Update on the predictability of tall stature from DNA markers in Europeans
•Current forensic DNA phenotyping focuses on pigmentation traits.•Update on DNA predictability of tall stature in Europeans is presented.•689 SNPs provided AUC of 0.79, while a subset of 412 SNPs achieved 0.76.•New models improved prediction accuracy compared to previous ones. Predicting adult heigh...
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| Published in: | Forensic science international : genetics Vol. 42; pp. 8 - 13 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.09.2019
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| ISSN: | 1872-4973, 1878-0326, 1878-0326 |
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| Abstract | •Current forensic DNA phenotyping focuses on pigmentation traits.•Update on DNA predictability of tall stature in Europeans is presented.•689 SNPs provided AUC of 0.79, while a subset of 412 SNPs achieved 0.76.•New models improved prediction accuracy compared to previous ones.
Predicting adult height from DNA has important implications in forensic DNA phenotyping. In 2014, we introduced a prediction model consisting of 180 height-associated SNPs based on data from 10,361 Northwestern Europeans enriched with tall individuals (770 > 1.88 standard deviation), which yielded a mid-ranged accuracy (AUC = 0.75 for binary prediction of tall stature and R2 = 0.12 for quantitative prediction of adult height). Here, we provide an update on DNA-based height predictability considering an enlarged list of subsequently-published height-associated SNPs using data from the same set of 10,361 Europeans. A prediction model based on the full set of 689 SNPs showed an improved accuracy relative to previous models for both tall stature (AUC = 0.79) and quantitative height (R2 = 0.21). A feature selection analysis revealed a subset of 412 most informative SNPs while the corresponding prediction model retained most of the accuracy (AUC = 0.76 and R2 = 0.19) achieved with the full model. Over all, our study empirically exemplifies that the accuracy for predicting human appearance phenotypes with very complex underlying genetic architectures, such as adult height, can be improved by increasing the number of phenotype-associated DNA variants. Our work also demonstrates that a careful sub-selection allows for a considerable reduction of the number of DNA predictors that achieve similar prediction accuracy as provided by the full set. This is forensically relevant due to restrictions in the number of SNPs simultaneously analyzable with forensically suitable DNA technologies in the current days of targeted massively parallel sequencing in forensic genetics. |
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| AbstractList | •Current forensic DNA phenotyping focuses on pigmentation traits.•Update on DNA predictability of tall stature in Europeans is presented.•689 SNPs provided AUC of 0.79, while a subset of 412 SNPs achieved 0.76.•New models improved prediction accuracy compared to previous ones.
Predicting adult height from DNA has important implications in forensic DNA phenotyping. In 2014, we introduced a prediction model consisting of 180 height-associated SNPs based on data from 10,361 Northwestern Europeans enriched with tall individuals (770 > 1.88 standard deviation), which yielded a mid-ranged accuracy (AUC = 0.75 for binary prediction of tall stature and R2 = 0.12 for quantitative prediction of adult height). Here, we provide an update on DNA-based height predictability considering an enlarged list of subsequently-published height-associated SNPs using data from the same set of 10,361 Europeans. A prediction model based on the full set of 689 SNPs showed an improved accuracy relative to previous models for both tall stature (AUC = 0.79) and quantitative height (R2 = 0.21). A feature selection analysis revealed a subset of 412 most informative SNPs while the corresponding prediction model retained most of the accuracy (AUC = 0.76 and R2 = 0.19) achieved with the full model. Over all, our study empirically exemplifies that the accuracy for predicting human appearance phenotypes with very complex underlying genetic architectures, such as adult height, can be improved by increasing the number of phenotype-associated DNA variants. Our work also demonstrates that a careful sub-selection allows for a considerable reduction of the number of DNA predictors that achieve similar prediction accuracy as provided by the full set. This is forensically relevant due to restrictions in the number of SNPs simultaneously analyzable with forensically suitable DNA technologies in the current days of targeted massively parallel sequencing in forensic genetics. Predicting adult height from DNA has important implications in forensic DNA phenotyping. In 2014, we introduced a prediction model consisting of 180 height-associated SNPs based on data from 10,361 Northwestern Europeans enriched with tall individuals (770 > 1.88 standard deviation), which yielded a mid-ranged accuracy (AUC = 0.75 for binary prediction of tall stature and R = 0.12 for quantitative prediction of adult height). Here, we provide an update on DNA-based height predictability considering an enlarged list of subsequently-published height-associated SNPs using data from the same set of 10,361 Europeans. A prediction model based on the full set of 689 SNPs showed an improved accuracy relative to previous models for both tall stature (AUC = 0.79) and quantitative height (R = 0.21). A feature selection analysis revealed a subset of 412 most informative SNPs while the corresponding prediction model retained most of the accuracy (AUC = 0.76 and R = 0.19) achieved with the full model. Over all, our study empirically exemplifies that the accuracy for predicting human appearance phenotypes with very complex underlying genetic architectures, such as adult height, can be improved by increasing the number of phenotype-associated DNA variants. Our work also demonstrates that a careful sub-selection allows for a considerable reduction of the number of DNA predictors that achieve similar prediction accuracy as provided by the full set. This is forensically relevant due to restrictions in the number of SNPs simultaneously analyzable with forensically suitable DNA technologies in the current days of targeted massively parallel sequencing in forensic genetics. Highlights•Current forensic DNA phenotyping focuses on pigmentation traits. •Update on DNA predictability of tall stature in Europeans is presented. •689 SNPs provided AUC of 0.79, while a subset of 412 SNPs achieved 0.76. •New models improved prediction accuracy compared to previous ones. Predicting adult height from DNA has important implications in forensic DNA phenotyping. In 2014, we introduced a prediction model consisting of 180 height-associated SNPs based on data from 10,361 Northwestern Europeans enriched with tall individuals (770 > 1.88 standard deviation), which yielded a mid-ranged accuracy (AUC = 0.75 for binary prediction of tall stature and R2 = 0.12 for quantitative prediction of adult height). Here, we provide an update on DNA-based height predictability considering an enlarged list of subsequently-published height-associated SNPs using data from the same set of 10,361 Europeans. A prediction model based on the full set of 689 SNPs showed an improved accuracy relative to previous models for both tall stature (AUC = 0.79) and quantitative height (R2 = 0.21). A feature selection analysis revealed a subset of 412 most informative SNPs while the corresponding prediction model retained most of the accuracy (AUC = 0.76 and R2 = 0.19) achieved with the full model. Over all, our study empirically exemplifies that the accuracy for predicting human appearance phenotypes with very complex underlying genetic architectures, such as adult height, can be improved by increasing the number of phenotype-associated DNA variants. Our work also demonstrates that a careful sub-selection allows for a considerable reduction of the number of DNA predictors that achieve similar prediction accuracy as provided by the full set. This is forensically relevant due to restrictions in the number of SNPs simultaneously analyzable with forensically suitable DNA technologies in the current days of targeted massively parallel sequencing in forensic genetics.Predicting adult height from DNA has important implications in forensic DNA phenotyping. In 2014, we introduced a prediction model consisting of 180 height-associated SNPs based on data from 10,361 Northwestern Europeans enriched with tall individuals (770 > 1.88 standard deviation), which yielded a mid-ranged accuracy (AUC = 0.75 for binary prediction of tall stature and R2 = 0.12 for quantitative prediction of adult height). Here, we provide an update on DNA-based height predictability considering an enlarged list of subsequently-published height-associated SNPs using data from the same set of 10,361 Europeans. A prediction model based on the full set of 689 SNPs showed an improved accuracy relative to previous models for both tall stature (AUC = 0.79) and quantitative height (R2 = 0.21). A feature selection analysis revealed a subset of 412 most informative SNPs while the corresponding prediction model retained most of the accuracy (AUC = 0.76 and R2 = 0.19) achieved with the full model. Over all, our study empirically exemplifies that the accuracy for predicting human appearance phenotypes with very complex underlying genetic architectures, such as adult height, can be improved by increasing the number of phenotype-associated DNA variants. Our work also demonstrates that a careful sub-selection allows for a considerable reduction of the number of DNA predictors that achieve similar prediction accuracy as provided by the full set. This is forensically relevant due to restrictions in the number of SNPs simultaneously analyzable with forensically suitable DNA technologies in the current days of targeted massively parallel sequencing in forensic genetics. |
| Author | Uitterlinden, André G. Zhong, Kaiyin Jing, Xiaoxi Hendriks, A. Emile J. Drop, Stenvert L.S. Kayser, Manfred Liu, Fan |
| Author_xml | – sequence: 1 givenname: Fan surname: Liu fullname: Liu, Fan email: liufan@big.ac.cn organization: CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China – sequence: 2 givenname: Kaiyin surname: Zhong fullname: Zhong, Kaiyin email: kindlychung@gmail.com organization: Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 3 givenname: Xiaoxi surname: Jing fullname: Jing, Xiaoxi email: jingxiaoxi@big.ac.cn organization: CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China – sequence: 4 givenname: André G. surname: Uitterlinden fullname: Uitterlinden, André G. email: a.g.uitterlinden@erasmusmc.nl organization: Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 5 givenname: A. Emile J. surname: Hendriks fullname: Hendriks, A. Emile J. email: a.e.j.hendriks@erasmusmc.nl organization: Department of Pediatrics, Pediatric Endocrinology and Diabetes, University of Cambridge, United Kingdom – sequence: 6 givenname: Stenvert L.S. surname: Drop fullname: Drop, Stenvert L.S. email: s.drop@chello.nl organization: Department of Pediatrics, Division of Endocrinology, Sophia Children’s Hospital, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 7 givenname: Manfred surname: Kayser fullname: Kayser, Manfred email: m.kayser@erasmusmc.nl organization: Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands |
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| Cites_doi | 10.1093/hmg/ddy271 10.1007/s00439-013-1394-0 10.1016/j.fsigen.2019.04.001 10.1038/ejhg.2009.5 10.1534/genetics.118.301267 10.1038/nature14558 10.1016/j.fsigen.2015.02.003 10.3390/genes9010049 10.1038/nature14317 10.1016/j.fsigen.2013.12.006 10.1186/gb-2011-12-9-r94 10.1016/j.cub.2009.01.027 10.1007/s00439-017-1808-5 10.1038/s41588-018-0100-5 10.1038/ng.3097 10.1098/rsnr.1976.0005 10.1002/humu.22905 10.1086/321287 10.1038/nature21039 10.1007/s00439-010-0939-8 10.1371/journal.pgen.1006594 10.1371/journal.pone.0127852 10.1038/s41467-018-04086-y 10.1016/j.fsigen.2018.08.017 10.1016/j.fsigen.2010.02.004 10.1007/s10654-017-0321-4 10.1093/hmg/ddx416 10.1007/s00439-006-0240-z 10.1002/humu.22713 10.1093/gerona/50A.4.B237 10.1016/j.fsigen.2015.02.002 10.1375/136905203770326402 10.1016/j.fsigen.2015.09.004 10.1038/ng1607 10.1371/journal.pgen.1002932 10.1038/nature09410 10.1016/j.fsigen.2012.07.005 10.1038/ncomms14694 |
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| Keywords | Body height DNA prediction Tall stature Forensic DNA phenotyping |
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| Snippet | •Current forensic DNA phenotyping focuses on pigmentation traits.•Update on DNA predictability of tall stature in Europeans is presented.•689 SNPs provided AUC... Highlights•Current forensic DNA phenotyping focuses on pigmentation traits. •Update on DNA predictability of tall stature in Europeans is presented. •689 SNPs... Predicting adult height from DNA has important implications in forensic DNA phenotyping. In 2014, we introduced a prediction model consisting of 180... |
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| SubjectTerms | Body height Body Height - genetics DNA - genetics DNA prediction European Continental Ancestry Group - genetics Forensic DNA phenotyping Genetic Markers Humans Logistic Models Models, Genetic Pathology Phenotype Polymorphism, Single Nucleotide Tall stature |
| Title | Update on the predictability of tall stature from DNA markers in Europeans |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1872497318305830 https://www.clinicalkey.es/playcontent/1-s2.0-S1872497318305830 https://dx.doi.org/10.1016/j.fsigen.2019.05.006 https://www.ncbi.nlm.nih.gov/pubmed/31207428 https://www.proquest.com/docview/2242816977 |
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