A linkage strategy for detection of human quantitative-trait loci. I. Generalized relative risk ratios and power of sib pairs with extreme trait values.

We generalize the concept of the relative risk ratio (lambda) to the case of quantitative traits, to take into account the various trait outcomes of a relative pair. Formulas are derived to express the expected proportions of genes shared identical by descent by a sib pair, in terms of the generalized lambda's for sib pairs (lambda S), parent-offspring pairs (lambda O), and monozygotic twins (lambda M) and in terms of the recombination fraction, with the assumption of no residual correlations. If residual correlations are nonzero among relative pairs, we assume that they are the same among sib pairs, parent-offspring pairs, and monozygotic twins, and we employ a slightly different definition for the generalized lambda so that the same set of formulas still hold. The power (or, the sample size necessary) to detect quantitative-trait loci (QTLs) by use of extreme sib pairs (ESPs) is shown to be a function of the three generalized lambda's. Since lambda M can be derived by use of values of lambda S and lambda O, estimates of the latter two lambda's will suffice for the analysis of power and the necessary sample sizes of ESPs, for a QTL linkage study.     

Mapping quantitative-trait loci in humans by use of extreme concordant sib pairs: selected sampling by parental phenotypes.

In two previous articles, we have considered sample sizes required to detect linkage for mapping quantitative-trait loci in humans, using extreme discordant sib pairs. Here, we examine further the use of extreme concordant sib pairs but consider the effect of parents' phenotypes. Sample sizes necessary to obtain a power of 80% with concordant sib pairs at a significance level of .0001 are given, stratified by parental phenotypes. When there is no residual correlation between sibs, the parental phenotypes have little impact on the sample sizes. When residual correlations between sibs exist, we show, however, that power can be considerably reduced by including extreme sib pairs when the parents also have similarly extreme values. Thus, we recommend the exclusion of such pairs from linkage studies. This recommendation reduces the required sample sizes by 3- to 28-fold. The degree of saving in the required sample sizes varies among different models and allele frequencies. The reduction is most dramatic (a 28-fold reduction) for a rare recessive gene.     

A two-stage variable-stringency semiparametric method for mapping quantitative-trait loci with the use of genomewide-scan data on sib pairs

Genomewide scans for mapping loci have proved to be extremely powerful and popular. We present a semiparametric method of mapping a quantitative-trait locus (QTL) or QTLs with the use of sib-pair data generated from a two-stage genomic scan. In a two-stage genomic scan, either the entire genome or a large portion of the genome is saturated with low-density markers at the first stage. At the second stage, the intervals that are identified as probable locations of the trait loci, by means of analysis of data from the first stage, are then saturated with higher-density markers. These data are then analyzed for fine mapping of the loci. Our statistical strategy for analysis of data from the first stage is a low-stringency method based on the rank correlation of squared trait-difference values of the sib pairs and the estimated identity-by-descent scores at the marker loci. We suggest the use of a low-stringency method at the first stage, to save on computational time and to avoid missing any marker interval that may contain the trait loci. For analysis of data from the second stage, we have developed a high-stringency nonparametric-regression approach, using the kernel-smoothing technique. Through extensive simulations, we show that this approach is more powerful than is a currently used method for mapping QTLs by use of sib pairs, particularly in the presence of dominance and epistatic effects at the trait loci.     

Segregation and linkage analysis of serum angiotensin I-converting enzyme levels: evidence for two quantitative-trait loci.

Human serum angiotensin I-converting enzyme (ACE) levels vary substantially between individuals and are highly heritable. Segregation analysis in European families has shown that more than half of the total variability in ACE levels is influenced by quantitative-trait loci (QTL). One of these QTLs is located within or close to the ACE locus itself. Combined segregation/linkage analysis in a series of African Caribbean families from Jamaica shows that the ACE insertion-deletion polymorphism is in moderate linkage disequilibrium with an ACE-linked QTL. Linkage analysis with a highly informative polymorphism at the neighboring growth-hormone gene (GH) shows surprisingly little support for linkage (LOD score [Z] = 0.12). An extended analysis with a two-QTL model, where an ACE-linked QTL interacts additively with an unlinked QTL, significantly improves both the fit of the model (P = .002) and the support for linkage between the ACe-linked QTL interacts additively with an unlinked QTL, significantly improves both the fit of the model (P = .002) and the support for linkage between the ACe-linked QTL and GH polymorphism (Z = 5.0). We conclude that two QTLs jointly influence serum ACE levels in this population. One QTL is located within or close to the ACE locus and explains 27% of the total variability; the second QTL is unlinked to the ACE locus and explains 52% of the variability. The identification of the molecular mechanisms underlying both QTLs is necessary in order to interpret the role of ACE in cardiovascular disease.     

A simulation study of the effects of assignment of prior identity-by-descent probabilities to unselected sib pairs, in covariance-structure modeling of a quantitative-trait locus.

Sib pair-selection strategies, designed to identify the most informative sib pairs in order to detect a quantitative-trait locus (QTL), give rise to a missing-data problem in genetic covariance-structure modeling of QTL effects. After selection, phenotypic data are available for all sibs, but marker data-and, consequently, the identity-by-descent (IBD) probabilities-are available only in selected sib pairs. One possible solution to this missing-data problem is to assign prior IBD probabilities (i.e., expected values) to the unselected sib pairs. The effect of this assignment in genetic covariance-structure modeling is investigated in the present paper. Two maximum-likelihood approaches to estimation are considered, the pi-hat approach and the IBD-mixture approach. In the simulations, sample size, selection criteria, QTL-increaser allele frequency, and gene action are manipulated. The results indicate that the assignment of prior IBD probabilities results in serious estimation bias in the pi-hat approach. Bias is also present in the IBD-mixture approach, although here the bias is generally much smaller. The null distribution of the log-likelihood ratio (i.e., in absence of any QTL effect) does not follow the expected null distribution in the pi-hat approach after selection. In the IBD-mixture approach, the null distribution does agree with expectation.     

A study on genetic linkage in chickens comprising six loci for polymorphic proteins and three othei gene pairs

Genetic linkage of six loci for polymorphic proteins in chickens ( Alb, Tf, Pa, Ov, G ₃ and G ₂) and three loci for somatic markers ( W/w , P/p and I/i ) was investigated. The existence of close linkage was proved between Ov and G₃. No sign of linkage was detected between the other loci.     

Anxiety and depression in twin and sib pairs extremely discordant and concordant for neuroticism: prodromus to a linkage study.

Multivariate modelling of anxiety and depression data in twins has suggested that the two phenotypes are largely underpinned by one genetic factor, while other studies have indicated a relationship between these disorders and the neuroticism personality trait. As part of a study to identify quantitative trait loci for anxiety and depression, questionnaire responses and interviews of 15,027 Australian twins and 11,389 of their family members conducted during the past 20 years were reviewed to identify individuals with neuroticism, anxiety and depression scores in the upper or lower deciles of the population. This information was then used to identify extreme discordant and concordant (EDAC) sib pairs. 1373 high-scoring and 1571 low-scoring subjects (2357 sib pairs) were selected for participation, and extremely high participation rates were achieved, with over 90% of contactable prospective participants completing the interview phase, and over 90% of these providing blood or buccal samples. Participation bias arising from the nature of the selection variables was minimal, with only a small difference between rates of interview participation among prospective participants with high and low selection scores (89.4% vs 91.6%). The interview permitted the diagnosis of depression and several anxiety disorders (OCD, agoraphobia, panic disorder, generalised anxiety disorder) in this sample according to DSM-IV criteria. The methodology for selection of prospective subjects was demonstrated to be extremely successful, with highly significant differences in depression and anxiety disorder prevalence rates between individuals in the two selection groups. The success of this EDAC sampling scheme will enhance the power for QTL linkage and association analysis in this sample.     

On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines

Summary The power of experiments aimed at detecting linkage between a quantitative locus and a marker locus, both segregating in the backross or F₂ generation of a cross between two inbred lines, is examined. Given that the two lines are close to fixation for alternative alleles of both marker locus and quantitative locus, it is concluded that experiments involving a few thousand offspring should be able to detect close linkages involving quantitative loci (or groups of loci) having rather modest effects (i.e., that contribute, say, 1% of the total phenotypic variance in the F₂).     

A Monte Carlo method for Bayesian analysis of linkage between single markers and quantitative trait loci. II. A simulation study

A Bayesian approach to the statistical mapping of Quantitative Trait Loci (QTLs) using single markers was implemented via Markov Chain Monte Carlo (MCMC) algorithms for parameter estimation and hypothesis testing. Parameters were estimated by marginal posterior means computed with a Gibbs sampler with data augmentation. Variables sampled included the augmented data (marker-QTL genotypes, polygenic effects), the event of linkage or nonlinkage, and the parameters (allele frequencies, QTL substitution effect, recombination rate, polygenic and residual variances). The analysis was evaluated empirically via application to simulated granddaughter designs consisting of 2000 sons, 20 related sires and their ancestors. Results obtained in this study and preliminary work on multiple linked markers and multiple QTLs support the usefulness of the Bayesian method for the statistical mapping of QTLs.     

Criteria to optimize designs for detection and estimation of linkage between marker loci from segregating populations containing several families

Summary Construction of a genome map of highly polymorphic markers has become possible in the past decade. Establishing a complete marker map is an enormous task. Therefore, designs to map molecular markers should be optimal. Designs to detect and estimate linkage between markers from segregating populations were studied. Two measures of design quality were used. The expectation of the maximum lod score indicates the possibility of designs to detect linkage. The accuracy of estimating recombination rate was measured as the probability that the true recombination rate is in a specified internal given the estimate. Accurate approximate methods were developed for rapid evaluation of designs. Seven family types (e.g., double backcross) can be distinguished that describe all families in a segregating population. The family type influences the expected maximum lod score and the accuracy of estimation. The frequency of favorable family types increased with increasing marker polymorphism. At a true recombination rate of 0.20,27 observations on offspring when five alleles were segregating, and 55 observations on offspring when two alleles were segregating, were necessary to obtain an expected maximum lod score of 3. The probability that the true recombination rate was between 0.15 and 0.25, given an estimate of 0.20, was about 0.85 for a design with 40 families with ten offspring and two alleles segregating and for a design with ten families with ten offspring and six alleles segregating. For smaller designs, accuracies were less, approximate evaluation of accuracy was not justified and, on average, true recombination rates were much greater than estimated given a specified value for the estimated recombination rate.     

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. II. An autosomal genome scan for diabetes-related quantitative-trait loci

Type 2 diabetes mellitus is a complex disorder encompassing multiple metabolic defects. We report results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sibling pair and analyzed by the variance components-based quantitative-trait locus (QTL) linkage approach. We analyzed diabetic and nondiabetic subjects separately, because of the possible impact of disease on the traits of interest. In diabetic individuals, our strongest results were observed on chromosomes 3 (fasting C-peptide/glucose: maximum LOD score [MLS] = 3.13 at 53.0 cM) and 13 (body-mass index: MLS = 3.28 at 5.0 cM). In nondiabetic individuals, the strongest results were observed on chromosomes 10 (acute insulin response: MLS = 3.11 at 21.0 cM), 13 (2-h insulin: MLS = 2.86 at 65.5 cM), and 17 (fasting insulin/glucose ratio: MLS = 3.20 at 9.0 cM). In several cases, there was evidence for overlapping signals between diabetic and nondiabetic individuals; therefore we performed joint analyses. In these joint analyses, we observed strong signals for chromosomes 3 (body-mass index: MLS = 3.43 at 59.5 cM), 17 (empirical insulin-resistance index: MLS = 3.61 at 0.0 cM), and 19 (empirical insulin-resistance index: MLS = 2.80 at 74.5 cM). Integrating genome-scan results from the companion article by Ghosh et al., we identify several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.     

A whole-genome scan in 164 Dutch sib pairs with attention-deficit/hyperactivity disorder: suggestive evidence for linkage on chromosomes 7p and 15q

A genome scan was performed on 164 Dutch affected sib pairs (ASPs) with attention-deficit/hyperactivity disorder (ADHD). All subjects were white and of Dutch descent and were phenotyped according to criteria set out in the Diagnostic and Statistical Manual Of Mental Disorders, 4th edition. Initially, a narrow phenotype was defined, in which all the sib pairs met the full ADHD criteria (117 ASPs). In a broad phenotype, additional sib pairs were included, in which one child had an autistic-spectrum disorder but also met the full ADHD criteria (164 ASPs). A set of 402 polymorphic microsatellite markers with an average intermarker distance of 10 cM was genotyped and analyzed using the Mapmaker/sibs program. Regions with multipoint maximum likelihood scores (MLSs) >1.5 in both phenotypes were fine mapped with additional markers. This genome scan indicated several regions of interest, two of which showed suggestive evidence for linkage. The most promising chromosome region was located at 15q, with an MLS of 3.54 under the broad phenotype definition. This region was previously implicated in reading disability and autism. In addition, MLSs of 3.04 and 2.05 were found for chromosome regions 7p and 9q in the narrow phenotype. Except for a region on chromosome 5, no overlap was found with regions mentioned in the only other independent genome scan in ADHD reported to date.     

A genome-wide study replicates linkage of 3p22-24 to extreme longevity in humans and identifies possible additional loci

Background

Although there is abundant evidence that human longevity is heritable, efforts to map loci responsible for variation in human lifespan have had limited success.

Methodology/Principal Findings

We identified individuals from a large multigenerational population database (the Utah Population Database) who exhibited high levels of both familial longevity and individual longevity. This selection identified 325 related “affected individuals”, defined as those in the top quartile for both excess longevity (EL = observed lifespan – expected lifespan) and familial excess longevity (FEL = weighted average EL across all relatives). A whole-genome scan for genetic linkage was performed on this sample using a panel of 1100 microsatellite markers. A strongly suggestive peak (Z = 4.2, Monte Carlo-adjusted p-value 0.09) was observed in the vicinity of D3S3547 on chromosome 3p24.1, at a point nearly identical to that reported recently by an independent team of researchers from Harvard Medical School (HMS) [1]. Meta-analysis of linkage scores on 3p from the two studies produced a minimum nominal p-value of 1.005×10⁻⁹ at 55 cM. Other potentially noteworthy peaks in our data occur on 18q23-24, 8q23, and 17q21. Meta-analysis results from combined UPDB and HMS data yielded additional support, but not formal replication, for linkage on 8q, 9q, and 17q.

Conclusions/Significance

Corroboration of the linkage of exceptional longevity to 3p22-24 greatly strengthens the case that genes in this region affect variation in longevity and suggest, therefore, an important role in the regulation of human lifespan. Future efforts should include intensive study of the 3p22-24 region.     

A genetic linkage map of 96 loci on the short arm of human chromosome 3.

We constructed a genetic map of 96 loci on the short arm of human chromosome 3 (3p) in 59 families provided by the Centre d'Etude du Polymorphisme Humaine (CEPH). Twenty-nine continuously linked loci were placed on the map with likelihood support of at least 1000:1; one locus, D3S213, was placed on the map with likelihood support of 871:1; D3Z1, an alpha satellite centromeric repeat probe, was placed on the map with likelihood support of 159:1; 65 loci were assigned regional locations. The average heterozygosity of the uniquely ordered markers was 49%. The map extends from 3p26, the terminal band of 3p, to the centromere (from D3S211 to D3Z1). Multipoint linkage analysis indicated that the male, female, and sex-averaged maps extend for 102, 147, and 116 cM, respectively. The mean genetic distance between uniquely ordered loci on the sex-averaged map was 4.0 cM. Probe density was greatest for the region of 3p between D3F15S2e and the telomere. The sex-averaged map contained two intervals greater than 10 cM. Seventeen probes were localized by fluorescence in situ hybridization. The loci described in this report will be useful in building an integrated genetic and physical map of this chromosome.     

A composite-conditional-likelihood approach for gene mapping based on linkage disequilibrium in windows of marker loci

A composite-conditional-likelihood (CCL) approach is proposed to map the position of a trait-influencing mutation (TIM) using the ancestral recombination graph (ARG) and importance sampling to reconstruct the genealogy of DNA sequences with respect to windows of marker loci and predict the linkage disequilibrium pattern observed in a sample of cases and controls. The method is designed to fine-map the location of a disease mutation, not as an association study. The CCL function proposed for the position of the TIM is a weighted product of conditional likelihood functions for windows of a given number of marker loci that encompass the TIM locus, given the sample configuration at the marker loci in those windows. A rare recessive allele is assumed for the TIM and single nucleotide polymorphisms (SNPs) are considered as markers. The method is applied to a range of simulated data sets. Not only do the CCL profiles converge more rapidly with smaller window sizes as the number of simulated histories of the sampled sequences increases, but the maximum-likelihood estimates for the position of the TIM remain as satisfactory, while requiring significantly less computing time. The simulations also suggest that non-random samples, more precisely, a non-proportional number of controls versus the number of cases, has little effect on the estimation procedure as well as sample size and marker density beyond some threshold values. Moreover, when compared with some other recent methods under the same assumptions, the CCL approach proves to be competitive.     

A gene-model-free method for linkage analysis of a disease-related-trait based on analysis of proband/sibling pairs

In this paper we investigate the power of finding linkage to a disease locus through analysis of the disease-related traits. We propose two family-based gene-model-free linkage statistics. Both involve considering the distribution of the number of alleles identical by descent with the proband and comparing siblings with the disease-related trait to those without the disease-related-trait. The objective is to find linkages to disease-related traits that are pleiotropic for both the disease and the disease-related-traits. The power of these statistics is investigated for Kofendrerd Personality Disorder-related traits a (Joining/founding cults) and trait b (Fear/discomfort with strangers) of the simulated data. The answers were known prior to the execution of the reported analyses. We find that both tests have very high power when applied to the samples created by combining the data of the three cities for which we have nuclear family data.     

A linkage map for sugi (Cryptomeria japonica) based on RFLP,RAPD, and isozyme loci

A linkage map for sugi was constructed on the basis of restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), and isozyme loci using a three-generation pedigree prepared for genetic analysis of heartwood color. A total of 128 RFLP (123 cDNA and 5 genomic probes), 33 RAPD, 2 isozyme, and 1 morphological (dwarf) loci segregated in 73 progeny. Of the 164 segregating loci, 145 loci were distributed in 20 linkage groups. Of these loci, 91 with confirmed map positions were assigned to 13 linkage groups, covering a total of 887.3 cM. A clustering of markers with distorted segregation was observed in 6 linkage groups. In the four clusters, distortions with a reduction in the number of homozygotes from one parent only were found.Abbreviations MAS marker-assisted selection - PAGE polyacrylamide gel electrophoresis - QTL quantitative traits of loci - RAPD random amplified polymorphic DNA - RFLP restriction fragment length polymorphism This work was supported by a Grant-in-Aid from the Ministry of Agriculture, Forestry and Fisheries of Japan (Integrated Research Program for the Use of Biotechnological Procedures for Plant Breeding) and by a Grant-in-Aid from the Ministry of Education, Science and Culture of Japan (Cooperative Research, no. 04304017)     

Novel linkage mapping approach using DNA pooling in human and animal genetics. II. Detection of quantitative traits loci in dairy cattle

Selective DNA pooling is an advanced methodology for linkage mapping of quantitative trait loci (QTL) in farm animals. The principle is based on densitometric estimates of marker allele frequency in pooled DNA samples of phenotypically extreme individuals from half-sib, backcross and F(2) experimental designs in farm animals. This methodology provides a rapid and efficient analysis of a large number of individuals with short tandem repeat markers that are essential to detect QTL through the genome - wide searching approach. Several strategies involving whole genome scanning with a high statistical power have been developed for systematic search to detect the quantitative traits loci and linked loci of complex traits. In recent studies, greater success has been achieved in mapping several QTLs in Israel-Holstein cattle using selective DNA pooling. This paper outlines the currently emerged novel strategies of linkage mapping to identify QTL based on selective DNA pooling with more emphasis on its theoretical pre-requisite to detect linked QTLs, applications, a general theory for experimental half-sib designs, the power of statistics and its feasibility to identify genetic markers linked QTL in dairy cattle. The study reveals that the application of selective DNA pooling in dairy cattle can be best exploited in the genome-wide detection of linked loci with small and large QTL effects and applied to a moderately sized half-sib family of about 500 animals.     

A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci*

Analysis of linkage disequilibrium (=mean squared correlation of allele frequencies at different gene loci) provides a means of estimating effective population size (N _e) from a single sample, but this method has seen much less use than the temporal method (which requires at least two samples). It is shown that for realistic numbers of loci and alleles, the linkage disequilibrium method can provide precision comparable to that of the temporal method. However, computer simulations show that estimates of N _e based on for unlinked, diallelic gene loci are sharply biased downwards ( in some cases) if sample size (S) is less than true N _e. The bias is shown to arise from inaccuracies in published formula for when S and/or N _e are small. Empirically derived modifications to for two mating systems (random mating and lifetime monogamy) effectively eliminate the bias (residual bias in % in most cases). The modified method also performs well in estimating N _e in non-ideal populations with skewed sex ratio or non-random variance in reproductive success. Recent population declines are not likely to seriously affect , but if N has recently increased from a bottleneck can be biased downwards for a few generations. These results should facilitate application of the disequilibrium method for estimating contemporary N _e in natural populations. However, a comprehensive assessment of performance of with highly polymorphic markers such as microsatellites is needed.The US Governmentȁ9s right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged.     

The detection of linkage and heterogeneity in nuclear families for complex disorders: one versus two marker loci.

Using exact expected likelihoods, we have computed the average number of phase-unknown nuclear families needed to detect linkage and heterogeneity. We have examined the case of both dominant and recessive inheritance with reduced penetrance and phenocopies. Most of our calculations have been carried out under the assumption that 50% of families are linked to a marker locus. We have varied both the number of offspring per family and the sampling scheme. We have also investigated the increased power when the disease locus is midway between two marker loci 10 cM apart. For recessive inheritance, both linkage and heterogeneity can be detected in clinically feasible sample sizes. For dominant inheritance, linkage can be detected but heterogeneity cannot be detected unless larger sibships (four offspring) are sampled or two linked markers are available. As expected, if penetrance is reduced, sampling families with all sibs affected is most efficient. Our results provide a basis for estimating the amount of resources needed to find genes for complex disorders under conditions of heterogeneity.