MCMC-based linkage analysis for complex traits on general pedigrees: multipoint analysis with a two-locus model and a polygenic component

A new Bayesian approach for QTL mapping of family data

In this paper, we propose a new Bayesian approach for QTL mapping of family data. The main purpose is to model a phenotype as a function of QTLs' effects. The model considers the detailed familiar dependence and it does not rely on random effects. It combines the probability for Mendelian inheritance of parents' genotype and the correlation between flanking markers and QTLs. This is an advance when compared with models which use only Mendelian segregation or only the correlation between markers and QTLs to estimate transmission probabilities. We use the Bayesian approach to estimate the number of QTLs, their location and the additive and dominance effects. We compare the performance of the proposed method with variance component and LASSO models using simulated and GAW17 data sets. Under tested conditions, the proposed method outperforms other methods in aspects such as estimating the number of QTLs, the accuracy of the QTLs' position and the estimate of their effects. The results of the application of the proposed method to data sets exceeded all of our expectations.

Detection of phenotype modifier genes using two-locus linkage analysis in complex disorders such as major psychosis

OBJECTIVE

To increase power to detect modifier loci conferring susceptibility to specific phenotypes such as disease diagnoses which are part of a broader disorder spectrum by jointly modeling a modifier and a broad susceptibility gene and to identify modifier loci conferring specific susceptibility to schizophrenia (SZ) or to bipolar disorder (BP) using the approach.

METHODS

We implemented a two-locus linkage analysis model where a gene 1 genotype increases the risk of a broad phenotype and a gene 2 genotype modifies the expression of gene 1 by conferring susceptibility to a specific phenotype.

RESULTS

Compared to a single-locus analysis within the broad phenotype, the proposed approach had greater power to detect the modifier gene 2 (0.96 vs. 0.54 under a simulation scenario including heterogeneity). In a sample of 12 mixed SZ and BP Eastern Quebec kindreds, D8S1110 at 8p22 showed the strongest evidence of linkage to a gene determining a specific phenotype (SZ or BP) among subjects susceptible to major psychosis because of putative genes at 10p13 (D10S245, conditional maximized LOD (cMOD) = 4.20, p = 0.0003) and 3q21-q23 (D3S2418, cMOD = 4.09, p = 0.0005).

CONCLUSION

The proposed strategy is useful to detect modifier loci conferring susceptibility to a specific phenotype within a broader phenotype.

Computationally efficient multipoint linkage analysis on extended pedigrees for trait models with two contributing major Loci

We have developed a computationally efficient method for multipoint linkage analysis on extended pedigrees for trait models having a two-locus quantitative trait loci (QTL) effect. The method has been implemented in the program, hg_lod, which uses the Markov chain Monte Carlo (MCMC) method to sample realizations of descent patterns conditional on marker data, then calculates the trait likelihood for each realization by efficient exact computation. Given its computational efficiency, hg_lod can handle data on large pedigrees with a lot of unobserved individuals, and can compute accurate estimates of logarithm of odds (lod) scores at a much larger number of hypothesized locations than can any existing method. We have compared hg_lod to lm_twoqtl, the first publically available linkage program for trait models with two major loci, using simulated data. Results show that our method is orders of magnitude faster while the accuracy of QTL localization is retained. The efficiency of our method also facilitates analyses with multiple trait models, for example, sensitivity analysis. Additionally, since the MCMC sampling conditions only on the marker data, there is no need to resample the descent patterns to compute likelihoods under alternative trait models. This achieves additional computational efficiency.

Identity by descent estimation with dense genome-wide genotype data

We present a novel method, IBDLD, for estimating the probability of identity by descent (IBD) for a pair of related individuals at a locus, given dense genotype data and a pedigree of arbitrary size and complexity. IBDLD overcomes the challenges of exact multipoint estimation of IBD in pedigrees of potentially large size and eliminates the difficulty of accommodating the background linkage disequilibrium (LD) that is present in high-density genotype data. We show that IBDLD is much more accurate at estimating the true IBD sharing than methods that remove LD by pruning SNPs and is highly robust to pedigree errors or other forms of misspecified relationships. The method is fast and can be used to estimate the probability for each possible IBD sharing state at every SNP from a high-density genotyping array for hundreds of thousands of pairs of individuals. We use it to estimate point-wise and genomewide IBD sharing between 185,745 pairs of subjects all of whom are related through a single, large and complex 13-generation pedigree and genotyped with the Affymetrix 500 k chip. We find that we are able to identify the true pedigree relationship for individuals who were misidentified in the collected data and estimate empirical kinship coefficients that can be used in follow-up QTL mapping studies. IBDLD is implemented as an open source software package and is freely available.

MQScore_SNP software for multipoint parametric linkage analysis of quantitative traits in large pedigrees

We describe software for multipoint parametric linkage analysis of quantitative traits using information about SNP genotypes. A mixed model of major gene and polygene inheritance is implemented in this software. Implementation of several algorithms to avoid computational underflow and decrease running time permits application of our software to the analysis of very large pedigrees collected in human genetically isolated populations. We tested our software by performing linkage analysis of adult height in a large pedigree from a Dutch isolated population. Three significant and four suggestive loci were identified with the help of our programs, whereas variance-component-based linkage analysis, which requires the pedigree fragmentation, demonstrated only three suggestive peaks. The software package MQScore_SNP is available at http://mga.bionet.nsc.ru/soft/index.html.

PedStr software for cutting large pedigrees for haplotyping, IBD computation and multipoint linkage analysis

We propose an automatic heuristic algorithm for splitting large pedigrees into fragments of no more than a user-specified bit size. The algorithm specifically aims to split large pedigrees where many close relatives are genotyped and to produce a set of sub-pedigrees for haplotype reconstruction, IBD computation or multipoint linkage analysis with the help of the Lander-Green-Kruglyak algorithm. We demonstrate that a set of overlapping pedigree fragments constructed with the help of our algorithm allows fast and effective haplotype reconstruction and detection of an allele's parental origin. Moreover, we compared pedigree fragments constructed with the help of our algorithm and existing programs PedCut and Jenti for multipoint linkage analysis. Our algorithm demonstrated significantly higher linkage power than the algorithm of Jenti and significantly shorter running time than the algorithm of PedCut. The software package PedStr implementing our algorithms is available at http://mga.bionet.nsc.ru/soft/index.html.

Same or different? Insights into the etiology of phonological awareness and rapid naming

This work's objective was to offer additional insights into the psychological and genetic bases of reading ability and disability, and to evaluate the plausibility of a variety of psychological models of reading involving phonological awareness (PA) and rapid naming (RN), both hypothesized to be principal components in such models. In Study 1, 488 unselected families were assessed with measures of PA and RN to investigate familial aggregation and to obtain estimates of both the number and effect-magnitude of genetic loci involved in these traits' transmission. The results of the analyses from Study 1 indicated the presence of genetic effects in the etiology of individual differences for PA and RN and pointed to both the shared and unique sources of this genetic variance, which appeared to be exerted by multiple (3-6 for PA and 3-5 for RN) genes. These results were used in Study 2 to parameterize a simulation of 3000 families with quantitatively distributed PA and RN, so that the robustness and generalizability of the Study 1 findings could be evaluated. The findings of both studies were interpreted according to established theories of reading and our own understanding of the etiology of complex developmental disorders.

Strategies for conditional two-locus nonparametric linkage analysis

In this article we deal with two-locus nonparametric linkage (NPL) analysis, mainly in the context of conditional analysis. This means that one incorporates single-locus analysis information through conditioning when performing a two-locus analysis. Here we describe different strategies for using this approach. Cox et al. [Nat Genet 1999;21:213-215] implemented this as follows: (i) Calculate the one-locus NPL process over the included genome region(s). (ii) Weight the individual pedigree NPL scores using a weighting function depending on the NPL scores for the corresponding pedigrees at speci fi c conditioning loci. We generalize this by conditioning with respect to the inheritance vector rather than the NPL score and by separating between the case of known (prede fi ned) and unknown (estimated) conditioning loci. In the latter case we choose conditioning locus, or loci, according to prede fi ned criteria. The most general approach results in a random number of selected loci, depending on the results from the previous one-locus analysis. Major topics in this article include discussions on optimal score functions with respect to the noncentrality parameter (NCP), and how to calculate adequate p values and perform power calculations. We also discuss issues related to multiple tests which arise from the two-step procedure with several conditioning loci as well as from the genome-wide tests.

An approach for cutting large and complex pedigrees for linkage analysis

Utilizing large pedigrees in linkage analysis is a computationally challenging task. The pedigree size limits applicability of the Lander-Green-Kruglyak algorithm for linkage analysis. A common solution is to split large pedigrees into smaller computable subunits. We present a pedigree-splitting method that, within a user supplied bit-size limit, identifies subpedigrees having the maximal number of subjects of interest (eg patients) who share a common ancestor. We compare our method with the maximum clique partitioning method using a large and complex human pedigree consisting of 50 patients with Alzheimer's disease ascertained from genetically isolated Dutch population. We show that under a bit-size limit our method can assign more patients to subpedigrees than the clique partitioning method, particularly when splitting deep pedigrees where the subjects of interest are scattered in recent generations and are relatively distantly related via multiple genealogic connections. Our pedigree-splitting algorithm and associated software can facilitate genome-wide linkage scans searching for rare mutations in large pedigrees coming from genetically isolated populations. The software package PedCut implementing our approach is available at http://mga.bionet.nsc.ru/soft/index.html.

Comparison of multipoint linkage analyses for quantitative traits in the CEPH data: parametric LOD scores, variance components LOD scores, and Bayes factors

We performed multipoint linkage analyses with multiple programs and models for several gene expression traits in the Centre d'Etude du Polymorphisme Humain families. All analyses provided consistent results for both peak location and shape. Variance-components (VC) analysis gave wider peaks and Bayes factors gave fewer peaks. Among programs from the MORGAN package, lm_multiple performed better than lm_markers, resulting in less Markov-chain Monte Carlo (MCMC) variability between runs, and the program lm_twoqtl provided higher LOD scores by also including either a polygenic component or an additional quantitative trait locus.

Breaking loops in large complex pedigrees

For pedigrees with multiple loops, exact likelihoods could not be computed in an acceptable time frame and thus, approximate methods are used. Some of these methods are based on breaking loops and approximations of complex pedigree likelihoods using the exact likelihood of the corresponding zero-loop pedigree. Due to ignoring loops, this method results in a loss of genetic information and a decrease in the power to detect linkage. To minimize this loss, an optimal set of loop breakers has to be selected. In this paper, we present a graph theory based algorithm for automatic selection of an optimal set of loop breakers. We propose using a total relationship between measured pedigree members as a proxy to power. To minimize the loss of genetic information, we suggest selection of such breakers whose duplication in a pedigree would be accompanied by a minimal loss of total relationship between measured pedigree members. We show that our algorithm compares favorably with other existing loop-breaker selection algorithms in terms of conservation of genetic information, statistical power and CPU time of subsequent linkage analysis. We implemented our method in a software package LOOP_EDGE, which is available at http://mga.bionet.nsc.ru/nlru/.

Accounting for Epistasis in Linkage Analysis of General Pedigrees

Complex traits are generally believed to be influenced by multiple loci. Identification of loci involved in complex traits is more difficult for interacting than for additive loci. Here we describe an extension of the program lm_twoqtl in the package MORGAN to handle two quantitative trait loci (QTLs) with gene-gene interaction. We investigate whether parametric linkage analysis that accounts for such epistasis improves prospects for linkage detection and accuracy of localization of QTLs. Through use of simulated data we show that analysis that accounts for epistasis provides higher lod scores and better localization than does analysis without epistasis. In addition, we demonstrate that the difference between lod scores in the presence vs. absence of use of an interaction model in analysis is greater in extended than in nuclear pedigrees.