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.     

Comparison of sequential and fixed-structure sampling of pedigrees in complex segregation analysis of a quantitative trait.

In designing a study to demonstrate the existence of a major locus for a quantitative trait, an investigator chooses a sampling rule to ascertain pedigrees. The choice of sampling rule can significantly affect the study's power. Here, we compare two types of sampling rules for family studies: fixed-structure rules, in which the same set of relatives are sampled for each proband, and sequential rules, in which the relative or relatives to be sampled next may depend on the trait values of the individuals already observed. We compare fixed-structure and sequential sampling in the setting of extended pedigrees, a quantitative trait, and the genetic mixed model. Using computer simulation, we show that sequential sampling can increase power to detect segregation at a dominant major locus by over 60% in comparison with fixed-structure sampling. Just as important, this substantially increased power is obtained with an easily implemented sampling rule, one that might reasonably be employed in a family study of a quantitative trait.     

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.     

PRIMUS: Rapid Reconstruction of Pedigrees from Genome-wide Estimates of Identity by Descent

Understanding and correctly utilizing relatedness among samples is essential for genetic analysis; however, managing sample records and pedigrees can often be error prone and incomplete. Data sets ascertained by random sampling often harbor cryptic relatedness that can be leveraged in genetic analyses for maximizing power. We have developed a method that uses genome-wide estimates of pairwise identity by descent to identify families and quickly reconstruct and score all possible pedigrees that fit the genetic data by using up to third-degree relatives, and we have included it in the software package PRIMUS (Pedigree Reconstruction and Identification of the Maximally Unrelated Set). Here, we validate its performance on simulated, clinical, and HapMap pedigrees. Among these samples, we demonstrate that PRIMUS can verify reported pedigree structures and identify cryptic relationships. Finally, we show that PRIMUS reconstructed pedigrees, all of which were previously unknown, for 203 families from a cohort collected in Starr County, TX (1,890 samples).     

Detection of parent-of-origin effects for quantitative traits using general pedigree data

Genomic imprinting is a genetic phenomenon in which certain alleles are differentially expressed in a parent-of-origin-specific manner, and plays an important role in the study of complex traits. For a diallelic marker locus in human, the parental-asymmetry tests Q-PAT(c) with any constant c were developed to detect parent-of-origin effects for quantitative traits. However, these methods can only be applied to deal with nuclear families and thus are not suitable for extended pedigrees. In this study, by making no assumption about the distribution of the quantitative trait, we first propose the pedigree parental-asymmetry tests Q-PPAT(c) with any constant c for quantitative traits to test for parent-of-origin effects based on nuclear families with complete information from general pedigree data, in the presence of association between marker alleles under study and quantitative traits. When there are any genotypes missing in pedigrees, we utilize Monte Carlo (MC) sampling and estimation and develop the Q-MCPPAT(c) statistics to test for parent-of-origin effects. Various simulation studies are conducted to assess the performance of the proposed methods, for different sample sizes, genotype missing rates, degrees of imprinting effects and population models. Simulation results show that the proposed methods control the size well under the null hypothesis of no parent-of-origin effects and Q-PPAT(c) are robust to population stratification. In addition, the power comparison demonstrates that Q-PPAT(c) and Q-MCPPAT(c) for pedigree data are much more powerful than Q-PAT(c) only using two-generation nuclear families selected from extended pedigrees.     

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.     

Extremely low penetrance of hearing loss in four Chinese families with the mitochondrial 12S rRNA A1555G mutation

Mutations in mitochondrial DNA (mtDNA) have been found to be associated with sensorineural hearing loss. We report here the clinical, genetic, and molecular characterization of four Chinese pedigrees with aminoglycoside-induced and nonsyndromic hearing impairment. Clinical evaluation revealed the variable phenotype of hearing impairment including audiometric configuration in these subjects, although these subjects share some common features: bilateral and sensorineural hearing impairment. Strikingly, these Chinese pedigrees exhibited extremely low penetrance of hearing loss (5.2%, 4.8%, 4.2%, and 13.3%, respectively, and with an average 8% penetrance). In particular, four of all five affected matrilineal relatives of these pedigrees had aminoglycoside-induced hearing loss. Sequence analysis of the complete mitochondrial genomes in these pedigrees showed the distinct sets of mtDNA polymorphism, in addition to the identical homoplasmic A1555G mutation, associated with hearing impairment in many families from different genetic backgrounds. The fact that mtDNA of those pedigrees belonged to different haplogroups R9a, N9a, D4a, and D4 suggested that the A1555G mutation occurred sporadically and multiplied through evolution of the mtDNA in China. However, there was the absence of functionally significant mutations in tRNA and rRNAs or secondary LHON mutations in these Chinese families. These data imply that the nuclear background or/and mitochondrial haplotype may not play a significant role in the phenotypic expression of the A1555G mutation in these Chinese pedigrees. However, aminoglycoside appears to be a major modifier factor for the phenotypic manifestation of the A1555G mutation in these Chinese families.     

Sample-size guidelines for linkage analysis of a dominant locus for a quantitative trait by the method of lod scores.

Sample-size guidelines for linkage studies of quantitative traits partially determined by a dominant major locus are needed to provide a rough estimate of the amount of pedigree material that should be sampled to map the loci that influence such traits. After pedigrees are sampled, a specific power calculation can be carried out to evaluate the linkage information provided by the sampled pedigrees. Using computer simulation, I provide sample-size guidelines for linkage studies by the method of lod scores of quantitative traits partially determined by a dominant major locus. I consider the effects of a trait model, marker characteristics, and sampling strategy, with particular attention to sampling strategy because it is the one factor which the investigator can fully control. My results suggest that linkage studies of quantitative traits are practical, particularly if the investigator chooses an efficient sampling design and an efficient strategy to select pedigrees for linkage analysis.     

A linkage study of cystic fibrosis in extended multigenerational pedigrees.

The linkage of polymorphic DNA markers on chromosome 7 to cystic fibrosis (CF) was examined in two pedigrees and a number of smaller nuclear families. The pedigrees are multigenerational and together consist of more than 300 members including 30 affected individuals, while the nuclear families each have two generations and either two or three children with CF. Tight linkage was observed between the CF locus and the met oncogene locus theta = 0, zeta = 15.45), pJ3.11 (theta = 0, zeta = 10.07), and 7C22 (theta = 0, zeta = 6.64) in both the pedigrees and nuclear families with no evidence for recombination between CF and any of the DNA markers. Weaker linkage between the CF locus and the locus for the serum enzyme activity marker paraoxonase (PON) was detected, theta = 0.18, zeta = 0.76. The two pedigrees were sufficiently informative to detect significant linkage between CF and each of the three DNA markers previously shown to be tightly linked to the CF locus. These results establish a locus for CF in these pedigrees in the region of chromosome 7 nearest the three DNA markers met, pJ3.11, and 7C22 and are consistent with locus homogeneity for the defect causing CF in these populations and others that have been examined to date.     

A major lung cancer susceptibility locus maps to chromosome 6q23-25

Lung cancer is a major cause of death in the United States and other countries. The risk of lung cancer is greatly increased by cigarette smoking and by certain occupational exposures, but familial factors also clearly play a major role. To identify susceptibility genes for familial lung cancer, we conducted a genomewide linkage analysis of 52 extended pedigrees ascertained through probands with lung cancer who had several first-degree relatives with the same disease. Multipoint linkage analysis, under a simple autosomal dominant model, of all 52 families with three or more individuals affected by lung, throat, or laryngeal cancer, yielded a maximum heterogeneity LOD score (HLOD) of 2.79 at 155 cM on chromosome 6q (marker D6S2436). A subset of 38 pedigrees with four or more affected individuals yielded a multipoint HLOD of 3.47 at 155 cM. Analysis of a further subset of 23 multigenerational pedigrees with five or more affected individuals yielded a multipoint HLOD score of 4.26 at the same position. The 14 families with only three affected relatives yielded negative LOD scores in this region. A predivided samples test for heterogeneity comparing the LOD scores from the 23 multigenerational families with those from the remaining families was significant (P=.007). The 1-HLOD multipoint support interval from the multigenerational families extends from C6S1848 at 146 cM to 164 cM near D6S1035, overlapping a genomic region that is deleted in sporadic lung cancers as well as numerous other cancer types. Parametric linkage and variance-components analysis that incorporated effects of age and personal smoking also supported linkage in this region, but with somewhat diminished support. These results localize a major susceptibility locus influencing lung cancer risk to 6q23-25.     

Robust score statistics for QTL linkage analysis

The traditional variance components approach for quantitative trait locus (QTL) linkage analysis is sensitive to violations of normality and fails for selected sampling schemes. Recently, a number of new methods have been developed for QTL mapping in humans. Most of the new methods are based on score statistics or regression-based statistics and are expected to be relatively robust to non-normality of the trait distribution and also to selected sampling, at least in terms of type I error. Whereas the theoretical development of these statistics is more or less complete, some practical issues concerning their implementation still need to be addressed. Here we study some of these issues such as the choice of denominator variance estimates, weighting of pedigrees, effect of parameter misspecification, effect of non-normality of the trait distribution, and effect of incorporating dominance. We present a comprehensive discussion of the theoretical properties of various denominator variance estimates and of the weighting issue and then perform simulation studies for nuclear families to compare the methods in terms of power and robustness. Based on our analytical and simulation results, we provide general guidelines regarding the choice of appropriate QTL mapping statistics in practical situations.     

Inter- and intrafamilial heterogeneity: effective sampling strategies and comparison of analysis methods.

Heterogeneity, both inter- and intrafamilial, represents a serious problem in linkage studies of common complex diseases. In this study we simulated different scenarios with families who phenotypically have identical diseases but who genotypically have two different forms of the disease (both forms genetic). We examined the proportion of families displaying intrafamilial heterogeneity, as a function of mode of inheritance, gene frequency, penetrance, and sampling strategies. Furthermore, we compared two different ways of analyzing linkage in these data sets: a two-locus (2L) analysis versus a one-locus (SL) analysis combined with an admixture test. Data were simulated with tight linkage between one disease locus and a marker locus; the other disease locus was not linked to a marker. Our findings are as follows: (1) In contrast to what has been proposed elsewhere to minimize heterogeneity, sampling only "high-density" pedigrees will increase the proportion of families with intrafamilial heterogeneity, especially when the two forms are relatively close in frequency. (2) When one form is dominant and one is recessive, this sampling strategy will greatly decrease the proportions of families with a recessive form and may therefore make it more difficult to detect linkage to the recessive form. (3) An SL analysis combined with an admixture test achieves about the same lod scores and estimate of the recombination fraction as does a 2L analysis. Also, a 2L analysis of a sample of families with intrafamilial heterogeneity does not perform significantly better than an SL analysis. (4) Bilineal pedigrees have little effect on the mean maximum lod score and mean maximum recombination fraction, and therefore there is little danger that including these families will lead to a false exclusion of linkage.     

Clinical evaluation and mitochondrial DNA sequence analysis in two Chinese families with aminoglycoside-induced and non-syndromic hearing loss

We report here the clinical, genetic, and molecular characterization of two Chinese pedigrees with aminoglycoside-induced and non-syndromic hearing impairment. Clinical evaluation revealed the variable phenotype of hearing impairment including audiometric configuration in these subjects. Penetrances of hearing loss in BJ105 and BJ106 pedigrees are 67% and 33%, respectively. In particular, three of 10 affected matrilineal relatives of BJ105 pedigree had aminoglycoside-induced hearing loss, while seven affected matrilineal relatives in BJ105 pedigree and six affected matrilineal relatives in BJ106 pedigree did not have a history of exposure to aminoglycosides. Sequence analysis of the complete mitochondrial genomes in these pedigrees showed the identical homoplasmic A1555G mutation and distinct sets of mtDNA variants belonging to haplogroups F3 and M7b. These variants showed no evolutionary conservation, implying that mitochondrial haplotype may not play a significant role in the phenotypic expression of the A1555G mutation in these Chinese pedigrees. However, aminoglycosides and nuclear backgrounds appear to be major modifier factors for the phenotypic manifestation of the A1555G mutation in these Chinese families.     

Statistics for Nonparametric Linkage Analysis of X-Linked Traits in General Pedigrees

We have compared the power of several allele-sharing statistics for "nonparametric" linkage analysis of X-linked traits in nuclear families and extended pedigrees. Our rationale was that, although several of these statistics have been implemented in popular software packages, there has been no formal evaluation of their relative power. Here, we evaluate the relative performance of five test statistics, including two new test statistics. We considered sibships of sizes two through four, four different extended pedigrees, 15 different genetic models (12 single-locus models and 3 two-locus models), and varying recombination fractions between the marker and the trait locus. We analytically estimated the sample sizes required for 80% power at a significance level of.001 and also used simulation methods to estimate power for a sample size of 10 families. We tried to identify statistics whose power was robust over a wide variety of models, with the idea that such statistics would be particularly useful for detection of X-linked loci associated with complex traits. We found that a commonly used statistic, S(all), generally performed well under various conditions and had close to the optimal sample sizes in most cases but that there were certain cases in which it performed quite poorly. Our two new statistics did not perform any better than those already in the literature. We also note that, under dominant and additive models, regardless of the statistic used, pedigrees with all-female siblings have very little power to detect X-linked loci.     

Inherent intractability of the ascertainment problem for pedigree data: a general likelihood framework.

The problem of ascertainment in segregation analysis arises when families are selected for study through ascertainment of affected individuals. In this case, ascertainment must be corrected for in data analysis. However, methods for ascertainment correction are not available for many common sampling schemes, e.g., sequential sampling of extended pedigrees (except in the case of "single" selection). Concerns about whether ascertainment correction is even required for large pedigrees, about whether and how multiple probands in the same pedigree can be taken into account properly, and about how to apply sequential sampling strategies have occupied many investigators in recent years. We address these concerns by reconsidering a central issue, namely, how to handle pedigree structure (including size). We introduce a new distinction, between sampling in such a way that observed pedigree structure does not depend on which pedigree members are probands (proband-independent [PI] sampling) and sampling in such a way that observed pedigree structure does depend on who are the probands (proband-dependent [PD] sampling). This distinction corresponds roughly (but not exactly) to the distinction between fixed-structure and sequential sampling. We show that conditioning on observed pedigree structure in ascertained data sets obtained under PD sampling is not in general correct (with the exception of "single" selection), while PI sampling of pedigree structures larger than simple sibships is generally not possible. Yet, in practice one has little choice but to condition on observed pedigree structure. We conclude that the problem of genetic modeling in ascertained data sets is, in most situations, literally intractable. We recommend that future efforts focus on the development of robust approximate approaches to the problem.     

Evidence for a major gene influence on persistent developmental stuttering

Stuttering is a complex developmental speech disorder of unknown etiology. There is a substantial aggregation of stuttering in families, suggesting a genetic component to the disorder. However, the exact mode of transmission is still unknown. An earlier study of 56 multigenerational pedigrees ascertained through single adult probands (38 males and 18 females) found that biological relatives of persistent developmental stutterers have an approximately 10-fold higher risk than in the general population; risk is higher for male relatives, and proband's sex does not affect recurrence and relative risks. In the present paper we conduct a complex segregation analysis of the same data, using the logistic regression model of the SAGE software. Based on the comparisons of model likelihoods, the Mendelian model was selected over all other nongenetic models and the general transmission model. This model was further refined into the most parsimonious model, which shows an autosomal dominant major gene effect influenced by two covariates: sex and affection status of parents. With this model applied to 47 informative multiplex pedigrees, a power calculation based on linkage simulation produced an average lod score of 6.8 for 10-cM density genome scan markers. These results give impetus for a genomewide linkage analysis of susceptibility to persistent developmental stuttering.     

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.     

Linkage analysis of quantitative trait loci in the presence of heterogeneity

Variance component modeling for linkage analysis of quantitative traits is a powerful tool for detecting and locating genes affecting a trait of interest, but the presence of genetic heterogeneity will decrease the power of a linkage study and may even give biased estimates of the location of the quantitative trait loci. Many complex diseases are believed to be influenced by multiple genes and therefore genetic heterogeneity is likely to be present for many real applications of linkage analysis. We consider a mixture of multivariate normals to model locus heterogeneity by allowing only a proportion of the sampled pedigrees to segregate trait-influencing allele(s) at a specific locus. However, for mixtures of normals the classical asymptotic distribution theory of the maximum likelihood estimates does not hold, so tests of linkage and/or heterogeneity are evaluated using resampling methods. It is shown that allowing for genetic heterogeneity leads to an increase in power to detect linkage. This increase is more prominent when the genetic effect of the locus is small or when the percentage of pedigrees not segregating trait-influencing allele(s) at the locus is high.     

A test for linkage and association in general pedigrees: the pedigree disequilibrium test

Family-based tests of linkage disequilibrium typically are based on nuclear-family data including affected individuals and their parents or their unaffected siblings. A limitation of such tests is that they generally are not valid tests of association when data from related nuclear families from larger pedigrees are used. Standard methods require selection of a single nuclear family from any extended pedigrees when testing for linkage disequilibrium. Often data are available for larger pedigrees, and it would be desirable to have a valid test of linkage disequilibrium that can use all potentially informative data. In this study, we present the pedigree disequilibrium test (PDT) for analysis of linkage disequilibrium in general pedigrees. The PDT can use data from related nuclear families from extended pedigrees and is valid even when there is population substructure. Using computer simulations, we demonstrated validity of the test when the asymptotic distribution is used to assess the significance, and examined statistical power. Power simulations demonstrate that, when extended pedigree data are available, substantial gains in power can be attained by use of the PDT rather than existing methods that use only a subset of the data. Furthermore, the PDT remains more powerful even when there is misclassification of unaffected individuals. Our simulations suggest that there may be advantages to using the PDT even if the data consist of independent families without extended family information. Thus, the PDT provides a general test of linkage disequilibrium that can be widely applied to different data structures.     

TNFR2基因的CA重复多态性在两个独立的白人群体中与肥胖表型的连锁和关联

我们先前通过全基因组扫描发现lp36与体重指数显提示性连锁（LOD=2．09）。肿瘤坏死因子受体2（1NFR2）定位于lp36,是肥胖的一个极好的图位和功能侯选基因。本研究采用数量传递连锁不平衡检验在两个大的独立的白人样本中进行了TNFR2基因与肥胖表型的连锁与关联检验。第一组受试者由来自79个多代家系的1836个个体组成;第二组受试者由来自157个核心家庭的636个个体组成。所检测的肥胖表型包括体重指数、脂肪量和脂肪量百分数。在多代家系中我们发现TNFR2基因变异与BMI显著连锁（P=0．0056）。结果表明,TNFR2基因是影响白人BMI变异的一个数量性状位点。