General models for segregation analysis.

Theoretical details are given of various oligogenic models for segregation analysis that are available as a general segregation analysis ("GENSEG") package, programmed in FORTRAN iv. The models allow for up to two autosomal loci and one X-linked locus, normally distributed or dichotomous phenotypes, variable age of onset, and various ascertainment functions (including one that allows the probability of becoming a proband to be dependent on the age of onset). Current programs are limited to the analysis of 2-generational data, using the joint likelihood of the sibship and parental phenotypes, unless it can be assumed that the pedigrees being analyzed are a random sample from the population; half-sibships and twins, however, are explicitly allowed.     

A single-gene explanation for the probability of having idiopathic talipes equinovarus.

It has been hypothesized that the pathogenesis of idiopathic talipes equinovarus (ITEV, or clubfoot) is explained by genetic regulation of development and growth. The objective of the present study was to determine whether a single Mendelian gene explains the probability of having ITEV in a sample of 143 Caucasian pedigrees from Iowa. These pedigrees were ascertained through probands with ITEV. Complex segregation analyses were undertaken using a regressive logistic model. The results of these analyses strongly rejected the hypotheses that the probability of having ITEV in these pedigrees was explained by a non-Mendelian pattern of transmission with residual sibling correlation, a nontransmitted (environmental) factor with residual sibling correlation, or residual sibling correlation alone. These results were consistent with the hypothesis that the probability of having ITEV was explained by the Mendelian segregation of a single gene with two alleles plus the effects of some unmeasured factor(s) shared among siblings. The segregation of alleles at this single Mendelian gene indicated that the disease allele A was incompletely dominant to the nondisease allele B. The disease allele A, associated with ITEV affection, was estimated to occur in the population of inference with a frequency of .007. After adjusting for sex-specific population incidences of ITEV, the conditional probability (penetrance) of ITEV affection given the AA, AB, and BB genotypes was computed to be 1.0, .039, and .0006, respectively. Individual pedigrees in this sample that most strongly supported the single Mendelian gene hypothesis were identified. These pedigrees are candidates for genetic linkage analyses or DNA association studies.     

Combined linkage and segregation analysis using regressive models.

Regressive models for segregation analysis have been extended to include multivariate data and linked marker loci. The new models have been applied to data from two pedigrees segregating a gene for cardiovascular disease.     

Selecting pedigrees for linkage analysis of a quantitative trait: the expected number of informative meioses.

With evidence of segregation at a major locus for a quantitative trait having been found, a logical next step is to select a subset of the pedigrees to include in a linkage study to map the major locus. Ideally this subset should include much of the linkage information in the sample but include only a fraction of the pedigrees. We previously described a strategy for selecting pedigrees for linkage analysis of a quantitative trait on the basis of a pedigree likelihood-ratio statistic. For quantitative traits controlled by a major locus with a rare dominant allele, the likelihood-ratio strategy extracted nearly all the information for linkage while typically requiring marker data on only about one-third of the pedigrees. Here, we describe a new strategy to select pedigrees for linkage analysis on the basis of the expected number of potentially informative meioses in each pedigree. We demonstrate that this informative-meioses strategy provides an efficient and more general means to select pedigrees for a linkage study of a quantitative trait.     

病理性近视的家系研究

为了探讨我国病理性近视的遗传模式,对90个病理性近视大家系进行了分离分析。简单分离分析采用先验法和SEGRAN-B软件,进行拟合优度卡方检验,比较实际分离比与理论分离比的符合程度;复合分离分析运用SAGE-REGD软件进行孟德尔遗传模型(主基因、显性、隐性、共显性)和非孟德尔遗传模型(非传递、环境、一般)的拟合。结果显示,婚配类型为A*N的家系符合常染色体显性遗传,散发概率为13．8％,婚配类型为N*N的家系符合常染色体隐性遗传,散发概率为16．3％,但常染色体显性遗传不能除外,复合分离分析接受孟德尔遗传的显性、隐性、共显性和主基因模型,共显性模型的可能性最大,基因频率为0．21442999。因此,我国病理性近视存在常染色体显性和隐性遗传模式,并有一定比例的散发病例,具有遗传异质性。     

Nonsyndromic cleft lip with or without cleft palate in west Bengal, India: evidence for an autosomal major locus.

Ninety extended families having one or more individuals affected with nonsyndromic cleft lip (CL) with or without cleft palate (CL/P) were ascertained in rural West Bengal, India. These families included 138 affected people, 64% of whom had CL alone and 66% of whom were male. Multiple-affected-member ("multiplex") pedigrees were less common than single-affected-member ("simplex") pedigrees, composing 34% of all extended pedigrees. There was no difference between multiplex and simplex pedigrees in the frequency of affected persons with CL alone, but multiplex pedigrees had a lower frequency of affected males (58%) than did simplex pedigrees (76%; P = .02). Complex segregation analysis using the POINTER computer program rejected both the hypothesis of no familial transmission (P < .0001) and the hypothesis that familiarity could be explained solely by a multifactorial/threshold model (P < .05). The hypothesis of major-locus inheritance alone could not be rejected. Among major-locus models examined, strictly recessive inheritance was rejected (P < .0001), but codominant and dominant models were not. Neither the addition of a multifactorial component nor the addition of a proportion of sporadic cases to the major-locus model improved the fit of the data. In conclusion, the results of complex segregation analysis were consistent with a dominant or codominant major-locus mode of inheritance of CL/P in these families.     

Genetic determination of plasma apolipoprotein AI in a population-based sample.

Apolipoprotein AI (apo AI) is the major protein of high-density lipoprotein (HDL). Using radioimmunoassay, we measured plasma apo AI levels in 1,880 individuals in 283 pedigrees randomly selected from the population with respect to disease status and risk factors for coronary artery disease. Apo AI levels were first adjusted for date of assay (6.8% of apo AI variation) and then adjusted for variability in age and body mass index (an additional 6.6%, 20.4%, and 23.0% of apo AI variations for males, females not using exogenous hormones, and females using exogenous hormones, respectively). A mixture of two normal distributions fit the adjusted data better than did a single normal distribution. Genetic and environmental models that could explain the mixture of normal distributions were investigated using complex segregation analysis. Heterogeneous etiologies for individual differences in adjusted apo AI levels were suggested by the data in the 283 pedigrees. In a subset of 126 pedigrees, there is evidence for the major effect of a nontransmitted environmental factor that explains the mixture of distributions as well as polygenic loci that influence apo AI levels within each distribution. The environmental factor and polygenic loci account for 32% and 65% of the adjusted variation, respectively. In the other 157 pedigrees there is strong support for a single locus with a major effect that accounts for 27% of the adjusted variation. The effect of the polygenic loci is not different from zero in these 157 pedigrees. This is the first study to present evidence for the segregation of a single unmeasured locus with a major effect on levels of apo AI in a population-based sample of pedigrees.     

The genetic determination of plasma apolipoprotein A-I levels measured by radioimmunoassay: a study of high-risk pedigrees.

Apolipoprotein A-I (apo A-I) is the major protein component of the high-density lipoprotein (HDL) found in all primates. Using radioimmunoassay, we measured plasma apo A-I levels in 97 individuals from 23 pedigrees ascertained through cases of hypertension or early coronary artery disease (CAD). Using complex segregation analysis, we found that a genetic model with both a single locus with a major effect and polygenic loci gave the best explanation for the distribution of apo A-I levels in these pedigrees. There was no evidence for a major locus effect on HDL cholesterol in these pedigrees. This is the first study to show evidence of a major effect of a single genetic locus on the quantitative variation of plasma apo A-I in a sample of pedigrees enriched for individuals at risk for CAD.     

Inheritance of guttural pouch tympany in the arabian horse

The objective of the present study was to analyze the mode of inheritance of guttural pouch tympany (GPT) using pedigrees of Arabian horses. Complex segregation analyses were employed to test for the significance of nongenetic transmission and for monogenic, polygenic, and mixed monogenic-polygenic modes of inheritance. Horses affected by GPT comprised 27 Arabian purebred foals. Of these 27 animals, 22 were patients at the Clinic for Horses, School of Veterinary Medicine Hannover, Hannover, Germany, between 1994 and 2001 and 5 Arabian foals were from stud farms. Information on the pedigrees of these patients allowed us to classify the affected foals into four families with a total of 276 animals. The regressive logistic model analysis took into account the nonrandomness of the pedigrees through multiple single ascertainment correction. The complex segregation analysis showed that, among all other models employed, a polygenic and a mixed monogenic-polygenic model best explained the segregation of Arabian foals with GPT. Models including only nongenetic distributions and monogenic inheritance could be significantly rejected. This is the first report in which a genetic component could be shown to be responsible for GPT in horses.     

Segregation analysis incorporating linkage markers. I. Single-locus models with an application to type I diabetes.

A method is described for segregation analysis that incorporates linkage markers. The model allows for segregation (penetrance), linkage (recombination fraction), and association (linkage disequilibrium) parameters. A single-locus-multiple-allele model underlying the trait phenotype is assumed. When families have been ascertained in a systematic fashion, a joint (markers, phenotypes) likelihood with ascertainment is advocated. When ascertainment correction is not feasible, a conditional (markers given phenotypes) approach is recommended, which is also valid in the presence of reduced fertility and assortative mating. This approach, oriented toward determining mode of inheritance, differs from conventional linkage analysis, which is oriented toward detection of linkage. Therefore, it is more appropriately considered an extension of the affected sib-pair method to arbitrary pedigrees, including association information and allowing for multiple alleles. Incorporation of coupling parameters allows for discrimination between pleiotropy and linkage disequilibrium. The method is demonstrated through a reanalysis of four recently published family studies on type 1 diabetes and HLA. Recessive inheritance is rejected in all four data sets. For three of them, dominant inheritance is not rejected, while in the fourth, all two-allele models are rejected in favor of three alleles. Although association with the DR3 and DR4 alleles is quite strong, pleiotropy with regard to these alleles is unlikely. The results also suggest an additional familial factor(s) (e.g., locus).     

Transition matrices with mutation

Transition matrices have been derived under mutation for segregation analysis of pedigrees.     

Identifying pedigrees segregating at a major locus for a quantitative trait: an efficient strategy for linkage analysis

Having found evidence for segregation at a major locus for a quantitative trait, a logical next step is to identify those pedigrees in which major-locus segregation is occurring. If the quantitative trait is a risk factor for an associated disease, identifying such segregating pedigrees can be important in classifying families by etiology, in risk assessment, and in suggesting treatment modalities. Identifying segregating pedigrees can also be helpful in selecting pedigrees to include in a subsequent linkage study to map the major locus. Here, we describe a strategy to identify pedigrees segregating at a major locus for a quantitative trait. We apply this pedigree selection strategy to simulated data generated under a major-locus or mixed model with a rare dominant allele and sampled according to one of several fixed-structure or sequential sampling designs. We demonstrate that for the situations considered, the pedigree selection strategy is sensitive and specific and that a linkage study based only on the pedigrees classified as segregating extracts essentially all the linkage information in the entire sample of pedigrees. Our results suggest that for large-scale linkage studies involving many genetic markers, the savings from this strategy can be substantial and that, compared with fixed-structure sampling, sequential sampling of pedigrees can greatly improve the efficiency for linkage analysis of a quantitative trait.     

Possibility of the current segregation analysis to discriminate between monogenic and multifactorial types of inheritance of traits. The effect of the structure of family data on model robustness and power of the analysis

The influence of sampling designs for robustness of the autosomal major locus model and the multifactorial model as well as possibility of segregation analysis to discriminate these models was studied. Nuclear families and 3-generation pedigrees were considered. It was found that robustness of models increased, when the size of sibships in nuclear families grows and when configuration of pedigrees is complicated. The resolution power of the analysis is always increased with size elevation of sibships, the highest effect of the analysis being observed for sibships of the size 3 or 4. Consideration of new generations is only advisable, if attracting sibs of these generations, the resolution power being increased, provided that the parameters of models are of high value.     

Two-trait-locus linkage analysis: a powerful strategy for mapping complex genetic traits.

Recent advances in molecular biology have provided geneticists with ever-increasing numbers of highly polymorphic genetic markers that have made possible linkage mapping of loci responsible for many human diseases. However, nearly all diseases mapped to date follow clear Mendelian, single-locus segregation patterns. In contrast, many common familial diseases such as diabetes, psoriasis, several forms of cancer, and schizophrenia are familial and appear to have a genetic component but do not exhibit simple Mendelian transmission. More complex models are required to explain the genetics of these important diseases. In this paper, we explore two-trait-locus, two-marker-locus linkage analysis in which two trait loci are mapped simultaneously to separate genetic markers. We compare the utility of this approach to standard one-trait-locus, one-marker-locus linkage analysis with and without allowance for heterogeneity. We also compare the utility of the two-trait-locus, two-marker-locus analysis to two-trait-locus, one-marker-locus linkage analysis. For common diseases, pedigrees are often bilineal, with disease genes entering via two or more unrelated pedigree members. Since such pedigrees often are avoided in linkage studies, we also investigate the relative information content of unilineal and bilineal pedigrees. For the dominant-or-recessive and threshold models that we consider, we find that two-trait-locus, two-marker-locus linkage analysis can provide substantially more linkage information, as measured by expected maximum lod score, than standard one-trait-locus, one-marker-locus methods, even allowing for heterogeneity, while, for a dominant-or-dominant generating model, one-locus models that allow for heterogeneity extract essentially as much information as the two-trait-locus methods. For these three models, we also find that bilineal pedigrees provide sufficient linkage information to warrant their inclusion in such studies. We also discuss strategies for assessing the significance of the two linkages assumed in two-trait-locus, two-marker-locus models.     

Detection of genetic heterogeneity among pedigrees through complex segregation analysis: an application to hypercholesterolemia

Several methods for investigating genetic heterogeneity for extreme levels of a quantitative trait with hypothesized multiple genetic etiologies require a priori stratification of families and/or identification of distinct phenotypes among affected individuals. We present a statistical approach for detecting genetic heterogeneity that does not rely on either a priori stratification or discrete disease phenotypes. Complex segregation analysis was applied to total serum cholesterol measurements in 709 relatives of 98 healthy index cases selected from 3,666 school children surveyed for lipid levels in Rochester, Minnesota. Thirty-three of the index cases and 109 relatives had hypercholesterolemia (cholesterol levels greater than the 95th percentile for their age and sex). Through application of the mixed genetic model and then estimation of conditional probabilities for having the mutant allele at the major locus, genetic heterogeneity for hypercholesterolemia was indicated. In three of 70 pedigrees with one or more hypercholesterolemics, there is strong evidence for segregation at a major locus. In the remaining pedigrees, only polygene variation and/or environmental variation are associated with cholesterol variability. Grandparents in the three pedigrees that were segregating at the major locus had the highest rates of death due to coronary heart disease. This study establishes that the mixed model has the potential to identify pedigrees with different genetic etiologies for variability in quantitative traits.     

Segregation analysis reveals a major gene effect in compact and cancellous bone mineral density in 2 populations

Involvement of genetic factors in determining bone mineral density (BMD) is doubtless. However, the exact nature of the genes governing BMD variation and sources for genetic determination of BMD of different parts of bone (compact and cancellous) have not been completely studied. The results of the complex segregation analyses performed in our previous study (Livshits et al. 1996) on a Turkmenian sample strongly support the hypothesis that a single Mendelian locus has a large effect on BMD. The parameter estimates for both types of bone tissue were so similar that we could assume a common gene effect for BMD variation of cancellous and compact bone. The objectives of the present study are to test again the possibility of major gene control of BMD in a different ethnic sample of pedigrees, namely, the Chuvasha. In addition, we report here the results of a bivariate segregation analysis of compact and cancellous BMD performed in both the Turkemenian and the Chuvasha samples of pedigrees. The results of the present study closely resemble the results obtained on the Turkmenian pedigrees. Likewise, the major finding of the present study is that there is a significant major gene effect on both compact and cancellous BMD; polygenic hypotheses were clearly rejected. Moreover, the results of the bivariate segregation analysis in both the Chuvasha and Turkmenian samples were similar. They lead to acceptance of the hypothesis that there is a single major locus with pleiotropy to both compact and cancellous bone.     

Further segregation analysis of the fragile X syndrome with special reference to transmitting males

Summary A new series of 96 pedigrees with the fra(X) syndrome was analysed using complex segregation analysis with pointers, defining affection as any degree of mental impairment. These families were found to exhibit the same segregation pattern as the first series of 110 pedigrees (Sherman et al. 1984). The best estimate for penetrance of mental impairment in males was 79% and in females was 35% for the combined data. Again, there was little evidence for sporadic cases among affected males.Many more intellectually normal transmitting males have been observed since the existence of such males and the concomitant need to investigate the paternal side of pedigrees was recognized. On further investigation of all 206 pedigrees from the old and new data sets, the sibships of nonexpressing males appeared to be different from those of expressing males. Our analysis, using mental impairment as the phenotype, suggested that obligate carrier mothers and daughters of intellectually normal transmitting males are rarely, if ever, mentally impaired and that the sibs of transmitting males are much less likely to be retarded than the sibs of mentally impaired males. Though mothers and daughters of transmitting males are similar in phenotype, the expression of the gene in their offspring appears to be different: the penetrance of mental impairment is higher in offspring of intellectually normal daughters of transmitting males than in offspring of intellectually normal mothers of transmitting males. The implications of these observations for genetic counseling and for genetic models of the fra(X) syndrome are discussed.     

Developmental choices in mating-type interconversion in fission yeast.

Fission yeast cells follow a specific pattern of mating (cell) type switching in single cell pedigrees. Asymmetric cell divisions producing sisters of different developmental fates result from inheritance of specific parental DNA strands according to the classical model of semiconservative replication and segregation.     

Genetic linkage and heterogeneity in type I Charcot-Marie-Tooth disease (hereditary motor and sensory neuropathy type I).

The segregation patterns of DNA markers from the pericentromeric regions of chromosomes 1 and 17 were studied in seven pedigrees segregating an autosomal dominant gene for Charcot-Marie-Tooth neuropathy type I (CMT I; hereditary motor and sensory neuropathy I). A multilocus analysis with four markers (pMCR-3, pMUC10, FY, and pMLAJ1) spanning the pericentromeric region of chromosome 1 excluded the CMT I gene from this region in six pedigrees but gave some evidence for linkage to the region of Duffy in one pedigree. Linkage of the CMT I gene to markers in the pericentromeric region of chromosome 17 (markers pA10-41, pEW301, p3.6, and pTH17.19) was established; however, in these seven pedigrees homogeneity analysis with chromosome 17 markers detected significant genetic heterogeneity. This analysis suggested that three of the seven pedigrees are not linked to this same region. Overall, two of the seven CMT I pedigrees were not linked to markers tested from chromosomes 1 or 17. These results confirm genetic heterogeneity in CMT I and implicate the existence of a third autosomal locus, in addition to a locus on chromosome 17, and a probable locus on chromosome 1. This evidence of etiological heterogeneity, supported by statistical tests, will have to be taken into consideration when fine-structure genetic maps of the regions around CMT I are constructed.     

Joint oligogenic segregation and linkage analysis using bayesian Markov chain Monte Carlo methods

One of the most challenging areas in human genetics is the dissection of quantitative traits. In this context, the efficient use of available data is important, including, when possible, use of large pedigrees and many markers for gene mapping. In addition, methods that jointly perform linkage analysis and estimation of the trait model are appealing because they combine the advantages of a model-based analysis with the advantages of methods that do not require prespecification of model parameters for linkage analysis. Here we review a Markov chain Monte Carlo approach for such joint linkage and segregation analysis, which allows analysis of oligogenic traits in the context of multipoint linkage analysis of large pedigrees. We provide an outline for practitioners of the salient features of the method, interpretation of the results, effect of violation of assumptions, and an example analysis of a two-locus trait to illustrate the method.