Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index
With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BioMed Central
2016-12-01
|
| Series: | Genomics & Informatics |
| Subjects: | |
| Online Access: | http://genominfo.org/upload/pdf/gni-14-149.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832569265999314944 |
|---|---|
| author | Sunghwan Bae Sungkyoung Choi Sung Min Kim Taesung Park |
| author_facet | Sunghwan Bae Sungkyoung Choi Sung Min Kim Taesung Park |
| author_sort | Sunghwan Bae |
| collection | DOAJ |
| description | With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar. |
| format | Article |
| id | doaj-art-78987be59ad7458e99291772fcde1d81 |
| institution | Kabale University |
| issn | 1598-866X 2234-0742 |
| language | English |
| publishDate | 2016-12-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-78987be59ad7458e99291772fcde1d812025-02-02T22:28:15ZengBioMed CentralGenomics & Informatics1598-866X2234-07422016-12-0114414915910.5808/GI.2016.14.4.149170Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass IndexSunghwan Bae0Sungkyoung Choi1Sung Min Kim2Taesung Park3Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.Bioinformatics and Biostatistics Lab, Seoul National University, Seoul 08826, Korea.Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.http://genominfo.org/upload/pdf/gni-14-149.pdfbody mass indexclinical prediction rulegenome-wide association studypenalized regression modelsvariable selection |
| spellingShingle | Sunghwan Bae Sungkyoung Choi Sung Min Kim Taesung Park Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index Genomics & Informatics body mass index clinical prediction rule genome-wide association study penalized regression models variable selection |
| title | Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index |
| title_full | Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index |
| title_fullStr | Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index |
| title_full_unstemmed | Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index |
| title_short | Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index |
| title_sort | prediction of quantitative traits using common genetic variants application to body mass index |
| topic | body mass index clinical prediction rule genome-wide association study penalized regression models variable selection |
| url | http://genominfo.org/upload/pdf/gni-14-149.pdf |
| work_keys_str_mv | AT sunghwanbae predictionofquantitativetraitsusingcommongeneticvariantsapplicationtobodymassindex AT sungkyoungchoi predictionofquantitativetraitsusingcommongeneticvariantsapplicationtobodymassindex AT sungminkim predictionofquantitativetraitsusingcommongeneticvariantsapplicationtobodymassindex AT taesungpark predictionofquantitativetraitsusingcommongeneticvariantsapplicationtobodymassindex |