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Human Molecular Genetics Advance Access originally published online on September 6, 2004
Human Molecular Genetics 2004 13(19):2173-2182; doi:10.1093/hmg/ddh239
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Human Molecular Genetics, Vol. 13, No. 19 © Oxford University Press 2004; all rights reserved

Genome-wide linkage analysis of a composite index of neuroticism and mood-related scales in extreme selected sibships

Matthew W. Nash1,{dagger}, Patricia Huezo-Diaz1,{dagger}, Richard J. Williamson1, Abraham Sterne1, Shaun Purcell1,4, Farzana Hoda1, Stacey S. Cherny5, Gonçalo R. Abecasis6, Martin Prince2, Jeffrey A. Gray3, David Ball1, Philip Asherson1, Anthony Mann2, David Goldberg2, Peter McGuffin1, Anne Farmer1, Robert Plomin1, Ian W. Craig1,* and Pak C. Sham1,7

1MRC Social, Genetic and Developmental Psychiatry Research Centre, 2Section of Epidemiology and 3Department of Psychology, Institute of Psychiatry, King's College, London, UK, 4Whitehead Institute, Center for Genome Research, Cambridge, MA, USA, 5Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK, 6Center for Statistical Genetics, University of Michigan, Ann Arbor, USA and 7Department of Psychiatry and Genome Research Centre, University of Hong Kong, Queen Mary Hospital, Pokfulam Road, Hong Kong, China

Received April 20, 2004; Accepted July 21, 2004


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 APPENDIX
 REFERENCES
 
There is considerable evidence to suggest that the genetic vulnerabilities to depression and anxiety substantially overlap and quantitatively act to alter risk to both disorders. Continuous scales can be used to index this shared liability and are a complementary approach to the use of clinical phenotypes in the genetic analysis of depression and anxiety. The aim of this study (Genetic and Environmental Nature of Emotional States in Siblings) was to identify genetic variants for the liability to depression and anxiety after the application of quantitative genetic methodology to a large community-based sample (n=34 371), using four well-validated questionnaires of depression and anxiety. Genetic model fitting was performed on 2658 unselected sibships, which provided evidence for a single common familial factor that accounted for a substantial proportion of the genetic variances and covariances of the four scales. Using the parameter estimates from this model, a composite index of liability (G) was constructed. This index was then used to select a smaller—but statistically powerful—sample for DNA collection (757 individuals, 297 sibships). These individuals were genotyped with more than 400 microsatellite markers. After the data were checked and cleaned, linkage analysis was performed on G and the personality scale of neuroticism using theregression-based linkage program MERLIN-REGRESS. The results indicated two potential quantitative trait loci (QTL): one on chromosome 1p (LOD 2.2) around 64 cM (43–70 cM) near marker D1S2892 and another on chromosome 6p (LOD 2.7) around 47 cM (34–63 cM) near marker D6S1610. Further exploratory sex-specific analyses suggested that these QTLs might have sex-limited effects.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 APPENDIX
 REFERENCES
 
Depression is a highly prevalent psychiatric disorder (1) with aprojected global disease burden second only to ischemic heart disease (2). There is substantial co-morbidity between depression and anxiety (3) owing to genetic overlap in liability to the disorders (4,5). This shared and continuous (6,7) liability can be indexed by self-report personality and mood scales such as neuroticism (8) and the general health questionnaire (9). These scales are heritable (10,11) and are genetically correlated with major depression (12,13). The use of continuous scales in the genetic analysis of affective psychopathology is complementary to clinical diagnoses and may provide greater statistical power (14).

Modern linkage designs have only been recently applied in the study of the liability to depression and anxiety (15–17). Harm avoidance, a personality measure similar to neuroticism known to index depression, has been examined in two studies (18,19). The initial study identified a locus on chromosome 8p21–23 (18), which was subsequently validated independently in a candidate linkage analysis (19). The published results from the first study on neuroticism (20) indicated quantitative trait loci (QTL) on chromosomes 1, 4, 7, 12 and 13. This study also identified certain regions as potentially sex specific.

Adverse life events are known to precipitate the onset of depression and anxiety (21–23). The complex interplay between life events and genetic liability is thought to be crucial to the aetiology of affective psychopathology (24,25). Two recent studies have reported genetic effects on depression that are moderated by childhood trauma or recent adverse events (26,27). To date no linkage analysis of depression, anxiety, or related personality traits has included environmental measures in the analysis.

The Genetic and Environmental Nature of Emotional States in Siblings (GENESiS) aims to identify genetic variants that influence the continuous liability to depression and anxiety by linkage analysis of selected sibships from a community sample. The study recruited 34 371 individuals through the general practices in England and Wales, on which phenotypic information was collected by self-report questionnaire, including four well-validated scales of depression and anxiety. Thescales were subjected to structural equation modelling to derive a composite index of liability, designated G (28). The most informative sibships to detect linkage to G were selected for DNA collection; this resulted in 711 individuals in 283 sibships who were genotyped for a set of 408 microsatellite markers. The regression-based linkage program MERLIN-REGRESS (29) was used to perform genome-wide linkage analysis. The initial intention of the project was to analyse G. However, as this is not a phenotype used in other papers, we have also undertook analysis with the EPQ-N to facilitate comparisons between studies. Here, we report the results of a genome-wide linkage analysis that identified two potential QTLs for the liability to depression and anxiety on chromosome 1p and 6p.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 APPENDIX
 REFERENCES
 
Selected sample
After data-cleaning, 283 sibships remained containing 711 individuals making up 601 pair-wise relationships. The sibships comprised of 150 sibling pairs, 99 trios, 21 quads and two quints. The female–male ratio of the selected sample was 2 : 1 with 474 females and 237 males giving 254 female and 86 male pair-wise relationships. The average age was 44 (range 23–60). The power of the sample, as measured by the ELOD20 was 2.5 for G and 2.8 for the EPQ-N. For G, this translates to 72% power for suggestive criterion (P=0.50) and 40% power for significant criterion (P=0.05). For the EPQ-N, this translates to 79% power for suggestive criterion (P=0.50) and 47% power for significant criterion (P=0.05).

Marker information
In total, three markers failed to work leaving 408 markers. Per marker, on an average 90% of the sample were genotyped successfully. The mean inter-marker distance was 8.77 cM; calculated using the Marshfield map with a mean marker heterozygosity value of 74.4%.

Genome-wide significance levels
As the resulting significance criteria were similar for G and EPQ-N phenotypes, the results are presented together. For the full sample, a LOD exceeding 1.7 is considered suggestive; and a LOD score exceeding 2.9 is considered significant. For sister-pairs, a suggestive threshold was equivalent to a LOD >1.7, and a significant threshold was equivalent to a LOD >2.8. For brother-pairs, a suggestive threshold was equivalent to a LOD >2.1, and a significant threshold was equivalent to a LOD >3.3.

Linkage analysis results
For each LOD score presented, the genetic map distances are accompanied by the interval over which the LOD is within one unit from the local maximum. Simulation-based empirical P-values are presented for each LOD.

Full sample analyses.
The results from the multipoint analyses using MERLIN-REGRESS are shown in Figure 1A and B for G and EPQ-N, respectively. Linkage analysis using G located the highest LOD score on chromosome 1 (LOD 2.2, P=0.21) around 64 cM (43–70 cM) near the marker D1S2892. No other peaks exceeded a LOD score of 1.7 for this measure. For the EPQ-N phenotype, the highest peak was found on chromosome 6 (LOD 2.7, P=0.07) around 47 cM (34–63 cM) near the marker D6S1610. A peak was also seen for chromosome 1 (LOD 1.6, P=0.62) around 80 cM (42–90 cM) near the marker D1S2890.



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  		Figure 1. Results of the genome-wide linkage analysis are illustrated in (A) and (B) for G and EPQ-N, respectively. The multipoint LOD scores are shown on the y-axis and the distance, in centimorgans (cM), is shown on the x-axis. Alternating numbers for each chromosome are shown on the top x-axis.

 
Analyses adjusted for LE.
Linkage analysis, after regressing out the LE composite, revealed little change to the results described earlier (data not shown). The largest changes found for G were on chromosomes 1, 6 and 14. The LOD peak on 1p decreased from a LOD of 2.2 to 1.2 (P=0.89), and the peak on chromosome 6 increased from a LOD of 0.9 to 1.6 (P=0.62). A peak on chromosome 14 around 102 cM (79–108 cM) increased from a LOD of 0.9 to 1.7 (P=0.49). No large changes were observed for the EPQ-N phenotype.

Same-sex pair analyses.
Figure 2A and B illustrates the same-sex pair linkage analysis for G and EPQ-N, respectively. In the sister-pair analysis, the highest LOD score was seen on chromosome 1 (LOD 2.6, P=0.08), again around 80 cM (57–89cM) for G. A second peak (221 cM, 181–226 cM) on chromosome 1 that was observed in the full analysis for G is observed once again in the sister-pair analysis for EPQ-N with a larger result of a LOD of 1.5 (P=0.66). The next highest peak was on chromosome 7 (LOD 1.9, P=0.34 and 1.8, P=0.41) for both the G and EPQ-N, respectively, at position 55 cM (32–68 cM) near the marker D7S484. A peak also appears on chromosome 12 (LOD 1.8, P=0.41 and 1.3, P=0.82) for both the G and EPQ-N, respectively, at position 90 cM (81–102 cM) in between the markers D12S326 and D12S351. Of these, the peaks on chromosome 1p and 7 meet suggestive criteria for both G and EPQ-N, whereas the peak on chromosome 12 meets suggestive criteria only for G. The highest peak from the brother-pair analysis was found on chromosome 6 (LOD 3.9, P=0.02) for EPQ-N around 44.5 cM (39–50 cM). The next highest peak was on chromosome 17 (LOD 2.1, P=0.51) for G around 6.63 cM (0–13 cM) near the marker D17S831. A peak also appears on chromosome 2 (LOD 1.7, P=0.81) for G at position 175 cM (161–187 cM) near the marker D2S335.



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  		Figure 2. Results of the genomewide same-sex pair linkage analysis are illustrated in (A) and (B) for G and EPQ-N, respectively. The multipoint LOD scores are shown on the y-axis and the distance, in centimorgans (cM), is shown on the x-axis. Alternating numbers for each chromosome are shown on the top x-axis. Sister-pair results are shown in red and brother-pair results are shown in black.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 APPENDIX
 REFERENCES
 
We have performed a QTL linkage analysis on extremes selected sibships using continuous measures of depression and anxiety. Our results from the full sample analysis indicate potential QTLs on chromosome 1p and 6p with the LOD peak at 6p almost reaching empirically determined genome-wide significance level (P-value <0.05). The linkage results were not substantially altered by LE regression from our measures. Further analyses also highlighted potential sex-specific loci. In the sister-pairs analysis, chromosomes 1, 7 and 12 showed statistically suggestive LOD scores that were not observed in brother-pairs. In the brother-pair analysis, chromosomes 6 and 17 gave statistically suggestive LOD scores that were not observed in sister-pairs. The probability of observing these findings was not corrected for multiply testing (i.e. two phenotypes, LE regressed and gender analyses). However, as the analyses are highly correlated, a Bonferroni correction would be too conservative.

As neuroticism and major depression show substantial genetic overlap (12), it is of interest to compare our results with other linkage studies of related disorders. The linkage peaks on chromosome 1 are noteworthy, as several previous studies of related disorders and traits have found putative QTLs on this chromosome (Table 1). In addition to a larger peak on chromosome 1p, a smaller peak on 1q is also observed, which is of interest as loci that influence emotionality in the rat have been found on chromosome 5, which is partly syntenic with chromosome 1p in humans (37). However, owing to the low resolution of both human and rat mapping studies it is difficult to say whether the same genes influence the trait in both species. In addition, high-resolution mapping of emotionality in mice has detected QTLs on chromosome, 1, syntenic with 1q31 in humans (38). Linkage around the chromosome 6p peak has been previously identified in studies that examined HLA haplotypes and MDD (39,40), but not in any modern linkage studies. Yet, in the study by Fullerton et al. (20) a small peak was observed in this region. This area has been also identified in a meta-analysis of schizophrenia (41), and although this might not appear an obvious choice for comparison, neuroticism is known to significantly predict later development of schizophrenia (42).


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  		Table 1. Positive results from linkage analyses of depression-related phenotypes
 
It is also of interest to consider our linkage results in relation to association findings for depression and related traits. The most notable candidate locus is the serotonin transporter (5HTT) (26,43). 5HTT is located on the q-arm of chromosome 17, and although a peak on chromosome 17 was observed in the brother-pair only analysis for G and EPQ-N it is located on the p-arm of the chromosome. Thus, none of the current candidate genes have been detected in this study, but this is not surprising given the small size of effects found in most association studies to date.

Previous quantitative genetic analyses have suggested the presence of sex-specific genetic effects for mood symptoms (44). Sex-specific findings have been noted in recent genome scan (20,45). We also identified potential sex-specific results. The locus on chromosome 1 appears to be female-specific, potentially overlapping with the female specific loci reported in the study by Fullerton et al. (20). Other female-specific loci that were not identified in the full analysis are located on the chromosomes 7 and 12. The specific location of the chromosome 12 peak is of particular interest as it has been identified as the largest peak in two recent linkage studies of neuroticism and major depression (20,45). In addition, our result is consistent with that of Fullerton et al. (20) finding of female specificity. However, chromosome 7 is potentially female specific in this study but male specific in the study of Fullerton et al. (20). A similar sex effect was observed for males on chromosomes 2, 6 and 17 with the loci on chromosome 6 increasing to a LOD of 3.9, yet none of these results overlap with any previously reported male-specific loci. Given the much lowered sample size of brother-pairs compared to sister-pairs, these results must be considered tentative.

Although we have sought to maximize statistical power by phenotype definition, sample selection and optimal linkage analysis (28,29,46), statistical power remains an important limitation of the study being only marginally adequate for detecting a QTL that explains as much as 20% of the trait variance. In the analysis of the full sample, the estimated effect size of chromosome 1 for G was 22% and chromosome 6 for EPQ-N was 27%. These are almost certainly over-estimates, and conversely many QTLs with similar or smaller effect sizes will have not been detected. Nevertheless, there does appear to be some consistency for chromosome 1 between our results and those of Fullerton et al. (20). A solution to the power issue would be an eventual meta-analysis of similar studies (15,16,20).

Discrepancies between studies might also be due to differences in the questionnaires used to measure depression and anxiety. For example, Fullerton et al. (20) used a larger 90-item EPQ-N questionnaire, whereas this study utilized a 12-item EPQ-N. The current analyses do not include higher-order effects, such as epistasis and gene–environment interaction, that may be present in depression and neuroticism (44). Epistasis that is not modelled tends to result in over-estimates of main genetic effects in linkage analysis (47). Although we considered sex in the linkage analysis by stratification of the sample, it was not possible to perform an analysis that included opposite-sex pairs or a formal test for sex-specific QTL effects. Currently, methodologies that incorporate these effects in a regression framework suitable for selected samples are under development and future work will utilize these to further the linkage analysis in this sample.

Our future aim is to focus our efforts on the fine mapping of the regions on chromosomes 1 and 6 to identify the specific molecular variants accounting for linkage findings in this study. We also plan to incorporate both sex-specific and life-events interactions into this association analysis and in doing so, identify important genes involved in mediating the response to environmental stressors, whereas also identifying genetic effects that are relatively independent of the environment.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
APPENDIX
 REFERENCES
 
Subjects and assessments
Subjects were recruited through general practices in England and Wales, made available through the Medical Research Council General Practice Research Framework. Index subjects were individuals registered with these practices in the age range 18–55 who had no current serious medical illness or disability, e.g. dementia. These individuals were sent a letter of notification detailing information about the proposed research project followed by a phenotypic questionnaire. In this questionnaire, they were asked about their siblings, if these siblings would like to participate, and the address of the siblings. The identified siblings who were not already among the index subjects were subsequently sent a phenotypic questionnaire. Total responses to date have led to a community-based sample of 34 371 individuals. Of these, 14 807 formed 6387 sibships of size 2 or greater, with the remaining 19 564 individuals not having a participating sibling. The sample is socio-demographically representative of the UK population with a slight over-representation of individuals identifying themselves as white. The majority were sib pairs (n=4824), with some larger sibships of size three (n=1171), four (n=322), five (n=63), six (n=6) and seven (n=1). Phenotypic measures included several well-validated and standardised instruments for the measurement of depression and anxiety symptoms, personality traits, and psychosocial adversity. The general health questionnaire (12-item version; GHQ-12) was used as a measure of psychological distress (9). The short form of the neuroticism scale from the revised Eysenck personality questionnaire (EPQ-N) was used as a measure of trait anxiety (8). Two subscales were used from the mood and anxiety symptoms questionnaire (48) to measure levels of anxious arousal (MASQ-AA), and high positive affect (MASQ-HPA). All measures were age and sex regressed, and then standardized. MASQ-HPA is scored inversely with respect to other measures such that higher scores indicate less depressive symptoms.

From the estimated factor loadings of a quantitative genetic model, the variables EPQ-N, MASQ-AA, MASQ-HPA and GHQ-12 were combined linearly using the weights 0.259, 0.103, –0.204 and 0.103, respectively, to generate a composite index, denoted G (2). To improve its properties, G was adjusted for age, log-transformed to approximately normality, and then separately standardized for males and females to have mean 0 and variance 1. A matrix of cross-phenotype correlations is given in Table 2 showing the high correlation of all four individual measures with G. Assuming the absence of shared environmental influences, the heritability of G in this sample was estimated at 42%; a result comparable to estimates for neuroticism (10), major depression (49) and generalized anxiety disorder (50).


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  		Table 2. Correlation matrix of the composite index G and the four individual measures of anxiety and depression
 
After an average of 6 months from returning the initial questionnaire, a second follow-up questionnaire was sent out to the full sample. As averaged repeated measurements reduce measurement error and increases heritability (51), the questionnaire scores from time 1 and 2 were averaged for use in the subsequent analyses. From the 34 371 individuals, 70% responded to the time 2 questionnaire. This included 78% of the sample selected for the linkage study. Individuals not replying to time 2 were included and a suitable adjustment was made in the linkage analysis for the number of measurements taken (Appendix).

Data from the ‘List of Threatening Experiences’ (LTE) (52) was also collected to incorporate the effects of the environment on the outcome variables. The LTE is comprised of four life-event categories: network, e.g. death of a close relative; relationships, e.g. marital difficulties; finances, e.g. loss of employment and personal, e.g. serious illness, injury or assault. A previous analysis on the relationship between LTE and the four measures of depression and anxiety has been performed in our unselected community sample (53). In these analyses, a canonical correlation analysis was performed to find a linear combination of the four life-events categories (rated as 1 for presence and 0 for absence) that most predicted the four measures of depression and anxiety (GHQ-12, EPQ-N, MASQ-AA and MASQ-HPA). A life-events linear composite (LE) was generated using the coefficients from the canonical correlation analysis (0.245, 1.219, 1.594 and 1.260 for network, relationships, finances and personal, respectively).

Selection of linkage sample
Sibships selected for linkage analysis were those with the greatest expected contributions to a true LOD peak, generalizing from previous methods of selecting extreme discordant or concordant sib pairs (46,54,55). An index of sibship informativeness for G was calculated for all sibships using the SEL program (46); the most informative 10% of all the sibships were selected for DNA sample collection. Informativeness was then re-calculated based only on individuals who returned DNA samples (65%), and this was used to rank and select sibships for inclusion into the linkage sample. The expected LOD of a QTL that accounts for 20% of the phenotypic variance (ELOD20) for each sibship and for the whole linkage sample was calculated using MERLIN-REGRESS (29) prior to genotyping, and again after correction of relationship errors using the genotype data on the linkage markers (see later).

DNA extraction and preparation
Selected sibships were sent cotton buds to collect buccal cells for DNA isolation with details of how to perform the collection. Buccal cell samples were sent back and processed (56). DNA quantification was performed by spectrophotometry (Molecular Devices, Spectra Max 384 Plus) and flurometry (Fluroskan Ascent FL).

Genotyping
Genotyping was performed using the ABI medium density 10 cM mapping set (PE Biosystems, CA, USA) with the inclusion of some additional markers. This gave a total of 411 markers. Methodology followed the manufacturer's specifications but only one-third reaction volumes and 5 ng of DNA were used. Thermal cycling was performed using the PTC-200 DNA engine (Peltier Thermal Cycler, MJ Research, MA, USA) and analysed on a ABI3100 (PE Biosystems, CA, USA) following manufacturer's guidelines. The resulting data were analysed using GENEMAPPERTM version 3.0 (PE Biosystems).

Data cleaning
Sibships without parental data pose significant problems in relation to detection of genotyping errors and unexpected genetic relationships. We employed various approaches to reduce such errors in the dataset to a minimum.

Relationship errors.
The first stage of data-cleaning examined the genetic relationships by a scatter plot of the mean against the variance of the number of alleles identity-by-state for the typed markers for all pairs of individuals in the sample. This produced a distribution of points that fell into recognizable clusters that represent different types of relationships, e.g. unrelated, full-sibs, half-sibs, etc. Using the program Graphical Relationship Representation (57), we detected 17 individuals from eight families who were actually half-siblings with respect to their self-reported siblings, 27 individuals from 18 families who were totally unrelated to their self-reported siblings and nine monozygotic twin (MZ) pairs. All unrelated individuals were removed, half-siblings were recoded as such and the phenotypes of the MZ pairs were averaged and used as a single data-point. In one case, a particular relationship did not fall into any recognizable relationship cluster. The relationship was tested using the program PREST and ALTERTEST (58), which performs multiple tests (EIBD, AIBS and IBS tests) of a number of possible relationships on the genetic data. From the results, one individual was identified as a third degree relative and was excluded from the analyses.

We reconfirmed the self-reported sex of the sample by examining the average heterozygosity level from X-chromosome markers. No women were fully homozygous, but five self-reported males were heterozygous. Given their unique and unknown impact, these individuals were all excluded from analysis.

Genotyping errors.
Without regenotyping, Mendelian incompatibilities can identify genotyping errors in sibships with three or more siblings. This strategy was applicable to 58% of our sample. Errors were systematically detected using the program PEDSTATS (59) and subsequently re-genotyped or removed. In total, we detected a rate of Mendelian incompatibilities of 0.6% in the sample. However, this method slightly underestimates the error rate. Examination of genotyping error in the MZ twins, where all genotyping errors can be detected, gave a total error rate of 1.1%.

An additional level of data checking was further performed to detect unlikely genotypes. This was on the basis of the detection of double recombinants using a method implemented in MERLIN (60). This produces a score where small values indicate unlikely genotypes. We assessed the distribution of these scores by replacing the actual genotype data with simulated chromosomes conditional on family structure, marker spacing, allele frequencies and missing data, whereas making no changes to either the family structure or the phenotypic scores. An examination of 1000 of these simulated gene-dropping datasets indicated that a score of 0.001 would only be observed once in 20 genome scans in the absence of error. Using this score, 15 unlikely genotypes were detected in our data and were consequently deleted.

Linkage analysis
Linkage was evaluated primarily using MERLIN-REGRESS (version 0.9.13-beta), a regression-based method (29) suitable for selected samples, where population parameters (mean, variance and heritability) are user-specified rather than calculated from the selected sample. All population parameters were calculated from the overall GENESiS sample (n= 34371). Phenotypic measures were standardized in the unselected sample and thus the mean and variance were set to 0 and 1, respectively, in the linkage analysis. The method allows repeated measurements to be entered as an average, with further specification of the number of repeated measurements from which the average was measured as well as the test-retest correlation (either 1 or 2 in this study) in order to readjust the trait variance (Appendix). The heritability and test–retest correlation parameters set for each phenotype are shown in Table 3. Analysis of the X-chromosome was performed using an X-chromosome specific version of MERLIN (60).


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  		Table 3. Heritability estimates and test–retest correlations of composite G and the individual measures
 
Linkage analysis was performed on G and EPQ-N, the latter was included in the analysis to aid comparison with a recent study (20). Additional analyses were performed on the residuals of G and EPQ-N after regressing out the LE composite, a method with equal power to detect a QTL as a means model or covariance approach (61). Sex specificity of the LOD peaks was explored by separate analysis of sister-pairs (474 individuals; 254 pairs) and brother-pairs (237 individuals; 86 pairs).

As standard statistical criteria for linkage (62) are based on the assumption of an infinitely dense marker map and are conservative when using a typical linkage marker set, we empirically derived genome-wide P-values from 10 000 simulated data sets using the gene-dropping method as previously described. These P-values were calculated for the full sample and the same-sex pair samples. To exemplify how this process works, if a LOD peak of 3 is reached in 100 out of the 10 000 simulated data sets, this would indicate a P-value of 0.01 or 1% genome-wide.


   ACKNOWLEDGEMENTS
 
We thank the general practitioners, the staff of the UK GP research framework, and the individuals who participated in this study. We also acknowledge the important contribution of our late colleague Professor David W. Fulker who conceived of the GENESiS project and was the Principal Applicant on the initial MRC grant which funded the first phase of the project. This work was supported by UK Medical Research Council grant G9700821. Development of methodology utilized in the present paper was supported in part by Grant EY-12562 from the US National Institutes of Health.


   APPENDIX
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 APPENDIX
REFERENCES
 
Extension of MERLIN-REGRESS to incorporate repeated measurements
The biometrical genetic model assumed in MERLIN-REGRESS is that the trait has components of variance Q (quantitative trait locus), A (residual polygenes) and E (non-shared environment), standardized to sum to 1. In reality, the non-shared environment contains measurement errors (M) as well as true environmental differences (E), which are confounded when the trait is measured only once. The intraclass correlation, c, of repeated trait measurements is expected to be 1–M, so that an estimate of M is given by 1–c.

MERLIN-REGRESS requires the user to specify the numerical value of the overall heritability which is (Q+A) under the assumed model. If an individual has been measured r times, then the average of these r measurements will have variance components Q, A, E and M/r. If every individual is measured the same number of times, then averaging over these repeated measurements will result in the same overall variance T=Q+A+E+M/r, which can be re-expressed in terms of the intraclass correlation and the number of repeated measurements as T=1–(1–c)(r–1)/r. If the average trait values are re-standardized to have variance 1, then these values can be analysed using MERLIN-REGRESS by specifying the overall heritability to be 1/T times the single-measurement heritability.

When the individuals have a variable number of repeated measurements, then the earlier mentioned method of re-standardization and re-assigning the overall heritability will not work, as the averages will have different variances depending on the number of repeated measurements over which the average is taken. A more appropriate method for analysing these averages would be to assign their variances to be given by 1–(1–c)(r–1)/r, where the value of c is a user-specified parameter, whereas r is specified for each individual in the pedigree file. The covariances on the other hand are unaffected by averaging. These variances and covariances are then used to calculate the variance–covariance matrix of the squared sums and squared differences that arerequired for the regression method. This modification toinclude c and r has been implemented in MERLIN–REGRESS.


   FOOTNOTES
 
* To whom correspondence should be addressed at: SGDP Centre, Institute of Psychiatry, De Crespigny Park, PO Box PO80 London SE5 8AF, UK. Tel: +44 2078480018; Fax: +44 2078480866; Email: i.craig{at}iop.kcl.ac.uk

{dagger} The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 APPENDIX
 REFERENCES
 

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