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Genotype imputation using the Positional Burrows Wheeler Transform [article]

Simone Rubinacci, Olivier Delaneau, Jonathan Marchini
2019 bioRxiv   pre-print
Genotype imputation is the process of predicting unobserved genotypes in a sample of individuals using a reference panel of haplotypes. Increasing reference panel size poses ever increasing computational challenges for imputation methods. Here we present IMPUTE5, a genotype imputation method that can scale to reference panels with millions of samples. It achieves fast and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT), which are used as
more » ... conditioning states within the IMPUTE model. IMPUTE5 is 20x faster than MINIMAC4 and 3x faster than BEAGLE5, when using the HRC reference panel, and uses less memory than both these methods. IMPUTE5 scales sub-linearly with reference panel size. Keeping the number of imputed markers constant, a 100 fold increase in reference panel size requires less than twice the computation time.
doi:10.1101/797944 fatcat:6fsnffyru5bwxauq7xi7lqfqui

Integrative haplotype estimation with sub-linear complexity [article]

Olivier Delaneau, Jean-Francois Zagury, Matthew R Robinson, Jonathan Marchini, Emmanouil Dermitzakis
2018 bioRxiv   pre-print
computations on compact graph representations of all possible haplotype configurations that are consistent with each individual (called genotype graph; see Supplementary Figure 5 for a graphical description and Delaneau  ... 
doi:10.1101/493403 fatcat:7a54li7x35e3pkue57keyv2q7e

Fast and efficient QTL mapper for thousands of molecular phenotypes [article]

Halit Ongen, Alfonso Buil, Andrew Brown, Emmanouil Dermitzakis, Olivier Delaneau
2015 bioRxiv   pre-print
Motivation: In order to discover quantitative trait loci (QTLs), multi-dimensional genomic data sets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing. Results: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to
more » ... control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted p-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot data sets can be now easily analyzed an order of magnitude faster than previous approaches. Availability: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/
doi:10.1101/022301 fatcat:ciiivjwlxbh43lgaxvodk2553a

Parent-of-origin effects in the UK Biobank [article]

Robin J Hofmeister, Simone Rubinacci, Diogo M Ribeiro, Zoltan Kutalik, Alfonso Buil, Olivier Delaneau
2021 bioRxiv   pre-print
Identical genetic variations can have different phenotypic effects depending on their parent of origin (PofO). Yet, studies focussing on PofO effects have been largely limited in terms of sample size due to the need of parental genomes or known genealogies. Here, we used a novel probabilistic approach to infer PofO of individual alleles in the UK Biobank that does not require parental genomes nor prior knowledge of genealogy. Our model uses Identity-By-Descent (IBD) sharing with second- and
more » ... d-degree relatives to assign alleles to parental groups and leverages chromosome X data in males to distinguish maternal from paternal groups. When combined with robust haplotype inference and haploid imputation, this allowed us to infer the PofO at 5.4 million variants genome-wide for 26,393 UK Biobank individuals. We used this large dataset to systematically screen 59 biomarkers and 38 anthropomorphic phenotypes for PofO effects and discovered 101 significant associations, demonstrating that this type of effects contributes to the genetics of complex traits. Notably, we retrieved well known PofO effects, such as the MEG3/DLK1 locus on platelet count, and we discovered many new ones at loci often unsuspected of being imprinted and, in some cases, previously thought to harbour additive associations.
doi:10.1101/2021.11.03.467079 fatcat:wloy6o2gsbb7dhwuvukty3pgne

Estimating the causal tissues for complex traits and diseases [article]

Halit Ongen, Andrew A Brown, Olivier Delaneau, Nikolaos Panousis, Alexandra C Nica, Emmanouil T Dermitzakis
2016 bioRxiv   pre-print
Interpretation of biological causes of the predisposing markers identified through Genome Wide Association Studies (GWAS) remains an open question. One direct and powerful way to assess the genetic causality behind GWAS is through expression quantitative trait loci (eQTLs). Here we describe a novel approach to estimate the tissues giving rise to the genetic causality behind a wide variety of GWAS traits, using the cis-eQTLs identified in 44 tissues of the GTEx consortium. We have adapted the
more » ... ulatory Trait Concordance (RTC) score, to on the one hand measure the tissue sharing probabilities of eQTLs, and also to calculate the probability that a GWAS and an eQTL variant tag the same underlying functional effect. We show that our tissue sharing estimates significantly correlate with commonly used estimates of tissue sharing. By normalizing the GWAS-eQTL probabilities with the tissue sharing estimates of the eQTLs, we can estimate the tissues from which GWAS genetic causality arises. Our approach not only indicates the gene mediating individual GWAS signals, but also can highlight tissues where the genetic causality for an individual trait is manifested.
doi:10.1101/074682 fatcat:55d7v35myzhureq4drcbltitla

Genotype imputation using the Positional Burrows Wheeler Transform

Simone Rubinacci, Olivier Delaneau, Jonathan Marchini, Paul Scheet
2020 PLoS Genetics  
Supporting information Rubinacci S, Delaneau O, Marchini J (2020) Genotype imputation using the Positional Burrows Wheeler Transform.  ... 
doi:10.1371/journal.pgen.1009049 pmid:33196638 fatcat:wxipvokoevdedj6d4w7saffesy

Accurate, scalable and integrative haplotype estimation

Olivier Delaneau, Jean-François Zagury, Matthew R. Robinson, Jonathan L. Marchini, Emmanouil T. Dermitzakis
2019 Nature Communications  
computations on compact graph representations of all possible haplotype configurations that are consistent with each individual (called genotype graph; see Supplementary Fig. 9 for a graphical description and Delaneau  ... 
doi:10.1038/s41467-019-13225-y pmid:31780650 pmcid:PMC6882857 fatcat:t76r6oqv7rd5bjoxm3d2iyomtm

Shape-IT: new rapid and accurate algorithm for haplotype inference

Olivier Delaneau, Cedric Coulonges, Jean-Francois Zagury
2008 BMC Bioinformatics  
We have developed a new computational algorithm, Shape-IT, to infer haplotypes under the genetic model of coalescence with recombination developed by Stephens et al in Phase v2.1. It runs much faster than Phase v2.1 while exhibiting the same accuracy. The major algorithmic improvements rely on the use of binary trees to represent the sets of candidate haplotypes for each individual. These binary tree representations: (1) speed up the computations of posterior probabilities of the haplotypes by
more » ... voiding the redundant operations made in Phase v2.1, and (2) overcome the exponential aspect of the haplotypes inference problem by the smart exploration of the most plausible pathways (ie. haplotypes) in the binary trees. Our results show that Shape-IT is several orders of magnitude faster than Phase v2.1 while being as accurate. For instance, Shape-IT runs 50 times faster than Phase v2.1 to compute the haplotypes of 200 subjects on 6,000 segments of 50 SNPs extracted from a standard Illumina 300 K chip (13 days instead of 630 days). We also compared Shape-IT with other widely used software, Gerbil, PL-EM, Fastphase, 2SNP, and Ishape in various tests: Shape-IT and Phase v2.1 were the most accurate in all cases, followed by Ishape and Fastphase. As a matter of speed, Shape-IT was faster than Ishape and Fastphase for datasets smaller than 100 SNPs, but Fastphase became faster -but still less accurate- to infer haplotypes on larger SNP datasets. Shape-IT deserves to be extensively used for regular haplotype inference but also in the context of the new high-throughput genotyping chips since it permits to fit the genetic model of Phase v2.1 on large datasets. This new algorithm based on tree representations could be used in other HMM-based haplotype inference software and may apply more largely to other fields using HMM.
doi:10.1186/1471-2105-9-540 pmid:19087329 pmcid:PMC2647951 fatcat:vs2vzn57xre4ropfrqftec42jm

Efficient phasing and imputation of low-coverage sequencing data using large reference panels [article]

Simone Rubinacci, Diogo Ribeiro, Robin Hofmeister, Olivier Delaneau
2020 bioRxiv   pre-print
This work was funded by a Swiss National 14 Science Foundation (SNSF) project grant (PP00P3_176977) 15 16 Author contribution 17 18 S Corresponding author 23 24 Olivier Delaneau (olivier.delaneau  ... 
doi:10.1101/2020.04.14.040329 fatcat:wwxr2p3ruze3pkq3ij5k4iffga

Haplotype Estimation Using Sequencing Reads

Olivier Delaneau, Bryan Howie, Anthony J. Cox, Jean-François Zagury, Jonathan Marchini
2013 American Journal of Human Genetics  
Precise details of the forward-backward algorithm used to calculate this model can be found in the Supplemental Data of Delaneau et al. 13 .  ... 
doi:10.1016/j.ajhg.2013.09.002 pmid:24094745 pmcid:PMC3791270 fatcat:3siuwgboqje4vohtzwx25zai4u

Improved whole-chromosome phasing for disease and population genetic studies

Olivier Delaneau, Jean-Francois Zagury, Jonathan Marchini
2013 Nature Methods  
doi:10.1038/nmeth.2307 pmid:23269371 fatcat:wiqlgh36snhulpcldq5knozple

Expression estimation and eQTL mapping for HLA genes with a personalized pipeline [article]

Vitor R.C. Aguiar, Jonatas E Cesar, Olivier Delaneau, Emmanouil T Dermitzakis, Diogo Meyer
2018 biorxiv/medrxiv   pre-print
We also used RTC to evaluate if eQTLs identified in our study capture the same signals as CRD-QTLs identified by Delaneau et al. [52] .  ...  Following the recommendation of Delaneau et al. (see Supplementary Note 7 in [47] ), we considered that two SNPs tagged the same functional signal if the RTC score was >0.9.  ... 
doi:10.1101/365957 fatcat:3qp7krxprra7lofbhmum4lj4qe

Fast and efficient QTL mapper for thousands of molecular phenotypes

Halit Ongen, Alfonso Buil, Andrew Anand Brown, Emmanouil T. Dermitzakis, Olivier Delaneau
2015 Bioinformatics  
Motivation: In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing. Results: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user-and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control
more » ... or multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches. Availability and implementation: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/
doi:10.1093/bioinformatics/btv722 pmid:26708335 pmcid:PMC4866519 fatcat:ygamesxp7vajhcsoal4ey35aze

Hundreds of putative non-coding cis-regulatory drivers in chronic lymphocytic leukaemia and skin cancer [article]

Halit Ongen, Olivier Delaneau, Michael W. Stevens, Cedric Howald, Emmanouil T. Dermitzakis
2017 bioRxiv   pre-print
RESULTS Identification of cis regulatory domains In order to define regions of the non-coding genome that are likely to be regulatory we have used data generated by the SysGenetiX (SGX) project (Delaneau  ... 
doi:10.1101/174219 fatcat:szcbdxfuu5gb3ninse3xlizv7e

Phasing for medical sequencing using rare variants and large haplotype reference panels

Kevin Sharp, Warren Kretzschmar, Olivier Delaneau, Jonathan Marchini
2016 Bioinformatics  
et al., 2012 Delaneau et al., , 2013b Li et al., 2010; Stephens et al., 2001; Scheet and Stephens, 2006) .  ...  We often refer to this as using the SHAPEIT1 model, as the compact graph structure was developed in SHAPEIT1 (Delaneau et al., 2012) .  ... 
doi:10.1093/bioinformatics/btw065 pmid:27153703 pmcid:PMC4920110 fatcat:nmuea5nx2fhqjj5gzyfb52pfli
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