Modeling linkage disequilibrium in exact linkage computations: a comparison of first-order Markov approaches and the clustered-markers approach

A system for exact and approximate genetic linkage analysis of SNP data in large pedigrees

MOTIVATION

The use of dense single nucleotide polymorphism (SNP) data in genetic linkage analysis of large pedigrees is impeded by significant technical, methodological and computational challenges. Here we describe Superlink-Online SNP, a new powerful online system that streamlines the linkage analysis of SNP data. It features a fully integrated flexible processing workflow comprising both well-known and novel data analysis tools, including SNP clustering, erroneous data filtering, exact and approximate LOD calculations and maximum-likelihood haplotyping. The system draws its power from thousands of CPUs, performing data analysis tasks orders of magnitude faster than a single computer. By providing an intuitive interface to sophisticated state-of-the-art analysis tools coupled with high computing capacity, Superlink-Online SNP helps geneticists unleash the potential of SNP data for detecting disease genes.

RESULTS

Computations performed by Superlink-Online SNP are automatically parallelized using novel paradigms, and executed on unlimited number of private or public CPUs. One novel service is large-scale approximate Markov Chain-Monte Carlo (MCMC) analysis. The accuracy of the results is reliably estimated by running the same computation on multiple CPUs and evaluating the Gelman-Rubin Score to set aside unreliable results. Another service within the workflow is a novel parallelized exact algorithm for inferring maximum-likelihood haplotyping. The reported system enables genetic analyses that were previously infeasible. We demonstrate the system capabilities through a study of a large complex pedigree affected with metabolic syndrome.

AVAILABILITY

Superlink-Online SNP is freely available for researchers at http://cbl-hap.cs.technion.ac.il/superlink-snp. The system source code can also be downloaded from the system website.

CONTACT

omerw@cs.technion.ac.il

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Finding disease genes: a fast and flexible approach for analyzing high-throughput data

Linkage disequilibrium (LD) is the non-random distribution of alleles across the genome, and it can create serious problems for modern linkage studies. In particular, computational feasibility is often obtained at the expense of power, precision, and/or accuracy. In our new approach, we combine linkage results over multiple marker subsets to provide fast, efficient, and robust analyses, without compromising power, precision, or accuracy. Allele frequencies and LD in the densely spaced markers are used to construct subsamples that are highly informative for linkage. We have tested our approach extensively, and implemented it in the software package EAGLET (Efficient Analysis of Genetic Linkage: Estimation and Testing). Relative to several commonly used methods we show that EAGLET has increased power to detect disease genes across a range of trait models, LD patterns, and family structures using both simulated and real data. In particular, when the underlying LD pattern is derived from real data, we find that EAGLET outperforms several commonly used linkage methods. In-depth analysis of family data, simulated with linkage and under the real-data derived LD pattern, showed that EAGLET had 78.1% power to detect a dominant disease with incomplete penetrance, whereas the method that uses one marker per cM had 69.7% power, and the cluster-based approach implemented in MERLIN had 76.7% power. In this same setting, EAGLET was three times faster than MERLIN, and it narrowed the MERLIN-based confidence interval for trait location by 29%. Overall, EAGLET gives researchers a fast, accurate, and powerful new tool for analyzing high-throughput linkage data, and large extended families are easily accommodated.

Linkage analysis with dense SNP maps in isolated populations

OBJECTIVE

SNP maps are becoming the gold standard for genetic markers, even for linkage analyses. However, because of the density of SNPs on most high throughput platforms, the resulting significant linkage disequilibrium (LD) can bias classical nonparametric multipoint linkage analyses. This problem may be even stronger in population isolates where LD can extend over larger distances and with a more stochastic pattern. We investigate the issue of linkage analysis with SNPs from the Affymetrix 500K GeneChip array in extended families from the isolated Hutterite population.

METHODS

We minimized LD between SNPs by two methods based on a LD block pattern (Merlin and SNPLINK) and by MASEL, a new algorithm that we proposed to select SNP subsets with minimum LD and with no prior hypothesis about the LD pattern.

RESULTS

Simulations, performed using the real LD pattern observed in the Hutterite population, show that sizeable inflation of linkage statistics persist when LD between SNPs is minimized by Merlin and SNPLINK. Inflation of linkage statistics is better controlled with MASEL.

CONCLUSION

In this population, it may be difficult to extract from standard GeneChip arrays a SNP map without LD-driven bias that is more informative than a dense microsatellite map.

Multipoint approximations of identity-by-descent probabilities for accurate linkage analysis of distantly related individuals

We propose an analytical approximation method for the estimation of multipoint identity by descent (IBD) probabilities in pedigrees containing a moderate number of distantly related individuals. We show that in large pedigrees where cases are related through untyped ancestors only, it is possible to formulate the hidden Markov model of the Lander-Green algorithm in terms of the IBD configurations of the cases. We use a first-order Markov approximation to model the changes in this IBD-configuration variable along the chromosome. In simulated and real data sets, we demonstrate that estimates of parametric and nonparametric linkage statistics based on the first-order Markov approximation are accurate. The computation time is exponential in the number of cases instead of in the number of meioses separating the cases. We have implemented our approach in the computer program ALADIN (accurate linkage analysis of distantly related individuals). ALADIN can be applied to general pedigrees and marker types and has the ability to model marker-marker linkage disequilibrium with a clustered-markers approach. Using ALADIN is straightforward: It requires no parameters to be specified and accepts standard input files.