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Ndila CM, Nyirongo V, Macharia AW, Jeffreys AE, Rowlands K, Hubbart C, Busby GBJ, Band G, Harding RM, Rockett KA, Williams TN. Haplotype heterogeneity and low linkage disequilibrium reduce reliable prediction of genotypes for the ‑α 3.7I form of α-thalassaemia using genome-wide microarray data. Wellcome Open Res 2021; 5:287. [PMID: 34632085 PMCID: PMC8474104 DOI: 10.12688/wellcomeopenres.16320.2] [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] [Accepted: 09/10/2021] [Indexed: 12/26/2022] Open
Abstract
Background: The -α
3.7I-thalassaemia deletion is very common throughout Africa because it protects against malaria. When undertaking studies to investigate human genetic adaptations to malaria or other diseases, it is important to account for any confounding effects of α-thalassaemia to rule out spurious associations. Methods: In this study, we have used direct α-thalassaemia genotyping to understand why GWAS data from a large malaria association study in Kilifi Kenya did not identify the α-thalassaemia signal. We then explored the potential use of a number of new approaches to using GWAS data for imputing α-thalassaemia as an alternative to direct genotyping by PCR. Results: We found very low linkage-disequilibrium of the directly typed data with the GWAS SNP markers around α-thalassaemia and across the haemoglobin-alpha (
HBA) gene region, which along with a complex haplotype structure, could explain the lack of an association signal from the GWAS SNP data. Some indirect typing methods gave results that were in broad agreement with those derived from direct genotyping and could identify an association signal, but none were sufficiently accurate to allow correct interpretation compared with direct typing, leading to confusing or erroneous results. Conclusions: We conclude that going forwards, direct typing methods such as PCR will still be required to account for α-thalassaemia in GWAS studies.
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Affiliation(s)
- Carolyne M Ndila
- Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme, Kilifi, PO BOX 230-80108, Kenya
| | - Vysaul Nyirongo
- United Nation Statistics Division, United Nations, New York, New York, 10017, USA
| | - Alexander W Macharia
- Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme, Kilifi, PO BOX 230-80108, Kenya
| | - Anna E Jeffreys
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, OX3 7BN, UK
| | - Kate Rowlands
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, OX3 7BN, UK
| | - Christina Hubbart
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, OX3 7BN, UK
| | - George B J Busby
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, OX3 7BN, UK.,Centre for Genomics and Global Health, Big Data Institute, University of Oxford, Oxford, Oxfordshire, OX3 7LF, UK
| | - Gavin Band
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, OX3 7BN, UK.,Parasites and Microbes Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK
| | - Rosalind M Harding
- Departments of Zoology and Statistics, University of Oxford, Oxford, Oxfordshire, OX1 3SZ, UK
| | - Kirk A Rockett
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, OX3 7BN, UK.,Parasites and Microbes Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK
| | - Thomas N Williams
- Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme, Kilifi, PO BOX 230-80108, Kenya.,Department of Infectious Diseases, Imperial College Faculty of Medicine, London, W2 1NY, UK
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Ndila CM, Nyirongo V, Macharia AW, Jeffreys AE, Rowlands K, Hubbart C, Busby GBJ, Band G, Harding RM, Rockett KA, Williams TN. Haplotype heterogeneity and low linkage disequilibrium reduce reliable prediction of genotypes for the ‑α3.7I form of α-thalassaemia using genome-wide microarray data. Wellcome Open Res 2020; 5:287. [DOI: 10.12688/wellcomeopenres.16320.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/26/2020] [Indexed: 11/20/2022] Open
Abstract
Background: The -α3.7I-thalassaemia deletion is very common throughout Africa because it protects against malaria. When undertaking studies to investigate human genetic adaptations to malaria or other diseases, it is important to account for any confounding effects of α-thalassaemia to rule out spurious associations. Methods: In this study we have used direct α-thalassaemia genotyping to understand why GWAS data from a large malaria association study in Kilifi Kenya did not identify the α-thalassaemia signal. We then explored the potential use of a number of new approaches to using GWAS data for imputing α-thalassaemia as an alternative to direct genotyping by PCR. Results: We found very low linkage-disequilibrium of the directly typed data with the GWAS SNP markers around α-thalassaemia and across the haemoglobin-alpha (HBA) gene region, which along with a complex haplotype structure, could explain the lack of an association signal from the GWAS SNP data. Some indirect typing methods gave results that were in broad agreement with those derived from direct genotyping and could identify an association signal, but none were sufficiently accurate to allow correct interpretation compared with direct typing, leading to confusing or erroneous results. Conclusions: We conclude that going forwards, direct typing methods such as PCR will still be required to account for α-thalassaemia in GWAS studies.
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Hu C, Duijts L, Erler NS, Elbert NJ, Piketty C, Bourdès V, Blanchet-Réthoré S, de Jongste JC, Pasmans SGMA, Felix JF, Nijsten T. Most associations of early-life environmental exposures and genetic risk factors poorly differentiate between eczema phenotypes: the Generation R Study. Br J Dermatol 2019; 181:1190-1197. [PMID: 30869802 PMCID: PMC6916296 DOI: 10.1111/bjd.17879] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2019] [Indexed: 12/23/2022]
Abstract
Background Childhood eczema is variable in onset and persistence. Objectives To identify eczema phenotypes during childhood, and their associations with early‐life environmental and genetic factors. Methods In this study of 5297 children from a multiethnic population‐based prospective cohort study, phenotypes based on parent‐reported physician‐diagnosed eczema from age 6 months to 10 years were identified using latent class growth analysis. Information on environmental factors was obtained using postal questionnaires. Four filaggrin mutations were genotyped and a risk score was calculated based on 30 genetic variants. Weighted adjusted multinomial models were used for association analyses. Results We identified the following five eczema phenotypes: never (76%), early transient (8%), mid‐transient (6%) and late transient (8%) and persistent eczema (2%). Early transient and persistent eczema were most common in first‐born children, those with a parental history of eczema, allergy or asthma and those with persistent wheezing [range of odds ratio (OR): 1.37, 95% confidence interval (CI) 1.07–1.74 and OR 3.38, 95%CI 1.95–5.85]. Early transient eczema was most common in male children only (OR 1·49, 95% CI 1·18–1·89). Children with late transient or persistent eczema were more often of Asian ethnicity (OR 2·04, 95% CI 1·14–3·65 and OR 3·08, 95% CI 1·34–7·10, respectively). Children with early, late transient and persistent eczema more often had a filaggrin mutation or additional risk alleles (range OR: 1.07, 95%CI 1.02–1.12 and OR 2.21, 95%CI 1.39–3.50). Eczema phenotypes were not associated with maternal education, breastfeeding, day care attendance and pet exposure. Conclusions Five eczema phenotypes were identified in a multiethnic paediatric population with limited differences in risk profiles, except for sex and ethnicity. What's already known about this topic? Two previous studies in longitudinal birth cohorts identified four and six different eczema phenotypes, predominantly in children of European ethnicity.
What does this study add? Five eczema phenotypes were identified in a multiethnic paediatric population using latent class growth analysis. Children with early transient and persistent eczema were most often first‐born children and had persistent wheezing, filaggrin mutation or additional risk alleles. Previously known eczema risk factors had limited differentiating capabilities for eczema phenotypes, except for the association of early transient eczema with male children, and late transient and persistent eczema with Asian ethnicity.
https://doi.org/10.1111/bjd.18575 available online https://www.bjdonline.com/article/most-associations-of-early-life-environmental-exposures-and-genetic-risk-factors-poorly-differentiate-between-eczema-phenotypes-the-generation-r-study/
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Affiliation(s)
- C Hu
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.,Department of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.,Department of Pediatrics, Division of Respiratory Medicine and Allergology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - L Duijts
- Department of Pediatrics, Division of Respiratory Medicine and Allergology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.,Department of Pediatrics, Division of Neonatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - N S Erler
- Department of Biostatistics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - N J Elbert
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.,Department of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - C Piketty
- CUTIS (Clinical Unit for Tests and Imaging of Skin), Evaluation Department, Nestlé Skin Health/Galderma Research and Development, Sophia-Antipolis, France
| | - V Bourdès
- CUTIS (Clinical Unit for Tests and Imaging of Skin), Evaluation Department, Nestlé Skin Health/Galderma Research and Development, Sophia-Antipolis, France
| | - S Blanchet-Réthoré
- CUTIS (Clinical Unit for Tests and Imaging of Skin), Evaluation Department, Nestlé Skin Health/Galderma Research and Development, Sophia-Antipolis, France
| | - J C de Jongste
- Department of Pediatrics, Division of Respiratory Medicine and Allergology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - S G M A Pasmans
- Department of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - J F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.,Department of Pediatrics, Division of Respiratory Medicine and Allergology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.,Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.,Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - T Nijsten
- Department of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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Sepúlveda N, Manjurano A, Campino SG, Lemnge M, Lusingu J, Olomi R, Rockett KA, Hubbart C, Jeffreys A, Rowlands K, Clark TG, Riley EM, Drakeley CJ. Malaria Host Candidate Genes Validated by Association With Current, Recent, and Historical Measures of Transmission Intensity. J Infect Dis 2017; 216:45-54. [PMID: 28541483 PMCID: PMC5853769 DOI: 10.1093/infdis/jix250] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 05/24/2017] [Indexed: 01/19/2023] Open
Abstract
Background Human malaria susceptibility is determined by multiple genetic factors. It is unclear, however, which genetic variants remain important over time. Methods Genetic associations of 175 high-quality polymorphisms within several malaria candidate genes were examined in a sample of 8096 individuals from northeast Tanzania using altitude, seroconversion rates, and parasite rates as proxies of historical, recent, and current malaria transmission intensity. A principal component analysis was used to derive 2 alternative measures of overall malaria propensity of a location across different time scales. Results Common red blood cell polymorphisms (ie, hemoglobin S, glucose-6-phosphate dehydrogenase, and α-thalassemia) were the only ones to be associated with all 3 measures of transmission intensity and the first principal component. Moderate associations were found between some immune response genes (ie, IL3 and IL13) and parasite rates, but these could not be reproduced using the alternative measures of malaria propensity. Conclusions We have demonstrated the potential of using altitude and seroconversion rate as measures of malaria transmission capturing medium- to long-term time scales to detect genetic associations that are likely to persist over time. These measures also have the advantage of minimizing the deleterious effects of random factors affecting parasite rates on the respective association signals.
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Affiliation(s)
- Nuno Sepúlveda
- London School of Hygiene and Tropical Medicine
- Centre of Statistics and Applications, University of Lisbon, Portugal
| | - Alphaxard Manjurano
- London School of Hygiene and Tropical Medicine
- Joint Malaria Programme, Kilimanjaro Christian Medical Centre, Moshi
| | - Susana G Campino
- London School of Hygiene and Tropical Medicine
- Wellcome Trust Sanger Institute, Hinxton
| | - Martha Lemnge
- National Institute for Medical Research, Dar es Salaam, Tanzania
| | - John Lusingu
- National Institute for Medical Research, Dar es Salaam, Tanzania
| | - Raimos Olomi
- Joint Malaria Programme, Kilimanjaro Christian Medical Centre, Moshi
| | - Kirk A Rockett
- Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
| | - Christina Hubbart
- Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
| | - Anna Jeffreys
- Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
| | - Kate Rowlands
- Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
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Sepúlveda N, Paulino CD, Drakeley C. Sample size and power calculations for detecting changes in malaria transmission using antibody seroconversion rate. Malar J 2015; 14:529. [PMID: 26715538 PMCID: PMC4696297 DOI: 10.1186/s12936-015-1050-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2015] [Accepted: 12/12/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Several studies have highlighted the use of serological data in detecting a reduction in malaria transmission intensity. These studies have typically used serology as an adjunct measure and no formal examination of sample size calculations for this approach has been conducted. METHODS A sample size calculator is proposed for cross-sectional surveys using data simulation from a reverse catalytic model assuming a reduction in seroconversion rate (SCR) at a given change point before sampling. This calculator is based on logistic approximations for the underlying power curves to detect a reduction in SCR in relation to the hypothesis of a stable SCR for the same data. Sample sizes are illustrated for a hypothetical cross-sectional survey from an African population assuming a known or unknown change point. RESULTS Overall, data simulation demonstrates that power is strongly affected by assuming a known or unknown change point. Small sample sizes are sufficient to detect strong reductions in SCR, but invariantly lead to poor precision of estimates for current SCR. In this situation, sample size is better determined by controlling the precision of SCR estimates. Conversely larger sample sizes are required for detecting more subtle reductions in malaria transmission but those invariantly increase precision whilst reducing putative estimation bias. CONCLUSIONS The proposed sample size calculator, although based on data simulation, shows promise of being easily applicable to a range of populations and survey types. Since the change point is a major source of uncertainty, obtaining or assuming prior information about this parameter might reduce both the sample size and the chance of generating biased SCR estimates.
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Affiliation(s)
- Nuno Sepúlveda
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. .,Center of Statistics and Applications of University of Lisbon, Faculdade de Ciências da Universidade de Lisboa, Bloco C6-Piso 4, 1749-1016, Lisbon, Portugal.
| | - Carlos Daniel Paulino
- Center of Statistics and Applications of University of Lisbon, Faculdade de Ciências da Universidade de Lisboa, Bloco C6-Piso 4, 1749-1016, Lisbon, Portugal. .,Instituto Superior Técnico, Universidade Técnica de Lisboa, Avenida Rovisco Pais, 1049-001, Lisbonn, Portugal.
| | - Chris Drakeley
- London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
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