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Weighted likelihood inference of genomic autozygosity patterns in dense genotype data
BACKGROUND: Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex dise...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709839/ https://www.ncbi.nlm.nih.gov/pubmed/29191164 http://dx.doi.org/10.1186/s12864-017-4312-3 |
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author | Blant, Alexandra Kwong, Michelle Szpiech, Zachary A. Pemberton, Trevor J. |
author_facet | Blant, Alexandra Kwong, Michelle Szpiech, Zachary A. Pemberton, Trevor J. |
author_sort | Blant, Alexandra |
collection | PubMed |
description | BACKGROUND: Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns. METHODS: We report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data. RESULTS: Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies. CONCLUSIONS: This weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-4312-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5709839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57098392017-12-06 Weighted likelihood inference of genomic autozygosity patterns in dense genotype data Blant, Alexandra Kwong, Michelle Szpiech, Zachary A. Pemberton, Trevor J. BMC Genomics Methodology Article BACKGROUND: Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns. METHODS: We report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data. RESULTS: Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies. CONCLUSIONS: This weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-4312-3) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-01 /pmc/articles/PMC5709839/ /pubmed/29191164 http://dx.doi.org/10.1186/s12864-017-4312-3 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Blant, Alexandra Kwong, Michelle Szpiech, Zachary A. Pemberton, Trevor J. Weighted likelihood inference of genomic autozygosity patterns in dense genotype data |
title | Weighted likelihood inference of genomic autozygosity patterns in dense genotype data |
title_full | Weighted likelihood inference of genomic autozygosity patterns in dense genotype data |
title_fullStr | Weighted likelihood inference of genomic autozygosity patterns in dense genotype data |
title_full_unstemmed | Weighted likelihood inference of genomic autozygosity patterns in dense genotype data |
title_short | Weighted likelihood inference of genomic autozygosity patterns in dense genotype data |
title_sort | weighted likelihood inference of genomic autozygosity patterns in dense genotype data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709839/ https://www.ncbi.nlm.nih.gov/pubmed/29191164 http://dx.doi.org/10.1186/s12864-017-4312-3 |
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