A comparison of three affected-sib-pair scoring methods to detect HLA-linked disease susceptibility genes.

Two widely used affected-sib-pair scoring procedures (the Green and Woodrow [1977] procedure, and the method of forming all possible affected-sib-pairs) are compared with a new method for their relative efficiency in detecting the presence of an HLA-linked disease susceptibility gene. Their relative performance is investigated by extensive computer simulations over a large number of disease transmission models. On the average, the new procedure appears to outperform the Green and Woodrow method and the "all-possible-pairs" method.

Power of the affected-sib-pair method to defect disease susceptibility loci of small effect: an application to multiple sclerosis.

The distribution of marker locus identity-by-descent scores in affected sib pairs provides a powerful tool for detecting the presence of a linked non-Mendelian disease susceptibility locus. This basic approach is here extended to include a trio of sibs. A special type of sib trio consisting of two affected and one unaffected sib is investigated. It is shown that compared to affected-sib-pairs, trios with the above configuration are less efficient in detecting the presence of a linked disease susceptibility locus. When the generalized two-allele single locus model is fitted to sib pairs affected with multiple sclerosis, an estimate of the recombination fraction of 0.21 between the putative disease susceptibility locus and the HLA complex is obtained. However, this transmission model is deemed inadequate since a recombination fraction this large is inconsistent with the variety of HLA associations observed at the population level.

Anxiety proneness linked to epistatic loci in genome scan of human personality traits.

A genome-wide scan between normal human personality traits and a set of genetic markers at an average interval of 13 centimorgans was carried out in 758 pairs of siblings in 177 nuclear families of alcoholics. Personality traits were measured by the Tridimensional Personality Questionnaire. We detected significant linkage between the trait Harm Avoidance, a measure of anxiety proneness, and a locus on chromosome 8p21-23 that explained 38% of the trait variance. There was significant evidence of epistasis between the locus on 8p and others on chromosomes 18p, 20p, and 21q. These oligogenic interactions explained most of the variance in Harm Avoidance. There was suggestive evidence of epistasis in other personality traits. These results confirm the important influence of epistasis on human personality suggested by twin and adoption studies.

Genome-wide search for genes affecting the risk for alcohol dependence.

Alcohol dependence is a leading cause of morbidity and premature death. Several lines of evidence suggest a substantial genetic component to the risk for alcoholism: sibs of alcoholic probands have a 3-8 fold increased risk of also developing alcoholism, and twin heritability estimates of 50-60% are reported by contemporary studies of twins. We report on the results of a six-center collaborative study to identify susceptibility loci for alcohol dependence. A genome-wide screen examined 291 markers in 987 individuals from 105 families. Two-point and multipoint nonparametric linkage analyses were performed to detect susceptibility loci for alcohol dependence. Multipoint methods provided the strongest suggestions of linkage with susceptibility loci for alcohol dependence on chromosomes 1 and 7, and more modest evidence for a locus on chromosome 2. In addition, there was suggestive evidence for a protective locus on chromosome 4 near the alcohol dehydrogenase genes, for which protective effects have been reported in Asian populations.

The familial transmission of primary major depressive disorder.

This is a study of the familial transmission of Primary Major Depressive Disorder in the families of 235 probands with this disorder ascertained as part of the NIMH-CRB Collaborative Depression Program. Eight hundred and twenty-six interviewed first degree relatives and 109 spouses are included. Research Diagnostic Criteria have been used and interviews were done using the SADS-L schedule. Prior analyses of these data have established the presence of strong secular trends in the age-of-onset and prevalence of Major Depressive Disorder in these families. Accordingly, new methods for the analysis of family data which incorporate secular variation were developed. Non-parametric Survival Analysis, using the Cox Proportional Hazards Model, guided the formulation of a quantitative family transmission model. Then a family analysis was conducted with the Multifactorial Model of Disease Transmission and the Tau Path Analytic Model. Using the non-parametric approach, only the sibs birth cohort, sex and affectational status of the mother were significantly related to the time of onset of illness in siblings. Proband sex, age-of-onset, and the presence of illness in the father were not significant. The quantitative analysis confirmed that more recently born cohorts of individuals had an increased expected lifetime prevalence and a decreased age-of-onset of Primary Major Depressive Disorder. Assortative mating was present and environmental factors common to siblings did not make a significant contribution to the phenotypic variance. Sex specific transmissibilities were found and the transmissibility in females (t2 = 0.62) was significantly greater than that of males (t2 = 0.28). There was a trend for the transmissibility of Primary Major Disorder to be greater in more recently born cohorts.

Schizo-affective disorders: bipolar-unipolar subtyping. Natural history variables: a discriminant analysis approach.

In view of tackling the problem of heterogeneity among the schizo-affectives, methods of univariate and multivariate statistical analysis (canonical discriminant analysis) were applied to the sociodemographic and natural history variables of four groups of affective disorder patients from the NIMH Collaborative Study on the Psychobiology of Depression Clinical section: the schizo-bipolar (SBP, n = 45), the schizo-unipolar (SUP, n = 30), the bipolar I (BP, n = 159) and the primary unipolar depressed (UP, n = 387) defined by Research Diagnostic Criteria. Two dimensions were identified among the four groups of 'affective' patients: the 'bipolar' and the 'schizophrenic' dimensions. They provided highly significant discrimination among the means of the four groups but were not very accurate in predicting group membership. The 'bipolar' dimension separates the UP from the BP and SBP, the SUP taking some intermediate value. The 'schizophrenic' dimension separates the BP and UP from the SUP, the SBP being intermediate. The two groups with the most similarities were the SBP and BP. The group with the most heterogeneity was the SUP, sharing similarities with the UP and SBP mostly. These conclusions are supported by results of familial aggregation on the same group of patients.

Linkage analysis with cohort effects: an application to X-linkage.

The LIPED program has been modified to incorporate cohort effects in a linkage analysis. Very significant positive lod scores are obtained between bipolar affective disorder and both the deutan and protan markers on the X-chromosome. Lod score methods are shown to be sensitive to the specification of the age of onset distributions and the parameters of the model of transmission.

Linkage analysis with inbreeding.

For computational reasons, linkage analysis of highly inbred pedigrees is often performed under the assumption that spouse pairs are genetically unrelated. A formulation of the likelihood of a pedigree which takes into account all of the genealogical information omega, for an arbitrary relationship between a pair of spouses, is proposed. Lod scores at a recombination fraction of 0 for an inbred three-member nuclear family are presented.

Multipoint linkage analysis of the pseudoautosomal regions, using affected sibling pairs.

Affected sibling pairs are often the design of choice in linkage-analysis studies with the goal of identifying the genes that increase susceptibility to complex diseases. Methods for multipoint analysis based on sibling amount of sharing that is identical by descent are widely available, for both autosomal and X-linked markers. Such methods have the advantage of making few assumptions about the mode of inheritance of the disease. However, with this approach, data from the pseudoautosomal regions on the X chromosome pose special challenges. Same-sex sibling pairs will share, in that region of the genome, more genetic material identical by descent, with and without the presence of a disease-susceptibility gene. This increased sharing will be more pronounced for markers closely linked to the sex-specific region. For the same reason, opposite-sex sibling pairs will share fewer alleles identical by descent. Failure to take this inequality in sharing into account may result in a false declaration of linkage if the study sample contains an excess of sex-concordant pairs, or a linkage may be missed when an excess of sex-discordant pairs is present. We propose a method to take into account this expected increase/decrease in sharing when markers in the pseudoautosomal region are analyzed. For quantitative traits, we demonstrate, using the Haseman-Elston method, (1) the same inflation in type I error, in the absence of an appropriate correction, and (2) the inadequacy of permutation tests to estimate levels of significance when all phenotypic values are permuted, irrespective of gender. The proposed method is illustrated with a genome screen on 350 sibling pairs affected with type I diabetes.

Multivariate genetic analysis of an oligogenic disease.

Joint multivariate segregation and linkage analysis provides a method for simultaneously analyzing data on affection status, correlated phenotypic traits, environmental risk factors, and other covariates. The power of this approach for mapping disease susceptibility loci of small effect (oligogenes) was evaluated by analyzing the GAW9 Problem 2 data set. The program REGRESS, which assumes a pleiotropy model in which one locus influences both affection status (AF) and a quantitative trait, was used to conduct joint segregation and linkage analysis of bivariate phenotypes, each comprising AF and one quantitative trait (Q2, Q3, Q4). A genome-wide search using markers spaced approximately 10 cM apart was conducted and regions on chromosomes 1, 2, and 5 were identified as demonstrating linkage with three respective bivariate phenotypes at the following markers: AF/Q2-D1G2; AF/Q3-D2G10; and AF/Q4-D5G18. The effects of other loci were included in a general model by specifying the quantitative traits they influenced as covariates along with age, sex, and an environmental effect. Use of covariate and quantitative trait data in each analysis resulted in respective chi 2 values with 1 df of 38.4, 65.4, and 22.0 to reject the no linkage hypothesis at theta = 0, with respective equivalent lod scores of 8.3, 14.2, and 4.8. Rejection at p < 0.0002 occurred using markers as far away as 20 cM. These loci were not detected when AF alone was analyzed.

Information content of data with respect to models.

A measure is proposed for the information content of data with respect to models. A model, defined by a set of parameter values in a mathematical framework, is considered a point in a hyperspace. The proposed measure expresses the information content of experimental data as the contribution they make, in units of information bits, in defining a model to within a desired region of the hyperspace. This measure is then normalized to conventional statistical measures of uncertainty. It is shown how the measure can be used to estimate the information of newly planned experiments and help in decisions on data collection strategies.

Type I (insulin dependent) diabetes mellitus. Is there strong evidence for a non-HLA linked gene?

A recent investigation of families containing two and three consecutive generations affected with Type I (insulin dependent) diabetes mellitus has led to speculation that there is a second susceptibility gene, not linked to the major histocompatibility complex. These families differ in important respects from families which have provided the strongest evidence for HLA linkage, namely, families with two or more affected siblings. Computer simulations were designed to test the hypothesis that these two types of families were drawn from the same population of insulin dependent patients. The results indicate that this hypothesis is tenable and that the consecutive generation families probably failed to yield strong evidence for an HLA linked susceptibility gene because of ascertainment bias combined with probable incorrect specifications of the mode of inheritance of insulin dependent diabetes.

Mathematical limits of multilocus models: the genetic transmission of bipolar disorder.

We describe a simple, graphical method for determining plausible modes of inheritance for complex traits and apply this to bipolar disorder. The constraints that allele frequencies and penetrances lie in the interval 0-1 impose limits on recurrence risks, KR, in relatives of an affected proband for a given population prevalence, KP. We have investigated these limits for KR in three classes of relatives (MZ co-twin, sibling, and parent/offspring) for the general single-locus model and for two types of multilocus models: heterogeneity and multiplicative. In our models we have assumed Hardy-Weinberg equilibrium, an all-or-none trait, absence of nongenetic resemblance between relatives, and negligible mutation at the disease loci. Although the true values of KP and the KR's are only approximately known, observed population and family data for bipolar disorder are inconsistent with a single-locus model or with any heterogeneity model. In contrast, multiplicative models involving three or more loci are consistent with observed data and, thus, represent plausible models for the inheritance of bipolar disorders. Studies to determine the genetic basis of most bipolar disorder should use methods capable of detecting interacting oligogenes.

HLA sharing in multiplex sibships.

For an arbitrary number of affected sibs with unaffected parents, the proportion that share both marker haplotypes identical by descent is derived for the generalized single locus model. This proportion is shown to depend on the underlying parameters of the disease transmission model, on the total sibship size and on the number of sibs who are affected. The variety of identity by descent patterns suggests that any attempt to infer the presence of other independent disease susceptibility loci solely on the basis of an observed inverse relationship between the degree of haplotype sharing and the number of affected sibs, is liable to result in spurious conclusions.

On the detection of linkage in multiple data sets: a comparison of various statistical approaches.

We contrast the pooling of multiple data sets with the compound HLOD (HLOD-C) and the posterior probability of linkage (PPL), two approaches that have been shown to have more power in the presence of genetic heterogeneity. We also propose and evaluate several multipoint extensions.

The importance of watching our weights: how the choice of weights for non-independent sib pairs can dramatically alter results.

Handling non-independent sib pairs in families with multiple affected sibs presents a problem in likelihood-based nonparametric linkage analyses. We contrast the more stable partial-likelihood solution in MAPMAKER/SIBS with the extremely variable partial-likelihood approach used in ASPEX, and the potential inflation of lods when the problem is ignored as in BETA.

Joint multipoint linkage analysis of multivariate qualitative and quantitative traits. I. Likelihood formulation and simulation results.

We describe a variance-components method for multipoint linkage analysis that allows joint consideration of a discrete trait and a correlated continuous biological marker (e.g., a disease precursor or associated risk factor) in pedigrees of arbitrary size and complexity. The continuous trait is assumed to be multivariate normally distributed within pedigrees, and the discrete trait is modeled by a threshold process acting on an underlying multivariate normal liability distribution. The liability is allowed to be correlated with the quantitative trait, and the liability and quantitative phenotype may each include covariate effects. Bivariate discrete-continuous observations will be common, but the method easily accommodates qualitative and quantitative phenotypes that are themselves multivariate. Formal likelihood-based tests are described for coincident linkage (i.e., linkage of the traits to distinct quantitative-trait loci [QTLs] that happen to be linked) and pleiotropy (i.e., the same QTL influences both discrete-trait status and the correlated continuous phenotype). The properties of the method are demonstrated by use of simulated data from Genetic Analysis Workshop 10. In a companion paper, the method is applied to data from the Collaborative Study on the Genetics of Alcoholism, in a bivariate linkage analysis of alcoholism diagnoses and P300 amplitude of event-related brain potentials.

Sib-based detection of QTLs.

Association and transmission/nontransmission analyses were used in a split sample design to identify disease susceptibility alleles at two loci. Sib-pair analysis on various subsets of the data identified an additional four regions that yielded signals of disease predisposing quantitative trait loci (QTLs). Three of these four regions represented Type I errors. A new simulation indicates that a multiplex sampling strategy would substantially improve QTL detection for this oligogenic transmission model.