Modelling and visualizing fine-scale linkage disequilibrium structure

Background Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or stati...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Edwards, David [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Linkage Disequilibrium Linkage Disequilibrium Block High Linkage Disequilibrium Monte Carlo Markov Chain Sampling Imputation Accuracy

Anmerkung:	© Edwards; licensee BioMed Central Ltd. 2013

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 14(2013), 1 vom: 06. Juni
Übergeordnetes Werk:	volume:14 ; year:2013 ; number:1 ; day:06 ; month:06

Links:	Volltext

DOI / URN:	10.1186/1471-2105-14-179

Katalog-ID:	SPR026884100

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allfields	10.1186/1471-2105-14-179 doi (DE-627)SPR026884100 (SPR)1471-2105-14-179-e DE-627 ger DE-627 rakwb eng Edwards, David verfasserin aut Modelling and visualizing fine-scale linkage disequilibrium structure 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Edwards; licensee BioMed Central Ltd. 2013 Background Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. Results An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. Conclusions The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 High Linkage Disequilibrium (dpeaa)DE-He213 Monte Carlo Markov Chain Sampling (dpeaa)DE-He213 Imputation Accuracy (dpeaa)DE-He213 Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 06. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:06 month:06 https://dx.doi.org/10.1186/1471-2105-14-179 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 06 06
spelling	10.1186/1471-2105-14-179 doi (DE-627)SPR026884100 (SPR)1471-2105-14-179-e DE-627 ger DE-627 rakwb eng Edwards, David verfasserin aut Modelling and visualizing fine-scale linkage disequilibrium structure 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Edwards; licensee BioMed Central Ltd. 2013 Background Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. Results An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. Conclusions The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 High Linkage Disequilibrium (dpeaa)DE-He213 Monte Carlo Markov Chain Sampling (dpeaa)DE-He213 Imputation Accuracy (dpeaa)DE-He213 Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 06. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:06 month:06 https://dx.doi.org/10.1186/1471-2105-14-179 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 06 06
allfields_unstemmed	10.1186/1471-2105-14-179 doi (DE-627)SPR026884100 (SPR)1471-2105-14-179-e DE-627 ger DE-627 rakwb eng Edwards, David verfasserin aut Modelling and visualizing fine-scale linkage disequilibrium structure 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Edwards; licensee BioMed Central Ltd. 2013 Background Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. Results An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. Conclusions The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 High Linkage Disequilibrium (dpeaa)DE-He213 Monte Carlo Markov Chain Sampling (dpeaa)DE-He213 Imputation Accuracy (dpeaa)DE-He213 Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 06. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:06 month:06 https://dx.doi.org/10.1186/1471-2105-14-179 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 06 06
allfieldsGer	10.1186/1471-2105-14-179 doi (DE-627)SPR026884100 (SPR)1471-2105-14-179-e DE-627 ger DE-627 rakwb eng Edwards, David verfasserin aut Modelling and visualizing fine-scale linkage disequilibrium structure 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Edwards; licensee BioMed Central Ltd. 2013 Background Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. Results An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. Conclusions The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 High Linkage Disequilibrium (dpeaa)DE-He213 Monte Carlo Markov Chain Sampling (dpeaa)DE-He213 Imputation Accuracy (dpeaa)DE-He213 Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 06. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:06 month:06 https://dx.doi.org/10.1186/1471-2105-14-179 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 06 06
allfieldsSound	10.1186/1471-2105-14-179 doi (DE-627)SPR026884100 (SPR)1471-2105-14-179-e DE-627 ger DE-627 rakwb eng Edwards, David verfasserin aut Modelling and visualizing fine-scale linkage disequilibrium structure 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Edwards; licensee BioMed Central Ltd. 2013 Background Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. Results An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. Conclusions The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 High Linkage Disequilibrium (dpeaa)DE-He213 Monte Carlo Markov Chain Sampling (dpeaa)DE-He213 Imputation Accuracy (dpeaa)DE-He213 Enthalten in BMC bioinformatics London : BioMed Central, 2000 14(2013), 1 vom: 06. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:14 year:2013 number:1 day:06 month:06 https://dx.doi.org/10.1186/1471-2105-14-179 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 1 06 06
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author	Edwards, David
spellingShingle	Edwards, David misc Linkage Disequilibrium misc Linkage Disequilibrium Block misc High Linkage Disequilibrium misc Monte Carlo Markov Chain Sampling misc Imputation Accuracy Modelling and visualizing fine-scale linkage disequilibrium structure
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topic_title	Modelling and visualizing fine-scale linkage disequilibrium structure Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 High Linkage Disequilibrium (dpeaa)DE-He213 Monte Carlo Markov Chain Sampling (dpeaa)DE-He213 Imputation Accuracy (dpeaa)DE-He213
topic	misc Linkage Disequilibrium misc Linkage Disequilibrium Block misc High Linkage Disequilibrium misc Monte Carlo Markov Chain Sampling misc Imputation Accuracy
topic_unstemmed	misc Linkage Disequilibrium misc Linkage Disequilibrium Block misc High Linkage Disequilibrium misc Monte Carlo Markov Chain Sampling misc Imputation Accuracy
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title	Modelling and visualizing fine-scale linkage disequilibrium structure
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title_full	Modelling and visualizing fine-scale linkage disequilibrium structure
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title_sort	modelling and visualizing fine-scale linkage disequilibrium structure
title_auth	Modelling and visualizing fine-scale linkage disequilibrium structure
abstract	Background Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. Results An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. Conclusions The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis. © Edwards; licensee BioMed Central Ltd. 2013
abstractGer	Background Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. Results An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. Conclusions The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis. © Edwards; licensee BioMed Central Ltd. 2013
abstract_unstemmed	Background Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. Results An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. Conclusions The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis. © Edwards; licensee BioMed Central Ltd. 2013
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title_short	Modelling and visualizing fine-scale linkage disequilibrium structure
url	https://dx.doi.org/10.1186/1471-2105-14-179
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up_date	2024-07-03T23:15:44.252Z
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Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models. Results An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity. 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Modelling and visualizing fine-scale linkage disequilibrium structure

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