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The germline mutation rate determines the pace of genome evolution and is an evolving parameter itself 1 . However, little is known about what determines its evolution, as most studies of mutation rates have focused on single species with different methodologies 2 . Here we quantify germline mutation rates across vertebrates by sequencing and comparing the high-coverage genomes of 151 parent-offspring trios from 68 species of mammals, fishes, birds and reptiles. We show that the per-generation mutation rate varies among species by a factor of 40, with mutation rates being higher for males than for females in mammals and birds, but not in reptiles and fishes. The generation time, age at maturity and species-level fecundity are the key life-history traits affecting this variation among species. Furthermore, species with higher long-term effective population sizes tend to have lower mutation rates per generation, providing support for the drift barrier hypothesis 3 . The exceptionally high yearly mutation rates of domesticated animals, which have been continually selected on fecundity traits including shorter generation times, further support the importance of generation time in the evolution of mutation rates. Overall, our comparative analysis of pedigree-based mutation rates provides ecological insights on the mutation rate evolution in vertebrates., (© 2023. The Author(s).)

The mutation rate of a specific position in the human genome depends on the sequence context surrounding it. Modeling the mutation rate by estimating a rate for each possible k-mer, however, only works for small values of k since the data becomes too sparse for larger values of k. Here we propose a new method that solves this problem by grouping similar k-mers. We refer to the method as k-mer pattern partition and have implemented it in a software package called kmerPaPa. We use a large set of human de novo mutations to show that this new method leads to improved prediction of mutation rates and makes it possible to create models using wider sequence contexts than previous studies. As the first method of its kind, it does not only predict rates for point mutations but also insertions and deletions. We have additionally created a software package called Genovo that, given a k-mer pattern partition model, predicts the expected number of synonymous, missense, and other functional mutation types for each gene. Using this software, we show that the created mutation rate models increase the statistical power to detect genes containing disease-causing variants and to identify genes under strong selective constraint., (© 2022. The Author(s).)

Sequencing of cell-free DNA (cfDNA) is currently being used to detect cancer by searching both for mutational and non-mutational alterations. Recent work has shown that the length distribution of cfDNA fragments from a cancer patient can inform tumor load and type. Here, we propose non-negative matrix factorization (NMF) of fragment length distributions as a novel and completely unsupervised method for studying fragment length patterns in cfDNA. Using shallow whole-genome sequencing (sWGS) of cfDNA from a cohort of patients with metastatic castration-resistant prostate cancer (mCRPC), we demonstrate how NMF accurately infers the true tumor fragment length distribution as an NMF component - and that the sample weights of this component correlate with ctDNA levels ( r =0.75). We further demonstrate how using several NMF components enables accurate cancer detection on data from various early stage cancers (AUC = 0.96). Finally, we show that NMF, when applied across genomic regions, can be used to discover fragment length signatures associated with open chromatin., Competing Interests: GR, MN, JL, HG, BD, JJ, MB, CA, KS, LM, SB No competing interests declared, (© 2022, Renaud et al.)

In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various nonhuman species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and appropriately accounting for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a 'Mutationathon,' a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a twofold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria, and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees., Competing Interests: LB, SB, TT, CV, RW, AP, EA, MR, JC, HC, MH, KH, AK, EL, PM, SP, GT, AY, GZ, MS No competing interests declared, (© 2022, Bergeron et al.)

Mantle cell lymphoma (MCL) is characterized by marked differences in outcome, emphasizing the need for strong prognostic biomarkers. Here, we explore expression patterns and prognostic relevance of circular RNAs (circRNAs), a group of endogenous non-coding RNA molecules, in MCL. We profiled the circRNA expression landscape using RNA-sequencing and explored the prognostic potential of 40 abundant circRNAs in samples from the Nordic MCL2 and MCL3 clinical trials, using NanoString nCounter Technology. We report a circRNA-based signature (circSCORE) developed in the training cohort MCL2 that is highly predictive of time to progression (TTP) and lymphoma-specific survival (LSS). The dismal outcome observed in the large proportion of patients assigned to the circSCORE high-risk group was confirmed in the independent validation cohort MCL3, both in terms of TTP (HR 3.0; P = 0.0004) and LSS (HR 3.6; P = 0.001). In Cox multiple regression analysis incorporating MIPI, Ki67 index, blastoid morphology and presence of TP53 mutations, circSCORE retained prognostic significance for TTP (HR 3.2; P = 0.01) and LSS (HR 4.6; P = 0.01). In conclusion, circRNAs are promising prognostic biomarkers in MCL and circSCORE improves identification of high-risk disease among younger patients treated with cytarabine-containing chemoimmunotherapy and autologous stem cell transplant., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

The lack of consensus methods to estimate germline mutation rates from pedigrees has led to substantial differences in computational pipelines in the published literature. Here, we answer Susanne Pfeifer's opinion piece discussing the pipeline choices of our recent article estimating the germline mutation rate of rhesus macaques (Macaca mulatta). We acknowledge the differences between the method that we applied and the one preferred by Pfeifer. Yet, we advocate for full transparency and justification of choices as long as rigorous comparison of pipelines remains absent because it is the only way to conclude on best practices for the field., (© The Author(s) 2021. Published by Oxford University Press GigaScience.)

Background: Understanding the rate and pattern of germline mutations is of fundamental importance for understanding evolutionary processes., Results: Here we analyzed 19 parent-offspring trios of rhesus macaques (Macaca mulatta) at high sequencing coverage of ∼76× per individual and estimated a mean rate of 0.77 × 10-8de novo mutations per site per generation (95% CI: 0.69 × 10-8 to 0.85 × 10-8). By phasing 50% of the mutations to parental origins, we found that the mutation rate is positively correlated with the paternal age. The paternal lineage contributed a mean of 81% of the de novo mutations, with a trend of an increasing male contribution for older fathers. Approximately 3.5% of de novo mutations were shared between siblings, with no parental bias, suggesting that they arose from early development (postzygotic) stages. Finally, the divergence times between closely related primates calculated on the basis of the yearly mutation rate of rhesus macaque generally reconcile with divergence estimated with molecular clock methods, except for the Cercopithecoidea/Hominoidea molecular divergence dated at 58 Mya using our new estimate of the yearly mutation rate., Conclusions: When compared to the traditional molecular clock methods, new estimated rates from pedigree samples can provide insights into the evolution of well-studied groups such as primates., (© The Author(s) 2021. Published by Oxford University Press GigaScience.)

A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to locate the primary cancer. As standard treatments are based on the cancer type, such cases are hard to treat and have very poor prognosis. Using molecular data from the metastatic cancer to predict the primary site can make treatment choice easier and enable targeted therapy. In this article, we first examine the ability to predict cancer type using different types of omics data. Methylation data lead to slightly better prediction than gene expression and both these are superior to classification using somatic mutations. After using 3 data types independently, we notice some differences between the classes that tend to be misclassified, suggesting that integrating the data might improve accuracy. In light of the different levels of information provided by different omics types and to be able to handle missing data, we perform multi-omics classification by hierarchically combining the classifiers. The proposed hierarchical method first classifies based on the most informative type of omics data and then uses the other types of omics data to classify samples that did not get a high confidence classification in the first step. The resulting hierarchical classifier has higher accuracy than any of the single omics classifiers and thus proves that the combination of different data types is beneficial. Our results show that using multi-omics data can improve the classification of cancer types. We confirm this by testing our method on metastatic cancers from the MET500 dataset., Competing Interests: Declaration of conflicting interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The human mutation rate per generation estimated from trio sequencing has revealed an almost linear relationship with the age of the father and the age of the mother, with fathers contributing about three times as many mutations per year as mothers. The yearly trio-based mutation rate estimate of around 0.43 × 10 -9 is markedly lower than previous indirect estimates of about 1 × 10 -9 per year from phylogenetic comparisons of the great apes calibrated by fossil evidence. This suggests either a slowdown in the accumulation of mutations per year in the human lineage over the past 10 million years or an inaccurate interpretation of the fossil record. Here we inferred de novo mutations in chimpanzee, gorilla, and orangutan parent-offspring trios. Extrapolating the relationship between the mutation rate and the age of parents from humans to these other great apes, we estimated that each species has higher mutation rates per year by factors of 1.50 ± 0.10, 1.51 ± 0.23, and 1.42 ± 0.22 for chimpanzee, gorilla, and orangutan, respectively, and by a factor of 1.48 ± 0.08 for the three species combined. These estimates suggest an appreciable slowdown in the yearly mutation rate in the human lineage that is likely to be recent as genome comparisons almost adhere to a molecular clock. If the nonhuman rates rather than the human rate are extrapolated over the phylogeny of the great apes, we estimate divergence and speciation times that are much more in line with the fossil record and the biogeography.

In this chapter, we give a short introduction to the genetics of complex diseases emphasizing evolutionary models for disease genes and the effect of different models on the genetic architecture, and we give a survey of the state-of-the-art of genome-wide association studies (GWASs).

Peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) constitutes a heterogeneous category of lymphomas, which do not fit into any of the specifically defined T-cell lymphoma entities. Both the pathogenesis and tumor biology in PTCL-NOS are poorly understood. Protein expression in pretherapeutic PTCL-NOS tumors was analyzed by proteomics. Differentially expressed proteins were compared in 3 distinct scenarios: (A) PTCL-NOS tumor tissue (n = 18) vs benign lymphoid tissue (n = 8), (B) clusters defined by principal component analysis (PCA), and (C) tumors from patients with chemosensitive vs refractory PTCL-NOS. Selected differentially expressed proteins identified by proteomics were correlated with clinico-pathological features and outcome in a larger cohort of patients with PTCL-NOS (n = 87) by immunohistochemistry (IHC). Most proteins with altered expression were identified comparing PTCL-NOS vs benign lymphoid tissue. PCA of the protein profile defined 3 distinct clusters. All benign samples clustered together, whereas PTCL-NOS tumors separated into 2 clusters with different patient overall survival rates ( P = .001). Differentially expressed proteins reflected large biological diversity among PTCL-NOS, particularly associated with alterations of "immunological" pathways. The 2 PTCL-NOS subclusters defined by PCA showed disturbance of "stress-related" and "protein metabolic" pathways. α-Enolase 1 (ENO1) was found differentially expressed in all 3 analyses, and high intratumoral ENO1 expression evaluated by IHC correlated with poor outcome (hazard ratio, 2.09; 95% confidence interval, 1.17-3.73; P = .013). High expression of triosephosphate isomerase (TPI1) also showed a tendency to correlate with poor survival ( P = .057). In conclusion, proteomic profiling of PTCL-NOS provided evidence of markedly altered protein expression and identified ENO1 as a novel potential prognostic marker., (© 2018 by The American Society of Hematology.)

Background: Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration., Results: To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures., Conclusion: We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

Mycosis fungoides (MF) is the most frequent form of cutaneous T-cell lymphoma. The disease often takes an indolent course, but in approximately one-third of the patients, the disease progresses to an aggressive malignancy with a poor prognosis. At the time of diagnosis, it is impossible to predict which patients develop severe disease and are in need of aggressive treatment. Accordingly, we investigated the prognostic potential of microRNAs (miRNAs) at the time of diagnosis in MF. Using a quantitative reverse transcription polymerase chain reaction platform, we analyzed miRNA expression in diagnostic skin biopsies from 154 Danish patients with early-stage MF. The patients were subdivided into a discovery cohort (n = 82) and an independent validation cohort (n = 72). The miRNA classifier was built using a LASSO (least absolute shrinkage and selection operator) Cox regression to predict progression-free survival (PFS). We developed a 3-miRNA classifier, based on miR-106b-5p, miR-148a-3p, and miR-338-3p, which successfully separated patients into high-risk and low-risk groups of disease progression. PFS was significantly different between these groups in both the discovery cohort and the validation cohort. The classifier was stronger than existing clinical prognostic factors and remained a strong independent prognostic tool after stratification and adjustment for these factors. Importantly, patients in the high-risk group had a significantly reduced overall survival. The 3-miRNA classifier is an effective tool to predict disease progression of early-stage MF at the time of diagnosis. The classifier adds significant prognostic value to existing clinical prognostic factors and may facilitate more individualized treatment of these patients., (© 2018 by The American Society of Hematology.)

Genes in the major histocompatibility complex (MHC, also known as HLA) play a critical role in the immune response and variation within the extended 4-Mb region shows association with major risks of many diseases. Yet, deciphering the underlying causes of these associations is difficult because the MHC is the most polymorphic region of the genome with a complex linkage disequilibrium structure. Here, we reconstruct full MHC haplotypes from de novo assembled trios without relying on a reference genome and perform evolutionary analyses. We report 100 full MHC haplotypes and call a large set of structural variants in the regions for future use in imputation with GWAS data. We also present the first complete analysis of the recombination landscape in the entire region and show how balancing selection at classical genes have linked effects on the frequency of variants throughout the region., (© 2017 Jensen et al.; Published by Cold Spring Harbor Laboratory Press.)

Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits. Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly. However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology. We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark.

A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to identify the location of the primary tumor. CUPs account for 3-5% of cancer cases. Using molecular data to determine the location of the primary tumor in such cases can help doctors make the right treatment choice and thus improve the clinical outcome. In this paper, we present a new method for predicting the location of the primary tumor using gene expression data: locating cancers of unknown primary (LoCUP). The method models the data as a mixture of normal and tumor cells and thus allows correct classification even in impure samples, where the tumor biopsy is contaminated by a large fraction of normal cells. We find that our method provides a significant increase in classification accuracy (95.8% over 90.8%) on simulated low-purity metastatic samples and shows potential on a small dataset of real metastasis samples with known origin.

Psoriasis is a chronic cutaneous inflammatory disease. The immunopathogenesis is a complex interplay between T cells, dendritic cells and the epidermis in which T cells and dendritic cells maintain skin inflammation. Anti-tumour necrosis factor (anti-TNF)-α agents have been approved for therapeutic use across a range of inflammatory disorders including psoriasis, but the anti-inflammatory mechanisms of anti-TNF-α in lesional psoriatic skin are not fully understood. We investigated early events in skin from psoriasis patients after treatment with anti-TNF-α antibodies by use of bioinformatics tools. We used the Human Gene 1.0 ST Array to analyse gene expression in punch biopsies taken from psoriatic patients before and also 4 and 14 days after initiation of treatment with the anti-TNF-α agent adalimumab. The gene expression was analysed by gene set enrichment analysis using the Functional Annotation Tool from DAVID Bioinformatics Resources. The most enriched pathway was visualised by the Pathview Package on Kyoto Encyclopedia of Genes and Genomes (KEGG) graphs. The analysis revealed new very early events in psoriasis after adalimumab treatment. Some of these events have been described after longer periods of anti-TNF-α treatment when clinical and histological changes appear, suggesting that effects of anti-TNF-α treatment on gene expression appear very early before clinical and histological changes. Combining microarray data on biopsies from psoriasis patients with pathway analysis allowed us to integrate in vitro findings into the identification of mechanisms that may be important in vivo. Furthermore, these results may reflect primary effect of anti-TNF-α treatment in contrast to studies of gene expression changes following clinical and histological changes, which may reflect secondary changes correlated to the healing of the skin., Competing Interests: The authors have declared that no competing interests exist.

Mutation of the DNA molecule is one of the most fundamental processes in biology. In this study, we use 283 parent-offspring trios to estimate the rate of mutation for both single nucleotide variants (SNVs) and short length variants (indels) in humans and examine the mutation process. We found 17812 SNVs, corresponding to a mutation rate of 1.29 × 10-8 per position per generation (PPPG) and 1282 indels corresponding to a rate of 9.29 × 10-10 PPPG. We estimate that around 3% of human de novo SNVs are part of a multi-nucleotide mutation (MNM), with 558 (3.1%) of mutations positioned less than 20kb from another mutation in the same individual (median distance of 525bp). The rate of de novo mutations is greater in late replicating regions (p = 8.29 × 10-19) and nearer recombination events (p = 0.0038) than elsewhere in the genome., Competing Interests: The authors have declared that no competing interests exist.

Building a population-specific catalogue of single nucleotide variants (SNVs), indels and structural variants (SVs) with frequencies, termed a national pan-genome, is critical for further advancing clinical and public health genetics in large cohorts. Here we report a Danish pan-genome obtained from sequencing 10 trios to high depth (50 × ). We report 536k novel SNVs and 283k novel short indels from mapping approaches and develop a population-wide de novo assembly approach to identify 132k novel indels larger than 10 nucleotides with low false discovery rates. We identify a higher proportion of indels and SVs than previous efforts showing the merits of high coverage and de novo assembly approaches. In addition, we use trio information to identify de novo mutations and use a probabilistic method to provide direct estimates of 1.27e-8 and 1.5e-9 per nucleotide per generation for SNVs and indels, respectively.

Background: Genome-wide association studies have identified many individual genes associated with complex traits. However, pathway and network information have not been fully exploited in searches for genetic determinants, and including this information may increase our understanding of the underlying biology of common diseases., Results: In this study, we propose a framework to address this problem in a principled way, with the underlying hypothesis that complex disease operates through multiple connected genes. Associations inferred from GWAS are translated into prior scores for vertices in a protein-protein interaction network, and these scores are propagated through the network. Permutation is used to select genes that are guilty-by-association and thus consistently obtain high scores after network propagation. We apply the approach to data of Crohn's disease and call candidate genes that have been reported by other independent GWAS, but not in the analysed data set. A prediction model based on these candidate genes show good predictive power as measured by Area Under the Receiver Operating Curve (AUC) in 10 fold cross-validations., Conclusions: Our network propagation method applied to a genome-wide association study increases association findings over other approaches.

In this chapter, we give a short introduction to the genetics of complex disease with special emphasis on evolutionary models for disease genes and the effect of different models on the genetic architecture, and finally give a survey of the state-of-the-art of genome-wide association studies.

We have characterized the development of seeds in the model legume Lotus japonicus. Like soybean (Glycine max) and pea (Pisum sativum), Lotus develops straight seed pods and each pod contains approximately 20 seeds that reach maturity within 40 days. Histological sections show the characteristic three developmental phases of legume seeds and the presence of embryo, endosperm, and seed coat in desiccated seeds. Furthermore, protein, oil, starch, phytic acid, and ash contents were determined, and this indicates that the composition of mature Lotus seed is more similar to soybean than to pea. In a first attempt to determine the seed proteome, both a two-dimensional polyacrylamide gel electrophoresis approach and a gel-based liquid chromatography-mass spectrometry approach were used. Globulins were analyzed by two-dimensional polyacrylamide gel electrophoresis, and five legumins, LLP1 to LLP5, and two convicilins, LCP1 and LCP2, were identified by matrix-assisted laser desorption ionization quadrupole/time-of-flight mass spectrometry. For two distinct developmental phases, seed filling and desiccation, a gel-based liquid chromatography-mass spectrometry approach was used, and 665 and 181 unique proteins corresponding to gene accession numbers were identified for the two phases, respectively. All of the proteome data, including the experimental data and mass spectrometry spectra peaks, were collected in a database that is available to the scientific community via a Web interface (http://www.cbs.dtu.dk/cgi-bin/lotus/db.cgi). This database establishes the basis for relating physiology, biochemistry, and regulation of seed development in Lotus. Together with a new Web interface (http://bioinfoserver.rsbs.anu.edu.au/utils/PathExpress4legumes/) collecting all protein identifications for Lotus, Medicago, and soybean seed proteomes, this database is a valuable resource for comparative seed proteomics and pathway analysis within and beyond the legume family.

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 approximately 317,000 markers and completed analysis within 5 CPU days.

Background: Identifying the genetic components of common diseases has long been an important area of research. Recently, genotyping technology has reached the level where it is cost effective to genotype single nucleotide polymorphism (SNP) markers covering the entire genome, in thousands of individuals, and analyse such data for markers associated with a diseases. The statistical power to detect association, however, is limited when markers are analysed one at a time. This can be alleviated by considering multiple markers simultaneously. The Haplotype Pattern Mining (HPM) method is a machine learning approach to do exactly this., Results: We present a new, faster algorithm for the HPM method. The new approach use patterns of haplotype diversity in the genome: locally in the genome, the number of observed haplotypes is much smaller than the total number of possible haplotypes. We show that the new approach speeds up the HPM method with a factor of 2 on a genome-wide dataset with 5009 individuals typed in 491208 markers using default parameters and more if the pattern length is increased., Conclusion: The new algorithm speeds up the HPM method and we show that it is feasible to apply HPM to whole genome association mapping with thousands of individuals and hundreds of thousands of markers.

Background: With current technology, vast amounts of data can be cheaply and efficiently produced in association studies, and to prevent data analysis to become the bottleneck of studies, fast and efficient analysis methods that scale to such data set sizes must be developed., Results: We present a fast method for accurate localisation of disease causing variants in high density case-control association mapping experiments with large numbers of cases and controls. The method searches for significant clustering of case chromosomes in the "perfect" phylogenetic tree defined by the largest region around each marker that is compatible with a single phylogenetic tree. This perfect phylogenetic tree is treated as a decision tree for determining disease status, and scored by its accuracy as a decision tree. The rationale for this is that the perfect phylogeny near a disease affecting mutation should provide more information about the affected/unaffected classification than random trees. If regions of compatibility contain few markers, due to e.g. large marker spacing, the algorithm can allow the inclusion of incompatibility markers in order to enlarge the regions prior to estimating their phylogeny. Haplotype data and phased genotype data can be analysed. The power and efficiency of the method is investigated on 1) simulated genotype data under different models of disease determination 2) artificial data sets created from the HapMap ressource, and 3) data sets used for testing of other methods in order to compare with these. Our method has the same accuracy as single marker association (SMA) in the simplest case of a single disease causing mutation and a constant recombination rate. However, when it comes to more complex scenarios of mutation heterogeneity and more complex haplotype structure such as found in the HapMap data our method outperforms SMA as well as other fast, data mining approaches such as HapMiner and Haplotype Pattern Mining (HPM) despite being significantly faster. For unphased genotype data, an initial step of estimating the phase only slightly decreases the power of the method. The method was also found to accurately localise the known susceptibility variants in an empirical data set--the DeltaF508 mutation for cystic fibrosis--where the susceptibility variant is already known--and to find significant signals for association between the CYP2D6 gene and poor drug metabolism, although for this dataset the highest association score is about 60 kb from the CYP2D6 gene., Conclusion: Our method has been implemented in the Blossoc (BLOck aSSOCiation) software. Using Blossoc, genome wide chip-based surveys of 3 million SNPs in 1000 cases and 1000 controls can be analysed in less than two CPU hours.

Summary: We have developed a tool implementing an efficient algorithm for refined Buneman tree reconstruction. The algorithm--which has the same complexity as the neighbour-joining method and the (plain) Buneman tree construction--enables refined Buneman tree reconstruction on large taxa sets., Availability: The source code for RBT, written in Java, is available under the GNU Public License (GPL) at http://www.birc.dk/Software/RBT CONTACT: besen@daimi.au.dk.