Population-specific reference panel improves imputation quality for genome-wide association studies conducted on the Japanese population Article Swipe
Jack U. Flanagan
,
Xiaoxi Liu
,
David Ortega-Reyes
,
Kohei Tomizuka
,
Nana Matoba
,
Masato Akiyama
,
Masaru Koido
,
Kazuyoshi Ishigaki
,
Kyota Ashikawa
,
Sadaaki Takata
,
Mingyang Shi
,
Tomomi Aoi
,
Yukihide Momozawa
,
Kaoru Ito
,
Yoshinori Murakami
,
Koichi Matsuda
,
Yoichiro Kamatani
,
Andrew P. Morris
,
Momoko Horikoshi
,
Chikashi Terao
,
Yoshinori Murakami
,
Yoichiro Kamatani
,
Kaori Muto
,
Akiko Nagai
,
Wataru Obara
,
Ken Yamaji
,
Kazuhisa Takahashi
,
Satoshi Asai
,
Yasuo Takahashi
,
Takao Suzuki
,
Nobuaki Sinozaki
,
Hiroki Yamaguchi
,
Shiro Minami
,
Shigeo Murayama
,
Kozo Yoshimori
,
Satoshi Nagayama
,
Daisuke Obata
,
Masahiko Higashiyama
,
Akihide Masumoto
,
Yukihiro Koretsune
,
Yoichiro Kamatani
,
Andrew P. Morris
,
Momoko Horikoshi
,
Chikashi Terao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s42003-024-07338-4
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s42003-024-07338-4
To improve imputation quality for genome-wide association studies (GWAS) conducted on the Japanese population, we developed and evaluated four Japanese population-specific reference panels. These panels were constructed through the augmentation of the 1000 Genomes Project (1KG) panel using Japanese whole genome sequencing (WGS) data, with sample sizes ranging from 1 K to 7 K individuals enrolled through the Biobank Japan (BBJ) project, and sequencing depths ranging from 3× to 30×. Among these panels, an augmented reference panel comprising 7472 WGS samples of mixed depth (1KG+7K) exhibit the greatest improvement in imputation quality relative to the Trans-Omics for Precision Medicine (TOPMed) reference panel. Notably, we observe these improvements primarily for rare variants with a minor allele frequency (MAF) <5%. To demonstrate the benefits of improved imputation quality in association analyses of complex traits, we conducted GWAS for serum uric acid and total cholesterol levels following imputation up to the 1KG+7K panel. The analysis reveals several loci reaching genome-wide significance (P < 5 × 10-8) in the 1KG+7K imputation output yet remaining undetected when the same sample set is imputed up to the TOPMed reference panel. In summary, the 1KG+7K panel demonstrates significant advantages in the discovery of trait-associated loci, particularly those influenced by low-frequency association signals.
Related Topics
Concepts
Imputation (statistics)
Genome-wide association study
Genetic association
Panel data
Population
Association (psychology)
Statistics
Biology
Medicine
Genetics
Psychology
Single-nucleotide polymorphism
Environmental health
Mathematics
Missing data
Genotype
Gene
Psychotherapist
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s42003-024-07338-4
- https://www.nature.com/articles/s42003-024-07338-4.pdf
- OA Status
- gold
- Cited By
- 8
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405573974
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405573974Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s42003-024-07338-4Digital Object Identifier
- Title
-
Population-specific reference panel improves imputation quality for genome-wide association studies conducted on the Japanese populationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-19Full publication date if available
- Authors
-
Jack U. Flanagan, Xiaoxi Liu, David Ortega-Reyes, Kohei Tomizuka, Nana Matoba, Masato Akiyama, Masaru Koido, Kazuyoshi Ishigaki, Kyota Ashikawa, Sadaaki Takata, Mingyang Shi, Tomomi Aoi, Yukihide Momozawa, Kaoru Ito, Yoshinori Murakami, Koichi Matsuda, Yoichiro Kamatani, Andrew P. Morris, Momoko Horikoshi, Chikashi Terao, Yoshinori Murakami, Yoichiro Kamatani, Kaori Muto, Akiko Nagai, Wataru Obara, Ken Yamaji, Kazuhisa Takahashi, Satoshi Asai, Yasuo Takahashi, Takao Suzuki, Nobuaki Sinozaki, Hiroki Yamaguchi, Shiro Minami, Shigeo Murayama, Kozo Yoshimori, Satoshi Nagayama, Daisuke Obata, Masahiko Higashiyama, Akihide Masumoto, Yukihiro Koretsune, Yoichiro Kamatani, Andrew P. Morris, Momoko Horikoshi, Chikashi TeraoList of authors in order
- Landing page
-
https://doi.org/10.1038/s42003-024-07338-4Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s42003-024-07338-4.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s42003-024-07338-4.pdfDirect OA link when available
- Concepts
-
Imputation (statistics), Genome-wide association study, Genetic association, Panel data, Population, Association (psychology), Statistics, Biology, Medicine, Genetics, Psychology, Single-nucleotide polymorphism, Environmental health, Mathematics, Missing data, Genotype, Gene, PsychotherapistTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Among | 70 |
| abstract_inverted_index.Japan | 59 |
| abstract_inverted_index.These | 23 |
| abstract_inverted_index.data, | 43 |
| abstract_inverted_index.depth | 83 |
| abstract_inverted_index.loci, | 197 |
| abstract_inverted_index.minor | 113 |
| abstract_inverted_index.mixed | 82 |
| abstract_inverted_index.panel | 36, 76, 188 |
| abstract_inverted_index.serum | 136 |
| abstract_inverted_index.sizes | 46 |
| abstract_inverted_index.these | 71, 105 |
| abstract_inverted_index.those | 199 |
| abstract_inverted_index.total | 140 |
| abstract_inverted_index.using | 37 |
| abstract_inverted_index.whole | 39 |
| abstract_inverted_index.(GWAS) | 8 |
| abstract_inverted_index.1KG+7K | 148, 165, 187 |
| abstract_inverted_index.TOPMed | 181 |
| abstract_inverted_index.allele | 114 |
| abstract_inverted_index.depths | 64 |
| abstract_inverted_index.genome | 40 |
| abstract_inverted_index.levels | 142 |
| abstract_inverted_index.output | 167 |
| abstract_inverted_index.panel. | 101, 149, 183 |
| abstract_inverted_index.panels | 24 |
| abstract_inverted_index.sample | 45, 174 |
| abstract_inverted_index.Biobank | 58 |
| abstract_inverted_index.Genomes | 33 |
| abstract_inverted_index.Project | 34 |
| abstract_inverted_index.complex | 130 |
| abstract_inverted_index.exhibit | 85 |
| abstract_inverted_index.improve | 1 |
| abstract_inverted_index.imputed | 177 |
| abstract_inverted_index.observe | 104 |
| abstract_inverted_index.panels, | 72 |
| abstract_inverted_index.panels. | 22 |
| abstract_inverted_index.quality | 3, 91, 125 |
| abstract_inverted_index.ranging | 47, 65 |
| abstract_inverted_index.reveals | 152 |
| abstract_inverted_index.samples | 80 |
| abstract_inverted_index.several | 153 |
| abstract_inverted_index.studies | 7 |
| abstract_inverted_index.through | 27, 56 |
| abstract_inverted_index.traits, | 131 |
| abstract_inverted_index.(1KG+7K) | 84 |
| abstract_inverted_index.(TOPMed) | 99 |
| abstract_inverted_index.Japanese | 12, 19, 38 |
| abstract_inverted_index.Medicine | 98 |
| abstract_inverted_index.Notably, | 102 |
| abstract_inverted_index.analyses | 128 |
| abstract_inverted_index.analysis | 151 |
| abstract_inverted_index.benefits | 121 |
| abstract_inverted_index.enrolled | 55 |
| abstract_inverted_index.greatest | 87 |
| abstract_inverted_index.improved | 123 |
| abstract_inverted_index.project, | 61 |
| abstract_inverted_index.reaching | 155 |
| abstract_inverted_index.relative | 92 |
| abstract_inverted_index.signals. | 204 |
| abstract_inverted_index.summary, | 185 |
| abstract_inverted_index.variants | 110 |
| abstract_inverted_index.Precision | 97 |
| abstract_inverted_index.augmented | 74 |
| abstract_inverted_index.conducted | 9, 133 |
| abstract_inverted_index.developed | 15 |
| abstract_inverted_index.discovery | 194 |
| abstract_inverted_index.evaluated | 17 |
| abstract_inverted_index.following | 143 |
| abstract_inverted_index.frequency | 115 |
| abstract_inverted_index.primarily | 107 |
| abstract_inverted_index.reference | 21, 75, 100, 182 |
| abstract_inverted_index.remaining | 169 |
| abstract_inverted_index.advantages | 191 |
| abstract_inverted_index.comprising | 77 |
| abstract_inverted_index.imputation | 2, 90, 124, 144, 166 |
| abstract_inverted_index.influenced | 200 |
| abstract_inverted_index.sequencing | 41, 63 |
| abstract_inverted_index.undetected | 170 |
| abstract_inverted_index.Trans-Omics | 95 |
| abstract_inverted_index.association | 6, 127, 203 |
| abstract_inverted_index.cholesterol | 141 |
| abstract_inverted_index.constructed | 26 |
| abstract_inverted_index.demonstrate | 119 |
| abstract_inverted_index.genome-wide | 5, 156 |
| abstract_inverted_index.improvement | 88 |
| abstract_inverted_index.individuals | 54 |
| abstract_inverted_index.population, | 13 |
| abstract_inverted_index.significant | 190 |
| abstract_inverted_index.augmentation | 29 |
| abstract_inverted_index.demonstrates | 189 |
| abstract_inverted_index.improvements | 106 |
| abstract_inverted_index.particularly | 198 |
| abstract_inverted_index.significance | 157 |
| abstract_inverted_index.low-frequency | 202 |
| abstract_inverted_index.10<sup>-8</sup>) | 162 |
| abstract_inverted_index.trait-associated | 196 |
| abstract_inverted_index.population-specific | 20 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5005705284 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 44 |
| corresponding_institution_ids | https://openalex.org/I150240954, https://openalex.org/I4210134357 |
| citation_normalized_percentile.value | 0.96389924 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |