Efficient strategies for genomic searching using the affected-pedigree-member method of linkage analysis.

The affected-pedigree-member (APM) method of linkage analysis is a nonparametric statistic that tests for nonrandom cosegregation of a disease and marker loci. The APM statistic is based on the observation that if a marker locus is near a disease-susceptibility locus, then affected individuals within a family should be more similar at the marker locus than is expected by chance. The APM statistic measures marker similarity in terms of identity by state (IBS) of marker alleles; that is, two alleles are IBS if they are the same, regardless of their ancestral origin. Since the APM statistic measures increased marker similarity, it makes no assumptions concerning how the disease is inherited; this can be an advantage when dealing with complex diseases for which the mode of inheritance is difficult to determine. We investigate here the power of the APM statistic to detect linkage in the context of a genomewide search. In such a search, the APM statistic is evaluated at a grid of markers. Then regions with high APM statistics are investigated more thoroughly by typing more markers in the region. Using simulated data, we investigate various search strategies and recommend an optimal search strategy that maximizes the power to detect linkage while minimizing the false-positive rate and number of markers. We determine an optimal series of three increasing cut-points and an independent criterion for significance.     

Candidate-gene studies of the atherogenic lipoprotein phenotype: a sib-pair linkage analysis of DZ women twins.

There is a growing body of evidence supporting the roles of small, dense LDL and plasma triglyceride (TG), both features of the atherogenic lipoprotein phenotype, as risk factors for coronary heart disease. Although family studies and twin studies have demonstrated genetic influences on these risk factors, the specific genes involved remain to be determined definitively. The purpose of this study was to investigate genetic linkage between LDL size, TG, and related atherogenic lipoproteins and candidate genes known to be involved in lipid metabolism. The linkage analysis was based on a sample of 126 DZ women twin pairs, which avoids the potentially confounding effects of both age and gender, by use of a quantitative sib-pair linkage-analysis approach. Eight candidate genes were examined, including those for microsomal TG-transfer protein (MTP), hepatic lipase, hormone-sensitive lipase, apolipoprotein (apo) B, apo CIII, apo E, insulin receptor, and LDL receptor. The analysis suggested genetic linkage between markers for the apo B gene and LDL size, plasma levels of TG, of HDL cholesterol, and of apo B, all features of the atherogenic lipoprotein phenotype. Furthermore, evidence for linkage was maintained when the analysis was limited to women with a major LDL-subclass diameter >255 A, indicating that the apo B gene may influence LDL heterogeneity in the intermediate-to-large size range. In addition, linkage was found between the MTP gene and TG, among all the women. These findings add to the growing evidence for genetic influences on the atherogenic lipoprotein phenotype and its role in genetic susceptibility to atherosclerosis.     

Comparison of linkage and association strategies for quantitative traits using the COGA dataset

Genome scans using dense single-nucleotide polymorphism (SNP) data have recently become a reality. It is thought that the increase in information content for linkage analysis as a result of the denser scans will help refine previously identified linkage regions and possibly identify new regions not identifiable using the sparser, microsatellite scans. In the context of the dense SNP scans, it is also possible to consider association strategies to provide even more information about potential regions of interest. To circumvent the multiple-testing issues inherent in association analysis, we use a recently developed strategy, implemented in PBAT, which screens the data to identify the optimal SNPs for testing, without biasing the nominal significance level. We compare the results from the PBAT analysis to that of quantitative linkage analysis on chromosome 4 using the Collaborative Study on the Genetics of Alcoholism data, as released through Genetic Analysis Workshop 14.     

Detection of linkage for heterogeneous disorders by using multipoint linkage analysis.

We have compared the efficiency of the lod score test which assumes heterogeneity (lod2) to the standard lod score test which assumes homogeneity (lod1) when three-point linkage analysis is used in successive map intervals. If it is assumed that a gene located midway between two linked marker loci is responsible for a proportion of disease cases, then the lod1 test loses power relative to the lod2 test, as the proportion of linked families decreases, as the flanking markers are more closely linked, and as more map intervals are tested. Moreover, when multipoint analysis is used, linkage for a disease gene is more likely to be incorrectly excluded from a complete and dense linkage map if true genetic heterogeneity is ignored. We thus conclude that, in general, the lod2 linkage test is more efficient for detecting a true linkage when a complete genetic marker map is screened for a heterogeneous disorder.     

Selection of genetic markers for association analyses,using linkage disequilibrium and haplotypes

The genotyping of closely spaced single-nucleotide polymorphism (SNP) markers frequently yields highly correlated data, owing to extensive linkage disequilibrium (LD) between markers. The extent of LD varies widely across the genome and drives the number of frequent haplotypes observed in small regions. Several studies have illustrated the possibility that LD or haplotype data could be used to select a subset of SNPs that optimize the information retained in a genomic region while reducing the genotyping effort and simplifying the analysis. We propose a method based on the spectral decomposition of the matrices of pairwise LD between markers, and we select markers on the basis of their contributions to the total genetic variation. We also modify Clayton's "haplotype tagging SNP" selection method, which utilizes haplotype information. For both methods, we propose sliding window-based algorithms that allow the methods to be applied to large chromosomal regions. Our procedures require genotype information about a small number of individuals for an initial set of SNPs and selection of an optimum subset of SNPs that could be efficiently genotyped on larger numbers of samples while retaining most of the genetic variation in samples. We identify suitable parameter combinations for the procedures, and we show that a sample size of 50-100 individuals achieves consistent results in studies of simulated data sets in linkage equilibrium and LD. When applied to experimental data sets, both procedures were similarly effective at reducing the genotyping requirement while maintaining the genetic information content throughout the regions. We also show that haplotype-association results that Hosking et al. obtained near CYP2D6 were almost identical before and after marker selection.     

Methods for genetic linkage analysis using trisomies.

Certain genetic disorders are rare in the general population, but more common in individuals with specific trisomies. Examples of this include leukemia and duodenal atresia in trisomy 21. This paper presents a linkage analysis method for using trisomic individuals to map genes for such traits. It is based on a very general gene-specific dosage model that posits that the trait is caused by specific effects of different alleles at one or a few loci and that duplicate copies of "susceptibility" alleles inherited from the nondisjoining parent give increased likelihood of having the trait. Our mapping method is similar to identity-by-descent-based mapping methods using affected relative pairs and also to methods for mapping recessive traits using inbred individuals by looking for markers with greater than expected homozygosity by descent. In the trisomy case, one would take trisomic individuals and look for markers with greater than expected homozygosity in the chromosomes inherited from the nondisjoining parent. We present statistical methods for performing such a linkage analysis, including a test for linkage to a marker, a method for estimating the distance from the marker to the trait gene, a confidence interval for that distance, and methods for computing power and sample sizes. We also resolve some practical issues involved in implementing the methods, including how to use partially informative markers and how to test candidate genes.     

Sample-size considerations and strategies for linkage analysis in autosomal recessive disorders.

The opportunity raised by recombinant DNA technology to develop a linkage marker panel that spans the human genome requires cost-efficient strategies for its optimal utilization. Questions arise as to whether it is more cost-effective to convert a dimorphic restriction enzyme marker system into a highly polymorphic system or, instead, to increase the number of families studied, simply using the available marker alleles. The choice is highly dependent on the population available for study, and, therefore, an examination of the informational content of the various family structures is important to obtain the most informative data. To guide such decisions, we have developed tables of the average sample number of families required to detect linkage for autosomal recessive disorders under single backcross and under "fully informative" matings. The latter cross consists of a marker locus with highly polymorphic codominant alleles such that the parental marker genotypes can be uniquely distinguished. The sampling scheme considers families with unaffected parents of known mating types ascertained via affected offspring, for sibship sizes ranging from two to four and various numbers of affected individuals. The sample-size tables, calculated for various values of the recombination fractions and lod scores, may serve as a guide to a more efficient application of the restriction fragment length polymorphism technology to sequential linkage analysis.     

Selection strategies for improved biocatalysts

Enzymes have become an attractive alternative to conventional catalysts in numerous industrial processes. However, their properties do not always meet the criteria of the application of interest. Directed evolution is a powerful tool for adopting the characteristics of an enzyme. However, selection of the evolved variants is a critical step, and therefore new strategies to enable selection of the desired enzymatic activity have been developed. This review focuses on these novel strategies for selecting enzymes from large libraries, in particular those that are used in the synthesis of pharmaceutical intermediates and pharmaceuticals.     

Preadolescent development: case studies in twins.

The authors studied eight sets of healthy twins, ranging in physical maturity from prepubertal to late pubertal, and their parents, to assess psychological changes in early adolescence. A phase-specific psychosocial regression correlates with the biological onset of puberty. We present preliminary findings related to twinship, parental reactions, and longitudinal development, based on detailed case studies. Parents experienced increased conflict, detachment, and pride in response to their twins'' psychosocial development at puberty. The more advanced child in a pair tended to lead in all spheres of adjustment-school success, heterosexual interest, peer friendships, and independent behavior. Although the more advanced child began to break away from his twin, in general the twin relationships remained close. Differences in personality traits and rate of psychosocial development within a twinship tended to remain consistent or to become accentuated. They were occasionally narrowed but rarely reversed from early childhood through early adolescence. Some of the differences were fostered by dissimilar patterns of identifying with the parents.     

Model-free linkage analysis using likelihoods.

Misspecification of transmission model parameters can produce artifactually negative lod scores at small recombination fractions and in multipoint analysis. To avoid this problem, we have tried to devise a test that aims to detect a genetic effect at a particular locus, rather than attempting to estimate the map position of a locus with specified effect. Maximizing likelihoods over transmission model parameters, as well as linkage parameters, can produce seriously biased parameter estimates and so yield tests that lack power for the detection of linkage. However, constraining the transmission model parameters to produce the correct population prevalence largely avoids this problem. For computational convenience, we recommend that the likelihoods under linkage and non-linkage are independently maximized over a limited set of transmission models, ranging from Mendelian dominant to null effect and from null effect to Mendelian recessive. In order to test for a genetic effect at a given map position, the likelihood under linkage is maximized over admixture, the proportion of families linked. Application to simulated data for a wide range of transmission models in both affected sib pairs and pedigrees demonstrates that the new method is well behaved under the null hypothesis and provides a powerful test for linkage when it is present. This test requires no specification of transmission model parameters, apart from an approximate estimate of the population prevalence. It can be applied equally to sib pairs and pedigrees, and, since it does not diminish the lod score at test positions very close to a marker, it is suitable for application to multipoint data.     

Genetic linkage studies of human neurodegenerative disorders.

Recombinant DNA technology has the ability to delineate the causes of several neurodegenerative disorders. Genetic linkage studies have been used successfully to localize gene defects and it is likely that in the near future the exact loci will be determined.     

Data acquisition for meta-analysis of genome-wide linkage studies using the genome search meta-analysis method

BACKGROUND: The Genome Search Meta-Analysis (GSMA) method enables researchers to pool results across genome-wide linkage studies, to increase the power to detect linkage. Results from individual studies must be extracted, with the maximum evidence for linkage placed into bins, usually of 30 cM width, and ranked within the study. Ranks are then summed across studies, with high summed ranks potentially showing evidence for linkage in the meta-analysis. OBJECTIVES: In this paper we study the properties of the GSMA method considering two different issues: (1) data binning from genome-wide results when indexed markers or graphs are available, based on either predefined boundary markers, or equal-length bins; (2) the use of selected instead of genome-wide results, using simulation to estimate power and type I error rates of GSMA. This is relevant when published papers show only summary results (e.g. with NPL score >1). Results: Using digitizing software to extract linkage statistics from graphs and assigning equal bin length is accurate, with the resulting ranking of bins similar to those defined through boundary markers. Simulation results show that power can fall substantially when genome-wide results are not available, particularly when only results from a single marker are available in a linked region. However there is no increase in false positive findings. CONCLUSIONS: The GSMA method is robust across different bin definitions and methods of data presentation and extraction. Using studies based on only the top ranked bins does not produce false positive results, but lacks power to detect genes conferring a modest increase in risk. Therefore, we advise that effort should be made to obtain genome-wide results from investigators or from published papers to avoid limiting the utility of the GSMA.     

DNA methylation studies using twins: what are they telling us?

Recent studies have identified both heritable DNA methylation effects and differential methylation in disease-discordant identical twins. Larger sample sizes, replication, genetic-epigenetic analyses and longitudinal assays are now needed to establish the role of epigenetic variants in disease.     

The role of linkage studies for common diseases

Linkage analysis when applied to common diseases has had limited success in mapping the genes contributing to them. We present a genealogic approach applied to the relatively isolated population of Iceland. We use an affecteds-only, allele-sharing method--which does not specify any particular inheritance model--implemented in the new statistical program, Allegro, which calculates lod scores based on multipoint calculations. We describe how this approach has helped us to map a gene contributing to the common late-onset form of Parkinson's disease to statistical significance.     

A general conditional-logistic model for affected-relative-pair linkage studies

Model-free LOD-score methods are often employed to detect linkage between marker loci and common diseases, with samples of affected sib pairs. Although extensions of the basic one-disease-locus model have been proposed that allow separate inclusion of other types of affected relative pairs, discordant relative pairs, covariates, or additional disease loci, a unified framework that can handle all of these features has been lacking. In this report, I propose a conditional-logistic parameterization that generalizes easily to include all of these features. Two data examples, one using simulated data and one using type 1 diabetes, illustrate applications of the models.     

Accurate inference of relationships in sib-pair linkage studies.

Relative-pair designs are routinely employed in linkage studies of complex genetic diseases and quantitative traits. Valid application of these methods requires correct specification of the relationships of the pairs. For example, within a sibship, presumed full sibs actually might be MZ twins, half sibs, or unrelated. Misclassification of half-sib pairs or unrelated individuals as full sibs can result in reduced power to detect linkage. When other family members, such as parents or additional siblings, are available, incorrectly specified relationships usually will be detected through apparent incompatibilities with Mendelian inheritance. Without other family members, sibling relationships cannot be determined absolutely, but they still can be inferred probabilistically if sufficient genetic marker data are available. In this paper, we describe a simple likelihood ratio method to infer the true relationship of a putative sibling pair. We explore the number of markers required to accurately infer relationships typically encountered in a sib-pair study, as a function of marker allele frequencies, marker spacing, and genotyping error rate, and we conclude that very accurate inference of relationships can be achieved, given the marker data from even part of a genome scan. We compare our method to related methods of relationship inference that have been suggested. Finally, we demonstrate the value of excluding non-full sibs in a genetic linkage study of non-insulin-dependent diabetes mellitus.     

Analysis of functional abilities for elderly Danish twins using GEE models.

In this work we present a new method for genetic analysis of twin data which is based on generalized estimating equations and allows for analysis of various response types (e.g., continuous, binary, counts) combined with estimation of residual correlations. The new approach allows for control of covariates of any kind (e.g., continuous, counts) by modeling the dependence of mean and variance on background variables. The proposed method was applied to identify the covariates that have a significant influence on elderly people's functional abilities, and find the estimates for the correlation coefficients of residuals for MZ and DZ twins in a sample of 2401 Danish twin 75 years of age or older. The bootstrap method was used to obtain standard errors for correlation coefficients. It was shown, that the chosen covariates have similar effects on MZ and DZ twins, and that the residual correlation in MZ twins is significantly higher than in DZ twins, which indicates that genetic factors play an etiological role in the determination of physical status of elderly people, controlled for 10 background variables.     

Zooplankton sampling strategies for environmental studies

This study characterizes sources of variation in total zooplankton abundance estimates at seven stations within the 5–10 m depth contour of southeastern Lake Michigan which were sampled monthly, April through October, for the 1975 to 1979 period. Month, year, and station were statistically significant factors affecting abundance estimates as were all interactions. Month was the largest source of variance either as a main effect or interaction. Smallest coefficients of variation were associated with subsampling (mean 6.1%) and replicate sampling (mean 15.1%). The between-station coefficient of variation averaged 39.0% and tended to be highest during the summer. For a given station and month, the between-year coefficient of variation averaged 73.4% while the between-month coefficient of variation for a single station in a given year averaged 95.1%. A table shows the estimated number of replications necessary to detect a true difference in two population means as a function of coefficient of variation. Environmental studies designed to detect spatial alterations should conduct such analyses on a cruise-by-cruise basis. Cruises should consist of a large number of stations and be conducted at least once during each season. Studies designed to detect temporal alterations require more frequent sampling because of the greater variability associated with temporal data sets. Because spatial variability adds little to the overall variability of such data sets, only a few representative stations need be sampled during each cruise.