Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage
Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from differ...
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| Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 113; no. 24; pp. 6713 - 6718 |
|---|---|
| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
National Academy of Sciences
14.06.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0027-8424 1091-6490 1091-6490 |
| DOI | 10.1073/pnas.1606460113 |
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| Abstract | Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Comparedwith GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome- wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions. |
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| AbstractList | Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Compared with GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome-wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions. Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Compared with GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome-wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions.Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Compared with GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome-wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions. We compared the information provided by whole-exome sequencing (WES) and genome-wide single-nucleotide variant arrays in terms of principal component analysis, homozygosity rate estimation, and linkage analysis using 110 subjects originating from different regions of the world. WES provided an accurate prediction of population substructure using high-quality variants with a minor allele frequency > 2% and reliable estimation of homozygosity rates using runs of homozygosity. Finally, homozygosity mapping in 15 consanguineous families showed that WES led to powerful linkage analyses, particularly in coding regions. Overall, our study shows that WES could be used for several analyses that are very helpful to optimize the search for disease-causing exome variants. Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Compared with GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome-wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions. Significance We compared the information provided by whole-exome sequencing (WES) and genome-wide single-nucleotide variant arrays in terms of principal component analysis, homozygosity rate estimation, and linkage analysis using 110 subjects originating from different regions of the world. WES provided an accurate prediction of population substructure using high-quality variants with a minor allele frequency > 2% and reliable estimation of homozygosity rates using runs of homozygosity. Finally, homozygosity mapping in 15 consanguineous families showed that WES led to powerful linkage analyses, particularly in coding regions. Overall, our study shows that WES could be used for several analyses that are very helpful to optimize the search for disease-causing exome variants. AbstractPrincipal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Compared with GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome-wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions. Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Comparedwith GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome- wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions. |
| Author | Cobat, Aurélie Abhyankar, Avinash Bousfiha, Aziz Shang, Lei Itan, Yuval El Baghdadi, Jamila Belkadi, Aziz Casanova, Jean-Laurent Abel, Laurent Pedergnana, Vincent Boisson, Bertrand Vincent, Quentin B. Alcais, Alexandre |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27247391$$D View this record in MEDLINE/PubMed https://hal.science/hal-03691490$$DView record in HAL |
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| Cites_doi | 10.1086/519795 10.1186/s12859-015-0727-5 10.1126/science.1177074 10.1534/genetics.114.164350 10.1038/ng.1074 10.1038/nrg3031 10.1126/science.1234864 10.1159/000358224 10.1371/journal.pgen.1003959 10.1038/nmeth.3739 10.1038/ng1847 10.1038/nature07331 10.1093/molbev/msu326 10.1371/journal.pgen.1000519 10.1038/ng.806 10.1086/378207 10.1038/nature09103 10.1084/jem.20130592 10.1002/gepi.0177 10.1038/ejhg.2015.143 10.1038/nrg3908 10.1186/gb-2011-12-9-r85 10.1038/nature11632 10.1073/pnas.1218167110 10.1016/j.immuni.2013.09.002 10.1371/journal.pgen.1002967 10.1002/humu.22220 10.1038/ng786 10.1093/bioinformatics/btp698 10.1126/science.1153717 10.1038/ng.499 10.1073/pnas.1313787111 10.1073/pnas.1518646112 10.1073/pnas.1515057112 10.1371/journal.pgen.0020190 10.1126/science.1251688 10.1371/journal.pcbi.1003757 10.1038/nature09298 10.1016/j.cell.2013.09.001 10.1016/j.ajhg.2008.08.007 10.1006/tpbi.2001.1543 10.1126/science.1219240 10.1126/science.2884728 10.1016/j.ajhg.2013.05.003 10.1093/bioinformatics/btu149 10.1016/j.ajhg.2010.10.028 10.1101/gr.107524.110 10.1038/ejhg.2013.297 10.1186/1753-6561-5-S9-S116 10.1038/ng.2303 10.1084/jem.20140520 10.1016/j.ajhg.2015.04.018 10.1073/pnas.0909559107 10.1126/science.aaa3650 10.1073/pnas.1418631112 |
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| Contributor | Arikan, Cigdem Ciancanelli, Michael Aydogmus, Cigdem Mahdaviani, Seyed Alireza Mahlhoui, Nizar Boussofara, Lobna Boutros, Jeannette Desai, Mukesh Cole, Theresa Bonnet, Damien Rezaei, Nima Ramon, Silvia Sanchez Vandenesch, François Al-Herz, Waleed Sanal, Ozden Parvaneh, Nima Keser-Emiroglu, Melike Stambouli, Omar Boudghene Franco, José Luis Bustamante, Jacinta Condino-Neto, Antonio Mansouri, Davood Zhang, Shen-Ying Puel, Anne Arkwright, Peter Ichai, Philippe Jouanguy, Emmanuelle Fieschi, Claire Kilic, Sara S Bernard, Olivier Blancas-Galicia, Lizbeth Picard, Capucine Boisson-Dupuis, Stéphanie Vogt, Guillaume Raoult, Didier |
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| Copyright | Volumes 1–89 and 106–113, copyright as a collective work only; author(s) retains copyright to individual articles Copyright National Academy of Sciences Jun 14, 2016 Distributed under a Creative Commons Attribution 4.0 International License |
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| DOI | 10.1073/pnas.1606460113 |
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| Keywords | exome sequencing linkage analysis population structure genotyping array homozygosity mapping |
| Language | English |
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| Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 PMCID: PMC4914194 Author contributions: A. Belkadi, V.P., A.C., Y.I., A. Alcais, J.-L.C., and L.A. designed research; A. Belkadi, V.P., A.C., Y.I., Q.B.V., A. Abhyankar, L.S., J.E.B., A. Bousfiha, E./A.C., and B.B. performed research; A. Belkadi, V.P., A.C., Y.I., Q.B.V., A. Abhyankar, and B.B. contributed new reagents/analytic tools; A. Belkadi, V.P., A.C., Y.I., Q.B.V., A. Abhyankar, L.S., A. Alcais, B.B., J.-L.C., and L.A. analyzed data; A. Belkadi, V.P., A.C., Y.I., A. Alcais, J.-L.C., and L.A. wrote the paper; and J.E.B., A. Bousfiha, and E./A.C. provided clinical data. Reviewers: L.B.B., Centre Hospitalo-Universitaire (CHU) Sainte-Justine/University of Montreal; and J.F., Ecole Polytechnique Fédérale de Lausanne (EPFL). 1A. Belkadi and V.P. contributed equally to this work. Contributed by Jean-Laurent Casanova, April 27, 2016 (sent for review March 3, 2016; reviewed by Luis B. Barreiro and Jacques Fellay) |
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| References | e_1_3_3_50_2 Kancheva D (e_1_3_3_37_2) 2015 e_1_3_3_16_2 e_1_3_3_18_2 e_1_3_3_39_2 e_1_3_3_12_2 e_1_3_3_14_2 e_1_3_3_35_2 e_1_3_3_56_2 e_1_3_3_33_2 e_1_3_3_54_2 e_1_3_3_10_2 e_1_3_3_31_2 e_1_3_3_52_2 e_1_3_3_40_2 e_1_3_3_5_2 e_1_3_3_7_2 e_1_3_3_9_2 e_1_3_3_27_2 e_1_3_3_29_2 e_1_3_3_23_2 e_1_3_3_48_2 e_1_3_3_25_2 e_1_3_3_46_2 e_1_3_3_1_2 e_1_3_3_44_2 e_1_3_3_3_2 e_1_3_3_21_2 e_1_3_3_42_2 e_1_3_3_51_2 e_1_3_3_17_2 e_1_3_3_19_2 e_1_3_3_38_2 e_1_3_3_13_2 e_1_3_3_36_2 e_1_3_3_15_2 e_1_3_3_34_2 e_1_3_3_32_2 e_1_3_3_55_2 e_1_3_3_11_2 e_1_3_3_30_2 e_1_3_3_53_2 e_1_3_3_6_2 e_1_3_3_8_2 e_1_3_3_28_2 e_1_3_3_49_2 e_1_3_3_24_2 e_1_3_3_47_2 e_1_3_3_26_2 e_1_3_3_45_2 e_1_3_3_2_2 e_1_3_3_20_2 e_1_3_3_43_2 e_1_3_3_4_2 e_1_3_3_22_2 e_1_3_3_41_2 |
| References_xml | – ident: e_1_3_3_39_2 doi: 10.1086/519795 – ident: e_1_3_3_17_2 doi: 10.1186/s12859-015-0727-5 – ident: e_1_3_3_30_2 doi: 10.1126/science.1177074 – ident: e_1_3_3_26_2 doi: 10.1534/genetics.114.164350 – ident: e_1_3_3_46_2 doi: 10.1038/ng.1074 – ident: e_1_3_3_3_2 doi: 10.1038/nrg3031 – ident: e_1_3_3_6_2 doi: 10.1126/science.1234864 – ident: e_1_3_3_44_2 doi: 10.1159/000358224 – ident: e_1_3_3_22_2 doi: 10.1371/journal.pgen.1003959 – ident: e_1_3_3_13_2 doi: 10.1038/nmeth.3739 – ident: e_1_3_3_53_2 doi: 10.1038/ng1847 – ident: e_1_3_3_27_2 doi: 10.1038/nature07331 – ident: e_1_3_3_24_2 doi: 10.1093/molbev/msu326 – ident: e_1_3_3_43_2 doi: 10.1371/journal.pgen.1000519 – ident: e_1_3_3_50_2 doi: 10.1038/ng.806 – ident: e_1_3_3_19_2 doi: 10.1086/378207 – ident: e_1_3_3_31_2 doi: 10.1038/nature09103 – ident: e_1_3_3_15_2 doi: 10.1084/jem.20130592 – year: 2015 ident: e_1_3_3_37_2 article-title: Novel mutations in genes causing hereditary spastic paraplegia and Charcot-Marie-Tooth neuropathy identified by an optimized protocol for homozygosity mapping based on whole-exome sequencing publication-title: Genet Med – ident: e_1_3_3_18_2 doi: 10.1002/gepi.0177 – ident: e_1_3_3_36_2 doi: 10.1038/ejhg.2015.143 – ident: e_1_3_3_49_2 doi: 10.1038/nrg3908 – ident: e_1_3_3_35_2 doi: 10.1186/gb-2011-12-9-r85 – ident: e_1_3_3_42_2 doi: 10.1038/nature11632 – ident: e_1_3_3_8_2 doi: 10.1073/pnas.1218167110 – ident: e_1_3_3_14_2 doi: 10.1016/j.immuni.2013.09.002 – ident: e_1_3_3_25_2 doi: 10.1371/journal.pgen.1002967 – ident: e_1_3_3_16_2 doi: 10.1002/humu.22220 – ident: e_1_3_3_56_2 doi: 10.1038/ng786 – ident: e_1_3_3_51_2 doi: 10.1093/bioinformatics/btp698 – ident: e_1_3_3_33_2 doi: 10.1126/science.1153717 – ident: e_1_3_3_1_2 doi: 10.1038/ng.499 – ident: e_1_3_3_29_2 doi: 10.1073/pnas.1313787111 – ident: e_1_3_3_11_2 doi: 10.1073/pnas.1518646112 – ident: e_1_3_3_12_2 doi: 10.1073/pnas.1515057112 – ident: e_1_3_3_34_2 doi: 10.1371/journal.pgen.0020190 – ident: e_1_3_3_32_2 doi: 10.1126/science.1251688 – ident: e_1_3_3_9_2 doi: 10.1371/journal.pcbi.1003757 – ident: e_1_3_3_41_2 doi: 10.1038/nature09298 – ident: e_1_3_3_7_2 doi: 10.1016/j.cell.2013.09.001 – ident: e_1_3_3_20_2 doi: 10.1016/j.ajhg.2008.08.007 – ident: e_1_3_3_21_2 doi: 10.1006/tpbi.2001.1543 – ident: e_1_3_3_5_2 doi: 10.1126/science.1219240 – ident: e_1_3_3_55_2 doi: 10.1126/science.2884728 – ident: e_1_3_3_40_2 doi: 10.1016/j.ajhg.2013.05.003 – ident: e_1_3_3_47_2 doi: 10.1093/bioinformatics/btu149 – ident: e_1_3_3_2_2 doi: 10.1016/j.ajhg.2010.10.028 – ident: e_1_3_3_52_2 doi: 10.1101/gr.107524.110 – ident: e_1_3_3_23_2 doi: 10.1038/ejhg.2013.297 – ident: e_1_3_3_54_2 doi: 10.1186/1753-6561-5-S9-S116 – ident: e_1_3_3_4_2 doi: 10.1038/ng.2303 – ident: e_1_3_3_45_2 doi: 10.1084/jem.20140520 – ident: e_1_3_3_38_2 doi: 10.1016/j.ajhg.2015.04.018 – ident: e_1_3_3_28_2 doi: 10.1073/pnas.0909559107 – ident: e_1_3_3_10_2 doi: 10.1126/science.aaa3650 – ident: e_1_3_3_48_2 doi: 10.1073/pnas.1418631112 |
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| Snippet | Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide... We compared the information provided by whole-exome sequencing (WES) and genome-wide single-nucleotide variant arrays in terms of principal component analysis,... Significance We compared the information provided by whole-exome sequencing (WES) and genome-wide single-nucleotide variant arrays in terms of principal... |
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| SubjectTerms | Biological Sciences Consanguinity Female Filters Genetic Linkage Genome-Wide Association Study Genomes Homozygosity Homozygote Humans Inbreeding Life Sciences Male Mapping Middle East North America Offspring Parents & parenting Population structure Principal components analysis Quality control |
| Title | Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage |
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