Genome‐Wide Association Studies

Morris, Andrew P.; Cardon, Lon R.

doi:10.1002/9781119487845.ch21

Cited by 5 publications

(29 citation statements)

References 157 publications

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“…As mentioned in the introduction, modeling phenotypic variation as a function of haplotype variation has extensive literature (Templeton et al, 1987;Balding, 2006;Thompson, 2013;Morris and Cardon, 2019). The prime motivation for this work is the recent growth in the generation of large scale genomic datasets and methods to build phylogenies (Kelleher et al, 2019).…”

Section: The Importance Of the Haplotype Network Modelmentioning

confidence: 99%

“…The haplotype network model can be seen as a hybrid between the mutation model (that models variation between the columns of a haplotype matrix) and the independent haplotype model (that models variation between the rows of a haplotype matrix). This hybrid view might improve genome-wide association studies (see review by Gibson, 2018;Simons et al, 2018;Uricchio, 2019;Morris and Cardon, 2019).…”

Section: The Importance Of the Haplotype Network Modelmentioning

confidence: 99%

“…The aim of geneticists is to infer which mutations are causing variation in phenotypes and what are their effects. This aim is nowadays approached with genome-wide association studies of regressing observed phenotypes on mutation genotypes (see the recent review of Morris and Cardon, 2019). However, mutations arise on specific haplotypes passed between generations, which limits accurate estimation due to low frequency of mutations, correlation with other mutations and limited ability to observe all mutations with a used genomic platform (e.g., see Gibson, 2018;Simons et al, 2018;Uricchio, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Instead of focusing on mutation effects we here focus on haplotype effects and their differences to estimate the effect of mutations on specific haplotypes. There is extensive literature on estimating haplotype effects (Balding, 2006;Thompson, 2013;Morris and Cardon, 2019). One issue with estimating haplotype effects is that there is usually an uneven distribution of haplotypes in a population (Ewens, 1972(Ewens, , 2004Walsh and Lynch, 2018) and estimating the effects of rare haplotypes is equally challenging as estimating the effect of rare mutations.…”

Section: Introductionmentioning

confidence: 99%

“…Others have used similar approaches to utilize this data structure (Balding, 2006;Thompson, 2013;Morris and Cardon, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical modeling of haplotype effects based on a phylogeny

Selle

Steinsland

Lindgren³

et al. 2020

Preprint

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This paper introduces a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are usually many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially -few haplotypes have high frequency and many haplotypes have low frequency. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations and leveraging these similarities can improve the estimation of haplotype effects. There is extensive literature on this topic. Here we build on these observations and develop an autoregressive model of order one that hierarchically models haplotype effects by leveraging phylogenetic relationships between the haplotypes described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network and we therefore refer to the model as the haplotype network model. The haplotype network model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. The key contribution of this work is that by leveraging the haplotype network structure we obtain a sparse model and by using hierarchical autoregression the flow of information between similar haplotypes is estimated from the data. We show with a simulation study that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially when there are few observations for a specific haplotype.We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a case study of modeling the effect of mitochondrial haplotypes on milk yield in cattle.

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Section: The Importance Of the Haplotype Network Modelmentioning

confidence: 99%

Section: The Importance Of the Haplotype Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Others have used similar approaches to utilize this data structure (Balding, 2006;Thompson, 2013;Morris and Cardon, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical modeling of haplotype effects based on a phylogeny

Selle

Steinsland

Lindgren³

et al. 2020

Preprint

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Computational Methods for the Identification of Genetic Variants in Complex Diseases

Antunes

Martins

Correia

et al. 2021

Practical Applications of Computational Biology &Amp; Bioinformatics, 15th International Conference (PACBB 2021)

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XSI—a genotype compression tool for compressive genomics in large biobanks

et al. 2022

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Motivation Generation of genotype data has been growing exponentially over the last decade. With the large size of recent datasets comes a storage and computational burden with ever increasing costs. To reduce this burden we propose XSI, a file format with reduced storage footprint that also allows computation on the compressed data and we show how this can improve future analyses. Results We show that XSI allows for a file size reduction of 4-20x compared to compressed BCF and demonstrate its potential for “compressive genomics” on the UK Biobank whole genome sequencing genotypes with 8x faster loading times, 5x faster run of homozygozity computation, 30x faster dot products computation, and 280x faster allele counts. Availability The xSqueezeIt file format (XSI) specifications, API, and command line tool are released under open-source (MIT) license and are available at https://github.com/rwk-unil/xSqueezeIt Supplementary information Supplementary materials are available at Bioinformatics online.

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Genome‐Wide Association Studies

Cited by 5 publications

References 157 publications

Hierarchical modeling of haplotype effects based on a phylogeny

Hierarchical modeling of haplotype effects based on a phylogeny

Computational Methods for the Identification of Genetic Variants in Complex Diseases

XSI—a genotype compression tool for compressive genomics in large biobanks

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