We have just published a paper on our Blossoc method in the latest issue of Genetics:
Besenbacher, Mailund and Schierup, Genetics, Vol. 181, 747-753, February 2009, Copyright © 2009 doi:10.1534/genetics.108.092643
We present a new method, termed QBlossoc, for linkage disequilibrium (LD) mapping of genetic variants underlying a quantitative trait. The method uses principles similar to a previously published method, Blossoc, for LD mapping of case/control studies. The method builds local genealogies along the genome and looks for a significant clustering of quantitative trait values in these trees. We analyze its efficiency in terms of localization and ranking of true positives among a large number of negatives and compare the results with single-marker approaches. Simulation results of markers at densities comparable to contemporary genotype chips show that QBlossoc is more accurate in localization of true positives as expected since it uses the additional information of LD between markers simultaneously. More importantly, however, for genomewide surveys, QBlossoc places regions with true positives higher on a ranked list than single-marker approaches, again suggesting that a true signal displays itself more strongly in a set of adjacent markers than a spurious (false) signal. The method is both memory and central processing unit (CPU) efficient. It has been tested on a real data set of height data for 5000 individuals measured at 317,000 markers and completed analysis within 5 CPU days.
The method works very similarly to our first paper on Blossoc. Running along the genome, we infer local genealogies and then scores the region according to how well the genealogy explains the phenotype under consideration.
What is new in this paper is the way we score threes when the phenotype is quantitative rather than qualitative (case/control status).
Also just published this month is results from the QTLMAS XII workshop held last year. As part of this workshop, a dataset with genome-wide genetic data and a quantitative phenotype was simulated, and groups could then compete in mapping the quantitative traits (Crooks et al. 2009).
One group used Blossoc. No, it wasn't us, but Ledur et al (2009). I am pretty proud to learn that Blossoc was considered the best performing method on this data.
Crooks et al's conclusion:
In this dataset, the best methods for detecting QTL were Blossoc  followed by a Bayesian linkage analysis , both of which used information from multiple markers to infer QTL genotypes. The two studies that aimed to increase the efficiency of QTL detection by reducing the amount of analysis had lowest power and were not effective in identifying the QTL with the largest effects. Estimates of QTL location were generally very good. There were bigger differences in how well the methods estimated the QTL effects. Here, two of the models that were most accurate used single markers in place of QTL genotype and simultaneously fit a polygenic effect. Although in this case estimates from a single locus model were as accurate as from a multilocus model, fitting multiple loci should allow closely linked QTL to be distinguished. A valuable approach might be to first locate QTL by a multimarker/haplotype method and then fit the closest markers in a multilocus model, to estimate QTL effects. For future such projects, we recommend that participants provide a list of their top-ranked effects, and report confidence intervals for QTL location and effect size estimates. Areas that we suggest for further work include significance thresholds, closely linked QTL and epistatic effects.
Actually, we do not try to estimate QTL effects in our method. I simply hadn't thought about that until I read the conclusions from QTLMAS XII. Well, that leaves a topic for future work...
- S. Besenbacher, T. Mailund, M. H. Schierup (2008). Local Phylogeny Mapping of Quantitative Traits: Higher Accuracy and Better Ranking Than Single-Marker Association in Genomewide Scans Genetics, 181 (2), 747-753 DOI: 10.1534/genetics.108.092643
- Lucy Crooks, Goutam Sahana, Dirk-Jan de Koning, Mogens Sandø Lund, Örjan Carlborg (2009). Comparison of analyses of the QTLMAS XII common dataset. II: genome-wide association and fine mapping BMC Proceedings
- Mônica Corrêa Ledur, Nicolas Navarro, Miguel Pérez-Enciso (2009). Data modeling as a main source of discrepancies in single and multiple marker association methods BMC Proceedings