1 Statistical Genetics Section, Inherited Disease Research Branch, National Human Genome Research Institute, Baltimore, MD
2 Department of Ophthalmology and Visual Sciences, University of Wisconsin Medical School, Madison, WI
Correspondence to Dr. Barbara E. K. Klein, Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Ocular Epidemiology, 610 North Walnut Street, Room 409, Madison, WI 53726 (e-mail: kleinb{at}epi.ophth.wisc.edu).
Received for publication June 25, 2004. Accepted for publication December 9, 2004.
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ABSTRACT |
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cataract; eye diseases; family; genes; genetic predisposition to disease; heredity; smoking
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INTRODUCTION |
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Previous studies, including those conducted within this cohort, the Beaver Dam Eye Study, have indicated that a family history of nuclear cataract (4, 5
), cigarette smoking (6
10
), female gender (11
13
), and increasing age are associated with an increased risk of nuclear cataract. A recent study by McCarty et al. (14
) found that the population attributable risk of cataract due to smoking was 17 percent (95 percent confidence interval: 16 percent, 18 percent).
Studies have shown that inherited genetic factors may also play a role in the development of age-related nuclear cataract. Hammond et al. (5) examined 506 pairs of female twins (226 monozygotic and 280 dizygotic) and concluded that additive genetic factors may explain 48 percent of the variation in the severity of nuclear sclerosis. There was no evidence in these data to support the influence of dominant genetic effects. Additionally, age explained 38 percent of the variation in nuclear sclerosis, and smoking accounted for 14 percent (5
). Previous analysis of 1,247 participants in the Beaver Dam Eye Study who could be classified into one of 564 sibships found that measurements of nuclear sclerosis for the right eye, the left eye, and the sum of the right and left eyes were highly correlated between siblings (4
). Segregation analysis of the sum of nuclear sclerosis measurements for the right and left eyes, adjusted for age and sex effects, supported the involvement of a major gene accounting for 35 percent of the variability in nuclear sclerosis within these 564 sibships (4
). However, because only sibship data were available previously, correlations between other pairs of relatives (parent-offspring, avuncular pairs, and cousins) as well as regressive familial effects (polygenic/multifactorial effects) within the segregation analysis could not be directly estimated. Additionally, the influence of cigarette smoking, a known risk factor for nuclear sclerosis, was not included in these analyses.
Therefore, to confirm results of the previous analyses, to further examine the influence of additional shared familial effects, and to examine the impact of incorporating cigarette smoking in the analysis, we examined familial correlations and performed segregation analyses on the extended pedigree data now available as part of the Beaver Dam Eye Study.
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MATERIALS AND METHODS |
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Study population
Of the 5,924 persons aged 4384 years who resided in the township of Beaver Dam, Wisconsin, 4,926 participated in the baseline examination of the Beaver Dam Eye Study conducted between 1988 and 1990 (15). The recruitment methods and study procedures have been described in detail elsewhere (16
). At baseline, complete eye examinations were given, including photography of the lens, and family relationship information was obtained from all participants. At the first follow-up visit, conducted between 1993 and 1995, family relationships, including extended pedigree information, were confirmed (17
). For the participants of the baseline examination, data on family relationships were available for 2,783 participants, 2,089 of whom had complete information on age, sex, and cigarette smoking (pack-years) and nuclear sclerosis measurements. Pack-years of cigarette smoking were determined by number of cigarettes smoked per day divided by 20, multiplied by the number of years of smoking.
Measurement of nuclear sclerosis
Nuclear sclerosis measurements were obtained by grading slit-lamp photographs of the lens. The details, including reliability, of these grading procedures have been described elsewhere (8, 18
). In brief, photographs were taken of each eye by using a Topcon SL5 photo slit-lamp camera (Topcon America Corp., Paramus, New Jersey). Each photograph was then graded for severity of nuclear sclerosis by comparing it to four standard photographs of increasing severity of nuclear sclerosis. The severity grades were defined as follows: grade 1, as clear or clearer than standard 1; grade 2, not as clear as standard 1 but as clear or clearer than standard 2; grade 3, not as clear as standard 2 but as clear or clearer than standard 3; grade 4, not as clear as standard 3 but as clear or clearer than standard 4; and grade 5, more severe than standard 4. Monocular cases and pseudophakic cases were excluded from the analyses. Only phenotype information from persons who participated in the baseline examination of the Beaver Dam Eye Study was included in this analysis.
Statistical analysis
Familial correlation analysis was performed by using FCOR, version 4.1 of S.A.G.E., and segregation analysis was performed by using REGC, version 2.1 of S.A.G.E. (19), and REGCHUNT (20
).
FCOR is used to compute the correlations in trait values between pairs of relatives. Correlations were calculated between the following relative pairs: parents and offspring, siblings, avuncular, and cousins. Equal weight was given to each pair of relatives (21) (i.e., each pair of siblings in a sibship of size two was given the same weight as each pair of siblings in a sibship of size three or larger).
REGC is used to perform segregation analysis of continuous traits and is based on the regressive models proposed by Bonney (19, 22
, 23
). These analyses test for autosomal inheritance of a single biallelic major locus that influences nuclear sclerosis by obtaining maximum likelihood estimates for parameters that describe the distribution of nuclear sclerosis in this population. If models in which these parameters are fixed to what is expected under Mendelian law describe the data as well as a more general model, then there is evidence for the influence of a major gene in the etiology of nuclear sclerosis. To estimate whether a single normal distribution with mean and variance denoted µ and
, respectively, provides an adequate description of the data, or whether a mixture of two or three normal distributions provides a significantly better description of the data, mixtures of distributions are fit to the observed data. Box-Cox transformation of the data is estimated as part of the analysis, denoted by parameters
1 and
2, respectively, to ensure that data are on the proper scale (24
).
Additionally, the proportion of persons in each of the distributions, known as the "type" frequencies, must also be estimated. This "type" represents an underlying discrete trait that influences nuclear sclerosis score (i.e., a person with a "low risk type" would have, on average, a lower degree of nuclear sclerosis than a person with a "high risk type") (23). In the models that test for inheritance of a major gene, type represents a genotype; however, for models that test for nongenetic factors, type is interpreted as levels of exposure to an unmeasured major environmental risk factor that is not correlated between family members. Three possible types are considered, which, for Mendelian inheritance, represent the two homozygotes AA and BB and the heterozygote AB. However, given that these types must sum to 1, only two parameters are estimated, denoted qA and qB. When Hardy-Weinberg equilibrium is assumed (
), only a single parameter qA is estimated.
To test whether each person's type is shared between parent and offspring in the proportions anticipated under Mendelian expectation, transmission parameters (denoted by ) are estimated. These parameters represent the probability that a parent will transmit A, given his/her own type (i.e., the probability that a parent with a given genotype will transmit an A allele for genetic models), to his/her offspring. Under Mendelian expectation, transmission parameters are fixed to 1 for parents of type AA, 0.5 for parents of type AB, and 0 for parents of type BB, denoted as
AA,
AB, and
BB, respectively. All of these transmission parameters not being constrained to their expectation under Mendelian law represent environmental factors influencing the phenotype.
Analysis was performed under class D models, which assume that dependency between sets of siblings is equal (i.e., not impacted by birth order, etc.) but not due to common parentage alone. Thus, additional familial correlations can be estimated within these analyses to account for other genes of small effect (polygenes) or other environmental factors shared among family members that influence the degree of nuclear sclerosis. These additional correlations include spousal (fm), parent and offspring (
po), and sibling (
ss). Furthermore, because age and sex are known to be important determinants of nuclear sclerosis, they were included in the analysis. Analysis was performed by including and excluding pack-years of cigarette smoking exposure as a covariate.
Likelihood ratio tests and Akaike Information Criterion A were used to select the most parsimonious model that adequately described the observed nuclear sclerosis data. Likelihood ratio tests were computed as 2 times the difference in lnLikelihood of the general model compared with a nested model. This test statistic was then compared with a 2 distribution in which the degrees of freedom were equal to the difference in the number of parameters estimated in the general compared with the nested model. When parameters in the general model maximized at a boundary, a mixture of
2 distributions was used to compute p values (25
). Akaike Information Criterion A allows nonnested models to be compared by taking 2 lnLikelihood of the model plus a correction of 2 (degrees of freedom of the model) to estimate additional parameters (26
).
Given that these data were obtained through a population-based survey, no correction for ascertainment was necessary in these segregation analyses.
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RESULTS |
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DISCUSSION |
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Unlike the previous segregation analysis performed on a subset of these data, which was limited to sibship data only and did not take into account personal cigarette smoking exposure (4), our results did not support the involvement of a recessive major gene. Even when smoking was not included in the analysis, recessive (results not shown) and codominant Mendelian (p = 0.02: model F vs. L (table 3)) models were rejected compared with the general model. Inclusion of additional pairs of relatives and linking of sibships to form larger pedigrees may have resulted in greater power to observe deviations from the patterns expected if there was a recessive major gene that influenced severity of nuclear sclerosis. This increased power could have resulted in rejection of the recessive model in favor of a more complex polygenic model.
The higher familial correlations observed between relative pairs in the same generations (siblings and cousins) compared with relative pairs in different generations (parents-offspring and avuncular pairs) even after adjustment for age, sex, and cigarette smoking indicated the potential for a cohort effect possibly due to shared environmental factors that we did not include in our analyses. Additionally, the differences could in part be due to residual confounding by age or smoking. When we examined the proportion of smokers by age, we found that about 70 percent of males smoked regardless of age group (above 65 years vs. 65 years or less). However, only 27 percent of females above the age of 65 years were ever smokers, whereas 48 percent of females aged 65 years or less were smokers. The total number of pack-years of smoking was similar among the older and younger women: 24.5 (SD, 28.9) and 24.7 (SD, 19.0), respectively. The younger male smokers had not yet had as much cumulative cigarette smoking exposure as the older men had: 34.8 (SD, 28.2) vs. 43.5 (SD, 39.4) pack-years. However, this difference could be due in large part to their younger age and thereby a fewer number of years of active smoking.
Although the overall reliability of our nuclear sclerosis grading procedure was high, weighted kappa = 0.76 (95 percent confidence interval: 0.70, 0.82) (18), there are limitations to categorizing a continuous trait (nuclear lens opacity) into five categories. Although a Box-Cox transformation of these data did enable us to adjust for the nonlinearity of the data, a certain amount of misclassification is inherent in the binning process. Nondifferential misclassification of nuclear sclerosis grade would bias the results toward the null by reducing the difference between persons with high and low nuclear lens opacity. In order for differential misclassification to be present in segregation analysis, the misclassification must be differential with respect to both nuclear cataract grade and family history of nuclear cataract grade. Therefore, because grading was performed without knowledge of family history, it is unlikely that differential misclassification would impact the results of these analyses.
Cigarette smoking has been shown to be an important risk factor for the development of both nuclear sclerosis and subsequent nuclear cataract. Although models in which we did not incorporate smoking as a covariate were also examined, they did not provide as good a fit to the observed data as did the models that included smoking (p < 0.0001; models B, F, and L vs. C, G, and M, respectively (table 3)). However, as with any adjustment, we may not have completely controlled for the influence of smoking on nuclear sclerosis in part because we did not adjust for current smoking, length of time since former smokers quit smoking, and passive smoke exposure, all of which may influence nuclear sclerosis. Cigarette smoking will be an important confounder to control for in future studies aimed at identifying the genes involved in nuclear sclerosis.
Our results suggest that the etiology of nuclear sclerosis is quite complex and may be due to a variety of genes of modest effect and environmental factors. These results are consistent with the findings of Hammond et al. (5), who found evidence supporting the involvement of additive genetic factors in the development of nuclear sclerosis. Although our major gene models were rejected, models with a polygenic component, which could be due to the additive effects of several genes, did provide a better fit to the data than did models that ignored the impact of polygenes. Linkage and association studies aimed at localizing the genes involved in the development of nuclear sclerosis are currently under way.
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ACKNOWLEDGMENTS |
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References |
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