Linkage strategies for genetically complex traits. II. The power of affected relative pairs.

The power to detect disease-susceptibility loci through linkage analysis using pairs of affected relatives and affected-unaffected pairs is examined. Allelic identity by descent (ibd) for a completely polymorphic marker for sibling, uncle-nephew, grandparent-grandchild, half-sib, and first-cousin pairs is considered. Affected-unaffected pairs generally represent a poor strategy. For single-locus models, ibd depends on lambda R, the risk ratio for type R relatives compared with population prevalence, and the recombination fraction theta. The ibd for grandparent-grandchild pairs is least affected by recombination, followed by sibs, half-sib, uncle-nephew, and first-cousin pairs. For diseases with large lambda values and for small theta values, distant relatives offer greater power. For larger theta values, grandparent-grandchild pairs are best; for small lambda values, sibs are best. Additive and multiplicative multilocus models are considered. For the multiplicative model, the same formulas as in the single-locus model apply, except that lambda iR (for the ith contributing locus) is substituted for lambda R. For the additive model, the deviation from null expectation for ibd is divided among all contributing loci. Compared with the multiplicative model, for an additive model there is usually greater advantage in distant relationships. Multipoint analysis using linked marker loci for affected relative pairs is described. Simultaneous use of multiple markers diminishes the effect of recombination and allows for localization of the disease-susceptibility locus.     

Linkage strategies for genetically complex traits. I. Multilocus models

In order to investigate linkage detection strategies for genetically complex traits, multilocus models of inheritance need to be specified. Here, two types of multilocus model are described: (1) a multiplicative model, representing epistasis (interaction) among loci, and (2) an additive model, which is shown to closely approximate genetic heterogeneity, which is characterized by no interlocus interaction. A ratio lambda R of risk for type R relatives that is compared with population prevalence is defined. For a single-locus model, lambda R - 1 decreases by a factor of two with each degree of relationship. The same holds true for an additive multilocus model. For a multiplicative (epistasis) model, lambda R - 1 decreases more rapidly than by a factor of two with degree of relationship. Examination of lambda R values for various classes of relatives can potentially suggest the presence of multiple loci and epistasis. For example, data for schizophrenia suggest multiple loci in interaction. It is shown in the second paper of this series that lambda R is the critical parameter in determining power to detect linkage by using affected relative pairs.     

The relationship between the sibling recurrence-risk ratio and genotype relative risk

The recurrence-risk ratio of disease in siblings, lambdaS, is a standard parameter used in genetic analysis to estimate the statistical power for detection of a disease locus. However, the relationship between the underlying risk conferred by a disease-susceptibility allele and lambdaS has not been well described. The former is generally quantified as a genotype relative risk, gamma, and measures the ratio of disease risks between those with and those without the susceptibility genotype(s). We demonstrate that lambdaS varies significantly more with respect to gamma and the disease-allele frequency for two-locus multiplicative models than for other two-locus and for single-locus models. For the single- and two-locus dominant-inheritance models that we studied, when a disease-susceptibility allele had a frequency >/=.2, lambdaS had an upper limit of <10. In general, lambdaS values >10 are possible only under recessive inheritance, dominant inheritance with relatively rare (<5%) disease-susceptibility alleles, or when two or more disease loci have alleles acting either epistatically or multiplicatively. We introduce the idea of a restricted sib recurrence-risk ratio (lambda*S) estimated by restriction of sibships to those ascertained through a proband who already has a putative high-risk allele. A lambda*S larger than the lambdaS value estimated from randomly selected probands can serve as an indirect way of testing whether the posited susceptibility allele increases disease risk. Our results demonstrate that a lambdaS of 2-3 may portend successful mapping for a variety of genetic models but that, for some two-locus models, a lambdaS as high as 10 does not guarantee underlying genes easily mapped by linkage.     

Familial recurrence-pattern analysis of cleft lip with or without cleft palate.

Cleft lip with or without cleft palate (CL/P) is a common congenital malformation with an incidence in European white populations of about 1/1,000. The familial clustering of CL/P has been extensively characterized, and epidemiological studies have proposed monogenic models (with reduced penetrance), multifactorial/threshold models, and mixed major-gene/multifactorial models to explain its inheritance. The recognition of an association between two RFLPs at the transforming growth factor alpha (TGFA) locus and CL/P supports a major-gene component to the etiology of CL/P. Risch has shown that the recurrence risk ratio lambda R (risk to relatives, vs. population prevalence) is a useful pointer to the mode of inheritance. Here we further develop the use of lambda R to analyze recurrence-risk data for CL/P. Recurrence risks for first-, second-, and third-degree relatives equate well with oligogenic models with as few as four loci. A monogenic/additive model is strongly rejected. The limited available twin data are also consistent with this model. A "major gene" interacting epistatically with an oligogenic background is shown to be a plausible alternative. Power calculations for a linkage study to map the CL/P major-risk locus suggest that a sample of 50 affected sib pairs will be adequate, but linkage to minor-risk loci will require very much larger samples.     

Assessing the role of HLA-linked and unlinked determinants of disease.

The relationship between increased risk in relatives over population prevalence (lambda R = KR/K) and probability of sharing zero marker alleles identical by descent (ibd) at a linked locus (such as HLA) by an affected relative pair is examined. For a model assuming a single disease-susceptibility locus or group of loci tightly linked to a marker locus, the relationship is remarkably simple and general. Namely, if phi R is the prior probability for the relative pair to share zero marker alleles identical by descent, then P (sharing 0 markers/both relatives are affected) is just phi R/lambda R. Alternatively, lambda AR, the increased risk over population prevalence to a relative R due to a disease locus tightly linked to marker locus A, equals the prior probability that the relative pair share zero A alleles ibd divided by the posterior probability that they share zero alleles ibd, given that they are both affected. For example, for affected sib pairs, P (sharing 0 markers/both sibs are affected) = .25/lambda S. This formula holds true for any number of alleles at the disease locus and for their frequencies, penetrances, and population prevalence. Similar formulas are derived for sharing one and two markers. Application of these formulas to several well-studied HLA-associated diseases yields the following results: For multiple sclerosis, insulin-dependent diabetes mellitus, and coeliac disease, a single-locus model of disease susceptibility is rejected, implying the existence of additional unlinked familial determinants. For all three diseases, the effect of the HLA-linked locus on familiality is minor: for multiple sclerosis, it accounts for only a 2.5-fold increased risk to sibs over the population prevalence, compared to an observed value of 20; for coeliac disease, it accounts for approximately a 5.25-fold increased risk to sibs, while the observed value is on the order of 60; for insulin-dependent diabetes mellitus, it accounts for a 3.42-fold increased risk in sibs, while the observed value is 15. In all cases, the secondary determinants must be outside the HLA region. For tuberculoid leprosy, an unlinked familial determinant is also implicated (increased risk to sibs due to HLA = 1.49; observed value = 2.38). For hemochromatosis and Hodgkin's disease, there is little evidence for HLA-unlinked familial determinants. With this formula, it is also possible to examine the hypothesis of pleiotropy versus linkage dis-equilibrium by comparing lambda AS with the increased risk to sibs due to the associated allele(s).(ABSTRACT TRUNCATED AT 400 WORDS)     

Familial recurrence-pattern analysis of nonsyndromic isolated cleft palate--a Danish Registry study.

The finding of an association between genetic variation at the transforming growth-factor alpha (TGFA) locus and nonsyndromic isolated cleft palate (CP) represents a potentially important breakthrough in our understanding of this condition. The present study was undertaken to assess the feasibility of detecting linkage to putative CP-susceptibility loci, such as TGFA. To this end, the familial recurrence pattern for CP was evaluated to determine the most likely mode of inheritance for this condition. The study took advantage of the high ascertainment and uniform registration of CP in Denmark. In addition, the study utilized estimates of familial recurrence that were obtained by register linkage and, hence, were not subject to either recall bias or the potentially biasing influence of nonresponders. The recurrence risks for first-, second-, and third-degree relatives of 1,364 nonsyndromic CP probands were estimated to be 2.74% (72/2,628), 0.28% (3/1,068), and 0.00% (0/360), respectively. These estimates are close to published estimates based on questionnaire and interview data. The population prevalence for nonsyndromic CP was, however, found to be considerable higher than usually reported (0.058% [1,456/2,523,023]). Analyses of these and previously published data, using the method presented by Risch, indicated that major-locus or additive multilocus inheritance of CP is unlikely. The familial recurrence pattern was, however, consistent with CP being determined by several interacting loci. Under such a model, a single locus accounting for more than a sixfold increase in the risk to first-degree relatives of CP probands is unlikely, whereas a single locus accounting for a threefold increase provided a good fit to the data. Such a locus could be detected in a realistic sample of affected sib pairs.     

Mutations in the BRCA1 gene: implications of inter-population differences for predicting the risk of radiation-induced breast cancers

The effects of cancer predisposition and increased tumorigenic radiosensitivity of the predisposed genotypes on radiation cancer risks (in the general population and in sisters and first cousins of affected probands) are studied using an autosomal dominant model of cancer predisposition and radiosensitivity. The model assumes that the predisposing alleles, which confer enhanced tumorigenic radiosensitivity, are incompletely penetrant. In addition, the model also allows for sporadic cancers, unrelated to the predisposing locus. The predictions of the model are illustrated using current estimates of BRCA1 mutant gene frequencies; the estimates of the strength of predisposition and radiosensitivity differentials used are based on animal and human studies. It is shown that, unless both the strength of predisposition and radiosensitivity differential are large (say, > 100-fold in comparison with normal homozygotes), (i) the effect of risk heterogeneity on cancer risk is marginal; (ii) dose-dependent radiation effect remains virtually the same as in a homogeneous irradiated population that has no predisposed subgroups; (iii) for the same radiation dose, relatives of affected probands show an enhancement of cancer risks; and (iv) most extra cancers in relatives can be attributed to radiosensitivity differentials. This simple model can give an upper bound of the effect of risk heterogeneity on radiation-induced breast cancer risks even when the cumulative breast cancer risk is age-dependent. Further, our model predicts that the benefits of mammography outweigh the risks.     

Mode of inheritance of nonsyndromic cleft lip with or without cleft palate: a reanalysis.

Nonsyndromic cleft lip with or without cleft palate (CL +/- P) is traditionally recognized as a multifactorial threshold trait (MFT). Recently, however, evidence for the involvement of a major gene in the etiology of CL +/- P has been reported. To assess the potential for major-gene involvement in the etiology of this trait, familial recurrence patterns from several family studies of CL +/- P were reanalyzed. The recurrence patterns in first-degree relatives of CL +/- P probands were found to be compatible with the expectations for either an MFT or a generalized single-major-locus (gSML) trait. The use of multiple thresholds based on proband sex, defect bilaterality, or palatal involvement did not help to discriminate between these models. However, the pattern of recurrence among MZ twins and more remote relatives of CL +/- P probands is not consistent with gSML inheritance but is compatible with either an MFT model or a model specifying multiple interacting loci. For such a model, no single locus can account for more than a sixfold increase in risk to first-degree relatives. These findings have important implications with regard to the feasibility of detecting linkage to loci conferring susceptibility to CL +/- P.     

General Epistatic Models of the Risk of Complex Diseases

The range of possible gene interactions in a multilocus model of a complex inherited disease is studied by exploring genotype-specific risks subject to the constraint that the allele frequencies and marginal risks are known. We quantify the effect of gene interactions by defining the interaction ratio, , where K_R is the recurrence risk to relatives with relationship R for the true model and is the recurrence risk to relatives for a multiplicative model with the same marginal risks. We use a Markov chain Monte Carlo (MCMC) procedure to sample from the space of possible models. We find that the average of C_R increases with the number of loci for both low frequency (p = 0.03) and higher frequency (p = 0.25) causative alleles. Furthermore, the probability that C_R > 1 is nearly 1. Similar results are obtained when more weight is given to risk models that are closer to the comparable multiplicative model. These results imply that, in general, gene interactions will result in greater heritability of a complex inherited disease than is expected on the basis of a multiplicative model of interactions and hence may provide a partial explanation for the problem of missing heritability of complex diseases.ALTHOUGH many genome-wide association studies (GWAS) have been performed and have found hundreds of SNPs associated with higher risk of complex inherited diseases, those SNPs so far account for only a small fraction of the inherited risk of those diseases (Altshuler et al. 2008). Several not mutually exclusive explanations have been proposed for the “missing heritability,” i.e., the heritability that is not yet accounted for by SNPs found in GWAS (Manolio et al. 2009): (i) common alleles of small effect that have not been found because GWAS done so far have been underpowered, (ii) low-frequency alleles of moderate effect that are difficult to find using HapMap SNPs, (iii) rare copy-number variants that are not in strong linkage disequilibrium (LD) with HapMap SNPs, (iv) inherited epigenetic factors that are not in strong LD with HapMap SNPs, and (v) interactions among causative alleles that conceal their true contribution to heritability. In this article we investigate the last possibility and determine the extent to which interactions may account for missing heritability.Our analysis is in the same spirit as that of Culverhouse et al. (2002). We assume that the risk of being affected by a complex disease is determined by an individual''s genotype at two or more loci and that the frequencies of causative alleles and the average risks for each one-locus genotype (the marginal risks) are known. Culverhouse et al. (2002) assumed the marginal risks were the same for all genotypes and all loci. In that case, causative alleles have odds ratios of 1; they contribute to risk only through their interactions. Culverhouse et al. found the risk function that maximized the heritability and showed that the maximum possible heritability attributable to interactions increased with the number of loci. They concluded that it is quite possible that interactions among loci that have no main effect could contribute substantially to the heritability of a complex disease and indeed could account for “virtually all the variation in affection status for diseases with any prevalence” (Culverhouse et al. 2002, p. 468).We generalize the analysis of Culverhouse et al. in three ways. First, we allow causative alleles to have odds ratios >1. Second, we explore the entire space of models instead of focusing only on the risk model that maximizes heritability. Third, we examine how the importance of gene interactions depends on the “distance” between a risk model and a comparable multiplicative model. We show that gene interactions can substantially increase the heritability of risk as measured by recurrence risk, K_R, and that the effect increases with the number of loci carrying causative alleles. Furthermore, we show that these results are true even if more weight is given to models that are closer to a comparable multiplicative model.Geometrically, the space of feasible genotype-specific risks subject to the aforementioned constraints (i.e., that the allele frequencies and marginal risks are known) corresponds to a high-dimensional convex polytope, and the computational problem of interest involves integrating a quadratic function over the polytope. The dimension of the polytope grows exponentially with the number of loci, and, therefore, analytic computation is intractable for more than two loci. Hence, we devise a Monte Carlo approach to tackle the problem. Note that, because of high dimensionality, rejection algorithms are not appropriate for this kind of problem. We instead employ a Markov chain Monte Carlo (MCMC) algorithm based on a random walk that always stays inside the polytope. We present empirical results for up to five loci and obtain a closed-form formula for the minimum of K_R over the polytope; the latter result applies to an arbitrary number of loci. Interestingly, the minimum of K_R decreases as the number L of loci increases, but the average of K_R over the polytope increases with L.     

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.     

Approaches to familial aggregation: hypothesis testing and estimation when families are selected through parent probands under a variant of single ascertainment

Epidemiologic approaches to testing and estimating familial aggregation of a disease consist of comparing rates of disease in relatives of individuals with the disease (known as case probands) with rates of disease in relatives of individuals without the disease (known as control probands). Gold et al. (J Am Stat Ass 1967;62: 409-420) derived an explicit mathematical model and sampling methods, under which this approach is equivalent to testing the null hypotheses that the disease risk in families is homogenous. A basic assumption of this model is that every family member has the same risk of disease and that disease status is independent among family members, although the disease risk may vary between families. When the disease is suspected of having a genetic component, rather than being purely environmental, this model has been shown to be appropriate for detecting disease aggregation in siblings, when relatives are siblings of probands. This model however is unrealistic for use in nuclear families when the affected status of offspring is not independent of the affected status of parents, and these families are selected through an affected or an unaffected parent, so that a parent is the proband and relatives are offspring of probands. We extend the Gold et al. model to allow for the disease risk in offspring to vary with the affected status of the parent. We assume that families are selected through affected and unaffected parents, under a variation of single ascertainment. Under this study design, we show that the usual test of association between affected status of probands and relatives, performed by comparing sample proportions of affected relatives of affected and unaffected probands, respectively, is no longer equivalent to a test of homogeneity of disease risk in offspring. Instead, it is equivalent to testing that the disease risk in offspring is independent of the number of affected parents. This test reduces to a test of homogeneity if and only if one assumes that the variation in disease risk in offspring, between families, is solely due to the variation in the number of affected parents. As a result, we show that under this study design, the standard chi2 test must be modified in order to obtain a valid test of familial aggregation. In addition the sample proportions of affected relatives of case and control probands, respectively, are shown to provide unbiased estimates of the expected risk of disease in an offspring given an affected/unaffected parent. We apply these results to methods of sample selection and discuss the practical implications of these findings.     

A family study of Gilles de la Tourette syndrome.

Previous studies have demonstrated that Gilles de la Tourette syndrome (TS) is a familial disorder and that chronic tics (CT) and obsessive compulsive disorder (OCD) appear to be etiologically related to the syndrome. In the present study we report the results from a study of 338 biological relatives of 86 TS probands, 21 biologically unrelated relatives of adopted TS probands, and 22 relatives of normal subjects. The 43 first-degree relatives of the adopted TS and normal probands constituted a control sample. The rates of TS, CT, and OCD in the total sample of biological relatives of TS probands were significantly greater than in the relatives of controls. In addition, the morbid risks of TS, OCD, and CT were not significantly different in families of probands with OCD when compared to relatives of probands without OCD. These findings provide further evidence that OCD is etiologically related to TS.     

A genetic study of Hirschsprung disease

Hirschsprung disease, or congenital aganglionic megacolon, is commonly assumed to be a sex-modified multifactorial trait. To test this hypothesis, complex segregation analysis was performed on data on 487 probands and their families. Demographic information on probands and the recurrence risk to relatives of probands are presented. An increased sex ratio (3.9 male:female) and an elevated risk to sibs (4%), as compared with the population incidence (0.02%), are observed, with the sex ratio decreasing and the recurrence risk to sibs increasing as the aganglionosis becomes more extensive. Down syndrome was found at an increased frequency among affected individuals but not among their unaffected sibs, and the increase was not associated with maternal age. Complex segregation analysis was performed on these family data. The families were classified into separate categories by extent of aganglionosis. For cases with aganglionosis beyond the sigmoid colon, the mode of inheritance is compatible with a dominant gene with incomplete penetrance, while for cases with aganglionosis extending no farther than the sigmoid colon, the inheritance pattern is equally likely to be either multifactorial or due to a recessive gene with very low penetrance. A model of gene action with random effects during morphogenesis is compatible with our observations.     

Case-control and case-only designs with genotype and family history data: estimating relative risk, residual familial aggregation, and cumulative risk

In case-control studies of inherited diseases, participating subjects (probands) are often interviewed to collect detailed data about disease history and age-at-onset information in their family members. Genotype data are typically collected from the probands, but not from their relatives. In this article, we introduce an approach that combines case-control analysis of data on the probands with kin-cohort analysis of disease history data on relatives. Assuming a marginally specified multivariate survival model for joint risk of disease among family members, we describe methods for estimating relative risk, cumulative risk, and residual familial aggregation. We also describe a variation of the methodology that can be used for kin-cohort analysis of the family history data from a sample of genotyped cases only. We perform simulation studies to assess performance of the proposed methodologies with correct and mis-specified models for familial aggregation. We illustrate the proposed methodologies by estimating the risk of breast cancer from BRCA1/2 mutations using data from the Washington Ashkenazi Study.     

Linkage strategies for genetically complex traits. III. The effect of marker polymorphism on analysis of affected relative pairs.

The results from the second paper of this series are reexamined for markers that are not completely polymorphic. A maximum lod score (MLS) criterion is defined for affected relative pairs. The expected MLS (EMLS) is calculated as a function of the marker polymorphic information content (PIC) for various values of lambda R (relative risk ratio) and different relative types by using simulations. An m-allele model with equal allele frequencies is employed. The EMLS is calculated for two sampling strategies: scheme 1, which uses pairs only, and scheme 2, which also includes additional informative relatives. For scheme 2, the percent of the maximum achievable EMLS (i.e., for a marker with a PIC of 1.0) is approximately equal to the marker PIC value for all relative types. For scheme 1, the EMLS is greatly diminished unless PIC is high, especially for distant relatives. For example, scheme 1 is not cost-effective for sibs unless PIC greater than .7; for second- and third-degree relatives, PIC must be greater than .85. Therefore, in general, it will be worthwhile to type additional relatives in linkage studies using affected pairs. The comparative value of sibs versus distant relatives depends on lambda R, recombination theta, and PIC. For large lambda R and PIC values, distant relatives are preferred. Alternatively, for smaller lambda R and PIC values, sibs are best.     

Genetic analysis of kifafa, a complex familial seizure disorder.

Kifafa is the Swahili name for an epileptic seizure disorder, first reported in the early 1960s, that is prevalent in the Wapogoro tribe of the Mahenge region of Tanzania in eastern Africa. A 1990 epidemiological survey of seizure disorders in this region reported a prevalence in the range of 19/1,000-36/1,000, with a mean age at onset of 11.6 years; 80% of those affected had onset prior to 20 years of age. A team of investigators returned to Tanzania in 1992 and collected data on > 1,600 relatives of 26 probands in 20 kifafa families. We have undertaken a genetic analysis of these data in order to detect the presence of familial clustering and whether such aggregation could be attributed to genetic factors. Of the 127 affected individuals in these pedigrees, 23 are first-degree relatives (parent, full sibling, or offspring) of the 26 probands; 20 are second-degree relatives (half-sibling, grandparent, uncle, or aunt). When corrected for age, the risk to first-degree relatives is .15; the risk to second-degree relatives is .063. These risks are significantly higher than would be expected if there were no familial clustering. Segregation analysis, using PAP (rev.4.0), was undertaken to clarify the mode of inheritance. Among the Mendelian single-locus models, an additive model was favored over either a dominant, recessive, or codominant model. The single-locus model could be rejected when compared with the mixed Mendelian model (inclusion of a polygenic background), although the major-gene component tends to be recessive.(ABSTRACT TRUNCATED AT 250 WORDS)     

Evidence for a third genetic locus causing familial hypercholesterolemia. A non-LDLR, non-APOB kindred.

Monogenically inherited hypercholesterolemia is most commonly caused by mutations at the low density lipoprotein receptor (LDLR) locus causing familial hypercholesterolemia (FH) or at the apolipoprotein B (APOB) locus causing the disorder familial defective apoB (FDB). Probands from 47 kindreds with a strict clinical diagnosis of FH were selected from the Cardiovascular Genetics Research Lipid Clinic, Utah, for molecular genetic analysis. Using a combination of single-strand conformation polymorphism (SSCP) and direct sequencing, 12 different LDLR gene mutations were found in 16 of the probands. Three of the probands were carriers of the APOB R3500Q mutation. In five of the remaining 28 pedigrees where no mutation had been detected, samples from enough relatives were available to examine co-segregation with the LDLR region using the microsatellite marker D19S221, which is within 1 Mb centromeric of the LDLR locus, and D19S394, sited within 150 kb telomeric of the LDLR locus. In four of the families there was strong evidence for co-segregation between the LDLR locus and the phenotype of hypercholesterolemia, but in one large family with 18 living affected members and clear-cut bimodal hypercholesterolemia, there were numerous exclusions of co-segregation. Using length polymorphic markers within and outside the APOB gene, linkage of phenotype in this family to the APOB region was similarly excluded. In this large family, the degree of hypercholesterolemia, prevalence of tendon xanthomata, and occurrence of early coronary disease were indistinguishable from the other families studied. In summary, the data provide unequivocal evidence that a third locus can be etiological for monogenic familial hypercholesterolemia and should be reinvigorating to research in this field.     

Exchangeable models of complex inherited diseases

A model of unlinked diallelic loci affecting the risk of a complex inherited disease is explored. The loci are equivalent in their effect on disease risk and are in Hardy-Weinberg and linkage equilibrium. The goal is to determine what assumptions about dependence of disease risk on genotype are consistent with data for diseases such as schizophrenia, bipolar disorder, autism, and multiple sclerosis that are relatively common (0.1-2% prevalence) and that have high concordance rates for monozygotic twins (30-50%) and high risks to first-degree relatives of affected individuals (risk ratios exceeding 4). These observations are consistent with a variety of models, including generalized additive, multiplicative, and threshold models, provided that disease risk increases rapidly for a narrow range of numbers of causative alleles. If causative alleles are in relatively high frequency, then the combined effects of numerous causative loci are necessary to substantially elevate disease risk.     

Mutational analysis of the <Emphasis Type="Italic">GLA</Emphasis> gene in Mexican families with Fabry disease

Fabry disease (FD) is a lysosomal storage disorder, which develops due to a deficiency in the hydrolytic enzyme, α-galactosidase A (α-Gal A). Alpha-Gal A hydrolyzes glycosphingolipid globotriaosylceramide (Gb3), and an α-Gal A deficiency leads to Gb3 accumulation in tissues and cells in the body. This pathology is likely to involve multiple systems, but it is generally considered to affect primarily vascular endothelium. In this study, we investigated mutations in the GLA gene, which encodes α-Gal A, in Mexican families with FD. We included seven probands with FD that carried known mutations. We analysed pedigrees of the probands, and performed molecular screening in 65 relatives with the potential of carrying a GLA mutation. Five mutations (P40S, IVS4 ⁺⁴, G328V, R363H, R404del) were detected in seven unrelated Mexican families with the classic FD phenotype. Of the 65 relatives examined, 42 (64.6%) had a GLA gene mutation. In summary, among seven Mexican probands with FD, 65 relatives were at risk of carrying a known GLA mutation, and molecular screening identified 42 individuals with the mutation. Thus, our findings showed that it is important to perform molecular analysis in families with FD to detect mutations and to provide accurate diagnoses for individuals that could be affected.     

Identifying families with likely genetic protective factors against Alzheimer disease

Elderly individuals who lived beyond the age of 90 years without dementia were hypothesized to have increased concentrations of genetic protective factors against Alzheimer disease (AD), conferring a reduced liability for this disease relative to less-aged nondemented elderly. However, testing this hypothesis is complicated by having to distinguish such a group from those who may lack genetic risk factors for AD, have had protective environmental exposures, or have escaped dementia for other reasons. Probands carrying genetic protective factors, however, should have relatives with lower illness rates not only for earlier-onset disease, when genetic risk factors are a strong contributing factor to the incidence of AD, but also for later-onset disease, when the role of these factors appears to be markedly diminished. AD dementia was assessed through family informants in 6,660 first-degree relatives of 1,049 nondemented probands aged 60-102 years. The probands were grouped by age (60-74, 75-89, and 90-102 years), and the cumulative survival from AD and 10-year-age-interval hazard rates of AD were calculated in their first-degree relatives. Cumulative survival from AD was significantly greater in the relatives of the oldest proband group (aged 90-102 years) than it was in the two younger groups. In addition, the reduction in the rate of illness for this group was relatively constant across the entire late life span. The results suggest that genetic factors conferring a lifelong reduced liability of AD may be more highly concentrated among nondemented probands aged >/=90 years and their relatives. Efforts to identify protective allele-bearing genes that are associated with very late-onset AD should target the families of nonagenarians and centenarians.