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Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: "RAVA-FIRST" (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the gnomAD populations, which are referred to as "CADD regions". (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 enriched for rare variants in early-onset patients. This region that was missed by standard sliding windows procedures is included in a TAD region that contains a strong candidate gene. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages. Author summary: Technological progresses have made possible whole-genome sequencing at an unprecedented scale, opening up the possibility to explore the role of genetic variants of low frequency in common diseases. The challenge is now methodological and requires the development of novel methods and strategies to analyse sequencing data that are not limited to assessing the role of coding variants. With RAVA-FIRST, we propose a novel strategy to investigate the role of rare variants in the whole-genome that takes benefit from biological information. Especially, RAVA-FIRST relies on testing units that go beyond genes to gather rare variants in the association tests. In this work, we show that this new strategy presents several advantages compared to existing methods. RAVA-FIRST offers an easy and straightforward analysis of genome-wide rare variants, especially the intergenic ones which are frequently left behind, making it a promising tool to get a better understanding of the biology of complex diseases. [ABSTRACT FROM AUTHOR]

This study aimed at identifying novel susceptibility genes for a mixed phenotype of Alzheimer's disease and vascular dementia. Results from a genome scan showed strongest linkage to 20p13 in 18 families, and subsequent fine mapping was performed with both microsatellites and single-nucleotide polymorphisms in 18 selected candidate transcripts in an extended sample set of 30 families. The multipoint linkage peak was located at marker rs2144151 in the ANGPT4 gene, which is a strong candidate gene for vascular disease because of its involvement in angiogenesis. Although the significance of the linkage decreased, we find this result intriguing, considering that we included additional families, and thus the reduced linkage signal may be caused by genetic heterogeneity. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disease that affects approximately 20 million persons all over the world. There are both sporadic and familial forms of AD. We have previously reported a genome-wide linkage analysis on 71 Swedish AD families using 365 genotyped microsatellite markers. In this study, we increased the number of individuals included in the original 71 analysed families besides adding 38 new families. These 109 families were genotyped for 1100 novel microsatellite markers. The present study reports on the linkage data generated from the non-overlapping genotypes from the first genome scan and the genotypes of the present scan, which results in a total of 1289 successfully genotyped markers at an average density of 2.85 cM on 468 individuals from 109 AD families. Non-parametric linkage analysis yielded a significant multipoint LOD score in chromosome 19q13, the region harbouring the major susceptibility gene APOE, both for the whole set of families (LOD=5.0) and the APOE ɛ4-positive subgroup made up of 63 families (LOD=5.3). Other suggestive linkage peaks that were observed in the original genome scan of 71 Swedish AD families were not detected in this extended analysis, and the previously reported linkage signals in chromosomes 9, 10 and 12 were not replicated.European Journal of Human Genetics (2008) 16, 202–208; doi:10.1038/sj.ejhg.5201946; published online 24 October 2007 [ABSTRACT FROM AUTHOR]

In genetics, with increasing data sizes and more advanced algorithms for mining complex data, a point is reached where increased computational capacity or alternative solutions becomes unavoidable. Most contemporary methods for linkage analysis are based on the Lander-Green hidden Markov model (HMM), which scales exponentially with the number of pedigree members. In whole genome linkage analysis, genotype simulations become prohibitively time consuming to perform on single computers. We have developed ‘Grid-Allegro’, a Grid aware implementation of the Allegro software, by which several thousands of genotype simulations can be performed in parallel in short time. With temporary installations of the Allegro executable and datasets on remote nodes at submission, the need of predefined Grid run-time environments is circumvented. We evaluated the performance, efficiency and scalability of this implementation in a genome scan on Swedish multiplex Alzheimer's disease families. We demonstrate that ‘Grid-Allegro’ allows for the full exploitation of the features available in Allegro for genome-wide linkage. The implementation of existing bioinformatics applications on Grids (Distributed Computing) represent a cost-effective alternative for addressing highly resource-demanding and data-intensive bioinformatics task, compared to acquiring and setting up clusters of computational hardware in house (Parallel Computing), a resource not available to most geneticists today.European Journal of Human Genetics (2007) 15, 694–702. doi:10.1038/sj.ejhg.5201815; published online publication, 21 March 2007 [ABSTRACT FROM AUTHOR]

The article summarizes a study which conducted a genome-wide transcriptomic analysis of protein-coding genes in sorted blood immune cell populations for the characterization of the expression levels of each individual gene across all cell types. Topics covered include detailed view of the gene expression in individual human blood cells by performing an in-depth characterization of the constituent cells in blood and presentation of all data in the open-access database Human Protein Atlas.