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Chen T, Zhang H, Mazumder R, Lin X. SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.14.618256. [PMID: 39464044 PMCID: PMC11507800 DOI: 10.1101/2024.10.14.618256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/29/2024]
Abstract
Polygenic risk scores are widely used in disease risk stratification, but their accuracy varies across diverse populations. Recent methods large-scale leverage multi-ancestry data to improve accuracy in under-represented populations but require labelling individuals by ancestry for prediction. This poses challenges for practical use, as clinical practices are typically not based on ancestry. We propose SPLENDID, a novel penalized regression framework for diverse biobank-scale data. Our method utilizes ancestry principal component interactions to model genetic ancestry as a continuum within a single prediction model for all ancestries, eliminating the need for discrete labels. In extensive simulations and analyses of 9 traits from the All of Us Research Program (N=224,364) and UK Biobank (N=340,140), SPLENDID significantly outperformed existing methods in prediction accuracy and model sparsity. By directly incorporating continuous genetic ancestry in model training, SPLENDID stands as a valuable tool for robust risk prediction across diverse populations and fairer clinical implementation.
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Rodriguez-Algarra F, Evans DM, Rakyan VK. Ribosomal DNA copy number variation associates with hematological profiles and renal function in the UK Biobank. CELL GENOMICS 2024; 4:100562. [PMID: 38749448 PMCID: PMC11228893 DOI: 10.1016/j.xgen.2024.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/19/2023] [Accepted: 04/21/2024] [Indexed: 06/15/2024]
Abstract
The phenotypic impact of genetic variation of repetitive features in the human genome is currently understudied. One such feature is the multi-copy 47S ribosomal DNA (rDNA) that codes for rRNA components of the ribosome. Here, we present an analysis of rDNA copy number (CN) variation in the UK Biobank (UKB). From the first release of UKB whole-genome sequencing (WGS) data, a discovery analysis in White British individuals reveals that rDNA CN associates with altered counts of specific blood cell subtypes, such as neutrophils, and with the estimated glomerular filtration rate, a marker of kidney function. Similar trends are observed in other ancestries. A range of analyses argue against reverse causality or common confounder effects, and all core results replicate in the second UKB WGS release. Our work demonstrates that rDNA CN is a genetic influence on trait variance in humans.
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Affiliation(s)
| | - David M Evans
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia; Frazer Institute, The University of Queensland, Brisbane, QLD 4102, Australia; MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Vardhman K Rakyan
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.
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Hamed HM, Bostany EE, Motawie AA, Abd Al-Aziz AM, Mourad AA, Salama HM, Kamel S, Hassan EM, Helmy NA, El-Saeed GS, Elghoroury EA. The association of TMPRSS6 gene polymorphism with iron status in Egyptian children (a pilot study). BMC Pediatr 2024; 24:105. [PMID: 38341535 PMCID: PMC10858485 DOI: 10.1186/s12887-024-04573-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024] Open
Abstract
Several studies have shown association of single nucleotide polymorphisms (SNPs) of hepcidin regulatory pathways genes with impaired iron status. The most common is in the TMPRSS6 gene. In Africa, very few studies have been reported. We aimed to investigate the correlation between the common SNPs in the transmembrane protease, serine 6 (TMPRSS6) gene and iron indicators in a sample of Egyptian children for identifying the suitable candidate for iron supplementation.Patients and methods One hundred and sixty children aged 5-13 years were included & classified into iron deficient, iron deficient anemia and normal healthy controls. All were subjected to assessment of serum iron, serum ferritin, total iron binding capacity, complete blood count, reticulocyte count, serum soluble transferrin receptor and serum hepcidin. Molecular study of TMPRSS6 genotyping polymorphisms (rs4820268, rs855791 and rs11704654) were also evaluated.Results There was an association of iron deficiency with AG of rs855791 SNP, (P = 0.01). The minor allele frequency for included children were 0.43, 0.45 & 0.17 for rs4820268, rs855791 & rs11704654 respectively. Genotype GG of rs4820268 expressed the highest hepcidin gene expression fold, the lowest serum ferroportin & iron store compared to AA and AG genotypes (p = 0.05, p = 0.05, p = 0.03 respectively). GG of rs855791 had lower serum ferritin than AA (p = 0.04), lowest iron store & highest serum hepcidin compared to AA and AG genotypes (p = 0.04, p = 0.01 respectively). Children having CC of rs11704654 had lower level of hemoglobin, serum ferritin and serum hepcidin compared with CT genotype (p = 0.01, p = 0.01, p = 0.02) respectively.Conclusion Possible contribution of SNPs (rs855791, rs4820268 and rs11704654) to low iron status.
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Grants
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- 11010150 National Research Centre, Egypt
- National Research Centre Egypt
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Affiliation(s)
- Hanan M Hamed
- Pediatrics Department, National Research Centre, Dokki, Cairo, 12622, Egypt.
| | - Eman El Bostany
- Pediatrics Department, National Research Centre, Dokki, Cairo, 12622, Egypt
| | - Ayat A Motawie
- Pediatrics Department, National Research Centre, Dokki, Cairo, 12622, Egypt
| | | | - Abbass A Mourad
- Pediatrics Department, National Research Centre, Dokki, Cairo, 12622, Egypt
| | - Hassan M Salama
- Pediatrics Department, National Research Centre, Dokki, Cairo, 12622, Egypt
| | - Solaf Kamel
- Clinical and Chemical Pathology Department, National Research Centre, Dokki, Cairo, 12622, Egypt
| | - Eman M Hassan
- Clinical and Chemical Pathology Department, National Research Centre, Dokki, Cairo, 12622, Egypt
| | - Neveen A Helmy
- Clinical and Chemical Pathology Department, National Research Centre, Dokki, Cairo, 12622, Egypt
| | - Gamila S El-Saeed
- Medical Biochemistry Department, National Research Centre, Dokki, Cairo, 12622, Egypt
| | - Eman A Elghoroury
- Clinical and Chemical Pathology Department, National Research Centre, Dokki, Cairo, 12622, Egypt
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Sakashita T, Nakamura Y, Sutoh Y, Shimizu A, Hachiya T, Otsuka-Yamasaki Y, Takashima N, Kadota A, Miura K, Kita Y, Ikezaki H, Otonari J, Tanaka K, Shimanoe C, Koyama T, Watanabe I, Suzuki S, Nakagawa-Senda H, Hishida A, Tamura T, Kato Y, Okada R, Kuriki K, Katsuura-Kamano S, Watanabe T, Tanoue S, Koriyama C, Oze I, Koyanagi YN, Nakamura Y, Kusakabe M, Nakatochi M, Momozawa Y, Wakai K, Matsuo K. Comparison of the loci associated with HbA1c and blood glucose levels identified by a genome-wide association study in the Japanese population. Diabetol Int 2023; 14:188-198. [PMID: 37090135 PMCID: PMC10113415 DOI: 10.1007/s13340-023-00618-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/15/2023] [Indexed: 01/28/2023]
Abstract
Aims Hemoglobin A1c (HbA1c) levels are widely employed to diagnose diabetes. However, estimates of the heritability of HbA1c and glucose levels are different. Therefore, we explored HbA1c- and blood glucose-associated loci in a non-diabetic Japanese population. Methods We conducted a two-stage genome-wide association study (GWAS) on variants associated with HbA1c and blood glucose levels in a Japanese population. In the initial stage, data of 4911 participants of the Japan Multi-Institutional Collaborative Cohort (J-MICC) were subjected to discovery analysis. In the second stage, two datasets from the Tohoku Medical Megabank project, with 8175 and 40,519 participants, were used for the replication study. Association of the imputed variants with HbA1c and blood glucose levels was determined via linear regression analyses adjusted for age, sex, body mass index (BMI), smoking, and genetic principal components (PC1-PC10). Moreover, we performed a BMI-stratified GWAS on HbA1c levels in the J-MICC. The discovery analysis and BMI-stratified GWAS results were validated with re-analyses of normalized HbA1c levels adjusted for site in addition to the above, and blood glucose adjusted for fasting time as an additional covariate. Results Genetic variants associated with HbA1c levels were identified in KCNQ1 and TMC6. None of the genetic variants associated with blood glucose levels in the discovery analysis were replicated. Association of rs2299620 in KCNQ1 with HbA1c levels showed heterogeneity between individuals with BMI ≥ 25 kg/m2 and BMI < 25 kg/m2. Conclusions The variant rs2299620 in KCNQ1 might affect HbA1c levels differentially based on BMI grouping in the Japanese population. Supplementary Information The online version contains supplementary material available at 10.1007/s13340-023-00618-0.
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Affiliation(s)
- Takuya Sakashita
- Department of Public Health, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga 520-2192 Japan
- TAKARA BIO INC., 7-4-38 Nojihigashi, Kusatsu, Shiga 525-0058 Japan
| | - Yasuyuki Nakamura
- Department of Public Health, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga 520-2192 Japan
- Takeda Hospital Medical Examination Center, Kyoto, Japan
| | - Yoichi Sutoh
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba-cho, Shiwa-gun, Iwate, 028-3694 Japan
| | - Atsushi Shimizu
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba-cho, Shiwa-gun, Iwate, 028-3694 Japan
| | - Tsuyoshi Hachiya
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba-cho, Shiwa-gun, Iwate, 028-3694 Japan
| | - Yayoi Otsuka-Yamasaki
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba-cho, Shiwa-gun, Iwate, 028-3694 Japan
| | - Naoyuki Takashima
- Department of Public Health, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga 520-2192 Japan
- Department of Public Health, Faculty of Medicine, Kindai University, 377-2 Ohnohigashi, Osaka-Sayama, Osaka 589-8511 Japan
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo- ku, Kyoto, 602-8566 Japan
| | - Aya Kadota
- Department of Public Health, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga 520-2192 Japan
- NCD Epidemiology Research Center, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga 520-2192 Japan
| | - Katsuyuki Miura
- Department of Public Health, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga 520-2192 Japan
- NCD Epidemiology Research Center, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga 520-2192 Japan
| | - Yoshikuni Kita
- Department of Public Health, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga 520-2192 Japan
- Faculty of Nursing Science, Tsuruga Nursing University, 78-2-1 Kizaki, Tsuruga, Fukui 914-0814 Japan
| | - Hiroaki Ikezaki
- Department of Comprehensive General Internal Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582 Japan
- Department of General Internal Medicine, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582 Japan
| | - Jun Otonari
- Department of Psychosomatic Medicine, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582 Japan
| | - Keitaro Tanaka
- Department of Preventive Medicine, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501 Japan
| | - Chisato Shimanoe
- Department of Pharmacy, Saga University Hospital, 5-1-1 Nabeshima, Saga, 849-8501 Japan
| | - Teruhide Koyama
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo- ku, Kyoto, 602-8566 Japan
| | - Isao Watanabe
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo- ku, Kyoto, 602-8566 Japan
| | - Sadao Suzuki
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, 467-8601 Japan
| | - Hiroko Nakagawa-Senda
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, 467-8601 Japan
| | - Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550 Japan
| | - Takashi Tamura
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550 Japan
| | - Yasufumi Kato
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550 Japan
| | - Rieko Okada
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550 Japan
| | - Kiyonori Kuriki
- Laboratory of Public Health, Division of Nutritional Sciences, School of Food and Nutritional Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka, 422-8526 Japan
| | - Sakurako Katsuura-Kamano
- Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503 Japan
| | - Takeshi Watanabe
- Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503 Japan
| | - Shiroh Tanoue
- Department of Epidemiology and Preventive Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544 Japan
| | - Chihaya Koriyama
- Department of Epidemiology and Preventive Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544 Japan
| | - Isao Oze
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-ku, Nagoya, 464-8681 Japan
| | - Yuriko N. Koyanagi
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-ku, Nagoya, 464-8681 Japan
| | - Yohko Nakamura
- Cancer Prevention Center, Chiba Cancer Center Research Institute, 666-2 Nitona-cho, Chuo-ku, Chiba, 260-8717 Japan
| | - Miho Kusakabe
- Cancer Prevention Center, Chiba Cancer Center Research Institute, 666-2 Nitona-cho, Chuo-ku, Chiba, 260-8717 Japan
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-ku, Nagoya, 461-8673 Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550 Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-ku, Nagoya, 464-8681 Japan
- Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550 Japan
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Gray OA, Yoo J, Sobreira DR, Jousma J, Witonsky D, Sakabe NJ, Peng YJ, Prabhakar NR, Fang Y, Nobréga MA, Di Rienzo A. A pleiotropic hypoxia-sensitive EPAS1 enhancer is disrupted by adaptive alleles in Tibetans. SCIENCE ADVANCES 2022; 8:eade1942. [PMID: 36417539 PMCID: PMC9683707 DOI: 10.1126/sciadv.ade1942] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
In Tibetans, noncoding alleles in EPAS1-whose protein product hypoxia-inducible factor 2α (HIF-2α) drives the response to hypoxia-carry strong signatures of positive selection; however, their functional mechanism has not been systematically examined. Here, we report that high-altitude alleles disrupt the activity of four EPAS1 enhancers in one or more cell types. We further characterize one enhancer (ENH5) whose activity is both allele specific and hypoxia dependent. Deletion of ENH5 results in down-regulation of EPAS1 and HIF-2α targets in acute hypoxia and in a blunting of the transcriptional response to sustained hypoxia. Deletion of ENH5 in mice results in dysregulation of gene expression across multiple tissues. We propose that pleiotropic adaptive effects of the Tibetan alleles in EPAS1 underlie the strong selective signal at this gene.
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Affiliation(s)
- Olivia A. Gray
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - Jennifer Yoo
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
- Institute for Integrative Physiology and Center for Systems Biology of O2 Sensing, The University of Chicago, Chicago, IL 60637, USA
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Débora R. Sobreira
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - Jordan Jousma
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - David Witonsky
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - Noboru J. Sakabe
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - Ying-Jie Peng
- Institute for Integrative Physiology and Center for Systems Biology of O2 Sensing, The University of Chicago, Chicago, IL 60637, USA
| | - Nanduri R. Prabhakar
- Institute for Integrative Physiology and Center for Systems Biology of O2 Sensing, The University of Chicago, Chicago, IL 60637, USA
| | - Yun Fang
- Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Marcelo A. Nobréga
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - Anna Di Rienzo
- Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA
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Yang W, He X, Yao Y, Lu H, Wang Y, Zhang Z, Wang Y, Wang L, He Y, Yuan D, Jin T. Genome-Wide Association Study on the Hematological Phenotypic Characteristics of the Han Population from Northwest China. Pharmgenomics Pers Med 2022; 15:743-763. [PMID: 35945964 PMCID: PMC9357418 DOI: 10.2147/pgpm.s361809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 06/14/2022] [Indexed: 11/23/2022] Open
Abstract
Background Hematological characteristics have positive reference value as clinical indicators in the evaluation of various diseases. The purpose of this study was to determine the gene loci associated with 20 hematological phenotypes in the Han population from northwest China. Methods A genome-wide association study (GWAS) was conducted on hematological indicators of 1005 Han people from northwest China. Genotyping was performed with a GeneTitan multichannel instrument and Axiom Analysis Suite 6.0. Using the 1000 Genomes Project (phase 3) as a reference, haplotype imputation was performed with IMPUTE2. SNVs (single nucleotide variants) significantly associated with hematological phenotypes were identified. The top SNV (p < 5E-7) was then selected for replication detection. Results Ninety genetic variations identified in the GWAS were significantly associated with hematological indicators. Among them, only rs35289401 (CCDC157) was significantly associated (genome-wide) with red blood cell distribution width (RDW) (p = 4.21E-08). The fourteen top SNVs were selected for replication verification and were significantly associated with hematological phenotypes. However, only HBS1 L-MYB rs1331309 was significantly associated with the mean hemoglobin content (p = 6.42E-07). We also found that the mean corpuscular hemoglobin (MCH) level in the rs1331309 GG/GT genotype was significantly higher than that in the TT genotype (p = 0.023). Conclusion The GWAS identified a total of 90 genetic variants significantly associated with hematological phenotypic indicators. In particular, rs1331309 (HBS1 L-MYB) is expected to be a biomarker for monitoring the dynamics of MCH levels. This study provides a reference for related studies on the genetic structure of hematological characteristics. It provides a valuable reference for the clinical diagnosis or prediction of a variety of diseases.
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Affiliation(s)
- Wei Yang
- Key Laboratory of Molecular Mechanism and Intervention Research for Plateau Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- Department of Emergency, The Affiliated Hospital of Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Xue He
- Key Laboratory of Molecular Mechanism and Intervention Research for Plateau Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Yuying Yao
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Hongyan Lu
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Yuliang Wang
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Zhanhao Zhang
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Yuhe Wang
- Key Laboratory of Molecular Mechanism and Intervention Research for Plateau Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- Department of Clinical Laboratory, The Affiliated Hospital of Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Li Wang
- Key Laboratory of Molecular Mechanism and Intervention Research for Plateau Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Yongjun He
- Key Laboratory of Molecular Mechanism and Intervention Research for Plateau Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Dongya Yuan
- Key Laboratory of Molecular Mechanism and Intervention Research for Plateau Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
| | - Tianbo Jin
- Key Laboratory of Molecular Mechanism and Intervention Research for Plateau Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
- Correspondence: Tianbo Jin, Key Laboratory of Molecular Mechanism and Intervention Research for Plateau Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, Shaanxi, 712082, People’s Republic of China, Tel/Fax +86-29-88895902, Email
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Cheng W, Ramachandran S, Crawford L. Uncertainty quantification in variable selection for genetic fine-mapping using bayesian neural networks. iScience 2022; 25:104553. [PMID: 35769876 PMCID: PMC9234235 DOI: 10.1016/j.isci.2022.104553] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/09/2022] [Accepted: 06/01/2022] [Indexed: 02/07/2023] Open
Abstract
In this paper, we propose a new approach for variable selection using a collection of Bayesian neural networks with a focus on quantifying uncertainty over which variables are selected. Motivated by fine-mapping applications in statistical genetics, we refer to our framework as an "ensemble of single-effect neural networks" (ESNN) which generalizes the "sum of single effects" regression framework by both accounting for nonlinear structure in genotypic data (e.g., dominance effects) and having the capability to model discrete phenotypes (e.g., case-control studies). Through extensive simulations, we demonstrate our method's ability to produce calibrated posterior summaries such as credible sets and posterior inclusion probabilities, particularly for traits with genetic architectures that have significant proportions of non-additive variation driven by correlated variants. Lastly, we use real data to demonstrate that the ESNN framework improves upon the state of the art for identifying true effect variables underlying various complex traits.
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Affiliation(s)
- Wei Cheng
- Department of Computer Science, Brown University, Providence, RI, USA
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
| | - Sohini Ramachandran
- Department of Computer Science, Brown University, Providence, RI, USA
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
- Microsoft Research New England, Cambridge, MA, USA
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8
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Identification of ATP2B4 Regulatory Element Containing Functional Genetic Variants Associated with Severe Malaria. Int J Mol Sci 2022; 23:ijms23094849. [PMID: 35563239 PMCID: PMC9101746 DOI: 10.3390/ijms23094849] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/15/2022] [Accepted: 04/22/2022] [Indexed: 12/04/2022] Open
Abstract
Genome-wide association studies for severe malaria (SM) have identified 30 genetic variants mostly located in non-coding regions. Here, we aimed to identify potential causal genetic variants located in these loci and demonstrate their functional activity. We systematically investigated the regulatory effect of the SNPs in linkage disequilibrium (LD) with the malaria-associated genetic variants. Annotating and prioritizing genetic variants led to the identification of a regulatory region containing five ATP2B4 SNPs in LD with rs10900585. We found significant associations between SM and rs10900585 and our candidate SNPs (rs11240734, rs1541252, rs1541253, rs1541254, and rs1541255) in a Senegalese population. Then, we demonstrated that both individual SNPs and the combination of SNPs had regulatory effects. Moreover, CRISPR/Cas9-mediated deletion of this region decreased ATP2B4 transcript and protein levels and increased Ca2+ intracellular concentration in the K562 cell line. Our data demonstrate that severe malaria-associated genetic variants alter the expression of ATP2B4 encoding a plasma membrane calcium-transporting ATPase 4 (PMCA4) expressed on red blood cells. Altering the activity of this regulatory element affects the risk of SM, likely through calcium concentration effect on parasitaemia.
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9
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Little A, Hu Y, Sun Q, Jain D, Broome J, Chen MH, Thibord F, McHugh C, Surendran P, Blackwell TW, Brody JA, Bhan A, Chami N, de Vries PS, Ekunwe L, Heard-Costa N, Hobbs BD, Manichaikul A, Moon JY, Preuss MH, Ryan K, Wang Z, Wheeler M, Yanek LR, Abecasis GR, Almasy L, Beaty TH, Becker LC, Blangero J, Boerwinkle E, Butterworth AS, Choquet H, Correa A, Curran JE, Faraday N, Fornage M, Glahn DC, Hou L, Jorgenson E, Kooperberg C, Lewis JP, Lloyd-Jones DM, Loos RJF, Min YI, Mitchell BD, Morrison AC, Nickerson DA, North KE, O'Connell JR, Pankratz N, Psaty BM, Vasan RS, Rich SS, Rotter JI, Smith AV, Smith NL, Tang H, Tracy RP, Conomos MP, Laurie CA, Mathias RA, Li Y, Auer PL, Thornton T, Reiner AP, Johnson AD, Raffield LM. Whole genome sequence analysis of platelet traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) initiative. Hum Mol Genet 2022; 31:347-361. [PMID: 34553764 PMCID: PMC8825339 DOI: 10.1093/hmg/ddab252] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 12/15/2022] Open
Abstract
Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI's Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.
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Affiliation(s)
- Amarise Little
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Yao Hu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Jai Broome
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Ming-Huei Chen
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
| | - Florian Thibord
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
| | - Caitlin McHugh
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB1 8RN, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Thomas W Blackwell
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | | | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Paul S de Vries
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Lynette Ekunwe
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Nancy Heard-Costa
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
| | - Brian D Hobbs
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Ani Manichaikul
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Kathleen Ryan
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Marsha Wheeler
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Lisa R Yanek
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Goncalo R Abecasis
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Lewis C Becker
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB1 8RN, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge CB1 8RN, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge CB1 8RN, UK
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Myriam Fornage
- University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Charles Kooperberg
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Joshua P Lewis
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Yuan-I Min
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Jeffrey R O'Connell
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle WA 98101, USA
| | - Ramachandran S Vasan
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
- Departments of Cardiology and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - Stephen S Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Albert V Smith
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
| | - Nicholas L Smith
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle WA 98101, USA
- Department of Veterans Affairs Office of Research and Development, Seattle Epidemiologic Research and Information Center, Seattle, WA 98108, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Russell P Tracy
- Department of Pathology and Laboratory Medicine and Biochemistry, University of Vermont Larner College of Medicine, Colchester, VT 05446, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Cecelia A Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Rasika A Mathias
- Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Yun Li
- Departments of Biostatistics, Genetics, Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Paul L Auer
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
| | | | - Timothy Thornton
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Andrew D Johnson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
- National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
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10
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Ngwa JS, Yanek LR, Kammers K, Kanchan K, Taub MA, Scharpf RB, Faraday N, Becker LC, Mathias RA, Ruczinski I. Secondary analyses for genome-wide association studies using expression quantitative trait loci. Genet Epidemiol 2022; 46:170-181. [PMID: 35312098 PMCID: PMC9086181 DOI: 10.1002/gepi.22448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 11/19/2021] [Accepted: 01/20/2022] [Indexed: 01/01/2023]
Abstract
Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits; however, the identified SNPs account for a fraction of trait heritability, and identifying the functional elements through which genetic variants exert their effects remains a challenge. Recent evidence suggests that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTL). Thus, incorporating eQTL information can potentially improve power to detect causal variants missed by traditional GWAS approaches. Using genomic, transcriptomic, and platelet phenotype data from the Genetic Study of Atherosclerosis Risk family-based study, we investigated the potential to detect novel genomic risk loci by incorporating information from eQTL in the relevant target tissues (i.e., platelets and megakaryocytes) using established statistical principles in a novel way. Permutation analyses were performed to obtain family-wise error rates for eQTL associations, substantially lowering the genome-wide significance threshold for SNP-phenotype associations. In addition to confirming the well known association between PEAR1 and platelet aggregation, our eQTL-focused approach identified a novel locus (rs1354034) and gene (ARHGEF3) not previously identified in a GWAS of platelet aggregation phenotypes. A colocalization analysis showed strong evidence for a functional role of this eQTL.
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Affiliation(s)
- Julius S. Ngwa
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Lisa R. Yanek
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Kai Kammers
- Department of OncologyJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Kanika Kanchan
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Margaret A. Taub
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Robert B. Scharpf
- Department of OncologyJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Lewis C. Becker
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Rasika A. Mathias
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Ingo Ruczinski
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
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11
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Soremekun O, Soremekun C, Machipisa T, Soliman M, Nashiru O, Chikowore T, Fatumo S. Genome-Wide Association and Mendelian Randomization Analysis Reveal the Causal Relationship Between White Blood Cell Subtypes and Asthma in Africans. Front Genet 2021; 12:749415. [PMID: 34925446 PMCID: PMC8674726 DOI: 10.3389/fgene.2021.749415] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/01/2021] [Indexed: 12/03/2022] Open
Abstract
Background: White blood cell (WBC) traits and their subtypes such as basophil count (Bas), eosinophil count (Eos), lymphocyte count (Lym), monocyte count (Mon), and neutrophil counts (Neu) are known to be associated with diseases such as stroke, peripheral arterial disease, and coronary heart disease. Methods: We meta-analyze summary statistics from genome-wide association studies in 17,802 participants from the African Partnership for Chronic Disease Research (APCDR) and African ancestry individuals from the Blood Cell Consortium (BCX2) using GWAMA. We further carried out a Bayesian fine mapping to identify causal variants driving the association with WBC subtypes. To access the causal relationship between WBC subtypes and asthma, we conducted a two-sample Mendelian randomization (MR) analysis using summary statistics of the Consortium on Asthma among African Ancestry Populations (CAAPA: n cases = 7,009, n control = 7,645) as our outcome phenotype. Results: Our metanalysis identified 269 loci at a genome-wide significant value of (p = 5 × 10-9) in a composite of the WBC subtypes while the Bayesian fine-mapping analysis identified genetic variants that are more causal than the sentinel single-nucleotide polymorphism (SNP). We found for the first time five novel genes (LOC126987/MTCO3P14, LINC01525, GAPDHP32/HSD3BP3, FLG-AS1/HMGN3P1, and TRK-CTT13-1/MGST3) not previously reported to be associated with any WBC subtype. Our MR analysis showed that Mon (IVW estimate = 0.38, CI: 0.221, 0.539, p < 0.001), Neu (IVW estimate = 0.189, CI: 0.133, 0.245, p < 0.001), and WBCc (IVW estimate = 0.185, CI: 0.108, 0.262, p < 0.001) are associated with increased risk of asthma. However, there was no evidence of causal relationship between Lym and asthma risk. Conclusion: This study provides insight into the relationship between some WBC subtypes and asthma and potential route in the treatment of asthma and may further inform a new therapeutic approach.
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Affiliation(s)
- Opeyemi Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Chisom Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Tafadzwa Machipisa
- Department of Medicine, University of Cape Town, Groote Schuur Hospital, Cape Town, South Africa
- The Department of Pathology and Molecular Medicine, Population Health Research Institute (PHRI), Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Mahmoud Soliman
- Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa
| | - Oyekanmi Nashiru
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Tinashe Chikowore
- Faculty of Health Sciences, Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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12
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Mikaelsdottir E, Thorleifsson G, Stefansdottir L, Halldorsson G, Sigurdsson JK, Lund SH, Tragante V, Melsted P, Rognvaldsson S, Norland K, Helgadottir A, Magnusson MK, Ragnarsson GB, Kristinsson SY, Reykdal S, Vidarsson B, Gudmundsdottir IJ, Olafsson I, Onundarson PT, Sigurdardottir O, Sigurdsson EL, Grondal G, Geirsson AJ, Geirsson G, Gudmundsson J, Holm H, Saevarsdottir S, Jonsdottir I, Thorgeirsson G, Gudbjartsson DF, Thorsteinsdottir U, Rafnar T, Stefansson K. Genetic variants associated with platelet count are predictive of human disease and physiological markers. Commun Biol 2021; 4:1132. [PMID: 34580418 PMCID: PMC8476563 DOI: 10.1038/s42003-021-02642-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 09/07/2021] [Indexed: 12/13/2022] Open
Abstract
Platelets play an important role in hemostasis and other aspects of vascular biology. We conducted a meta-analysis of platelet count GWAS using data on 536,974 Europeans and identified 577 independent associations. To search for mechanisms through which these variants affect platelets, we applied cis-expression quantitative trait locus, DEPICT and IPA analyses and assessed genetic sharing between platelet count and various traits using polygenic risk scoring. We found genetic sharing between platelet count and counts of other blood cells (except red blood cells), in addition to several other quantitative traits, including markers of cardiovascular, liver and kidney functions, height, and weight. Platelet count polygenic risk score was predictive of myeloproliferative neoplasms, rheumatoid arthritis, ankylosing spondylitis, hypertension, and benign prostate hyperplasia. Taken together, these results advance understanding of diverse aspects of platelet biology and how they affect biological processes in health and disease. Evgenia Mikaelsdottir et al. report a study of variants associated with platelet count among European individuals where they identify 577 associations. They also report a genetic overlap between platelet count and human diseases, including myeloproliferative neoplasms, rheumatoid arthritis, and hypertension, as well as a genetic overlap between platelet count and various physiological markers.
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Affiliation(s)
| | | | | | | | | | - Sigrun H Lund
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland
| | | | - Pall Melsted
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland.,School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Magnus K Magnusson
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Gunnar B Ragnarsson
- Department of Oncology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | - Sigurdur Y Kristinsson
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland.,Department of Hematology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | - Sigrun Reykdal
- Department of Hematology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | - Brynjar Vidarsson
- Department of Hematology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | | | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | - Pall T Onundarson
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland.,Laboratory Hematology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | - Olof Sigurdardottir
- Department of Clinical Biochemistry, Akureyri Hospital, 600, Akureyri, Iceland
| | | | - Gerdur Grondal
- Department of Rheumatology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | - Arni J Geirsson
- Department of Rheumatology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | - Gudmundur Geirsson
- Department of Urology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | | | - Hilma Holm
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland
| | - Saedis Saevarsdottir
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland.,Department of Rheumatology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | - Ingileif Jonsdottir
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Gudmundur Thorgeirsson
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland.,Department of Cardiology, Landspitali-University Hospital, 101, Reykjavik, Iceland
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland.,School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Thorunn Rafnar
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE Genetics/Amgen, Sturlugata 8, 101, Reykjavik, Iceland. .,Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland.
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13
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Ebel ER, Kuypers FA, Lin C, Petrov DA, Egan ES. Common host variation drives malaria parasite fitness in healthy human red cells. eLife 2021; 10:e69808. [PMID: 34553687 PMCID: PMC8497061 DOI: 10.7554/elife.69808] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/22/2021] [Indexed: 12/11/2022] Open
Abstract
The replication of Plasmodium falciparum parasites within red blood cells (RBCs) causes severe disease in humans, especially in Africa. Deleterious alleles like hemoglobin S are well-known to confer strong resistance to malaria, but the effects of common RBC variation are largely undetermined. Here, we collected fresh blood samples from 121 healthy donors, most with African ancestry, and performed exome sequencing, detailed RBC phenotyping, and parasite fitness assays. Over one-third of healthy donors unknowingly carried alleles for G6PD deficiency or hemoglobinopathies, which were associated with characteristic RBC phenotypes. Among non-carriers alone, variation in RBC hydration, membrane deformability, and volume was strongly associated with P. falciparum growth rate. Common genetic variants in PIEZO1, SPTA1/SPTB, and several P. falciparum invasion receptors were also associated with parasite growth rate. Interestingly, we observed little or negative evidence for divergent selection on non-pathogenic RBC variation between Africans and Europeans. These findings suggest a model in which globally widespread variation in a moderate number of genes and phenotypes modulates P. falciparum fitness in RBCs.
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Affiliation(s)
- Emily R Ebel
- Department of Biology, Stanford UniversityStanfordUnited States
- Department of Pediatrics, Stanford University School of MedicineStanfordUnited States
| | - Frans A Kuypers
- Children's Hospital Oakland Research InstituteOaklandUnited States
| | - Carrie Lin
- Department of Pediatrics, Stanford University School of MedicineStanfordUnited States
| | - Dmitri A Petrov
- Department of Biology, Stanford UniversityStanfordUnited States
| | - Elizabeth S Egan
- Department of Pediatrics, Stanford University School of MedicineStanfordUnited States
- Department of Microbiology & Immunology, Stanford University School of MedicineStanfordUnited States
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14
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Thompson A, King K, Morris AP, Pirmohamed M. Assessing the impact of alcohol consumption on the genetic contribution to mean corpuscular volume. Hum Mol Genet 2021; 30:2040-2051. [PMID: 34104963 PMCID: PMC8522631 DOI: 10.1093/hmg/ddab147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/08/2021] [Accepted: 05/25/2021] [Indexed: 11/13/2022] Open
Abstract
The relationship between the genetic loci that influence mean corpuscular volume (MCV) and those associated with excess alcohol drinking are unknown. We used white British participants from the UK Biobank (n = 362 595) to assess the association between alcohol consumption and MCV, and whether this was modulated by genetic factors. Multivariable regression was applied to identify predictors of MCV. GWAS, with and without stratification for alcohol consumption, determined how genetic variants influence MCV. SNPs in ADH1B, ADH1C and ALDH1B were used to construct a genetic score to test the assumption that acetaldehyde formation is an important determinant of MCV. Additional investigations using mendelian randomisation and phenome-wide association analysis were conducted. Increasing alcohol consumption by 40 g/week resulted in a 0.30% (95% CI: 0.30 to 0.31%) increase in MCV (P < 1.0x10-320). Unstratified (irrespective of alcohol intake) GWAS identified 212 loci associated with MCV, of which 108 were novel. There was no heterogeneity of allelic effects by drinking status. No association was found between MCV and the genetic score generated from alcohol metabolising genes. Mendelian randomisation demonstrated a causal effect for alcohol on MCV. Seventy-one SNP-outcome pairs reached statistical significance in phenome-wide association analysis, with evidence of shared genetic architecture for MCV and thyroid dysfunction, and mineral metabolism disorders. MCV increases linearly with alcohol intake in a causal manner. Many genetic loci influence MCV, with new loci identified in this analysis that provide novel biological insights. However, there was no interaction between alcohol consumption and the allelic variants associated with MCV.
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Affiliation(s)
- Andrew Thompson
- Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,MRC Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Liverpool Centre for Alcohol Research, University of Liverpool, Liverpool, UK
| | - Katharine King
- Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,MRC Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK.,Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Munir Pirmohamed
- Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,MRC Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Liverpool Centre for Alcohol Research, University of Liverpool, Liverpool, UK.,Liverpool University Hospital, Liverpool, UK.,Liverpool Health Partners, Liverpool, UK
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15
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Jallow MW, Campino S, Prentice AM, Cerami C. Association of common TMPRSS6 and TF gene variants with hepcidin and iron status in healthy rural Gambians. Sci Rep 2021; 11:8075. [PMID: 33850216 PMCID: PMC8044158 DOI: 10.1038/s41598-021-87565-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 03/25/2021] [Indexed: 11/08/2022] Open
Abstract
Genome-wide association studies in Europeans and Asians have identified numerous variants in the transmembrane protease serine 6 (TMPRSS6) and transferrin (TF) genes that are associated with changes in iron status. We sought to investigate the effects of common TMPRSS6 and TF gene SNPs on iron status indicators in a cohort of healthy Africans from rural Gambia. We measured iron biomarkers and haematology traits on individuals participating in the Keneba Biobank with genotype data on TMPRSS6 (rs2235321, rs855791, rs4820268, rs2235324, rs2413450 and rs5756506) and TF (rs3811647 and rs1799852), n = 1316. After controlling for inflammation, age and sex, we analysed the effects of carrying either single or multiple iron-lowering alleles on iron status. TMPRSS6 rs2235321 significantly affected plasma hepcidin concentrations (AA genotypes having lower hepcidin levels; F ratio 3.7, P = 0.014) with greater impact in individuals with low haemoglobin or ferritin. No other TMPRSS6 variant affected hepcidin. None of the TMPRSS6 variants nor a TMPRSS6 allele risk score affected other iron biomarkers or haematological traits. TF rs3811647 AA carriers had 21% higher transferrin (F ratio 16.0, P < 0.0001), 24% higher unsaturated iron-binding capacity (F ratio 12.8, P < 0.0001) and 25% lower transferrin saturation (F ratio 4.3, P < 0.0001) compared to GG carriers. TF rs3811647 was strongly associated with transferrin, unsaturated iron-binding capacity (UIBC) and transferrin saturation (TSAT) with a single allele effect of 8-12%. There was no association between either TF SNP and any haematological traits or iron biomarkers. We identified meaningful associations between TMPRSS6 rs2235321 and hepcidin and replicated the previous findings on the effects of TF rs3811647 on transferrin and iron binding capacity. However, the effects are subtle and contribute little to population variance. Further genetic and functional studies, including polymorphisms frequent in Africa populations, are needed to identify markers for genetically stratified approaches to prevention or treatment of iron deficiency anaemia.
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Affiliation(s)
- Momodou W Jallow
- Nutrition Theme, MRC Unit, The Gambia at London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, P.O. Box 273, Banjul, The Gambia
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Susana Campino
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Andrew M Prentice
- Nutrition Theme, MRC Unit, The Gambia at London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, P.O. Box 273, Banjul, The Gambia
| | - Carla Cerami
- Nutrition Theme, MRC Unit, The Gambia at London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, P.O. Box 273, Banjul, The Gambia.
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16
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RCL1 copy number variants are associated with a range of neuropsychiatric phenotypes. Mol Psychiatry 2021; 26:1706-1718. [PMID: 33597717 PMCID: PMC8159744 DOI: 10.1038/s41380-021-01035-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 12/29/2020] [Accepted: 01/15/2021] [Indexed: 12/18/2022]
Abstract
Mendelian and early-onset severe psychiatric phenotypes often involve genetic variants having a large effect, offering opportunities for genetic discoveries and early therapeutic interventions. Here, the index case is an 18-year-old boy, who at 14 years of age had a decline in cognitive functioning over the course of a year and subsequently presented with catatonia, auditory and visual hallucinations, paranoia, aggression, mood dysregulation, and disorganized thoughts. Exome sequencing revealed a stop-gain mutation in RCL1 (NM_005772.4:c.370 C > T, p.Gln124Ter), encoding an RNA 3'-terminal phosphate cyclase-like protein that is highly conserved across eukaryotic species. Subsequent investigations across two academic medical centers identified eleven additional cases of RCL1 copy number variations (CNVs) with varying neurodevelopmental or psychiatric phenotypes. These findings suggest that dosage variation of RCL1 contributes to a range of neurological and clinical phenotypes.
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17
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Bhatlekar S, Manne BK, Basak I, Edelstein LC, Tugolukova E, Stoller ML, Cody MJ, Morley SC, Nagalla S, Weyrich AS, Rowley JW, O'Connell RM, Rondina MT, Campbell RA, Bray PF. miR-125a-5p regulates megakaryocyte proplatelet formation via the actin-bundling protein L-plastin. Blood 2020; 136:1760-1772. [PMID: 32844999 PMCID: PMC7544541 DOI: 10.1182/blood.2020005230] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 05/24/2020] [Indexed: 12/17/2022] Open
Abstract
There is heritability to interindividual variation in platelet count, and better understanding of the regulating genetic factors may provide insights for thrombopoiesis. MicroRNAs (miRs) regulate gene expression in health and disease, and megakaryocytes (MKs) deficient in miRs have lower platelet counts, but information about the role of miRs in normal human MK and platelet production is limited. Using genome-wide miR profiling, we observed strong correlations among human bone marrow MKs, platelets, and differentiating cord blood-derived MK cultures, and identified MK miR-125a-5p as associated with human platelet number but not leukocyte or hemoglobin levels. Overexpression and knockdown studies showed that miR-125a-5p positively regulated human MK proplatelet (PP) formation in vitro. Inhibition of miR-125a-5p in vivo lowered murine platelet counts. Analyses of MK and platelet transcriptomes identified LCP1 as a miR-125a-5p target. LCP1 encodes the actin-bundling protein, L-plastin, not previously studied in MKs. We show that miR-125a-5p directly targets and reduces expression of MK L-plastin. Overexpression and knockdown studies show that L-plastin promotes MK progenitor migration, but negatively correlates with human platelet count and inhibits MK PP formation (PPF). This work provides the first evidence for the actin-bundling protein, L-plastin, as a regulator of human MK PPF via inhibition of the late-stage MK invagination system, podosome and PPF, and PP branching. We also provide resources of primary and differentiating MK transcriptomes and miRs associated with platelet counts. miR-125a-5p and L-plastin may be relevant targets for increasing in vitro platelet manufacturing and for managing quantitative platelet disorders.
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Affiliation(s)
- Seema Bhatlekar
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
| | - Bhanu K Manne
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
| | - Indranil Basak
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
| | - Leonard C Edelstein
- Cardeza Foundation for Hematologic Research, Thomas Jefferson University, Philadelphia, PA
| | - Emilia Tugolukova
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
| | | | - Mark J Cody
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
| | - Sharon C Morley
- Division of Infectious Diseases, Department of Pediatrics and
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO
| | - Srikanth Nagalla
- Division of Hematology and Oncology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
| | - Andrew S Weyrich
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
- Division of Pulmonary, Department of Internal Medicine
| | - Jesse W Rowley
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
- Division of Pulmonary, Department of Internal Medicine
| | - Ryan M O'Connell
- Division of Microbiology and Immunology, Department of Pathology, and
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
| | - Matthew T Rondina
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
- Geriatric Research, Education and Clinical Center, George E. Wahlen VAMC GRECC, Salt Lake City, UT; and
- Division of General Internal Medicine and
| | - Robert A Campbell
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
- Division of General Internal Medicine and
| | - Paul F Bray
- Program in Molecular Medicine, University of Utah, Salt Lake City, UT
- Division of Hematology and Hematologic Malignancies, Department of Internal Medicine, University of Utah, Salt Lake City, UT
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18
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Kessler T, Schunkert H, von Hundelshausen P. Novel Approaches to Fine-Tune Therapeutic Targeting of Platelets in Atherosclerosis: A Critical Appraisal. Thromb Haemost 2020; 120:1492-1504. [PMID: 32772352 DOI: 10.1055/s-0040-1714352] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The pathogenesis of atherosclerotic vascular disease is driven by a multitude of risk factors intertwining metabolic and inflammatory pathways. Increasing knowledge about platelet biology sheds light on how platelets take part in these processes from early to later stages of plaque development. Recent insights from experimental studies and mouse models substantiate platelets as initiators and amplifiers in atherogenic leukocyte recruitment. These studies are complemented by results from genetics studies shedding light on novel molecular mechanisms which provide an interesting prospect as novel targets. For instance, experimental studies provide further details how platelet-decorated von Willebrand factor tethered to activated endothelial cells plays a role in atherogenic monocyte recruitment. Novel aspects of platelets as atherogenic inductors of neutrophil extracellular traps and particularities in signaling pathways such as cyclic guanosine monophosphate and the inhibitory adaptor molecule SHB23/LNK associating platelets with atherogenesis are shared. In summary, it was our intention to balance insights from recent experimental data that support a plausible role for platelets in atherogenesis against a paucity of clinical evidence needed to validate this concept in humans.
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Affiliation(s)
- Thorsten Kessler
- Deutsches Herzzentrum München, Klinik für Herz- und Kreislauferkrankungen, Technische Universität München, Munich, Germany.,Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK) e.V., Partner Site Munich Heart Alliance, Munich, Germany
| | - Heribert Schunkert
- Deutsches Herzzentrum München, Klinik für Herz- und Kreislauferkrankungen, Technische Universität München, Munich, Germany.,Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK) e.V., Partner Site Munich Heart Alliance, Munich, Germany
| | - Philipp von Hundelshausen
- Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK) e.V., Partner Site Munich Heart Alliance, Munich, Germany.,Institut für Prophylaxe und Epidemiologie der Kreislaufkrankheiten, Klinikum der Universität, Ludwig-Maximilians-Universität, Partner Site Munich Heart Alliance, Munich, Germany
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19
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Lee J, Godfrey AL, Nangalia J. Genomic heterogeneity in myeloproliferative neoplasms and applications to clinical practice. Blood Rev 2020; 42:100708. [PMID: 32571583 DOI: 10.1016/j.blre.2020.100708] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 03/22/2020] [Accepted: 04/18/2020] [Indexed: 12/14/2022]
Abstract
The myeloproliferative neoplasms (MPN) polycythaemia vera, essential thrombocythaemia and primary myelofibrosis are chronic myeloid disorders associated most often with mutations in JAK2, MPL and CALR, and in some patients with additional acquired genomic lesions. Whilst the molecular mechanisms downstream of these mutations are now clearer, it is apparent that clinical phenotype in MPN is a product of complex interactions, acting between individual mutations, between disease subclones, and between the tumour and background host factors. In this review we first discuss MPN phenotypic driver mutations and the factors that interact with them to influence phenotype. We consider the importance of ongoing studies of clonal haematopoiesis, which may inform a better understanding of why MPN develop in specific individuals. We then consider how best to deploy genomic testing in a clinical environment and the challenges as well as opportunities that may arise from more routine, comprehensive genomic analysis of patients with MPN.
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Affiliation(s)
- Joe Lee
- Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK; Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Puddicombe Way, Cambridge, UK; Department of Haematology, University of Cambridge, Cambridge, UK
| | - Anna L Godfrey
- Haematopathology and Oncology Diagnostics Service/ Department of Haematology, Cambridge University Hospitals NHS Foundation Trust, Hills Rd, Cambridge CB2 0QQ, UK
| | - Jyoti Nangalia
- Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK; Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Puddicombe Way, Cambridge, UK; Department of Haematology, University of Cambridge, Cambridge, UK; Haematopathology and Oncology Diagnostics Service/ Department of Haematology, Cambridge University Hospitals NHS Foundation Trust, Hills Rd, Cambridge CB2 0QQ, UK.
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20
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Cheng W, Ramachandran S, Crawford L. Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits. PLoS Genet 2020; 16:e1008855. [PMID: 32542026 PMCID: PMC7316356 DOI: 10.1371/journal.pgen.1008855] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 06/25/2020] [Accepted: 05/13/2020] [Indexed: 12/22/2022] Open
Abstract
Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-ε efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways.
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Affiliation(s)
- Wei Cheng
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
| | - Sohini Ramachandran
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
- * E-mail: (SR); (LC)
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
- Department of Biostatistics, Brown University, Providence, Rhode Island, United States of America
- Center for Statistical Sciences, Brown University, Providence, Rhode Island, United States of America
- * E-mail: (SR); (LC)
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21
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Hodonsky CJ, Baldassari AR, Bien SA, Raffield LM, Highland HM, Sitlani CM, Wojcik GL, Tao R, Graff M, Tang W, Thyagarajan B, Buyske S, Fornage M, Hindorff LA, Li Y, Lin D, Reiner AP, North KE, Loos RJF, Kooperberg C, Avery CL. Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics. BMC Genomics 2020; 21:228. [PMID: 32171239 PMCID: PMC7071748 DOI: 10.1186/s12864-020-6626-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 02/26/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. RESULTS We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP. CONCLUSION This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.
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Affiliation(s)
- Chani J. Hodonsky
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
- University of Virginia Center for Public Health Genomics, 1355 Lee St, Charlottesville, VA 22908 USA
| | - Antoine R. Baldassari
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Stephanie A. Bien
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109 USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599 USA
| | - Heather M. Highland
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Colleen M. Sitlani
- University of Washington, 1730 Minor Ave, Ste 1360, Seattle, WA 98101 USA
| | - Genevieve L. Wojcik
- Stanford University School of Medicine, 291 Campus Dr, Stanford, CA 94305 USA
| | - Ran Tao
- Vanderbilt University, 2525 West End Ave #1100, Nashville, TN 37203 USA
| | - Marielisa Graff
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Weihong Tang
- University of Minnesota, 420 Delaware St SE, Minneapolis, MN 55455 USA
| | | | - Steve Buyske
- Rutgers University, 683 Hoes Ln W, Piscataway, NJ 08854 USA
| | - Myriam Fornage
- University of Texas Houston, 7000 Fannin Street, Houston, TX 77030 USA
| | - Lucia A. Hindorff
- National Human Genome Research Institute, 31 Center Dr, Bethesda, MD 20894 USA
| | - Yun Li
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Danyu Lin
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
| | - Alex P. Reiner
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109 USA
- University of Washington, 1705 NE Pacific St, Seattle, WA 98195 USA
| | - Kari E. North
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599 USA
| | - Ruth J. F. Loos
- Icahn School of Medicine at Mount Sinai, 1468 Madison Ave, New York, NY 10029 USA
| | | | - Christy L. Avery
- University of North Carolina Gillings School of Public Health, 135 Dauer Dr, Chapel Hill, NC 27599 USA
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22
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Lin SH, Loftfield E, Sampson JN, Zhou W, Yeager M, Freedman ND, Chanock SJ, Machiela MJ. Mosaic chromosome Y loss is associated with alterations in blood cell counts in UK Biobank men. Sci Rep 2020; 10:3655. [PMID: 32108144 PMCID: PMC7046668 DOI: 10.1038/s41598-020-59963-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 02/04/2020] [Indexed: 12/31/2022] Open
Abstract
Mosaic loss of Y chromosome (mLOY) is the most frequently detected somatic copy number alteration in leukocytes of men. In this study, we investigate blood cell counts as a potential mechanism linking mLOY to disease risk in 206,353 UK males. Associations between mLOY, detected by genotyping arrays, and blood cell counts were assessed by multivariable linear models adjusted for relevant risk factors. Among the participants, mLOY was detected in 39,809 men. We observed associations between mLOY and reduced erythrocyte count (−0.009 [−0.014, −0.005] × 1012 cells/L, p = 2.75 × 10−5) and elevated thrombocyte count (5.523 [4.862, 6.183] × 109 cells/L, p = 2.32 × 10−60) and leukocyte count (0.218 [0.198, 0.239] × 109 cells/L, p = 9.22 × 10−95), particularly for neutrophil count (0.174 × [0.158, 0.190]109 cells/L, p = 1.24 × 10−99) and monocyte count (0.021 [0.018 to 0.024] × 109 cells/L, p = 6.93 × 10−57), but lymphocyte count was less consistent (0.016 [0.007, 0.025] × 109 cells/L, p = 8.52 × 10−4). Stratified analyses indicate these associations are independent of the effects of aging and smoking. Our findings provide population-based evidence for associations between mLOY and blood cell counts that should stimulate investigation of the underlying biological mechanisms linking mLOY to cancer and chronic disease risk.
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Affiliation(s)
- Shu-Hong Lin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, 9609 Medical Center Drive MSC 9776, Bethesda, Maryland, 20892, USA
| | - Erikka Loftfield
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, 9609 Medical Center Drive MSC 9776, Bethesda, Maryland, 20892, USA
| | - Josh N Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, 9609 Medical Center Drive MSC 9776, Bethesda, Maryland, 20892, USA
| | - Weiyin Zhou
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, 9609 Medical Center Drive MSC 9776, Bethesda, Maryland, 20892, USA.,Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland, 8717 Grovemont Circle, Gaithersburg, MD, 20877, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, 9609 Medical Center Drive MSC 9776, Bethesda, Maryland, 20892, USA.,Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland, 8717 Grovemont Circle, Gaithersburg, MD, 20877, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, 9609 Medical Center Drive MSC 9776, Bethesda, Maryland, 20892, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, 9609 Medical Center Drive MSC 9776, Bethesda, Maryland, 20892, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, 9609 Medical Center Drive MSC 9776, Bethesda, Maryland, 20892, USA.
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Hegedűs L, Zámbó B, Pászty K, Padányi R, Varga K, Penniston JT, Enyedi Á. Molecular Diversity of Plasma Membrane Ca2+ Transporting ATPases: Their Function Under Normal and Pathological Conditions. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1131:93-129. [DOI: 10.1007/978-3-030-12457-1_5] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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24
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Kowalski MH, Qian H, Hou Z, Rosen JD, Tapia AL, Shan Y, Jain D, Argos M, Arnett DK, Avery C, Barnes KC, Becker LC, Bien SA, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Buyske S, Cai J, Cho MH, Choi SH, Choquet H, Cupples LA, Cushman M, Daya M, de Vries PS, Ellinor PT, Faraday N, Fornage M, Gabriel S, Ganesh SK, Graff M, Gupta N, He J, Heckbert SR, Hidalgo B, Hodonsky CJ, Irvin MR, Johnson AD, Jorgenson E, Kaplan R, Kardia SLR, Kelly TN, Kooperberg C, Lasky-Su JA, Loos RJF, Lubitz SA, Mathias RA, McHugh CP, Montgomery C, Moon JY, Morrison AC, Palmer ND, Pankratz N, Papanicolaou GJ, Peralta JM, Peyser PA, Rich SS, Rotter JI, Silverman EK, Smith JA, Smith NL, Taylor KD, Thornton TA, Tiwari HK, Tracy RP, Wang T, Weiss ST, Weng LC, Wiggins KL, Wilson JG, Yanek LR, Zöllner S, North KE, Auer PL, Raffield LM, Reiner AP, Li Y. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet 2019; 15:e1008500. [PMID: 31869403 PMCID: PMC6953885 DOI: 10.1371/journal.pgen.1008500] [Citation(s) in RCA: 171] [Impact Index Per Article: 34.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 01/10/2020] [Accepted: 10/30/2019] [Indexed: 01/10/2023] Open
Abstract
Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.
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Affiliation(s)
- Madeline H. Kowalski
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Huijun Qian
- Department of Statistics and Operation Research, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Ziyi Hou
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jonathan D. Rosen
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Amanda L. Tapia
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Yue Shan
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Maria Argos
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Donna K. Arnett
- College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America
| | - Christy Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Kathleen C. Barnes
- Department of Medicine, Anschutz Medical Campus, University of Colorado Denver, Aurora, Colorado, United States of America
| | - Lewis C. Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Stephanie A. Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, Texas, United States of America
| | - Eric Boerwinkle
- Human Genome Sequencing Center, University of Texas Health Science Center at Houston; Baylor College of Medicine, Houston, Texas, United States of America
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Donald W. Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Jianwen Cai
- Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Seung Hoan Choi
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Mary Cushman
- Departments of Medicine & Pathology, Larner College of Medicine, University of Vermont, Colchester, Vermont, United States of America
| | - Michelle Daya
- Department of Medicine, Anschutz Medical Campus, University of Colorado Denver, Aurora, Colorado, United States of America
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Patrick T. Ellinor
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Nauder Faraday
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Myriam Fornage
- School of Public Health, The University of Texas Health Science Center, Houston, Texas, United States of America
| | - Stacey Gabriel
- Genomics Platform, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Santhi K. Ganesh
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Misa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Namrata Gupta
- Genomics Platform, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Los Angeles, United States of America
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America
| | - Bertha Hidalgo
- Department of Epidemiology, Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Chani J. Hodonsky
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Marguerite R. Irvin
- Department of Epidemiology, Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Andrew D. Johnson
- Framingham Heart Study, Framingham, Massachusetts, United States of America
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, Massachusetts, United States of America
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Tanika N. Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Los Angeles, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Jessica A. Lasky-Su
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Steven A. Lubitz
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Rasika A. Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Caitlin P. McHugh
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Courtney Montgomery
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Jee-Young Moon
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - George J. Papanicolaou
- National Heart, Lung, and Blood Institute, Division of Cardiovascular Sciences, PPSP/EB, NIH, Bethesda, Maryland, United States of America
| | - Juan M. Peralta
- Department of Human Genetics and South Texas Diabetes Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, Texas, United States of America
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Nicholas L. Smith
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, Washington, United States of America
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Hemant K. Tiwari
- Department of Biostatistics, Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Russell P. Tracy
- Departments of Pathology & Laboratory Medicine and Biochemistry, Larrner College of Medicine, University of Vermont, Colchester, Vermont, United States of America
| | - Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Lu-Chen Weng
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Kerri L. Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Lisa R. Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Sebastian Zöllner
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kari E. North
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center of Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Paul L. Auer
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America
| | | | | | - Laura M. Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States of America
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Sangurdekar D, Sun C, McLaughlin H, Ayling-Rouse K, Allaire NE, Penny MA, Bronson PG. Genetic Study of Severe Prolonged Lymphopenia in Multiple Sclerosis Patients Treated With Dimethyl Fumarate. Front Genet 2019; 10:1039. [PMID: 31749835 PMCID: PMC6844186 DOI: 10.3389/fgene.2019.01039] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 09/27/2019] [Indexed: 12/31/2022] Open
Abstract
In delayed-release dimethyl fumarate (DMF)-treated patients, absolute lymphocyte count (ALC) often declines in the first year and stabilizes thereafter; early declines have been associated with development of severe prolonged lymphopenia (SPL). Prolonged moderate or severe lymphopenia is a known risk factor for progressive multifocal leukoencephalopathy (PML); DMF-associated PML is very rare. It is unknown whether genetic predictors of SPL secondary to DMF treatment exist. We aimed to identify genetic predictors of reduced white blood cell (WBC) counts in DMF-treated multiple sclerosis (MS) patients. Genotyping (N = 1,258) and blood transcriptional profiling (N = 1,133) were performed on MS patients from DEFINE/CONFIRM. ALCs were categorized as: SPL, < 500 cells/µL for ≥6 months; moderate prolonged lymphopenia (MPL), < 800 cells/µL for ≥6 months, excluding SPL; mildly reduced lymphocytes, < 910 cells/µL at any point, excluding SPL and MPL; no lymphopenia, ≥910 cells/µL. Genome-wide association, HLA, and cross-sectional gene expression studies were performed. No common variants, HLA alleles, or expression profiles clinically useful for predicting SPL or MPL were identified. There was no overlap between genetic peaks and genetic loci known to be associated with WBC. Gene expression profiles were not associated with lymphopenia status. A classification model including gene expression features was not more predictive of lymphopenia status than standard covariates. There were no genetic predictors of SPL (or MPL) secondary to DMF treatment. Our results support ALC monitoring during DMF treatment as the most effective way to identify patients at risk of SPL.
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Affiliation(s)
- Dipen Sangurdekar
- Translational Genome Sciences, Translational Biology, Biogen, Cambridge, MA, United States
| | - Chao Sun
- Translational Genome Sciences, Translational Biology, Biogen, Cambridge, MA, United States
| | - Helen McLaughlin
- Human Target Validation Core, Translational Biology, Biogen, Cambridge, MA, United States
| | | | - Normand E Allaire
- Translational Genome Sciences, Translational Biology, Biogen, Cambridge, MA, United States
| | - Michelle A Penny
- Translational Genome Sciences, Translational Biology, Biogen, Cambridge, MA, United States
| | - Paola G Bronson
- Human Target Validation Core, Translational Biology, Biogen, Cambridge, MA, United States
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Yasukochi Y, Sakuma J, Takeuchi I, Kato K, Oguri M, Fujimaki T, Horibe H, Yamada Y. Evolutionary history of disease-susceptibility loci identified in longitudinal exome-wide association studies. Mol Genet Genomic Med 2019; 7:e925. [PMID: 31402603 PMCID: PMC6732299 DOI: 10.1002/mgg3.925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 06/12/2019] [Accepted: 07/26/2019] [Indexed: 12/17/2022] Open
Abstract
Background Our longitudinal exome‐wide association studies previously detected various genetic determinants of complex disorders using ~26,000 single‐nucleotide polymorphisms (SNPs) that passed quality control and longitudinal medical examination data (mean follow‐up period, 5 years) in 4884–6022 Japanese subjects. We found that allele frequencies of several identified SNPs were remarkably different among four ethnic groups. Elucidating the evolutionary history of disease‐susceptibility loci may help us uncover the pathogenesis of the related complex disorders. Methods In the present study, we conducted evolutionary analyses such as extended haplotype homozygosity, focusing on genomic regions containing disease‐susceptibility loci and based on genotyping data of our previous studies and datasets from the 1000 Genomes Project. Results Our evolutionary analyses suggest that derived alleles of rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs closely located at 12q24.1 associated with type 2 diabetes mellitus, obesity, dyslipidemia, and three complex disorders (hypertension, hyperuricemia, and dyslipidemia), respectively, rapidly expanded after the human dispersion from Africa (Out‐of‐Africa). Allele frequencies of GGA3 and six SNPs at 12q24.1 appeared to have remarkably changed in East Asians, whereas the derived alleles of rs34902660 of SLC17A3 and rs7656604 at 4q13.3 might have spread across Japanese and non‐Africans, respectively, although we cannot completely exclude the possibility that allele frequencies of disease‐associated loci may be affected by demographic events. Conclusion Our findings indicate that derived allele frequencies of nine disease‐associated SNPs (rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs at 12q24.1) identified in the longitudinal exome‐wide association studies largely increased in non‐Africans after Out‐of‐Africa.
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Affiliation(s)
- Yoshiki Yasukochi
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,CREST, Japan Science and Technology Agency, Kawaguchi, Japan
| | - Jun Sakuma
- CREST, Japan Science and Technology Agency, Kawaguchi, Japan.,Computer Science Department, College of Information Science, University of Tsukuba, Tsukuba, Japan.,RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Ichiro Takeuchi
- CREST, Japan Science and Technology Agency, Kawaguchi, Japan.,RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.,Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan
| | - Kimihiko Kato
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,Department of Internal Medicine, Meitoh Hospital, Nagoya, Japan
| | - Mitsutoshi Oguri
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,Department of Cardiology, Kasugai Municipal Hospital, Kasugai, Japan
| | - Tetsuo Fujimaki
- Department of Cardiovascular Medicine, Inabe General Hospital, Inabe, Japan
| | - Hideki Horibe
- Department of Cardiovascular Medicine, Gifu Prefectural Tajimi Hospital, Tajimi, Japan
| | - Yoshiji Yamada
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,CREST, Japan Science and Technology Agency, Kawaguchi, Japan
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27
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Damena D, Denis A, Golassa L, Chimusa ER. Genome-wide association studies of severe P. falciparum malaria susceptibility: progress, pitfalls and prospects. BMC Med Genomics 2019; 12:120. [PMID: 31409341 PMCID: PMC6693204 DOI: 10.1186/s12920-019-0564-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Accepted: 07/29/2019] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND P. falciparum malaria has been recognized as one of the prominent evolutionary selective forces of human genome that led to the emergence of multiple host protective alleles. A comprehensive understanding of the genetic bases of severe malaria susceptibility and resistance can potentially pave ways to the development of new therapeutics and vaccines. Genome-wide association studies (GWASs) have recently been implemented in malaria endemic areas and identified a number of novel association genetic variants. However, there are several open questions around heritability, epistatic interactions, genetic correlations and associated molecular pathways among others. Here, we assess the progress and pitfalls of severe malaria susceptibility GWASs and discuss the biology of the novel variants. RESULTS We obtained all severe malaria susceptibility GWASs published thus far and accessed GWAS dataset of Gambian populations from European Phenome Genome Archive (EGA) through the MalariaGen consortium standard data access protocols. We noticed that, while some of the well-known variants including HbS and ABO blood group were replicated across endemic populations, only few novel variants were convincingly identified and their biological functions remain to be understood. We estimated SNP-heritability of severe malaria at 20.1% in Gambian populations and showed how advanced statistical genetic analytic methods can potentially be implemented in malaria susceptibility studies to provide useful functional insights. CONCLUSIONS The ultimate goal of malaria susceptibility study is to discover a novel causal biological pathway that provide protections against severe malaria; a fundamental step towards translational medicine such as development of vaccine and new therapeutics. Beyond singe locus analysis, the future direction of malaria susceptibility requires a paradigm shift from single -omics to multi-stage and multi-dimensional integrative functional studies that combines multiple data types from the human host, the parasite, the mosquitoes and the environment. The current biotechnological and statistical advances may eventually lead to the feasibility of systems biology studies and revolutionize malaria research.
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Affiliation(s)
- Delesa Damena
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, Cape Town, 7700 South Africa
| | - Awany Denis
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, Cape Town, 7700 South Africa
| | - Lemu Golassa
- Aklilu Lema Institute of Pathobiology, Addis Ababa University, PO box 1176, Addis Ababa, Ethiopia
| | - Emile R. Chimusa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, Cape Town, 7700 South Africa
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28
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Ma X, Jia C, Fu D, Chu M, Ding X, Wu X, Guo X, Pei J, Bao P, Liang C, Yan P. Analysis of Hematological Traits in Polled Yak by Genome-Wide Association Studies Using Individual SNPs and Haplotypes. Genes (Basel) 2019; 10:E463. [PMID: 31212963 PMCID: PMC6627507 DOI: 10.3390/genes10060463] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/11/2019] [Accepted: 06/12/2019] [Indexed: 12/21/2022] Open
Abstract
Yak (Bos grunniens) is an important domestic animal living in high-altitude plateaus. Due to inadequate disease prevention, each year, the yak industry suffers significant economic losses. The identification of causal genes that affect blood- and immunity-related cells could provide preliminary reference guidelines for the prevention of diseases in the population of yaks. The genome-wide association studies (GWASs) utilizing a single-marker or haplotype method were employed to analyze 15 hematological traits in the genome of 315 unrelated yaks. Single-marker GWASs identified a total of 43 significant SNPs, including 35 suggestive and eight genome-wide significant SNPs, associated with nine traits. Haplotype analysis detected nine significant haplotype blocks, including two genome-wide and seven suggestive blocks, associated with seven traits. The study provides data on the genetic variability of hematological traits in the yak. Five essential genes (GPLD1, EDNRA,APOB, HIST1H1E, and HIST1H2BI) were identified, which affect the HCT, HGB, RBC, PDW, PLT, and RDWSD traits and can serve as candidate genes for regulating hematological traits. The results provide a valuable reference to be used in the analysis of blood properties and immune diseases in the yak.
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Affiliation(s)
- Xiaoming Ma
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Congjun Jia
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Donghai Fu
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Min Chu
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Xuezhi Ding
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Xiaoyun Wu
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Xian Guo
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Jie Pei
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Pengjia Bao
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Chunnian Liang
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
| | - Ping Yan
- Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
- Key Laboratory for Yak Genetics, Breeding, and Reproduction Engineering of Gansu Province, Lanzhou 730050, China.
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Read RW, Schlauch KA, Elhanan G, Metcalf WJ, Slonim AD, Aweti R, Borkowski R, Grzymski JJ. GWAS and PheWAS of red blood cell components in a Northern Nevadan cohort. PLoS One 2019; 14:e0218078. [PMID: 31194788 PMCID: PMC6564422 DOI: 10.1371/journal.pone.0218078] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 05/21/2019] [Indexed: 01/20/2023] Open
Abstract
In this study, we perform a full genome-wide association study (GWAS) to identify statistically significantly associated single nucleotide polymorphisms (SNPs) with three red blood cell (RBC) components and follow it with two independent PheWASs to examine associations between phenotypic data (case-control status of diagnoses or disease), significant SNPs, and RBC component levels. We first identified associations between the three RBC components: mean platelet volume (MPV), mean corpuscular volume (MCV), and platelet counts (PC), and the genotypes of approximately 500,000 SNPs on the Illumina Infimum DNA Human OmniExpress-24 BeadChip using a single cohort of 4,673 Northern Nevadans. Twenty-one SNPs in five major genomic regions were found to be statistically significantly associated with MPV, two regions with MCV, and one region with PC, with p<5x10-8. Twenty-nine SNPs and nine chromosomal regions were identified in 30 previous GWASs, with effect sizes of similar magnitude and direction as found in our cohort. The two strongest associations were SNP rs1354034 with MPV (p = 2.4x10-13) and rs855791 with MCV (p = 5.2x10-12). We then examined possible associations between these significant SNPs and incidence of 1,488 phenotype groups mapped from International Classification of Disease version 9 and 10 (ICD9 and ICD10) codes collected in the extensive electronic health record (EHR) database associated with Healthy Nevada Project consented participants. Further leveraging data collected in the EHR, we performed an additional PheWAS to identify associations between continuous red blood cell (RBC) component measures and incidence of specific diagnoses. The first PheWAS illuminated whether SNPs associated with RBC components in our cohort were linked with other hematologic phenotypic diagnoses or diagnoses of other nature. Although no SNPs from our GWAS were identified as strongly associated to other phenotypic components, a number of associations were identified with p-values ranging between 1x10-3 and 1x10-4 with traits such as respiratory failure, sleep disorders, hypoglycemia, hyperglyceridemia, GERD and IBS. The second PheWAS examined possible phenotypic predictors of abnormal RBC component measures: a number of hematologic phenotypes such as thrombocytopenia, anemias, hemoglobinopathies and pancytopenia were found to be strongly associated to RBC component measures; additional phenotypes such as (morbid) obesity, malaise and fatigue, alcoholism, and cirrhosis were also identified to be possible predictors of RBC component measures.
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Affiliation(s)
- Robert W. Read
- Applied Innovation Center, Renown Institute for Health Innovation, Desert Research Institute, Reno, NV, United States of America
| | - Karen A. Schlauch
- Applied Innovation Center, Renown Institute for Health Innovation, Desert Research Institute, Reno, NV, United States of America
| | - Gai Elhanan
- Applied Innovation Center, Renown Institute for Health Innovation, Desert Research Institute, Reno, NV, United States of America
| | - William J. Metcalf
- Applied Innovation Center, Renown Institute for Health Innovation, Desert Research Institute, Reno, NV, United States of America
| | | | - Ramsey Aweti
- 23andMe, Inc., Mountain View, CA, United States of America
| | | | - Joseph J. Grzymski
- Applied Innovation Center, Renown Institute for Health Innovation, Desert Research Institute, Reno, NV, United States of America
- Renown Health, Reno, NV, United States of America
- * E-mail:
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30
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Lakhotia R, Aggarwal A, Link ME, Rodgers GP, Hsieh MM. Natural history of benign ethnic neutropenia in individuals of African ancestry. Blood Cells Mol Dis 2019; 77:12-16. [PMID: 30909074 DOI: 10.1016/j.bcmd.2019.01.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 01/17/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Benign ethnic neutropenia (BEN), defined by neutrophil count <1.5 k/μL in the absence of other causes, is an asymptomatic condition more commonly observed in individuals of African ancestry. However, the natural history of this condition has been less well described. METHODS Individuals with BEN were retrospectively identified by chart review or referral to hematology clinics. They were then invited to enroll in a prospective natural history study. Retrospective and prospective clinical and laboratory data were combined for descriptive analyses. FINDINGS 46 participants, younger and older adults from 2 institutions, had BEN. Hypertension was reported in 30%, musculoskeletal disorders in 15%, and upper respiratory infection in 33% of these adults. Their leukopenia resulted from isolated neutropenia, ranging from 1000 and 1500 cells/μL. The severity of infections was mild and the frequency was similar to other healthy individuals in the ambulatory clinic. INTERPRETATION In this group of BEN participants, their leukopenia was stable over time, and they had low rates of infections or common medical disorders, confirming the benign nature of this condition. The presence of BEN in children, younger adults, and older adults suggest a hereditary pattern for BEN.
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Affiliation(s)
- Rahul Lakhotia
- Hematology Branch, NHLBI, NIH, United States of America.
| | - Anita Aggarwal
- Department of Hematology and Oncology, Veterans Administration Hospital, Washington DC, United States of America.
| | - Mary E Link
- Molecular and Clinical Hematology Branch, NHLBI, NIH, United States of America.
| | - Griffin P Rodgers
- Molecular and Clinical Hematology Branch, NHLBI, NIH, United States of America.
| | - Matthew M Hsieh
- Molecular and Clinical Hematology Branch, NHLBI, NIH, United States of America.
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Abstract
After more than 10 years of accumulated efforts, genome-wide association studies (GWAS) have led to many findings, most of which have been deposited into the GWAS Catalog. Between GWAS's inception and March 2017, the GWAS Catalog has collected 2429 studies, 1818 phenotypes, and 28,462 associated SNPs. We reclassified the psychology-related phenotypes into 217 reclassified phenotypes, which accounted for 514 studies and 7052 SNPs. In total, 1223 of the SNPs reached genome-wide significance. Of these, 147 were replicated for the same psychological trait in different studies. Another 305 SNPs were replicated within one original study. The SNPs rs2075650 and rs4420638 were linked to the most replications within a single reclassified phenotype or very similar reclassified phenotypes; both were associated with Alzheimer's disease (AD). Schizophrenia was associated with 74 within-phenotype SNPs reported in independents studies. Alzheimer's disease and schizophrenia were both linked to some physical phenotypes, including cholesterol and body mass index, through common GWAS signals. Alzheimer's disease also shared risk SNPs with age-related phenotypes such as age-related macular degeneration and longevity. Smoking-related SNPs were linked to lung cancer and respiratory function. Alcohol-related SNPs were associated with cardiovascular and digestive system phenotypes and disorders. Two separate studies also identified a shared risk SNP for bipolar disorder and educational attainment. This review revealed a list of reproducible SNPs worthy of future functional investigation. Additionally, by identifying SNPs associated with multiple phenotypes, we illustrated the importance of studying the relationships among phenotypes to resolve the nature of their causal links. The insights within this review will hopefully pave the way for future evidence-based genetic studies.
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32
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Gouveia MH, Bergen AW, Borda V, Nunes K, Leal TP, Ogwang MD, Yeboah ED, Mensah JE, Kinyera T, Otim I, Nabalende H, Legason ID, Mpoloka SW, Mokone GG, Kerchan P, Bhatia K, Reynolds SJ, Birtwum RB, Adjei AA, Tettey Y, Tay E, Hoover R, Pfeiffer RM, Biggar RJ, Goedert JJ, Prokunina-Olsson L, Dean M, Yeager M, Lima-Costa MF, Hsing AW, Tishkoff SA, Chanock SJ, Tarazona-Santos E, Mbulaiteye SM. Genetic signatures of gene flow and malaria-driven natural selection in sub-Saharan populations of the "endemic Burkitt Lymphoma belt". PLoS Genet 2019; 15:e1008027. [PMID: 30849090 PMCID: PMC6426263 DOI: 10.1371/journal.pgen.1008027] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 03/20/2019] [Accepted: 02/17/2019] [Indexed: 12/13/2022] Open
Abstract
Populations in sub-Saharan Africa have historically been exposed to intense selection from chronic infection with falciparum malaria. Interestingly, populations with the highest malaria intensity can be identified by the increased occurrence of endemic Burkitt Lymphoma (eBL), a pediatric cancer that affects populations with intense malaria exposure, in the so called "eBL belt" in sub-Saharan Africa. However, the effects of intense malaria exposure and sub-Saharan populations' genetic histories remain poorly explored. To determine if historical migrations and intense malaria exposure have shaped the genetic composition of the eBL belt populations, we genotyped ~4.3 million SNPs in 1,708 individuals from Ghana and Northern Uganda, located on opposite sides of eBL belt and with ≥ 7 months/year of intense malaria exposure and published evidence of high incidence of BL. Among 35 Ghanaian tribes, we showed a predominantly West-Central African ancestry and genomic footprints of gene flow from Gambian and East African populations. In Uganda, the North West population showed a predominantly Nilotic ancestry, and the North Central population was a mixture of Nilotic and Southern Bantu ancestry, while the Southwest Ugandan population showed a predominant Southern Bantu ancestry. Our results support the hypothesis of diverse ancestral origins of the Ugandan, Kenyan and Tanzanian Great Lakes African populations, reflecting a confluence of Nilotic, Cushitic and Bantu migrations in the last 3000 years. Natural selection analyses suggest, for the first time, a strong positive selection signal in the ATP2B4 gene (rs10900588) in Northern Ugandan populations. These findings provide important baseline genomic data to facilitate disease association studies, including of eBL, in eBL belt populations.
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Affiliation(s)
- Mateus H. Gouveia
- Instituto de Pesquisa René Rachou, Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brazil
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Center for Research on Genomics & Global Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - Andrew W. Bergen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - Victor Borda
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Kelly Nunes
- Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil
| | - Thiago P. Leal
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Department of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Martin D. Ogwang
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | | | | | - Tobias Kinyera
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Isaac Otim
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | | | | | | | - Gaonyadiwe George Mokone
- Department of Biomedical Sciences, University of Botswana School of Medicine, Gaborone, Botswana
| | - Patrick Kerchan
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Kishor Bhatia
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - Steven J. Reynolds
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | | | | | - Yao Tettey
- University of Ghana Medical School, Accra, Ghana
| | - Evelyn Tay
- University of Ghana Medical School, Accra, Ghana
| | - Robert Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - Ruth M. Pfeiffer
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - Robert J. Biggar
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - James J. Goedert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - Ludmila Prokunina-Olsson
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - Michael Dean
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - Meredith Yeager
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, US Department of Health and Human Services, Frederick, Maryland, United States of America
| | - M. Fernanda Lima-Costa
- Instituto de Pesquisa René Rachou, Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brazil
| | - Ann W. Hsing
- Stanford Cancer Institute, Stanford University, Stanford, California, United States of America
| | - Sarah A. Tishkoff
- Department of Genetics and Biology, University of Pennsylvania, Philadelphia, United States of America
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
| | - Eduardo Tarazona-Santos
- Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Sam M. Mbulaiteye
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland, United States of America
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Genetic associations of hemoglobin in children with chronic kidney disease in the PediGFR Consortium. Pediatr Res 2019; 85:324-328. [PMID: 30140068 PMCID: PMC6377354 DOI: 10.1038/s41390-018-0148-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 05/30/2018] [Accepted: 06/04/2018] [Indexed: 12/04/2022]
Abstract
BACKGROUND Genome-wide association studies (GWAS) in healthy populations have identified variants associated with erythrocyte traits, but genetic causes of hemoglobin variation in children with CKD are incompletely understood. METHODS The Pediatric Investigation of Genetic Factors Linked with Renal Progression (PediGFR) Consortium comprises three pediatric CKD cohorts: Chronic Kidney Disease in Children (CKiD), Effect of Strict Blood Pressure Control and ACE Inhibition on the Progression of CRF in Pediatric Patients (ESCAPE), and Cardiovascular Comorbidity in Children with CKD (4C). We performed cross-sectional and longitudinal association studies of single-nucleotide polymorphisms (SNPs) in 1125 patients. RESULTS Children of European (n = 725) or Turkish (n = 400) ancestry (EA or TA) were included. In cross-sectional analysis, two SNPs (rs10758658 and rs12718597) previously associated with RBC traits were significantly associated with hemoglobin levels in children of EA and TA. In longitudinal analysis, SNP rs2540917 was nominally associated with hemoglobin in EA and TA children. CONCLUSIONS SNPs associated with erythrocyte traits in healthy populations were marginally significant for an association with hemoglobin. Further analyses/replication studies are needed in larger CKD cohorts to investigate SNPs of unknown significance associated with hemoglobin. Functional studies will be required to confirm that the observed associations between SNPs and clinical phenotype are causal.
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Vasudeva K, Munshi A. Genetics of platelet traits in ischaemic stroke: focus on mean platelet volume and platelet count. Int J Neurosci 2018; 129:511-522. [PMID: 30371123 DOI: 10.1080/00207454.2018.1538991] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Purpose/Aim of the study: The aim of this review is to summarize the role of genetic variants affecting mean platelet volume (MPV) and platelet count (PLT) leading to higher platelet reactivity and in turn to thrombotic events like stroke and cardiovascular diseases. MATERIALS AND METHODS A search was conducted in PUBMED, MEDLINE, EMBASE, PROQUEST, Science Direct, Cochrane Library, and Google Scholar related to the studies focussing on genome-wide association studies (GWAS), whole exome sequencing (WES), whole genome sequencing (WGS), phenome-wide association studies (PheWAS) and multi-omic analysis that have been employed to identify the genetic variants influencing MPV and PLT. RESULTS Antiplatelet agents underscore the crucial role of platelets in the pathogenesis of stroke. Higher platelet reactivity in terms of mean platelet volume (MPV) and platelet count (PLT) contributes significantly to the interindividual variation in platelet reaction at the site of vessel wall injury. Some individuals encounter thrombotic events as platelets get occluded at the site of vessel wall injury whereas others heal the injury without occluding the circulation. Evidence suggests that MPV and PLT have a strong genetic component. High throughput techniques including genome-wide association studies (GWAS), whole exome sequencing (WES), whole genome sequencing (WGS), phenome-wide association studies (PheWAS) and multi-omic analysis have identified different genetic variants influencing MPV and PLT. CONCLUSIONS Identification of complex genetic cross talks affecting PLT and MPV might help to develop novel treatment strategies in treating neurovascular diseases like stroke.
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Affiliation(s)
- Kanika Vasudeva
- a Department of Human Genetics and Molecular Medicine , Central University of Punjab Bathinda , Punjab , India
| | - Anjana Munshi
- a Department of Human Genetics and Molecular Medicine , Central University of Punjab Bathinda , Punjab , India
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Unfolding of hidden white blood cell count phenotypes for gene discovery using latent class mixed modeling. Genes Immun 2018; 20:555-565. [PMID: 30459343 DOI: 10.1038/s41435-018-0051-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 09/24/2018] [Accepted: 10/24/2018] [Indexed: 12/26/2022]
Abstract
Resting-state white blood cell (WBC) count is a marker of inflammation and immune system health. There is evidence that WBC count is not fixed over time and there is heterogeneity in WBC trajectory that is associated with morbidity and mortality. Latent class mixed modeling (LCMM) is a method that can identify unobserved heterogeneity in longitudinal data and attempts to classify individuals into groups based on a linear model of repeated measurements. We applied LCMM to repeated WBC count measures derived from electronic medical records of participants of the National Human Genetics Research Institute (NHRGI) electronic MEdical Record and GEnomics (eMERGE) network study, revealing two WBC count trajectory phenotypes. Advancing these phenotypes to GWAS, we found genetic associations between trajectory class membership and regions on chromosome 1p34.3 and chromosome 11q13.4. The chromosome 1 region contains CSF3R, which encodes the granulocyte colony-stimulating factor receptor. This protein is a major factor in neutrophil stimulation and proliferation. The association on chromosome 11 contain genes RNF169 and XRRA1; both involved in the regulation of double-strand break DNA repair.
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Yasukochi Y, Sakuma J, Takeuchi I, Kato K, Oguri M, Fujimaki T, Horibe H, Yamada Y. Identification of nine novel loci related to hematological traits in a Japanese population. Physiol Genomics 2018; 50:758-769. [PMID: 29958078 PMCID: PMC6172615 DOI: 10.1152/physiolgenomics.00088.2017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Recent genome-wide association studies have identified various genetic variants associated with hematological traits. Although it is possible that quantitative data of hematological traits are varied among the years examined, conventional genome-wide association studies have been conducted in a cross-sectional manner that measures traits at a single point in time. To address this issue, we have traced blood profiles in 4,884 Japanese individuals who underwent annual health check-ups for several years. In the present study, longitudinal exome-wide association studies were conducted to identify genetic variants related to 13 hematological phenotypes. The generalized estimating equation model showed that a total of 67 single nucleotide polymorphisms (SNPs) were significantly [false discovery rate (FDR) of <0.01] associated with hematological phenotypes. Of the 67 SNPs, nine SNPs were identified as novel hematological markers: rs4686683 of SENP2 for red blood cell count (FDR = 0.008, P = 5.5 × 10−6); rs3917688 of SELP for mean corpuscular volume (FDR = 0.005, P = 2.4 × 10−6); rs3133745 of C8orf37-AS1 for white blood cell count (FDR = 0.003, P = 1.3 × 10−6); rs13121954 at 4q31.2 for basophil count (FDR = 0.007, P = 3.1 × 10−5); rs7584099 at 2q22.3 (FDR = 2.6 × 10−5, P = 8.8 × 10−8), rs1579219 of HCG17 (FDR = 0.003, P = 2.0 × 10−5), and rs10757049 of DENND4C (FDR = 0.008, P = 5.6 × 10−5) for eosinophil count; rs12338 of CTSB for neutrophil count (FDR = 0.007, P = 2.9 × 10−5); and rs395967 of OSMR-AS1 for monocyte count (FDR = 0.008, P = 3.2 × 10−5).
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Affiliation(s)
- Yoshiki Yasukochi
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Mie , Japan.,CREST, Japan Science and Technology Agency, Kawaguchi, Saitama , Japan
| | - Jun Sakuma
- CREST, Japan Science and Technology Agency, Kawaguchi, Saitama , Japan.,Computer Science Department, College of Information Science, University of Tsukuba, Tsukuba, Ibaraki , Japan.,RIKEN Center for Advanced Intelligence Project , Tokyo , Japan
| | - Ichiro Takeuchi
- CREST, Japan Science and Technology Agency, Kawaguchi, Saitama , Japan.,RIKEN Center for Advanced Intelligence Project , Tokyo , Japan.,Department of Computer Science, Nagoya Institute of Technology, Nagoya, Aichi , Japan
| | - Kimihiko Kato
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Mie , Japan.,Department of Internal Medicine, Meitoh Hospital, Nagoya, Aichi , Japan
| | - Mitsutoshi Oguri
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Mie , Japan.,Department of Cardiology, Kasugai Municipal Hospital, Kasugai, Aichi , Japan
| | - Tetsuo Fujimaki
- Department of Cardiovascular Medicine, Inabe General Hospital, Inabe, Mie , Japan
| | - Hideki Horibe
- Department of Cardiovascular Medicine, Gifu Prefectural Tajimi Hospital, Tajimi, Gifu , Japan
| | - Yoshiji Yamada
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Mie , Japan.,CREST, Japan Science and Technology Agency, Kawaguchi, Saitama , Japan
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Jo Hodonsky C, Schurmann C, Schick UM, Kocarnik J, Tao R, van Rooij FJ, Wassel C, Buyske S, Fornage M, Hindorff LA, Floyd JS, Ganesh SK, Lin DY, North KE, Reiner AP, Loos RJ, Kooperberg C, Avery CL. Generalization and fine mapping of red blood cell trait genetic associations to multi-ethnic populations: The PAGE Study. Am J Hematol 2018; 93:10.1002/ajh.25161. [PMID: 29905378 PMCID: PMC6300146 DOI: 10.1002/ajh.25161] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/29/2018] [Accepted: 05/29/2018] [Indexed: 12/17/2022]
Abstract
Red blood cell (RBC) traits provide insight into a wide range of physiological states and exhibit moderate to high heritability, making them excellent candidates for genetic studies to inform underlying biologic mechanisms. Previous RBC trait genome-wide association studies were performed primarily in European- or Asian-ancestry populations, missing opportunities to inform understanding of RBC genetic architecture in diverse populations and reduce intervals surrounding putative functional SNPs through fine-mapping. Here, we report the first fine-mapping of six correlated (Pearson's r range: |0.04 - 0.92|) RBC traits in up to 19,036 African Americans and 19,562 Hispanic/Latinos participants of the Population Architecture using Genomics and Epidemiology (PAGE) consortium. Trans-ethnic meta-analysis of race/ethnic- and study-specific estimates for approximately 11,000 SNPs flanking 13 previously identified association signals as well as 150,000 additional array-wide SNPs was performed using inverse-variance meta-analysis after adjusting for study and clinical covariates. Approximately half of previously reported index SNP-RBC trait associations generalized to the trans-ethnic study population (p<1.7x10-4 ); previously unreported independent association signals within the ABO region reinforce the potential for multiple functional variants affecting the same locus. Trans-ethnic fine-mapping did not reveal additional signals at the HFE locus independent of the known functional variants. Finally, we identified a potential novel association in the Hispanic/Latino study population at the HECTD4/RPL6 locus for RBC count (p=1.9x10-7 ). The identification of a previously unknown association, generalization of a large proportion of known association signals, and refinement of known association signals all exemplify the benefits of genetic studies in diverse populations. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Chani Jo Hodonsky
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ursula M Schick
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Jonathan Kocarnik
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Frank Ja van Rooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3000, the Netherlands
| | - Christina Wassel
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Vermont, Burlington, VT
| | - Steve Buyske
- Department of Statistics and Biostatistics, Hill Center, Rutgers, The State University of New Jersey, 110 Frelinghuysen Rd. Piscataway, NY
| | - Myriam Fornage
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX
| | - Lucia A Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National institutes of Health, Bethesda, MD
| | - James S Floyd
- Departments of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Santhi K Ganesh
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
| | - Kari E North
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC
| | - Alex P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Ruth Jf Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC
- Carolina Population Center, University of North Carolina, Chapel Hill, NC
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Verma A, Lucas A, Verma SS, Zhang Y, Josyula N, Khan A, Hartzel DN, Lavage DR, Leader J, Ritchie MD, Pendergrass SA. PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger. Am J Hum Genet 2018; 102:592-608. [PMID: 29606303 PMCID: PMC5985339 DOI: 10.1016/j.ajhg.2018.02.017] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 02/20/2018] [Indexed: 01/23/2023] Open
Abstract
Most phenome-wide association studies (PheWASs) to date have used a small to moderate number of SNPs for association with phenotypic data. We performed a large-scale single-cohort PheWAS, using electronic health record (EHR)-derived case-control status for 541 diagnoses using International Classification of Disease version 9 (ICD-9) codes and 25 median clinical laboratory measures. We calculated associations between these diagnoses and traits with ∼630,000 common frequency SNPs with minor allele frequency > 0.01 for 38,662 individuals. In this landscape PheWAS, we explored results within diseases and traits, comparing results to those previously reported in genome-wide association studies (GWASs), as well as previously published PheWASs. We further leveraged the context of functional impact from protein-coding to regulatory regions, providing a deeper interpretation of these associations. The comprehensive nature of this PheWAS allows for novel hypothesis generation, the identification of phenotypes for further study for future phenotypic algorithm development, and identification of cross-phenotype associations.
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Affiliation(s)
- Anurag Verma
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Anastasia Lucas
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shefali S Verma
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Yu Zhang
- Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA
| | - Navya Josyula
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Anqa Khan
- Mount Holyoke College, South Hadley, MA 01075, USA
| | - Dustin N Hartzel
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Daniel R Lavage
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Joseph Leader
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA; Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Sarah A Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA.
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Duan Q, Xu Z, Raffield L, Chang S, Wu D, Lange EM, Reiner AP, Li Y. A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations. Genet Epidemiol 2018; 42:288-302. [PMID: 29226381 PMCID: PMC5851818 DOI: 10.1002/gepi.22104] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 09/07/2017] [Accepted: 10/20/2017] [Indexed: 12/23/2022]
Abstract
Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two-step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry-specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups' previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA.
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Affiliation(s)
- Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of North Carolina, Chapel Hill, NC, USA
| | - Zheng Xu
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE
- Initiative of Quantitative Life Sciences, University of Nebraska-Lincoln, Lincoln, NE
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Suhua Chang
- Institute of Psychology, Chinese Academy of Science, Beijing, China
| | - Di Wu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Periodontology, University of North Carolina, Chapel Hill, NC, USA
| | - Ethan M. Lange
- Department of Medicine, University of Colorado at Denver, Anschutz Medical Campus, Aurora, CO, USA
- Department of Biostatistics and Informatics, University of Colorado at Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Alex P. Reiner
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
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Charles BA, Hsieh MM, Adeyemo AA, Shriner D, Ramos E, Chin K, Srivastava K, Zakai NA, Cushman M, McClure LA, Howard V, Flegel WA, Rotimi CN, Rodgers GP. Analyses of genome wide association data, cytokines, and gene expression in African-Americans with benign ethnic neutropenia. PLoS One 2018; 13:e0194400. [PMID: 29596498 PMCID: PMC5875757 DOI: 10.1371/journal.pone.0194400] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 03/04/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Benign ethnic neutropenia (BEN) is a hematologic condition associated with people of African ancestry and specific Middle Eastern ethnic groups. Prior genetic association studies in large population showed that rs2814778 in Duffy Antigen Receptor for Chemokines (DARC) gene, specifically DARC null red cell phenotype, was associated with BEN. However, the mechanism of this red cell phenotype leading to low white cell count remained elusive. METHODS We conducted an extreme phenotype design genome-wide association study (GWAS), analyzed ~16 million single nucleotide polymorphisms (SNP) in 1,178 African-Americans individuals from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study and replicated from 819 African-American participants in the Atherosclerosis Risk in Communities (ARIC) study. Conditional analyses on rs2814778 were performed to identify additional association signals on chromosome 1q22. In a separate cohort of healthy individuals with and without BEN, whole genome gene expression from peripheral blood neutrophils were analyzed for DARC. RESULTS We confirmed that rs2814778 in DARC was associated with BEN (p = 4.09×10-53). Conditioning on rs2814778 abolished other significant chromosome 1 associations. Inflammatory cytokines (IL-2, 6, and 10) in participants in the Howard University Family Study (HUFS) and Multi-Ethnic Study in Atherosclerosis (MESA) showed similar levels in individuals homozygous for the rs2814778 allele compared to others, indicating cytokine sink hypothesis played a minor role in leukocyte homeostasis. Gene expression in neutrophils of individuals with and without BEN was also similar except for low DARC expression in BEN, suggesting normal function. BEN neutrophils had slightly activated profiles in leukocyte migration and hematopoietic stem cell mobilization pathways (expression fold change <2). CONCLUSIONS These results in humans support the notion of DARC null erythroid progenitors preferentially differentiating to myeloid cells, leading to activated DARC null neutrophils egressing from circulation to the spleen, and causing relative neutropenia. Collectively, these human data sufficiently explained the mechanism DARC null red cell phenotype causing BEN and further provided a biologic basis that BEN is clinically benign.
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Affiliation(s)
- Bashira A. Charles
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Matthew M. Hsieh
- Molecular and Clinical Hematology Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, Maryland, United States of America
| | - Adebowale A. Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Edward Ramos
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland, United States of America
- National Institute of Biomedical Imaging and Bioengineering, NIH, Bethesda, Maryland, United States of America
| | - Kyung Chin
- Molecular and Clinical Hematology Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, Maryland, United States of America
| | - Kshitij Srivastava
- Warren Grant Magnuson Clinical Center, NIH, Bethesda, Maryland, United States of America
| | - Neil A. Zakai
- Departments of Pathology and Medicine, University of Vermont Larner College of Medicine, Burlington, Vermont, United States of America
| | - Mary Cushman
- Departments of Pathology and Medicine, University of Vermont Larner College of Medicine, Burlington, Vermont, United States of America
| | - Leslie A. McClure
- School of Public Health, University of Alabama, Birmingham, Alabama, United States of America
- Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, Pennsylvania, United States of America
| | - Virginia Howard
- School of Public Health, University of Alabama, Birmingham, Alabama, United States of America
| | - Willy A. Flegel
- Warren Grant Magnuson Clinical Center, NIH, Bethesda, Maryland, United States of America
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland, United States of America
- * E-mail: (CNR); (GPR)
| | - Griffin P. Rodgers
- Molecular and Clinical Hematology Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, Maryland, United States of America
- * E-mail: (CNR); (GPR)
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41
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Handtke S, Steil L, Greinacher A, Thiele T. Toward the Relevance of Platelet Subpopulations for Transfusion Medicine. Front Med (Lausanne) 2018; 5:17. [PMID: 29459897 PMCID: PMC5807390 DOI: 10.3389/fmed.2018.00017] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 01/18/2018] [Indexed: 12/11/2022] Open
Abstract
Circulating platelets consist of subpopulations with different age, maturation state and size. In this review, we address the association between platelet size and platelet function and summarize the current knowledge on platelet subpopulations including reticulated platelets, procoagulant platelets and platelets exposing signals to mediate their clearance. Thereby, we emphasize the impact of platelet turnover as an important condition for platelet production in vivo. Understanding of the features that characterize platelet subpopulations is very relevant for the methods of platelet concentrate production, which may enrich or deplete particular platelet subpopulations. Moreover, the concept of platelet size being associated with platelet function may be attractive for transfusion medicine as it holds the perspective to separate platelet subpopulations with specific functional capabilities.
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Affiliation(s)
- Stefan Handtke
- Institut für Immunologie und Transfusionsmedizin, Greifswald, Germany
| | - Leif Steil
- Interfakultäres Institut für Funktionelle Genomforschung, Greifswald, Germany
| | | | - Thomas Thiele
- Institut für Immunologie und Transfusionsmedizin, Greifswald, Germany
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42
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Lin BD, Carnero Montoro E, Bell JT, Boomsma DI, de Geus EJ, Jansen R, Kluft C, Mangino M, Penninx B, Spector TD, Willemsen G, Hottenga JJ. 2SNP heritability and effects of genetic variants for neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio. J Hum Genet 2017; 62:979-988. [PMID: 29066854 PMCID: PMC5669488 DOI: 10.1038/jhg.2017.76] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 05/24/2017] [Accepted: 06/13/2017] [Indexed: 01/13/2023]
Abstract
Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are important biomarkers for disease development and progression. To gain insight into the genetic causes of variance in NLR and PLR in the general population, we conducted genome-wide association (GWA) analyses and estimated SNP heritability in a sample of 5901 related healthy Dutch individuals. GWA analyses identified a new genome-wide significant locus on the HBS1L-MYB intergenic region for PLR, which replicated in a sample of 2538 British twins. For platelet count, we replicated three known genome-wide significant loci in our cohort (at CCDC71L-PIK3CG, BAK1 and ARHGEF3). For neutrophil count, we replicated the PSMD3 locus. For the identified top SNPs, we found significant cis and trans expression quantitative trait loci effects for several loci involved in hematological and immunological pathways. Linkage Disequilibrium score (LD) regression analyses for PLR and NLR confirmed that both traits are heritable, with a polygenetic SNP heritability for PLR of 14.1%, and for NLR of 2.4%. Genetic correlations were present between ratios and the constituent counts, with the genetic correlation (r=0.45) of PLR with platelet count reaching statistical significance. In conclusion, we established that two important biomarkers have a significant heritable SNP component, and identified the first genome-wide locus for PLR.
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Affiliation(s)
- Bochao Danae Lin
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Elena Carnero Montoro
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Eco J. de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- EMGO+ Institute for Health & Care Research, VU Medical Center, Amsterdam, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, VU Medical Center, Amsterdam, The Netherlands
| | | | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London SE1 9RT, UK
| | - Brenda Penninx
- Department of Psychiatry, VU Medical Center, Amsterdam, The Netherlands
| | - Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- EMGO+ Institute for Health & Care Research, VU Medical Center, Amsterdam, The Netherlands
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Lessard S, Gatof ES, Beaudoin M, Schupp PG, Sher F, Ali A, Prehar S, Kurita R, Nakamura Y, Baena E, Ledoux J, Oceandy D, Bauer DE, Lettre G. An erythroid-specific ATP2B4 enhancer mediates red blood cell hydration and malaria susceptibility. J Clin Invest 2017; 127:3065-3074. [PMID: 28714864 PMCID: PMC5531409 DOI: 10.1172/jci94378] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 06/01/2017] [Indexed: 12/12/2022] Open
Abstract
The lack of mechanistic explanations for many genotype-phenotype associations identified by GWAS precludes thorough assessment of their impact on human health. Here, we conducted an expression quantitative trait locus (eQTL) mapping analysis in erythroblasts and found erythroid-specific eQTLs for ATP2B4, the main calcium ATPase of red blood cells (rbc). The same SNPs were previously associated with mean corpuscular hemoglobin concentration (MCHC) and susceptibility to severe malaria infection. We showed that Atp2b4-/- mice demonstrate increased MCHC, confirming ATP2B4 as the causal gene at this GWAS locus. Using CRISPR-Cas9, we fine mapped the genetic signal to an erythroid-specific enhancer of ATP2B4. Erythroid cells with a deletion of the ATP2B4 enhancer had abnormally high intracellular calcium levels. These results illustrate the power of combined transcriptomic, epigenomic, and genome-editing approaches in characterizing noncoding regulatory elements in phenotype-relevant cells. Our study supports ATP2B4 as a potential target for modulating rbc hydration in erythroid disorders and malaria infection.
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Affiliation(s)
- Samuel Lessard
- Montreal Heart Institute and Université de Montréal, Montréal, Québec, Canada
| | - Emily Stern Gatof
- Division of Hematology/Oncology, Boston Children’s Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
- Tufts University School of Medicine, Boston, Massachusetts, USA
| | - Mélissa Beaudoin
- Montreal Heart Institute and Université de Montréal, Montréal, Québec, Canada
| | - Patrick G. Schupp
- Division of Hematology/Oncology, Boston Children’s Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Falak Sher
- Division of Hematology/Oncology, Boston Children’s Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Adnan Ali
- Cancer Research UK Manchester Institute, and
| | - Sukhpal Prehar
- Division of Cardiovascular Sciences, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Ryo Kurita
- Cell Engineering Division, RIKEN BioResource Center, Tsukuba, Ibaraki, Japan
| | - Yukio Nakamura
- Cell Engineering Division, RIKEN BioResource Center, Tsukuba, Ibaraki, Japan
- Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | | | - Jonathan Ledoux
- Montreal Heart Institute and Université de Montréal, Montréal, Québec, Canada
| | - Delvac Oceandy
- Division of Cardiovascular Sciences, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Daniel E. Bauer
- Division of Hematology/Oncology, Boston Children’s Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Guillaume Lettre
- Montreal Heart Institute and Université de Montréal, Montréal, Québec, Canada
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Abstract
The last decade has witnessed an explosion in the depth, variety, and amount of human genetic data that can be generated. This revolution in technical and analytical capacities has enabled the genetic investigation of human traits and disease in thousands to now millions of participants. Investigators have taken advantage of these advancements to gain insight into platelet biology and the platelet's role in human disease. To do so, large human genetics studies have examined the association of genetic variation with two quantitative traits measured in many population and patient based cohorts: platelet count (PLT) and mean platelet volume (MPV). This article will review the many human genetic strategies-ranging from genome-wide association study (GWAS), Exomechip, whole exome sequencing (WES), to whole genome sequencing (WGS)-employed to identify genes and variants that contribute to platelet traits. Additionally, we will discuss how these investigations have examined and interpreted the functional implications of these newly identified genetic factors and whether they also impart risk to human disease. The depth and size of genetic, phenotypic, and other -omic data are primed to continue their growth in the coming years and provide unprecedented opportunities to gain critical insights into platelet biology and how platelets contribute to disease.
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Affiliation(s)
- John D Eicher
- a Population Sciences Branch , National Heart Lung and Blood Institute, The Framingham Heart Study , Framingham , MA , USA
| | - Guillaume Lettre
- b Department of Medicine , Université de Montréal , Montréal , Québec , Canada.,c Montreal Heart Institute , Montréal , Québec , Canada
| | - Andrew D Johnson
- a Population Sciences Branch , National Heart Lung and Blood Institute, The Framingham Heart Study , Framingham , MA , USA
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SNP in human ARHGEF3 promoter is associated with DNase hypersensitivity, transcript level and platelet function, and Arhgef3 KO mice have increased mean platelet volume. PLoS One 2017; 12:e0178095. [PMID: 28542600 PMCID: PMC5441597 DOI: 10.1371/journal.pone.0178095] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2016] [Accepted: 05/07/2017] [Indexed: 12/20/2022] Open
Abstract
Genome-wide association studies have identified a genetic variant at 3p14.3 (SNP rs1354034) that strongly associates with platelet number and mean platelet volume in humans. While originally proposed to be intronic, analysis of mRNA expression in primary human hematopoietic subpopulations reveals that this SNP is located directly upstream of the predominantly expressed ARHGEF3 isoform in megakaryocytes (MK). We found that ARHGEF3, which encodes a Rho guanine exchange factor, is dramatically upregulated during both human and murine MK maturation. We show that the SNP (rs1354034) is located in a DNase I hypersensitive region in human MKs and is an expression quantitative locus (eQTL) associated with ARHGEF3 expression level in human platelets, suggesting that it may be the causal SNP that accounts for the variations observed in human platelet traits and ARHGEF3 expression. In vitro human platelet activation assays revealed that rs1354034 is highly correlated with human platelet activation by ADP. In order to test whether ARHGEF3 plays a role in MK development and/or platelet function, we developed an Arhgef3 KO/LacZ reporter mouse model. Reflecting changes in gene expression, LacZ expression increases during MK maturation in these mice. Although Arhgef3 KO mice have significantly larger platelets, loss of Arhgef3 does not affect baseline MK or platelets nor does it affect platelet function or platelet recovery in response to antibody-mediated platelet depletion compared to littermate controls. In summary, our data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human MKs and human platelet function—a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.
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Grinfeld J, Godfrey AL. After 10 years of JAK2V617F: Disease biology and current management strategies in polycythaemia vera. Blood Rev 2017; 31:101-118. [DOI: 10.1016/j.blre.2016.11.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Revised: 11/08/2016] [Accepted: 11/14/2016] [Indexed: 12/12/2022]
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Hodonsky CJ, Jain D, Schick UM, Morrison JV, Brown L, McHugh CP, Schurmann C, Chen DD, Liu YM, Auer PL, Laurie CA, Taylor KD, Browning BL, Li Y, Papanicolaou G, Rotter JI, Kurita R, Nakamura Y, Browning SR, Loos RJF, North KE, Laurie CC, Thornton TA, Pankratz N, Bauer DE, Sofer T, Reiner AP. Genome-wide association study of red blood cell traits in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos. PLoS Genet 2017; 13:e1006760. [PMID: 28453575 PMCID: PMC5428979 DOI: 10.1371/journal.pgen.1006760] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 05/12/2017] [Accepted: 04/12/2017] [Indexed: 01/13/2023] Open
Abstract
Prior GWAS have identified loci associated with red blood cell (RBC) traits in populations of European, African, and Asian ancestry. These studies have not included individuals with an Amerindian ancestral background, such as Hispanics/Latinos, nor evaluated the full spectrum of genomic variation beyond single nucleotide variants. Using a custom genotyping array enriched for Amerindian ancestral content and 1000 Genomes imputation, we performed GWAS in 12,502 participants of Hispanic Community Health Study and Study of Latinos (HCHS/SOL) for hematocrit, hemoglobin, RBC count, RBC distribution width (RDW), and RBC indices. Approximately 60% of previously reported RBC trait loci generalized to HCHS/SOL Hispanics/Latinos, including African ancestral alpha- and beta-globin gene variants. In addition to the known 3.8kb alpha-globin copy number variant, we identified an Amerindian ancestral association in an alpha-globin regulatory region on chromosome 16p13.3 for mean corpuscular volume and mean corpuscular hemoglobin. We also discovered and replicated three genome-wide significant variants in previously unreported loci for RDW (SLC12A2 rs17764730, PSMB5 rs941718), and hematocrit (PROX1 rs3754140). Among the proxy variants at the SLC12A2 locus we identified rs3812049, located in a bi-directional promoter between SLC12A2 (which encodes a red cell membrane ion-transport protein) and an upstream anti-sense long-noncoding RNA, LINC01184, as the likely causal variant. We further demonstrate that disruption of the regulatory element harboring rs3812049 affects transcription of SLC12A2 and LINC01184 in human erythroid progenitor cells. Together, these results reinforce the importance of genetic study of diverse ancestral populations, in particular Hispanics/Latinos.
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Affiliation(s)
- Chani J. Hodonsky
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC, United States of America
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Ursula M. Schick
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Jean V. Morrison
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Lisa Brown
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Caitlin P. McHugh
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
- New York Genome Center, New York, NY, United States of America
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Diane D. Chen
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, United States of America
| | - Yong Mei Liu
- School of Medicine, Wake Forest University, Winston-Salem, NC, United States of America
| | - Paul L. Auer
- Joseph J. Zilber School of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, United States of America
| | - Cecilia A. Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA United States of America
- Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA United States of America
| | - Brian L. Browning
- Department of Medicine, University of Washington, Seattle, WA United States of America
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, United States of America
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United States of America
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, United States of America
| | - George Papanicolaou
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD, United States of America
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA United States of America
- Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA United States of America
| | - Ryo Kurita
- Research and Development Department, Central Blood Institute, Blood Service Headquarters, Japanese Red Cross Society, Tokyo, Japan
| | - Yukio Nakamura
- Cell Engineering Division, RIKEN BioResource Center, Tsukuba, Ibaraki, Japan
- Comprehensive Human Sciences, Tsukuba University, Tsukuba, Ibaraki, Japan
| | - Sharon R. Browning
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Kari E. North
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC, United States of America
- Department of Genetics, University of North Carolina, Chapel Hill, NC, United States of America
| | - Cathy C. Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Nathan Pankratz
- Division of Laboratory Medicine & Pathology, University of Minnesota, Minneapolis, MN, United States of America
| | - Daniel E. Bauer
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, United States of America
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America
- Department of Pediatrics, Harvard Medical School and Harvard Stem Cell Institute, Harvard University, Boston, MA, United States of America
| | - Tamar Sofer
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Alex P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
- * E-mail:
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48
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Joehanes R, Zhang X, Huan T, Yao C, Ying SX, Nguyen QT, Demirkale CY, Feolo ML, Sharopova NR, Sturcke A, Schäffer AA, Heard-Costa N, Chen H, Liu PC, Wang R, Woodhouse KA, Tanriverdi K, Freedman JE, Raghavachari N, Dupuis J, Johnson AD, O'Donnell CJ, Levy D, Munson PJ. Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Genome Biol 2017; 18:16. [PMID: 28122634 PMCID: PMC5264466 DOI: 10.1186/s13059-016-1142-6] [Citation(s) in RCA: 118] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 12/20/2016] [Indexed: 12/21/2022] Open
Abstract
Background Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2. Results We detected over 19,000 independent lead cis-eQTLs and over 6000 independent lead trans-eQTLs, targeting over 10,000 gene targets (eGenes), with a false discovery rate (FDR) < 5%. Of previously published significant GWAS SNPs, 48% are identified to be significant eQTLs in our study. Some trans-eQTLs point toward novel mechanistic explanations for the association of the SNP with the GWAS-related phenotype. We also identify 59 distinct blocks or clusters of trans-eQTLs, each targeting the expression of sets of six to 229 distinct trans-eGenes. Ten of these sets of target genes are significantly enriched for microRNA targets (FDR < 5%). Many of these clusters are associated in GWAS with multiple phenotypes. Conclusions These findings provide insights into the molecular regulatory patterns involved in human physiology and pathophysiology. We illustrate the value of our eQTL database in the context of a recent GWAS meta-analysis of coronary artery disease and provide a list of targeted eGenes for 21 of 58 GWAS loci. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1142-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Roby Joehanes
- The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.,Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA.,Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, Boston, MA, USA
| | - Xiaoling Zhang
- The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.,Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.,Section of Biomedical Genetics, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Tianxiao Huan
- The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA
| | - Chen Yao
- The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA
| | - Sai-Xia Ying
- Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | - Quang Tri Nguyen
- Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | - Cumhur Yusuf Demirkale
- Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | - Michael L Feolo
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Nataliya R Sharopova
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Anne Sturcke
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Alejandro A Schäffer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Nancy Heard-Costa
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Han Chen
- School of Public Health, Harvard University, Boston, MA, USA.,Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Po-Ching Liu
- DNA Sequencing and Genomics Core, National Institutes of Health, Bethesda, MD, USA
| | - Richard Wang
- DNA Sequencing and Genomics Core, National Institutes of Health, Bethesda, MD, USA
| | - Kimberly A Woodhouse
- DNA Sequencing and Genomics Core, National Institutes of Health, Bethesda, MD, USA
| | - Kahraman Tanriverdi
- Department of Medicine, University of Massachusetts Medical School, Worchester, MA, USA
| | - Jane E Freedman
- Department of Medicine, University of Massachusetts Medical School, Worchester, MA, USA
| | - Nalini Raghavachari
- National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Josée Dupuis
- The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.,Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Andrew D Johnson
- The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA
| | - Christopher J O'Donnell
- The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.,Cardiology Section, Department of Medicine, Boston VA Healthcare, Boston, MA, USA
| | - Daniel Levy
- The Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.
| | - Peter J Munson
- Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA. .,National Institutes of Health, Bldg 12A, Room 2003, Bethesda, MD, 20892-5626, USA.
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49
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Li W, Liu Q, Tang Y. Platelet to lymphocyte ratio in the prediction of adverse outcomes after acute coronary syndrome: a meta-analysis. Sci Rep 2017; 7:40426. [PMID: 28071752 PMCID: PMC5223131 DOI: 10.1038/srep40426] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 12/05/2016] [Indexed: 12/17/2022] Open
Abstract
Recent studies have shown platelet to lymphocyte ratio (PLR) to be a potential inflammatory marker in cardiovascular diseases. We performed a meta-analysis to systematically evaluate the prognostic role of PLR in acute coronary syndrome (ACS). A comprehensive literature search up to May 18, 2016 was conducted from PUBMED, EMBASE and Web of science to identify related studies. The risk ratio (RR) with 95% confidence interval (CI) was extracted or calculated for effect estimates. Totally ten studies involving 8932 patients diagnosed with ACS were included in our research. We demonstrated that patients with higher PLR level had significantly higher risk of in-hospital adverse outcomes (RR = 2.24, 95%CI = 1.81–2.77) and long-term adverse outcomes (RR = 2.32, 95%CI = 1.64–3.28). Sensitivity analyses confirmed the stability of our results. We didn’t detect significant publication bias by Begg’s and Egger’s test (p > 0.05). In conclusion, our meta-analysis revealed that PLR is promising biomarker in predicting worse prognosis in ACS patients. The results should be validated by future large-scale, standard investigations.
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Affiliation(s)
- Wenzhang Li
- Department of Cardiology, First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China
| | - Qianqian Liu
- Department of Respiratory Diseases, Chengdu Municipal First People's Hospital, Chengdu, Sichuan, China
| | - Yin Tang
- State Key Laboratory of Oral Disease, West China School &Hospital of Stomotology, Sichuan University, Chengdu, Sichuan, China
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50
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van Rooij FJ, Qayyum R, Smith AV, Zhou Y, Trompet S, Tanaka T, Keller MF, Chang LC, Schmidt H, Yang ML, Chen MH, Hayes J, Johnson AD, Yanek LR, Mueller C, Lange L, Floyd JS, Ghanbari M, Zonderman AB, Jukema JW, Hofman A, van Duijn CM, Desch KC, Saba Y, Ozel AB, Snively BM, Wu JY, Schmidt R, Fornage M, Klein RJ, Fox CS, Matsuda K, Kamatani N, Wild PS, Stott DJ, Ford I, Slagboom PE, Yang J, Chu AY, Lambert AJ, Uitterlinden AG, Franco OH, Hofer E, Ginsburg D, Hu B, Keating B, Schick UM, Brody JA, Li JZ, Chen Z, Zeller T, Guralnik JM, Chasman DI, Peters LL, Kubo M, Becker DM, Li J, Eiriksdottir G, Rotter JI, Levy D, Grossmann V, Patel KV, Chen CH, Ridker PM, Tang H, Launer LJ, Rice KM, Li-Gao R, Ferrucci L, Evans MK, Choudhuri A, Trompouki E, Abraham BJ, Yang S, Takahashi A, Kamatani Y, Kooperberg C, Harris TB, Jee SH, Coresh J, Tsai FJ, Longo DL, Chen YT, Felix JF, Yang Q, Psaty BM, Boerwinkle E, Becker LC, Mook-Kanamori DO, Wilson JG, Gudnason V, O'Donnell CJ, Dehghan A, Cupples LA, Nalls MA, Morris AP, Okada Y, Reiner AP, Zon LI, Ganesh SK, Ganesh SK. Genome-wide Trans-ethnic Meta-analysis Identifies Seven Genetic Loci Influencing Erythrocyte Traits and a Role for RBPMS in Erythropoiesis. Am J Hum Genet 2017; 100:51-63. [PMID: 28017375 DOI: 10.1016/j.ajhg.2016.11.016] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Accepted: 11/16/2016] [Indexed: 11/26/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPMS/GTF2E2) associated with mean corpuscular hemoglobin and mean corpuscular volume. Statistical fine-mapping at this locus pointed to RBPMS at this locus and excluded nearby GTF2E2. Using zebrafish morpholino to evaluate loss of function, we observed a strong in vivo erythropoietic effect for RBPMS but not for GTF2E2, supporting the statistical fine-mapping at this locus and demonstrating that RBPMS is a regulator of erythropoiesis. Our findings show the utility of trans-ethnic GWASs for discovery and characterization of genetic loci influencing hematologic traits.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Santhi K Ganesh
- Division of Cardiovascular Medicine, Department of Internal Medicine, Department of Human Genetics, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI 48109, USA.
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