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Genotype imputation using the Positional Burrows Wheeler Transform
[article]
2019
bioRxiv
pre-print
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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
doi:10.1101/797944
fatcat:6fsnffyru5bwxauq7xi7lqfqui
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... 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.
Integrative haplotype estimation with sub-linear complexity
[article]
2018
bioRxiv
pre-print
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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]
2015
bioRxiv
pre-print
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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
doi:10.1101/022301
fatcat:ciiivjwlxbh43lgaxvodk2553a
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... 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/
Parent-of-origin effects in the UK Biobank
[article]
2021
bioRxiv
pre-print
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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
doi:10.1101/2021.11.03.467079
fatcat:wloy6o2gsbb7dhwuvukty3pgne
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... 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.
Estimating the causal tissues for complex traits and diseases
[article]
2016
bioRxiv
pre-print
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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
doi:10.1101/074682
fatcat:55d7v35myzhureq4drcbltitla
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... 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.
Genotype imputation using the Positional Burrows Wheeler Transform
2020
PLoS Genetics
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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
2019
Nature Communications
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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
2008
BMC Bioinformatics
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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
doi:10.1186/1471-2105-9-540
pmid:19087329
pmcid:PMC2647951
fatcat:vs2vzn57xre4ropfrqftec42jm
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... 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.
Efficient phasing and imputation of low-coverage sequencing data using large reference panels
[article]
2020
bioRxiv
pre-print
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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
2013
American Journal of Human Genetics
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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
2013
Nature Methods
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Expression estimation and eQTL mapping for HLA genes with a personalized pipeline
[article]
2018
biorxiv/medrxiv
pre-print
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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
2015
Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
doi:10.1093/bioinformatics/btv722
pmid:26708335
pmcid:PMC4866519
fatcat:ygamesxp7vajhcsoal4ey35aze
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/
Hundreds of putative non-coding cis-regulatory drivers in chronic lymphocytic leukaemia and skin cancer
[article]
2017
bioRxiv
pre-print
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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
2016
Bioinformatics
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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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