the data for this model can therefore be written as follows: +oo
L = l i=1 -[
(2ntrE)l/Eexp
[
-~ exp
FL
"1-I
l 1/2 ~.freq~(g) (2/tO'2w)
U/ J=l g=l
'l
(1)
(Yo - & - ac - P c - ui - x o f ) . , dui 2ffw
Where: N is the number of full-sib families; n,- is the number of sibs in the ith family; u~ is the random effect of the ith family, u ~ N ( 0 , a ~ ); c is the cross type of the individual; freqc (g) is the expected frequency of major genotype g in cross type c; Yo is the phenotype of the jth sib in the ith family; ~g is the effect of the gth major genotype; o~ and fl are, respectively, the additive and dominance effects of the background genes; xij is a design vector for fixed effects and the covariate for individual ij; f is a vector of estimates of fixed effects and the partial regression coefficient; a2 w is the residual variance. To analyse the loglo transformed LH concentrations the likelihood was implemented in a FORTRAN program, with integration carried out numerically using Hermite polynomials to provide suitable weights and abscissae (e.g. Knott and Haley, 1992 ). The likelihood was maximised using the quasi-Newton routine E04JAF (Numerical Algorithms Group, 1990). Estimates and likelihoods from three alternative analyses were compared. Firstly, assuming the absence of any major gene and hence fixing its effect at zero. Secondly, assuming the presence of a major gene which was fixed for alternative alleles in the high and low line and hence fixing PH= 1 and PL = 0. Thirdly, assuming the presence of a major gene which may be segregating within the high and low lines and hence estimating all parameters simultaneously. The significance of any improvement in the likelihood resulting from increasing the complexity of the model can be judged from the test statistic obtained as twice the difference between the maximised log~ likelihoods of the more complex and simpler models. Under the null hypothesis this test statistic is expected
GENETIC CONTROL OF LH RELEASEIN SHEEP
159
to follow a chi-squared distribution with degrees of freedom equal to the number of parameters which are fixed in the simpler model but estimated in the more complex model (Wilks, 1938 ). RESULTS
Genotypic means and crossbreeding parameters There were a total of 518 LH response records and the distribution of these over genotypes and sexes is shown in Table 2. The estimates of sire, dam within sire and residual variance components from the model fitted to all genotypes and sexes simultaneously were 0.0015, 0.0092, and 0.0342, respectively. Thus the estimate of the phenotypic variance was 0.0449 (a phenotypic standard deviation of 0.212 ). The means estimated from this model for each genotype and sex of lamb are given in Table 2. In both sexes the difference between the high and low lines in mean log~o LH response is about 0.65, equivalent to about three standard deviations between the lines. The within genotype and sex phenotypic variances estimated by fitting the REML crossbreeding model to each combination of genotype and sex separately are also given in Table 2. It can be seen that the variances within the high and low lines are similar on the loglo scale. The distribution of records of mean loglo LH release within each of the five basic generations (high and low selection lines, Fl cross and the backcrosses to the high and low lines) is shown in Fig. 2. The data have been adjusted by removal of the fixed effects of sex, year of birth, size of birth litter and date of birth. Estimates of crossbreeding parameters are shown in Table 3 for three models: the full model of all estimable crossbreeding parameters, the model TABLE2 Number of observations and estimated means and total variance of mean log~o LH response for each sex in each cross type Cross ~
High line Low line HXL L×H HLXH LHXH HL×L LH×L
Males
(H) (L) (HL) (LH) (HL(H)) (LH ( H ) ) (HL(L)) (LH(L))
Females
No. of records
mean
variance
No. of records
mean
variance
91 73 17 20 32 24 32 28
1.766 1.117 1.526 1.449 1.637 1.627 1.260 1.215
0.0363 0.0575 0.0715 0.0381 0.0268 0.0157 0.1065 0.0514
57 52 0 0 26 22 27 19
1.496 0.857 1.321 1.270 1.038 0.951
0.0374 0.0405 0.0553 0.0711 0.0591 0.0340
~Genotype of the sire of the cross is shown first.
160
C.S. HALEYET AL.
Highline4 0 " l j No.of 30" records 20"
~
10" 0
0.3
0.9
'°1
No. of 20 records 10i
0.3
0.9
1.5
F1 2 0 ~ l ~ j ~ l ~ No. of 10" records 0 0.3 0.9 1.5
.x,o Ot
2.1
2.1
uul
No. of 2 0 L ~ ~ L records 10
4°L 0
2.1
I,
F1 x Higg 0
0
1.5
0.3
0.9
~ 1.5
2.1
30 Lowline No. of 20" records 10" 0
0.3
0.9 1.5 2.1 Adjustedmeanlog LH.release
Fig. 2. Distribution of mean log~o LH release in the five basic generations (high and low selection lines, F1 cross and backcrosses to high and low lines). The raw data have been adjusted by removal of the effects of sex, year of birth, size of birth litter and date of birth within year with the residuals being given around the mean of the appropriate generation.
161
GENETIC CONTROL OF LH RELEASE IN SHEEP
TABLE3 E s t i m a t e d crossbreeding p a r a m e t e r s for m e a n loglo L H response f r o m R E M L analyses Effect ~
Full m o d e l
Males /t a 6 am aSm asf ~sf Sex difference P h e n o t y p i c variance
Females
1.454+0.027 1.187+0.030 0.330+0.038 0.414+0.079 0.029+0.040 -0.026+0.084 - 0 . 0 3 1 -+0.0512 0.036 + 0.056 . . - 0 . 0 6 1 + 0.047 -0.015+0.045 0.270 + 0.023 0.0447
Sex-limited direct effect m o d e l
Direct effect model
Males
Both sexes
Females
1.453+0.027 1.177+0.028 0.336_+0.017 0.315+0.021 0.028_+0.040 -0.036_+0.072 _3 _ . . 0.276 + 0.020 0.0446
1.315+0.025 0.328_+0.014 0.018+0.038 _ 0.275 + 0.020 0.0446
1Abbreviations: #, m e a n ; a , additive direct effect; & d o m i n a n c e direct effect; a m , additive m a t e r n a l effect; asm, additive sex-linked effect in males; asf, additive sex-linked effect in females; 6sf, d o m i n a n c e sex-linked effect in females. 2 C o m m o n p a r a m e t e r e s t i m a t e d for the two sexes. 3 p a r a m e t e r n o t estimated.
of sex-limited direct effects and the model of direct effects which are not sexlimited. None of the more complicated models were a significant improvement over the model which included only direct effects which were not sexlimited and the only estimates of genetic effects which are significantly greater than zero are those for 04 the additive direct effect. There is thus no evidence for dominance at the autosomal genes or for sex-linked genes or maternal effects contributing to the line differences. The mean difference between sexes is significant, but despite this the estimate of a differs little between the sexes, i.e. there is little evidence that the genes controlling the line difference are sex-limited on the scale on which the data were analysed.
Segregation analyses The results of the segregation analyses are given in Table 4. No major gene was included in the first model, which hence assumed all genotype differences were polygenic in origin. This model is very similar to that with direct effects which are not sex-limited (the results of which are given in Table 3 ) with the exception that the segregation analysis is a m a x i m u m likelihood full-sib family model rather than a restricted m a x i m u m likelihood sire and dam within sire model. Inspection of Tables 3 and 4 shows that estimates of crossbreeding parameters from the two models were very similar. A major gene was included in the second model but it was assumed that it was fixed for alternative alleles in the high and low selection lines and so the allele frequencies were fixed and not estimated in this analysis. The estimates
162
c.s. HALEYET AL.
TABLE4
Estimated parameters from segregation analyses Effect
Model Polygenic effect only
Polygenic effect + fixed major gene
Polygenic effects Additive effect (i.e. c~) Dominance effect (i.e. 8)
0.328 0.019
0.238 -0.070
0.312 0.023
0.281 0.018
Major genie effects Additive effect Dominance effect
_1 -
0.090 0.090
0.360 0.094
0.187 0.076
1.0 0.0
0.900 0.844
0.762 0.479
0.273 0.0234 32.64
0.276 0.0225 9.32
Frequencies of increasing allele High line (Pn) Low line (PL) Sex difference Phenotypic variance Test statistic2
0.273 0.0433 -
0.274 0.0416 1.96
Polygenic effect + segregating major gene
Polygenic effect + segregating major gene 3
'Parameter not estimated. 2Twice improvement in log~-likelihood compared to previous model. 3Analysis of data omitting seven most deviant records.
which maximised the likelihood included a dominant major gene responsible for around one third of the difference between the high and low lines. Comparison of the log-likelihoods, however, indicates that this model was not a significant improvement over the model with no major gene. The third model included a major gene for which allele frequencies in each of the high and low lines were estimated. Comparison of the log-likelihoods shows that this model is a significant improvement over the previous two models. The estimated effect of the major gene is relatively large, at around 2.5 phenotypic standard deviations between the two homozygotes, and dominance is in the direction of the high allele. High estimates of the frequency of the allele which increases LH release were obtained for both the high and low selection lines, being marginally higher for the low line. These results appear inconsistent with the history of selection in the lines, which would be expected to result in the allele frequency for a gene of such large effect to be at opposite extreme frequencies in the two lines. Inspection of the data (Fig. 2 ) reveals that there is a tendency for there to be one or two animals with very low values within each cross type. The major gene model would explain these
GENETIC CONTROL OF LH RELEASE IN SHEEP
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few low values as, in a population with a dominant increasing allele at high frequency, there would be a few extremely low scoring animals which were homozygous for the decreasing allele. DISCUSSION
The crossbreeding analyses show that the responses to selection for mean log~o LH release in the high and low lines can be explained by a very simple genetic model, with the line difference controlled by autosomal genes of additive effect. Despite a relatively large difference between the means of the sexes, the difference between the lines was very similar in the two sexes and there was no evidence for the actions of the genes being sex-limited. This latter conclusion is consistent with that of Haley et al. (1989) that the genetic correlation between the trait in males and females was close to unity. Our segregation analyses provided no evidence for a major gene being involved in the response to selection. The apparent major gene effect detected did not explain the difference between the selection lines as the estimated allele frequency was not markedly different between the lines. Additionally, the estimates of the polygenic components in the model containing the major gene effect were very similar to those in the model from which it was absent. It seems unlikely that the apparent major gene effect was real, as selection would have been expected to change the allele frequencies of a gene of such large effect to opposite extremes in the two selection lines. Our analysis does not suggest that this has occurred and in fact the estimated frequency of the increasing allele was higher in the low selection line, the opposite of expectation. The detection of a putative major gene effect may be associated with the presence within some of the genotypic classes of a few animals with low values for log~o LH release. It is possible that such records were due to accidental events, such as the mis-identification of samples or animals. Alternatively, such cases may have been due to occasional sporadic environmental events affecting individual animals, such as disease. In order to explore the effect these few extreme records have on the results of the analysis, we re-analysed the data after removal of animals with values for log~o LH release which were more than three within-line standard deviations from the mean of a cross. This resulted in the removal of a total of seven animals: one with a low record and one with a high record from the high line and animals with low records from the low line (one animal), the F~ crosses (one animal), the backcrosses to the high line (one animal) and the backcrosses to the low line (two animals). The results of the analysis of the reduced data with a model in which the major gene was allowed to segregate within the selection lines are shown in Table 4. A comparison of the log-likelihoods of this model with one in which the major gene is assumed fixed for alternative alleles in the two selection lines still suggests a significant effect of a segregating major gene. Compared to the complete data, however, the test
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statistic and the estimate of the additive effect of the major gene were greatly reduced and the estimates of allele frequencies within the selection lines have been changed. In view of the effects of a few extreme animals on the results of the analyses and the inconsistency of the results with expectation, we are reluctant to accept this effect as due to a major gene without further supporting evidence. One concern about the conclusion that a major gene was not involved in the response to selection is whether the trial was sufficiently powerful to det e c t any major gene. The best case for detection would have been if a single gene completely explained the responses to selection. It is unlikely that such a gene would have been completely fixed by selection during the course of experiment, but this would probably not greatly influence the power of its detection. This is because most of the evidence for the presence of a major gene is likely to come from the inflation it produces in the variance of the backcross generations. For a gene of additive effect, this inflation of the variance will be the same if a given line difference is produced by a gene fixed for alternative alleles in the two lines or by a gene of (necessarily) larger effect which is at less extreme frequencies in the two lines. If the gene were fixed for alternative alleles, our crossbreeding analyses suggest that it would be autosomal with an additive effect of three phenotypic standard deviations on log lo LH release between the two homozygotes. We have used simulation in order to investigate the power to detect such a gene. The same data structure was simulated as was analysed (i.e. same number of animals of each cross type, same sex difference, same family and residual variance, etc. ) with a single gene fixed for alternative alleles explaining the whole of the difference between the high and low lines. Segregation analysis detected a significant effect of a major gene in 94 of 100 simulated replicates. When data were simulated with the major gene explaining only half of the line difference (the rest being due to genes of small effect), however, it was detected in only 13 of 100 replicates. Thus we can conclude that the experiment was likely to detect a single gene explaining most or all of the line difference, but a larger trial would have been needed to detect genes of smaller effect. The data were analysed on the log~0 scale in this paper and that of Haley et al. (1989). This transform was necessary to remove the strong positive relationship between the mean and the variance which is present on the nominal scale. The success of this transform is evident from the similarity of the variance within the high and low lines (Table 2). It could be argued that this transformation may obscure evidence for the presence of a major gene. It is certainly the case that a major gene fixed for one allele in the low line but segregating in the high line would lead to a positive relationship between the mean and the variance and the log~o transformation could remove this evidence. On the other hand, any relationship between mean and variance due to a scale effect is likely to cause spurious detection of a major gene (Elston,
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1984). Furthermore, due to the selection applied to the lines, any gene of large effect is likely to be fixed or at opposite extreme frequencies for alternative alleles in the high and low lines. Such a gene would not cause differences in variance between the high and low lines, but its segregation in the backcrosses would inflate their variances. In this situation, the loglo transform would not remove all evidence of a major gene, as the variance in the backcrosses would remain above those in the high and low lines and the F1 after transformation. Thus the use of transformation to equalise the variance in the high and low lines seems reasonable, as it should reduce the chance of detecting a spurious major gene without removing all evidence for a gene fixed for alternative alleles in the two lines. In summary, selection responses in LH release after a GnRH challenge have been achieved by changes at additive autosomal loci which have the same effect in both sexes. A single major gene does not seem to explain the response to selection, but the effects of several genes of large effect can not be ruled out at this stage. This conclusion is consistent with the results of detailed studies of the high and low selection lines (e.g. Evans, et al., 1991 a,b; McNeilly et al., 1993 ), which indicate a number of differences at the physiological and molecular level. Therefore the observed asymmetry in the selection response (Fig. 1 ) may simply be due to an environmental trend. Continuing physiological studies should identify potential candidate loci, changes at which may have contributed to the selection responses. In this case, as the genetic map of the ovine is developed, allowing markers close to any chosen locus to be identified, looking for evidence of co-segregation of markers close to candidate loci with loglo LH release is likely to provide a more powerful test for an effect of these loci, and thus for major genetic effects, than does segregation analysis (Knott and Haley, 1992 ). ACKNOWLEDGEMENTS
We are very grateful to staff at Blythbank farm, particularly John Bracken and Phil Davies, for their care of the animals and to staffat the Large Animal Unit, particularly Marjorie Ritchie, and to Clair Warren and Gerry Baxter for their excellent technical assistance. This work was supported by the Ministry of Agriculture, Fisheries and Food and the Agricultural and Food Research Council.
REFERENCES Elston, R.C. 1984. The genetic analysis of quantitative trait differences between two homozygous lines. Genetics, 108: 733-744. Elston, R.C. and Stewart, J. 1971. A general model for the genetic analysis of pedigree data. Human Hered., 21: 523-542.
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Elston, R.C. and Stewart, J. 1973. The analysis of quantitative traits for simple genetic models from parental, F~ and backcross data. Genetics, 73:695-711. Evans, N.P., Land, R.B., McNeilly, J.R. and Webb, R. 1991a. Role of gonadal negative feedback on the gonadotrophin responses to gonadotrophin-releasing hormone (GnRH) in ram lambs from two lines of sheep selected for their luteinizing hormone response to GnRH. J. Reprod. Felt., 93: 549-558. Evans, N.P., McNeilly, J.R., Springbett, A.J. and Webb, R. 199 lb. Alterations in pituitary gland sensitivity in ram lambs to physiological doses of gonadotrophin-releasing hormone (GnRH), after divergent selection based on the luteinizing hormone response to pharmacological GnRH challenge. J. Reprod. Fert., 93: 559-567. Falconer, D.S. 1989. Introduction to Quantitative Genetics (Third Edition). Longman, Harlow, U.K. GENSTAT 5 COMMITTEE, 1989. Genstat 5 Reference Manual, Clarendon Press, Oxford, U.K. Haley, C.S., Lee, G.J., Fordyce, M., Baxter, G., Land, R.B. and Webb, R. 1989. Study of LH response to GnRH in the young male as a criterion of genetic merit for female reproduction in sheep. J. Reprod. Fert., 86:119-133. Hill, W.G. and Knott, S.A. 1990. Detection of genes of large effect. In: K. Hammond and D. Gianola, (eds), Advances in Statistical Methods for Genetic Improvement of Livestock, Springer-Verlag, Berlin, Germany. pp. 477-495. Knott S.A. and Haley, C.S. 1992. Maximum likelihood mapping of quantitative trait loci using full-sib families. Genetics, 132:1211-1222. Knott, S.A., Haley, C.S. and Thompson, R. 1992. Methods of segregation analysis for animal breeding data: a comparison of power. Heredity, 68:299-311. Land, R.B. 1973. The expression of female sex-limited characters in the male. Nature, 241: 208209. Land, R.B., Bodin, U Driancourt, M.A., Haley, C.S. and McNeilly, J.R. 1988. Physiological prediction of genetic merit for female reproduction in the sheep. Proc. 3rd World Congr. Sheep Beef Cattle Breed., Vol. 2:611-622. Martensz, N.D., Baird, D.T., Scaramuzzi, R.J. and Van Look, P.F.A. 1976. Androstenedione and the control ofluteinizing hormone in the ewe during anoestrus. J. Endocrinol., 69: 22i7237. McNeilly, J.R., Evans, N.P., Bramley, T.A., Brown, P., Clark, A.J. and Webb, R. 1993. The relationship between selection for pituitary responsiveness to gonadotrophin releasing hormone in sheep and differences in gonadotrophin subunit mRNAs. J. Reprod. Fert., 97:311315. Numerical Algorithms Group, 1990. The NAG Fortran Library Manual - Mark 14. NAG Ltd., Oxford, U.K. Patterson, H.D. and Thompson, R. 1971. The recovery of inter-block information when block sizes are unequal. Biometrika, 58: 545-554. Walkley, J.R.W. and Smith, C. 1980. The use of physiological traits in genetic selection for litter size in sheep. J. Reprod. Fert., 59: 83-88. Webb, R., Baxter, G. Preece, R.D., Land, R.B. and Springbett, A.J. 1985. Control ofgonadotrophin release in Scottish Blackface and Finnish Landrace ewes during seasonal anoestrus. J. Reprod. Fert., 73: 369-378. Wilks, S.S. 1938. The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat., 9: 60-62.
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RESUME Haley, C.S., Lee, C.J., Webb, R. et Knott, S.A., 1993. Indications sur le d6terminisme g6n6tique de la s6cr6tion de LH en r6ponse h l'injection de GnRH fournies par les croisements entre lign6es ovines s61ectionn6es. Livest. Prod. Sci., 37:15 3-167 (en anglais). L'objectif de cette 6tude 6tait de d6terminer la base g6n6tique de la r6ponse ~ la s61ection divergente sur la quantit6 d'hormone luteinisante (LH) s6cr6t6e par des agneaux de 10 semaines apr~s une injection de GnRH. Des donn6es provenant de 518 animaux des lign6es s61ectionn6esl, des F~ et des backcrosses ont 6t6 analys6es par le maximum de vraisemblance r6siduelle pour estimer les effets g6n6tiques du croisement. On n'a pas trouv6 d'indication d'effets li6s au sexe, d'effets maternels, d'effets d6pendant du sexe ou de dominance pour les effets directs autosomaux. Les r6ponses h la s61ection ont ainsi 6t6 obtenues par des variations ~ des loci autosomaux additifs qui ont le m6me effet dans les deux sexes. L'analyse de s6gr6gation par le maximum de vraisemblance a 6t6 utilis6e pour rechercher un g~ne majeur affectant la s6cr6tion de LH. On a d6tect6 apparemment un g~ne majeur de m~mes fr6quences all61iques dans les lign6es s61ectionn6es mais ce r6sultat s'expliquait par la non normalit6 des donn6es, car il ne se maintenait pas aprbs transformation de cell-ci. Ii n'y a eu aucune preuve qu'un g~ne majeur simple explique la diff6rence entre lign6es s61ectionn6es mais l'6tude n'a pas 6t6 suffisamment puissante pour exclure la contribution de g~nes d'effets plus petits mais appr6ciables. KURZFASSUNG Haley, C.S., Lee, G.J., Webb, R. and Knott, S., 1993. Erkentnisse fiber die genetische Steuerung der LH Ausschiittung durch GnRH bei Kreuzungen von selektierten Schaflinien. Livest. Prod. Sci., 37:153-167 (aufenglisch). Ziel dieser Studie war es, die genetischen Grundlagen des Selektionserfolges bei divergierender Selektion auf die von zehn Wochen alten L~immern ausgeschfittete Menge luteinisierenden Hormons (LH) nach einer Verabreichung yon Gonadotropin Releasing Hormon (GnRH) zu bestimmen. Daten von 518 Tieren in den selektierten Linien, der F1-Kreuzung dieser Linien und beiden Rfickkreuzungen wurden mit der Residual Maximum Likelihood Methode analysiert, um Kreuzungsparameter zu sch~itzen. Weder geschlechtsgekoppelte Effekte, noch maternale, geschlechtsspezifische oder DOminanzeffekte der autosomalen direkten Effekte Liel3en sich nachweisen. Folglich wurde der Selektionserfolg durch Vedinderungen von additiven, autosomalen Loci herbeigef'tihrt, die dieselbe Wirkung in beiden Geschlechtern besitzen. Eine Maximum Likelihood Segregationsanalyse wurde durchgeftihrt, um das Vorhandensein eines major genes ftir die LH-Ausschfittung zu prfifen. Die Analyse zeigte das Vorhandensein eines major genes mit ahnlichen Allelfrequenzen in beiden Selektionslinien an. Dieses Ergebnis wurde j edoch auf die Nichtnormalitiit der Residuen zurufickgeftihrt, die durch die verwendete Transformation nicht beseitigt werden konnte. Es gab keine Hinweise darauf, dab ein einzelnes major gene die Differenz zwischen den beiden Selektionslinien erkl~iren konnte. Die M~ichtigkeit des Testverfahrens reichte jedoch nicht aus, die Wirkung eines oder mehrerer Gene mit kleineren, jedoch immer noch bedeutenden Effekten nachzuweisen.