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Results for “multifactorial inheritance”

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PubMed1986

The inheritance of pyloric stenosis explained by a multifactorial threshold model with sex dimorphism for liability.

The inheritance of pyloric stenosis is explained by a multifactorial threshold model with an underlying assumption that the liability for the disease is distributed in males and females showing a sex dimorphism. From the available data on familial occurrences of pyloric stenosis, it is shown, that an extra maternal effect is not required to explain the familial risk of pyloric stenosis, as opposed to the earlier literature. Explicit expressions for familial risks of a discontinuous trait exhibiting dimorphism of liability are presented, based on a model originally proposed by Rice et al [1981], which do not require the approximation of univariate normality of a conditional bivariate normal distribution.

Factor Analysis, Statistical↗
PubMed2003

Genetic interaction between a maternal factor and the zygotic genome controls the intestine length in PRM/Alf mice.

Postoperative management of small and large bowel resections would be helped by use of intestinotrophic molecules. Here, we present a mouse inbred strain called PRM/Alf that is characterized by a selective intestinal lengthening. We show that PRM/Alf intestine is one-third longer compared with other inbred strains. The phenotype is acquired mostly during the postnatal period, before weaning. Its genetic determinism is polygenic, and involves a strong maternal effect. Cross-fostering experiments revealed that the dam's genotype acts synergistically with the offspring's genotype to confer the longest intestine. Moreover, genes in the offspring have a direct effect on intestine length. Possible involvement of milk growth factors and identification of candidate genes are discussed.

Animals↗
PubMed2000

Linkage analysis in the presence of errors IV: joint pseudomarker analysis of linkage and/or linkage disequilibrium on a mixture of pedigrees and singletons when the mode of inheritance cannot be accurately specified.

There is a lot of confusion in the literature about the "differences" between "model-based" and "model-free" methods and about which approach is better suited for detection of the genes predisposing to complex multifactorial phenotypes. By starting from first principles, we demonstrate that the differences between the two approaches have more to do with study design than statistical analysis. When simple data structures are repeatedly ascertained, no assumptions about the genotype-phenotype relationship need to be made for the analysis to be powerful, since simple data structures admit only a small number of df. When more complicated and/or heterogeneous data structures are ascertained, however, the number of df in the underlying probability model is too large to have a powerful, truly "model-free" test. So-called "model-free" methods typically simplify the underlying probability model by implicitly assuming that, in some sense, all meioses connecting two affected individuals are informative for linkage with identical probability and that the affected individuals in a pedigree share as many disease-predisposing alleles as possible. By contrast, "model-based" methods add structure to the underlying parameter space by making assumptions about the genotype-phenotype relationship, making it possible to probabilistically assign disease-locus genotypes to all individuals in the data set on the basis of the observed phenotypes. In this study, we demonstrate the equivalence of these two approaches in a variety of situations and exploit this equivalence to develop more powerful and efficient likelihood-based analogues of "model-free" tests of linkage and/or linkage disequilibrium. Through the use of a "pseudomarker" locus to structure the space of observations, sib-pairs, triads, and singletons can be analyzed jointly, which will lead to tests that are more well-behaved, efficient, and powerful than traditional "model-free" tests such as the affected sib-pair, transmission/disequilibrium, haplotype relative risk, and case-control tests. Also described is an extension of this approach to large pedigrees, which, in practice, is equivalent to affected relative-pair analysis. The proposed methods are equally applicable to two-point and multipoint analysis (using complex-valued recombination fractions).

Case-Control Studies↗
PubMed1981

Age-of-onset and genetic transmission in affective disorders.

Age-of-onset data were gathered on first-degree relatives of 252 probands with bipolar and unipolar affective disorders. Early onset probands (younger than 40 at onset) had more early onset relatives and a greater risk for affective disorder among their relatives than late onset probands (40 or older). This indicates that age-of-onset is a familial factor correlated with the liability to affective illness. Multiple threshold models of inheritance were applied to the data using age-of-onset as a liability-threshold determinant. The hypothesis of autosomal single-major locus was ruled out. Multifactorial-polygenic inheritance provided a better fit to the data. The data suggest that early and late onset affective disorders can be placed at different thresholds on a genetic environmental continuum and that the early onset form is more deviant genetically than the late onset type. The implications for genetic research in affective disorder are discussed.

Affective Disorders, Psychotic↗
PubMed1981

Application of the major gene index and offspring-between-parents function to dermatoglyphic fingertip variables.

Forty-eight digital dermatoglyphic variables in 192 nuclear families were analyzed to search for evidence of major gene effects, utilizing a pair of recently developed statistics called the major gene index (MGI) and offspring-between-parents (OBP) function. They operate on the principle that under a multifactorial blending inheritance scheme an offspring's phenotypic value approximates the midparental value, whereas under major gene inheritance, the child's value more closely resembles that of one of his parents. Both statistics yielded comparable results. All ridge-count variables showed no strong deviation from a multifactorial model. Pattern-type variables gave values suggesting the presence of major gene effects, but these results were probably the consequence of the variables' relatively discrete distributions, since a departure of a variable from a reasonably continuous phenotypic distribution was shown to interfere significantly with the interpretation of both statistics.

Chromosome Mapping↗
PubMed2004

Genetic basis of endometriosis.

Endometriosis is a complex gynecologic disorder that has long been recognized as showing heritable tendencies, with recurrence risks of 5-7% for first-degree relatives. Familial and epidemiologic studies support that this disease is a genetic disorder of polygenic/multifactorial inheritance. The current investigational challenge is to determine the number and location of causative genes. Recent advances in molecular technology make identification and elucidation of these genes now possible. In this review, we update previous communications in which we also reviewed heritability studies supporting polygenic/multifactorial inheritance, discuss the scientific basis of genomewide strategies for identifying causative genes, and identify potential candidate genes.

Endometriosis↗
PubMed1982

Genetic models of schizophrenia.

Multiple threshold models of inheritance are applied to a large sample of Franz Kallmann's (1938) pedigree data on schizophrenia. Paranoid and nonparanoid subtypes are represented in the models at different thresholds on a continuum of genetic-environmental liability. Single major locus and multifactorial-polygenic inheritance are ruled out as modes of transmission. These findings suggest that the paranoid-non-paranoid dichotomy cannot be used as a genetic threshold determinant in the population studied.

Gene Frequency↗
PubMed2004

The oligogenic properties of Bardet-Biedl syndrome.

Bardet-Biedl syndrome (BBS: OMIM 209900) is a rare developmental disorder that exhibits significant clinical and genetic heterogeneity. Although modeled initially as a purely recessive trait, recent data have unmasked an oligogenic mode of disease transmission, in which mutations at different BBS loci can interact genetically in some families to cause and/or modify the phenotype. Here, I will review and discuss recent advances in elucidating both genetic and cellular aspects of this phenotype and their potential application in understanding the genetic basis of phenotypic variability and oligogenic inheritance.

Adaptor Proteins, Signal Transducing↗
PubMed2006

Segregation analysis of a complex quantitative trait: approaches for identifying influential data points.

BACKGROUND/AIMS: Complex traits pose a particular challenge to standard methods for segregation analysis (SA), and for such traits it is difficult to assess the ability of complex SA (CSA) to approximate the true mode of inheritance. Here we use an oligogenic Bayesian Markov chain Monte Carlo method for SA (OSA) to verify results from a single-locus likelihood-based CSA for data on a quantitative measure of reading ability. METHODS: We compared the profile likelihood from CSA, maximized over the trait allele frequency, to the posterior distribution of genotype effects from OSA to explore differences in the overall parameter estimates from SA on the original phenotype data and the same data Winsorized to reduce the potential influence of three outlying data points. RESULTS: Bayesian OSA revealed two modes of inheritance, one of which coincided with the QTL model from CSA. Winsorizing abolished the model originally estimated by CSA; both CSA and OSA identified only the second OSA model. CONCLUSION: Differences between the results from the two methods alerted us to the presence of influential data points, and identified the QTL model best supported by the data. Thus, the Bayesian OSA proved a valuable tool for assessing and verifying inheritance models from CSA.

Alleles↗
PubMed2006

Mendelian disorders deserve more attention.

The study of inherited monogenic diseases has contributed greatly to our mechanistic understanding of pathogenic mutations and gene regulation, and to the development of effective diagnostic tools. But interest has gradually shifted away from monogenic diseases, which collectively affect only a small fraction of the world's population, towards multifactorial, common diseases. The quest for the genetic variability associated with common traits should not be done at the expense of Mendelian disorders, because the latter could still contribute greatly to understanding the aetiology of complex traits.

Animals↗
PubMed2001

Determining trait locus position from multipoint analysis: accuracy and power of three different statistics.

Previous work using two-point linkage analysis showed that performing a lod score (LOD) analysis twice, once assuming dominant and once assuming recessive inheritance, and then taking the larger of the two values (designated MMLS) usually has more power to detect linkage than any other method tested. Using computer simulation for a variety of complex inheritance models, we demonstrated power for the MMLS comparable with analysis assuming the true model. However, reports in the literature suggested that the MMLS approach might fail to detect linkage using multipoint analysis due to genetic model misspecification. Here, we tested the robustness of the MMLS approach under multipoint analysis. We simulated data under complex inheritance models, including heterogeneity, epistatic, and additive models. We examined the expected maximum LOD, LOD assuming heterogeneity (HLOD), and nonparametric linkage statistics and the corresponding estimated position in a chromosomal interval of 10 markers with 10% recombination between markers. The mean estimates of position were generally good for all three statistics except when heterogeneity existed, where the LOD and the NPL did not perform as well as the HLOD. The MMLS approach was as robust using multipoint as using two-point linkage analysis. LOD and/or the HLOD generally had more power to detect linkage than NPL across a variety of generating models, even after correcting for the multiple tests. For finding linkage to one locus of several contributing to disease expression, assuming the dominant and recessive models with reduced penetrance is a good approximation of the mode of inheritance at that locus.

Bias↗
PubMed2002

Hirschsprung, RET-SOX and beyond: the challenge of examining non-mendelian traits (Review).

Hirschsprung disease (HSCR), or congenital intestinal aganglionosis, is a common hereditary disorder causing intestinal obstruction, thereby showing considerable phenotypic variation in conjunction with complex inheritance. Moreover, phenotypic assessment of the disease has been complicated since a subset of the observed mutations is also associated with several additional syndromic anomalies. Coding sequence mutations in e.g. RET, GDNF, EDNRB, EDN3, and SOX10 lead to long-segment (L-HSCR) as well as syndromic HSCR but fail to explain the transmission of the much more common short-segment form (S-HSCR). Furthermore, mutations in the RET gene are responsible for approximately half of the familial and some sporadic cases, strongly suggesting, on the one hand, the importance of non-coding variations and, on the other hand, that additional genes involved in the development of the enteric nervous system still await their discovery. For almost all of the identified HSCR genes incomplete penetrance of the HSCR phenotype has been reported, probably due to modifier loci. Therefore, HSCR has become a model for a complex oligo-/polygenic disorder in which the relationship between different genes creating a non-mendelian inheritance pattern still remains to be elucidated.

DNA-Binding Proteins↗
PubMed2004

Genetics of endometriosis: heritability and candidate genes.

Endometriosis is a complex gynecologic disorder that affects as many as 10-15% of premenopausal women. Epidemiologic studies have confirmed that this disease is a genetic disorder of polygenic/multifactorial inheritance. The disorder has long been recognized to show heritable tendencies with recurrence risks of 5-7% for first-degree relatives. The current investigational goal is to determine the number and location of causative genes, a process that has been made possible by recent advances in molecular technology. This chapter discusses heritability studies supporting polygenic/multifactorial inheritance, the scientific basis of genome-wide strategies for identifying causative genes and potential candidate genes.

Endometriosis↗