Osteopenia in 37 members of seven families: analysis based on a model of dominant inheritance.

BACKGROUND: The genetic factors involved in determining bone mineral density (BMD) have not been fully elucidated. We have begun genetic linkage analysis of seven families in which many members are osteopenic, in order to identify chromosomal loci that are potentially involved in determining BMD. MATERIALS AND METHODS: Spine BMD was measured in 143 members of seven kindred with familial osteopenia. The absolute BMD values for the spine (L2-L4) were converted to the age-, gender-, and weight-adjusted Z scores, and this corrected value was used as the quantitative trait on which to base subsequent genetic analyses. Simulations of linkage were performed in order to determine the information content of the pedigree set, and actual linkage analysis was conducted using polymorphic markers either within or near three candidate loci: COL1A1, COL1A2, and vitamin D receptor (VDR). RESULTS: The distribution of the corrected Z scores was bimodal (p = 0.001) suggesting a monogenic mode of inheritance of the low BMD trait. Simulation of linkage analysis suggested that the family data set was sufficient to detect linkage under a single major gene model. Actual linkage analysis did not support linkage to the three candidate loci. In addition, the VDR genotype was not statistically associated with low bone density at the spine. CONCLUSIONS: Loci other than COL1A1, COL1A2 and VDR are very likely responsible for the low BMD trait observed in these families. These families are suitable for a genome-wide screen using microsatellite repeats in order to identify the loci that are involved in osteopenia.     

Association between a genetic trait and a marker: Discrimination between epistasis and gametic disequilibrium

Summary We reconsider the method of Ott and Falk (1982) for the analysis of genetic linkage and of epistasis in the presence of phenotypic association. Their approach is extended to allow for gametic disequilibrium between marker and trait loci. We show that epistasis and tight linkage with gametic disequilibrium may be indistinguishable as explanations of association even in a very large pedigree.     

Using the noninformative families in family-based association tests: a powerful new testing strategy

For genetic association studies with multiple phenotypes, we propose a new strategy for multiple testing with family-based association tests (FBATs). The strategy increases the power by both using all available family data and reducing the number of hypotheses tested while being robust against population admixture and stratification. By use of conditional power calculations, the approach screens all possible null hypotheses without biasing the nominal significance level, and it identifies the subset of phenotypes that has optimal power when tested for association by either univariate or multivariate FBATs. An application of our strategy to an asthma study shows the practical relevance of the proposed methodology. In simulation studies, we compare our testing strategy with standard methodology for family studies. Furthermore, the proposed principle of using all data without biasing the nominal significance in an analysis prior to the computation of the test statistic has broad and powerful applications in many areas of family-based association studies.     

High-resolution joint linkage disequilibrium and linkage mapping of quantitative trait loci based on sibship data

This paper proposes variance component models for high resolution joint linkage disequilibrium (LD) and linkage mapping of quantitative trait loci (QTL) based on sibship data; this can include population data if independent individuals are treated as single sibships. One application of these models is late onset complex disease gene mapping, when parental data are not available. The models simultaneously incorporate both LD and linkage information. The LD information is contained in mean coefficients of sibship data. The linkage information is contained in the variance-covariance matrices of trait values for sibships with at least two siblings. We derive formulas for calculating the probability of sharing two trait alleles identical by descent (IBD) for sibpairs in interval mapping of QTL; this is the coefficient of dominant variance of the trait covariance of sibpairs on major QTL. To investigate the performance of the formulas, we calculate the numerical values via the formulas and get satisfactory approximations. We compare the power and sample sizes for both LD and linkage mapping. By simulation and theoretical analysis, we compare the results with those of Fulker and Abecasis "AbAw" approach. It is well known that the resolution of linkage analysis can be low for complex disease gene mapping. LD mapping, on the other hand, can increase mapping precision and is useful in high resolution mapping. Linkage analysis is less sensitive to population subdivisions and admixtures. The level of LD is sensitive to population stratification which may easily lead to spurious association. Performing a joint analysis of LD and linkage mapping can help to overcome the limits of both approaches. Moreover, the advantages of the two complementary strategies can be utilized maximally. In practice, linkage analysis may be performed using pedigree data to identify suggestive linkage between markers and trait loci based on a sparse marker map. In the presence of linkage, joint LD and linkage mapping can be carried out to do fine gene mapping based on a dense genetic map using both pedigree and population data. Population and pedigree data of any type can be combined to perform a joint analysis of high resolution LD and linkage mapping of QTL by generalizing the method.     

Handling marker-marker linkage disequilibrium: pedigree analysis with clustered markers

Single-nucleotide polymorphisms (SNPs) are rapidly replacing microsatellites as the markers of choice for genetic linkage studies and many other studies of human pedigrees. Here, we describe an efficient approach for modeling linkage disequilibrium (LD) between markers during multipoint analysis of human pedigrees. Using a gene-counting algorithm suitable for pedigree data, our approach enables rapid estimation of allele and haplotype frequencies within clusters of tightly linked markers. In addition, with the use of a hidden Markov model, our approach allows for multipoint pedigree analysis with large numbers of SNP markers organized into clusters of markers in LD. Simulation results show that our approach resolves previously described biases in multipoint linkage analysis with SNPs that are in LD. An updated version of the freely available Merlin software package uses the approach described here to perform many common pedigree analyses, including haplotyping and haplotype frequency estimation, parametric and nonparametric multipoint linkage analysis of discrete traits, variance-components and regression-based analysis of quantitative traits, calculation of identity-by-descent or kinship coefficients, and case selection for follow-up association studies. To illustrate the possibilities, we examine a data set that provides evidence of linkage of psoriasis to chromosome 17.     

Combining the meiosis Gibbs sampler with the random walk approach for linkage and association studies with a general complex pedigree and multimarker loci

A linkage analysis for finding inheritance states and haplotype configurations is an essential process for linkage and association mapping. The linkage analysis is routinely based upon observed pedigree information and marker genotypes for individuals in the pedigree. It is not feasible for exact methods to use all such information for a large complex pedigree especially when there are many missing genotypic data. Proposed Markov chain Monte Carlo approaches such as a single-site Gibbs sampler or the meiosis Gibbs sampler are able to handle a complex pedigree with sparse genotypic data; however, they often have reducibility problems, causing biased estimates. We present a combined method, applying the random walk approach to the reducible sites in the meiosis sampler. Therefore, one can efficiently obtain reliable estimates such as identity-by-descent coefficients between individuals based on inheritance states or haplotype configurations, and a wider range of data can be used for mapping of quantitative trait loci within a reasonable time.     

Parametric and nonparametric linkage analysis: a unified multipoint approach.

In complex disease studies, it is crucial to perform multipoint linkage analysis with many markers and to use robust nonparametric methods that take account of all pedigree information. Currently available methods fall short in both regards. In this paper, we describe how to extract complete multipoint inheritance information from general pedigrees of moderate size. This information is captured in the multipoint inheritance distribution, which provides a framework for a unified approach to both parametric and nonparametric methods of linkage analysis. Specifically, the approach includes the following: (1) Rapid exact computation of multipoint LOD scores involving dozens of highly polymorphic markers, even in the presence of loops and missing data. (2) Non-parametric linkage (NPL) analysis, a powerful new approach to pedigree analysis. We show that NPL is robust to uncertainty about mode of inheritance, is much more powerful than commonly used nonparametric methods, and loses little power relative to parametric linkage analysis. NPL thus appears to be the method of choice for pedigree studies of complex traits. (3) Information-content mapping, which measures the fraction of the total inheritance information extracted by the available marker data and points out the regions in which typing additional markers is most useful. (4) Maximum-likelihood reconstruction of many-marker haplotypes, even in pedigrees with missing data. We have implemented NPL analysis, LOD-score computation, information-content mapping, and haplotype reconstruction in a new computer package, GENEHUNTER. The package allows efficient multipoint analysis of pedigree data to be performed rapidly in a single user-friendly environment.     

Genetic determinants of bone mass.

A genetic contribution to bone mass determination was first described in the early 70s. Elucidation of gene contribution to this has since been attempted through studies analyzing associations between bone mass acquisition and/or maintenance and polymorphic variations of several genes. The first to be described was the vitamin D receptor gene (VDR), initially claimed to contribute to almost 75% of the genetic variation in bone mineral density (BMD) in twin and general population studies. Not all of the studies published to date conclude that a clear relationship exists between polymorphic VDR alleles and BMD, and the molecular basis for the VDR gene polymorphisms influence on bone mineralization has not yet been clarified. Since then, other genes with a significant role in bone metabolism such as estradiol receptor, collagen type 1alpha1, TGF-beta1, interleukin-6, calcitonin receptor, alpha2-HS-glycoprotein, osteocalcin, calcium-sensing receptor, interleukin-1 receptor antagonist, beta3-adrenergic receptor, apolipoprotein E, PTH, IGF-I and glucocorticoid receptor have been analyzed. Some polymorphic variations in these genes have been associated in some works with significant differences in BMD, with even more significant contributions when associations of different gene polymorphisms were analyzed. Again, the molecular basis for the contribution of these alleles to bone mass determination has not yet been described. A different approach has been attempted by linkage analysis of loci involved in bone density in pedigrees with low BMD using BMD as a quantitative trait. Recent results do not confirm, in these families, any association with any of the previously reported genes, but rather with other as yet unidentified genes. The genetic contribution to mild variations in the general population, as a result of environmental and endogenous individual influences, probably differs completely from that providing a pathologic BMD.     

Screening and replication using the same data set: testing strategies for family-based studies in which all probands are affected

For genome-wide association studies in family-based designs, we propose a powerful two-stage testing strategy that can be applied in situations in which parent-offspring trio data are available and all offspring are affected with the trait or disease under study. In the first step of the testing strategy, we construct estimators of genetic effect size in the completely ascertained sample of affected offspring and their parents that are statistically independent of the family-based association/transmission disequilibrium tests (FBATs/TDTs) that are calculated in the second step of the testing strategy. For each marker, the genetic effect is estimated (without requiring an estimate of the SNP allele frequency) and the conditional power of the corresponding FBAT/TDT is computed. Based on the power estimates, a weighted Bonferroni procedure assigns an individually adjusted significance level to each SNP. In the second stage, the SNPs are tested with the FBAT/TDT statistic at the individually adjusted significance levels. Using simulation studies for scenarios with up to 1,000,000 SNPs, varying allele frequencies and genetic effect sizes, the power of the strategy is compared with standard methodology (e.g., FBATs/TDTs with Bonferroni correction). In all considered situations, the proposed testing strategy demonstrates substantial power increases over the standard approach, even when the true genetic model is unknown and must be selected based on the conditional power estimates. The practical relevance of our methodology is illustrated by an application to a genome-wide association study for childhood asthma, in which we detect two markers meeting genome-wide significance that would not have been detected using standard methodology.     

The role of pedigree information in combined linkage disequilibrium and linkage mapping of quantitative trait loci in a general complex pedigree

Combined linkage disequilibrium and linkage (LDL) mapping can exploit historical as well as recent and observed recombinations in a recorded pedigree. We investigated the role of pedigree information in LDL mapping and the performance of LDL mapping in general complex pedigrees. We compared using complete and incomplete genotypic data, spanning 5 or 10 generations of known pedigree, and we used bi- or multiallelic markers that were positioned at 1- or 5-cM intervals. Analyses carried out with or without pedigree information were compared. Results were compared with linkage mapping in some of the data sets. Linkage mapping or LDL mapping with sparse marker spacing ( approximately 5 cM) gave a poorer mapping resolution without considering pedigree information compared to that with considering pedigree information. The difference was bigger in a pedigree of more generations. However, LDL mapping with closely linked markers ( approximately 1 cM) gave a much higher mapping resolution regardless of using pedigree information. This study shows that when marker spacing is dense and there is considerable linkage disequilibrium generated from historical recombinations between flanking markers and QTL, the loss of power due to ignoring pedigree information is negligible and mapping resolution is very high.     

Application of genome-wide SNP data for uncovering pairwise relationships and quantitative trait loci

The genetic analysis of quantitative traits in humans is changing as a result of the availability of whole-genome SNP data. Heritability analysis can make use of actual genetic sharing between pairs of individuals estimated from the genotype data, rather than the expected genetic sharing implied by their family relationship. This could provide more accurate heritability estimates and help to overcome the equal environment assumption. Quantitative trait locus (QTL) linkage mapping can make use of local genetic sharing inferred from very dense local genotype data from pedigree members or individuals not previously known to be related. This approach may be particularly suited for detecting loci that contain rare variants with major effect on the phenotype. Finally, whole-genome SNP data can be used to measure the genetic similarity between individuals to provide matched sets for association studies, in order to avoid spurious association from population stratification.     

Multilocus association testing of quantitative traits based on partial least-squares analysis

Because of combining the genetic information of multiple loci, multilocus association studies (MLAS) are expected to be more powerful than single locus association studies (SLAS) in disease genes mapping. However, some researchers found that MLAS had similar or reduced power relative to SLAS, which was partly attributed to the increased degrees of freedom (dfs) in MLAS. Based on partial least-squares (PLS) analysis, we develop a MLAS approach, while avoiding large dfs in MLAS. In this approach, genotypes are first decomposed into the PLS components that not only capture majority of the genetic information of multiple loci, but also are relevant for target traits. The extracted PLS components are then regressed on target traits to detect association under multilinear regression. Simulation study based on real data from the HapMap project were used to assess the performance of our PLS-based MLAS as well as other popular multilinear regression-based MLAS approaches under various scenarios, considering genetic effects and linkage disequilibrium structure of candidate genetic regions. Using PLS-based MLAS approach, we conducted a genome-wide MLAS of lean body mass, and compared it with our previous genome-wide SLAS of lean body mass. Simulations and real data analyses results support the improved power of our PLS-based MLAS in disease genes mapping relative to other three MLAS approaches investigated in this study. We aim to provide an effective and powerful MLAS approach, which may help to overcome the limitations of SLAS in disease genes mapping.     

Association analysis of vitamin D receptor gene polymorphisms with bone mineral density in young women with Graves' disease

Graves' (GD) hyperthyroidism induces accelerated bone turnover that leads to decreased bone mineral density (BMD). The role of the VDR gene in predisposition to primary osteoporosis has been recognized. Recent studies show associations between the VDR gene polymorphisms and susceptibility to autoimmune diseases. Here we analyzed if VDR gene polymorphisms: BsmI, ApaI, TaqI, and FokI may predispose women with Graves' hyperthyroidism to BMD reduction or to disease development. The subjects were 75 premenopausal female Polish patients with GD and 163 healthy women. The genotyping was performed by the use of the restriction fragment length polymorphism analysis (RFLP). We studied the association of the VDR polymorphisms and their haplotypes with patients' BMD and also SNPs and haplotypes association with Graves' disease. We found a strong linkage disequilibrium for the BsmI, ApaI, and TaqI polymorphims that formed three most frequent haplotypes in Graves' women: baT (47.9%), BAt (34.9%), and bAT (16.4%). We did not show statistically significant association of analyzed VDR polymorphisms or haplotypes with decreased bone mineral density in Graves' patients. However, the presence of F allele had a weak tendency to be associated with Graves' disease (with OR=1.93; 95% CI: 0.97-3.84; p=0.058). In conclusion: VDR gene polymorphisms do not predict the risk of decreased BMD in Polish women with Graves'. It may be speculated that the F allele carriers of the VDR-FokI polymorphism are predisposed to Graves' disease development.     

Gene mapping by linkage and association analysis

Genetic analysis is used to map genes, including disease loci, to positions within the human genome. Linkage analysis depends on the co-segregation of a gene (locus) and a phenotype through a pedigree, while association analysis, or linkage disequilibrium mapping, depends on measuring deviation from the random occurrence of alleles in a haplotype in unrelated individuals or nuclear families. Complex computer programs may be used in both forms of analysis. In recent years most interest has focused on identifying genes involved in common, multifactorial diseases. Here I review some current and developing techniques of genetic analysis and give references to where further information can be obtained.     

Association between VDR Apal Polymorphism and Hip Bone Mineral Density Can Be Modified by Body Mass Index:A Study on Postmenopausal Chinese Women

Osteoporosis is a major public health problem for old people.Genetic factors are considered tobe major contributors to the pathogenesis of postmenopausal osteoporosis.The vitamin D receptor(VDR)gene is a prominent candidate gene for the regulation of postmenopausal bone mass;however,despite exten-sive studies,controversy remains regarding its association with postmenopausal body mineral density(BMD)variation.In this study,a total of 260 healthy postmenopausal Chinese women were genotyped at the VDRApaI locus using polymerase chain reaction(PCR)-restriction fragment length polymorphism (RFLP).Rawhip BMD was significantly associated with VDR ApaI polymorphism with and without adjusting for age(P=0.015 and 0.040,respectively).This genetic effect can explain 3.32% of hip BMD variation.However,the significant association vanished after correcting for both age and body mass index(BMI)(P=0.169).Inaddition,we observed a significant association between VDR ApaI polymorphism with unadjusted BMI(P=0.042)or BMI adjusted for age(P=0.049).The raw hip BMD was also found to be significantly corre-lated with BMI(r=0.517,P=0.0001),with BMI explaining 26.35% of the variation of hip BMD.All of thesefacts prompt us to conclude that the significant association between the VDR ApaI genotype and hip BMDmay be modified by BMI in postmenopausal Chinese women.Our findings may partially explain the earlierinconsistent association results concerning the VDR gene and BMD,and highlight the importance of incorpo-rating covariates such as BMI into osteoporosis association studies.     

关联分析及其在植物遗传学研究中的应用

植物的很多重要经济性状均属于复杂性状。基于连锁分析的QTL作图是研究复杂性状的有效手段, 但其尚存在一定的局限性。随着现代生物学的发展, 一种基于连锁不平衡的新剖分复杂性状方法--关联分析法, 开始应用于植物遗传学研究。与QTL作图法相比, 应用关联分析法具有不需要构建特殊的群体, 可同时对多个等位基因进行分析, 定位QTL精度可达到单基因水平等优势。该文介绍了关联分析方法学的基础和特性, 简述了其在植物遗传学研究中的进展情况, 并对其未来发展和在植物遗传学研究中的应用进行了展望。     

Power and design considerations for a general class of family-based association tests: quantitative traits

In the present article, we address family-based association tests (FBATs) for quantitative traits. We propose an approach to analytical power and sample-size calculations for general FBATs; this approach can be applied to virtually any scenario (missing parental information, multiple offspring per family, etc.). The power calculations are used to discuss optimal choices of the phenotypes for the FBAT statistic and its power's dependence on ascertainment conditions, on study design, and on the correct specification of the distributional assumptions for the phenotypes. We also compare the general FBAT approach with PDT and QTDT. The practical relevance of our theoretical considerations is illustrated by their application to an asthma study.     

Association between VDR ApaI Polymorphism and Hip Bone Mineral Density Can Be Modified by Body Mass Index： A Study on Postmenopausal Chinese Women

Osteoporosis is a major public health problem for old people. Genetic factors are considered to be major contributors to the pathogenesis of postmenopausal osteoporosis. The vitamin D receptor (VDR) gene is a prominent candidate gene for the regulation of postmenopausal bone mass; however, despite extensive studies, controversy remains regarding its association with postmenopausal body mineral density (BMD) variation. In this study, a total of 260 healthy postmenopausal Chinese women were genotyped at the VDR ApaI locus using polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP). Raw hip BMD was significantly associated with VDR ApaI polymorphism with and without adjusting for age (P=0.015 and 0.040, respectively). This genetic effect can explain 3.32% of hip BMD variation. However, the significant association vanished after correcting for both age and body mass index (BMI) (P=0.169). In addition, we observed a significant association between VDR ApaI polymorphism with unadjusted BMI(P=0.042) or BMI adjusted for age (P=0.049). The raw hip BMD was also found to be significantly correlated with BMI (r=0.517, P=0.0001), with BMI explaining 26.35% of the variation of hip BMD. All of these facts prompt us to conclude that the significant association between the VDR ApaI genotype and hip BMD may be modified by BMI in postmenopausal Chinese women. Our findings may partially explain the earlier inconsistent association results concerning the VDR gene and BMD, and highlight the importance of incorporating covariates such as BMI into osteoporosis association studies.     

Combined linkage disequilibrium and linkage mapping: Bayesian multilocus approach

Quantitative trait loci (QTL) affecting the phenotype of interest can be detected using linkage analysis (LA), linkage disequilibrium (LD) mapping or a combination of both (LDLA). The LA approach uses information from recombination events within the observed pedigree and LD mapping from the historical recombinations within the unobserved pedigree. We propose the Bayesian variable selection approach for combined LDLA analysis for single-nucleotide polymorphism (SNP) data. The novel approach uses both sources of information simultaneously as is commonly done in plant and animal genetics, but it makes fewer assumptions about population demography than previous LDLA methods. This differs from approaches in human genetics, where LDLA methods use LA information conditional on LD information or the other way round. We argue that the multilocus LDLA model is more powerful for the detection of phenotype–genotype associations than single-locus LDLA analysis. To illustrate the performance of the Bayesian multilocus LDLA method, we analyzed simulation replicates based on real SNP genotype data from small three-generational CEPH families and compared the results with commonly used quantitative transmission disequilibrium test (QTDT). This paper is intended to be conceptual in the sense that it is not meant to be a practical method for analyzing high-density SNP data, which is more common. Our aim was to test whether this approach can function in principle.     

Automating data manipulation for genetic analysis using a data base management system

Inefficient coding and manipulation of pedigree data have often hindered the progress of genetic studies. In this paper we present the methodology for interfacing a data base management system (DBMS) called MEGADATS with a linkage analysis program called LIPED. Two families that segregate a dominant trait and one test marker were used in a simulated exercise to demonstrate how a DBMS can be used to automate tedious clerical steps and improve the efficiency of a genetic analysis. The merits of this approach to data management are discussed. We conclude that a standardized format for genetic analysis programs would greatly facilitate data analysis.