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Scurrah KJ, Lamantia A, Ellis JA, Harrap SB. Familial Analysis of Epistatic and Sex-Dependent Association of Genes of the Renin-Angiotensin-Aldosterone System and Blood Pressure. ACTA ACUST UNITED AC 2018; 10:CIRCGENETICS.116.001595. [PMID: 28506960 DOI: 10.1161/circgenetics.116.001595] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 03/02/2017] [Indexed: 01/11/2023]
Abstract
BACKGROUND Renin-angiotensin-aldosterone system genes have been inconsistently associated with blood pressure, possibly because of unrecognized influences of sex-dependent genetic effects or gene-gene interactions (epistasis). METHODS AND RESULTS We tested association of systolic blood pressure with single-nucleotide polymorphisms (SNPs) at renin (REN), angiotensinogen (AGT), angiotensin-converting enzyme (ACE), angiotensin II type 1 receptor (AGTR1), and aldosterone synthase (CYP11B2), including sex-SNP or SNP-SNP interactions. Eighty-eight tagSNPs were tested in 2872 white individuals in 809 pedigrees from the Victorian Family Heart Study using variance components models. Three SNPs (rs8075924 and rs4277404 at ACE and rs12721297 at AGTR1) were individually associated with lower systolic blood pressure with significant (P<0.00076) effect sizes ≈1.7 to 2.5 mm Hg. Sex-specific associations were seen for 3 SNPs in men (rs2468523 and rs2478544 at AGT and rs11658531 at ACE) and 1 SNP in women (rs12451328 at ACE). SNP-SNP interaction was suggested (P<0.005) for 14 SNP pairs, none of which had shown individual association with systolic blood pressure. Four SNP pairs were at the same gene (2 for REN, 1 for AGT, and 1 for AGTR1). The SNP rs3097 at CYP11B2 was represented in 5 separate pairs. CONCLUSIONS SNPs at key renin-angiotensin-aldosterone system genes associate with systolic blood pressure individually in both sexes, individually in one sex only and only when combined with another SNP. Analyses that incorporate sex-dependent and epistatic effects could reconcile past inconsistencies and account for some of the missing heritability of blood pressure and are generally relevant to SNP association studies for any phenotype.
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Affiliation(s)
- Katrina J Scurrah
- From the Department of Physiology (K.J.S., A.L., S.B.H.), Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health (K.J.S.), and Department of Paediatrics (J.A.E.), The University of Melbourne, Australia; Genes, Environment & Complex Disease Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia (J.A.E.); and Centre for Social and Early Emotional Development, Faculty of Health, Deakin University, Victoria, Australia (J.A.E.)
| | - Angela Lamantia
- From the Department of Physiology (K.J.S., A.L., S.B.H.), Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health (K.J.S.), and Department of Paediatrics (J.A.E.), The University of Melbourne, Australia; Genes, Environment & Complex Disease Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia (J.A.E.); and Centre for Social and Early Emotional Development, Faculty of Health, Deakin University, Victoria, Australia (J.A.E.)
| | - Justine A Ellis
- From the Department of Physiology (K.J.S., A.L., S.B.H.), Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health (K.J.S.), and Department of Paediatrics (J.A.E.), The University of Melbourne, Australia; Genes, Environment & Complex Disease Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia (J.A.E.); and Centre for Social and Early Emotional Development, Faculty of Health, Deakin University, Victoria, Australia (J.A.E.)
| | - Stephen B Harrap
- From the Department of Physiology (K.J.S., A.L., S.B.H.), Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health (K.J.S.), and Department of Paediatrics (J.A.E.), The University of Melbourne, Australia; Genes, Environment & Complex Disease Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia (J.A.E.); and Centre for Social and Early Emotional Development, Faculty of Health, Deakin University, Victoria, Australia (J.A.E.).
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Abstract
This chapter describes the main issues that genetic epidemiologists usually consider in the design of linkage and association studies. For linkage, we briefly consider the situation of rare highly penetrant alleles showing a disease pattern consistent with Mendelian inheritance investigated through parametric methods in large pedigrees, or with autozygosity mapping in inbred families, and we then turn our focus to the most common design, the affected sibling pair design that is of more relevance for common, complex diseases. Power and sample size calculations are provided as a function of the strength of the genetic effect being investigated. We also discuss the impact of other determinants of statistical power such as disease heterogeneity, pedigree and genotyping errors and the effect of the type and density of genetic markers. For association studies, we consider the popular case-control design for dichotomous phenotypes and we provide power and sample size calculations for one-stage and multistage designs. For candidate genes, guidelines are given on the prioritization of genetic variants, and for genome-wide association studies (GWAS) the issue of choosing an appropriate SNP array is discussed. A warning is issued regarding the danger of designing an underpowered replication study following an initial GWAS. The risk of finding spurious association due to population stratification, cryptic relatedness, and differential bias is underlined.
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Affiliation(s)
- Jérémie Nsengimana
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - D Timothy Bishop
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.
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3
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Blue EM, Sun L, Tintle NL, Wijsman EM. Value of Mendelian laws of segregation in families: data quality control, imputation, and beyond. Genet Epidemiol 2014; 38 Suppl 1:S21-8. [PMID: 25112184 DOI: 10.1002/gepi.21821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
When analyzing family data, we dream of perfectly informative data, even whole-genome sequences (WGSs) for all family members. Reality intervenes, and we find that next-generation sequencing (NGS) data have errors and are often too expensive or impossible to collect on everyone. The Genetic Analysis Workshop 18 working groups on quality control and dropping WGSs through families using a genome-wide association framework focused on finding, correcting, and using errors within the available sequence and family data, developing methods to infer and analyze missing sequence data among relatives, and testing for linkage and association with simulated blood pressure. We found that single-nucleotide polymorphisms, NGS data, and imputed data are generally concordant but that errors are particularly likely at rare variants, for homozygous genotypes, within regions with repeated sequences or structural variants, and within sequence data imputed from unrelated individuals. Admixture complicated identification of cryptic relatedness, but information from Mendelian transmission improved error detection and provided an estimate of the de novo mutation rate. Computationally, fast rule-based imputation was accurate but could not cover as many loci or subjects as more computationally demanding probability-based methods. Incorporating population-level data into pedigree-based imputation methods improved results. Observed data outperformed imputed data in association testing, but imputed data were also useful. We discuss the strengths and weaknesses of existing methods and suggest possible future directions, such as improving communication between data collectors and data analysts, establishing thresholds for and improving imputation quality, and incorporating error into imputation and analytical models.
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Affiliation(s)
- Elizabeth M Blue
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, United States of America
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Cheung CYK, Thompson EA, Wijsman EM. Detection of Mendelian consistent genotyping errors in pedigrees. Genet Epidemiol 2014; 38:291-9. [PMID: 24718985 DOI: 10.1002/gepi.21806] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2013] [Revised: 03/03/2014] [Accepted: 03/04/2014] [Indexed: 11/12/2022]
Abstract
Detection of genotyping errors is a necessary step to minimize false results in genetic analysis. This is especially important when the rate of genotyping errors is high, as has been reported for high-throughput sequence data. To detect genotyping errors in pedigrees, Mendelian inconsistent (MI) error checks exist, as do multi-point methods that flag Mendelian consistent (MC) errors for sparse multi-allelic markers. However, few methods exist for detecting MC genotyping errors, particularly for dense variants on large pedigrees. Here, we introduce an efficient method to detect MC errors even for very dense variants (e.g., SNPs and sequencing data) on pedigrees that may be large. Our method first samples inheritance vectors (IVs) using a moderately sparse but informative set of markers using a Markov chain Monte Carlo-based sampler. Using sampled IVs, we considered two test statistics to detect MC genotyping errors: the percentage of IVs inconsistent with observed genotypes (A1) or the posterior probability of error configurations (A2). Using simulations, we show that this method, even with the simpler A1 statistic, is effective for detecting MC genotyping errors in dense variants, with sensitivity almost as high as the theoretical best sensitivity possible. We also evaluate the effectiveness of this method as a function of parameters, when including the observed pattern for genotype, density of framework markers, error rate, allele frequencies, and number of sampled inheritance vectors. Our approach provides a line of defense against false findings based on the use of dense variants in pedigrees.
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Affiliation(s)
- Charles Y K Cheung
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, United States of America
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Li B, Wang G, Leal SM. PhenoMan: phenotypic data exploration, selection, management and quality control for association studies of rare and common variants. Bioinformatics 2014; 30:442-4. [PMID: 24336645 DOI: 10.1093/bioinformatics/btt682] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Next-generation sequencing and other high-throughput technology advances have promoted great interest in detecting associations between complex traits and genetic variants. Phenotype selection, quality control (QC) and control of confounders are crucial and can have a great impact on the ability to detect associations. Although there are programs to perform association analyses, e.g. PLINK and GenABEL, they cannot be used for comprehensive management and QC of phenotype data. To address this need PhenoMan was developed: to select individuals based on multiple phenotype criteria or population membership; control for missing covariate data; remove related individuals, duplicate samples and individuals with incorrect sex specification; recode primary traits and covariates; transform data; remove or winsorize outliers; select covariates for analysis; and create residuals. To ensure consistency and harmonization between analyses, a report is generated for every dataset. Summary statistics are also provided in graphical or text format. PhenoMan can be used for selection and manipulation of quantitative, disease and control data. SUMMARY Phenoman is freeware that provides approaches for efficient exploration and management of phenotype data. Proper QC of phenotypes before proceeding to the association analysis is critical to ensure control of type I and II errors, reliable effect estimates and consistent results between studies. PhenoMan is highly beneficial for the preparation of qualitative and quantitative trait data for association studies using new datasets as well as those obtained from public repositories. AVAILABILITY AND IMPLEMENTATION code.google.com/p/phenoman
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Affiliation(s)
- Biao Li
- Center for Statistical Genetics, Department of Molecular and Human Genetics, One Baylor Plaza 700D, Baylor College of Medicine, Houston, TX 77030, USA
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Abstract
This chapter describes the main issues that genetic epidemiologists usually consider in the design of linkage and association studies. For linkage, we briefly consider the situation of rare, highly penetrant alleles showing a disease pattern consistent with Mendelian inheritance investigated through parametric methods in large pedigrees or with autozygosity mapping in inbred families, and we then turn our focus to the most common design, affected sibling pairs, of more relevance for common, complex diseases. Theoretical and more practical power and sample size calculations are provided as a function of the strength of the genetic effect being investigated. We also discuss the impact of other determinants of statistical power such as disease heterogeneity, pedigree, and genotyping errors, as well as the effect of the type and density of genetic markers. Linkage studies should be as large as possible to have sufficient power in relation to the expected genetic effect size. Segregation analysis, a formal statistical technique to describe the underlying genetic susceptibility, may assist in the estimation of the relevant parameters to apply, for instance. However, segregation analyses estimate the total genetic component rather than a single-locus effect. Locus heterogeneity should be considered when power is estimated and at the analysis stage, i.e. assuming smaller locus effect than the total the genetic component from segregation studies. Disease heterogeneity should be minimised by considering subtypes if they are well defined or by otherwise collecting known sources of heterogeneity and adjusting for them as covariates; the power will depend upon the relationship between the disease subtype and the underlying genotypes. Ultimately, identifying susceptibility alleles of modest effects (e.g. RR≤1.5) requires a number of families that seem unfeasible in a single study. Meta-analysis and data pooling between different research groups can provide a sizeable study, but both approaches require even a higher level of vigilance about locus and disease heterogeneity when data come from different populations. All necessary steps should be taken to minimise pedigree and genotyping errors at the study design stage as they are, for the most part, due to human factors. A two-stage design is more cost-effective than one stage when using short tandem repeats (STRs). However, dense single-nucleotide polymorphism (SNP) arrays offer a more robust alternative, and due to their lower cost per unit, the total cost of studies using SNPs may in the future become comparable to that of studies using STRs in one or two stages. For association studies, we consider the popular case-control design for dichotomous phenotypes, and we provide power and sample size calculations for one-stage and multistage designs. For candidate genes, guidelines are given on the prioritisation of genetic variants, and for genome-wide association studies (GWAS), the issue of choosing an appropriate SNP array is discussed. A warning is issued regarding the danger of designing an underpowered replication study following an initial GWAS. The risk of finding spurious association due to population stratification, cryptic relatedness, and differential bias is underlined. GWAS have a high power to detect common variants of high or moderate effect. For weaker effects (e.g. relative risk<1.2), the power is greatly reduced, particularly for recessive loci. While sample sizes of 10,000 or 20,000 cases are not beyond reach for most common diseases, only meta-analyses and data pooling can allow attaining a study size of this magnitude for many other diseases. It is acknowledged that detecting the effects from rare alleles (i.e. frequency<5%) is not feasible in GWAS, and it is expected that novel methods and technology, such as next-generation resequencing, will fill this gap. At the current stage, the choice of which GWAS SNP array to use does not influence the power in populations of European ancestry. A multistage design reduces the study cost but has less power than the standard one-stage design. If one opts for a multistage design, the power can be improved by jointly analysing the data from different stages for the SNPs they share. The estimates of locus contribution to disease risk from genome-wide scans are often biased, and relying on them might result in an underpowered replication study. Population structure has so far caused less spurious associations than initially feared, thanks to systematic ethnicity matching and application of standard quality control measures. Differential bias could be a more serious threat and must be minimised by strictly controlling all the aspects of DNA acquisition, storage, and processing.
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Affiliation(s)
- Jérémie Nsengimana
- Section of Epidemiology and Biostatistics, Leeds Institute of Molecular Medicine, University of Leeds, Cancer Genetics Building, Leeds, UK.
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Åberg K, Dai F, Sun G, Keighley ED, Indugula SR, Roberts ST, Zhang Q, Smelser D, Viali S, Tuitele J, Jin L, Deka R, Weeks DE, McGarvey ST. Susceptibility loci for adiposity phenotypes on 8p, 9p, and 16q in American Samoa and Samoa. Obesity (Silver Spring) 2009; 17:518-24. [PMID: 19238140 PMCID: PMC2879592 DOI: 10.1038/oby.2008.558] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Obesity is a complex phenotype affected by genetic and environmental influences such as sociocultural factors and individual behaviors. Previously, we performed two separate genome-wide investigations for adiposity-related traits (BMI, percentage body fat (%BF), abdominal circumference (ABDCIR), and serum leptin and serum adiponectin levels) in families from American Samoa and in families from Samoa. The two polities have a common evolutionary history but have lately been influenced by variations in economic development, leading to differences in income and wealth and in dietary and physical activity patterns. We now present a genome-wide linkage scan of the combined samples from the two polities. We adjust for environmental covariates, including polity of residence, education, cigarette smoking, and farm work, and use variance component methods to calculate univariate and bivariate multipoint lod scores. We identified a region on 9p22 with genome-wide significant linkage for the bivariate phenotypes ABDCIR-%BF (1-d.f. lod 3.30) and BMI-%BF (1-d.f. lod 3.31) and two regions with genome-wide suggestive linkage on 8p12 and 16q23 for adiponectin (lod 2.74) and the bivariate phenotype leptin-ABDCIR (1-d.f. lod 3.17), respectively. These three regions have previously been reported to be linked to adiposity-related phenotypes in independent studies. However, the differences in results between this study and our previous polity-specific studies suggest that environmental effects are of different importance in the samples. These results strongly encourage further genetic studies of adiposity-related phenotypes where extended sets of carefully measured environmental factors are taken into account.
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Affiliation(s)
- Karolina Åberg
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Feng Dai
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Guangyun Sun
- Center for Genome Information, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio, USA
| | - Ember D. Keighley
- International Health Institute, Brown University, Providence, Rhode Island, USA
| | - Subba R. Indugula
- Center for Genome Information, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio, USA
| | - Sarah T. Roberts
- International Health Institute, Brown University, Providence, Rhode Island, USA
| | - Qi Zhang
- Center for Genome Information, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio, USA
| | - Diane Smelser
- Center for Genome Information, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio, USA
| | - Satupaitea Viali
- Tupua Tamasese Meaole Hospital, Ministry of Health, Government of Samoa, Apia, Samoa
| | - John Tuitele
- Tafuna Family Health Center, Department of Health, American Samoa Government, Pago Pago, American Samoa, USA
| | - Li Jin
- Center for Genome Information, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio, USA
| | - Ranjan Deka
- Center for Genome Information, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio, USA
| | - Daniel E. Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Stephen T. McGarvey
- International Health Institute, Brown University, Providence, Rhode Island, USA
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Miller MB, Schwander K, Rao D. Genotyping Errors and Their Impact on Genetic Analysis. GENETIC DISSECTION OF COMPLEX TRAITS 2008; 60:141-52. [DOI: 10.1016/s0065-2660(07)00406-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Greenwood TA, Libiger O, Kardia S, Hanis C, Morrison AC, Gu CC, Rice T, Miller M, Turner ST, Myers RH, Grove J, Hsiao CF, Weder AB, Schork NJ. Comprehensive linkage and linkage heterogeneity analysis of 4344 sibling pairs affected with hypertension from the Family Blood Pressure Program. Genet Epidemiol 2007; 31:195-210. [PMID: 17266112 DOI: 10.1002/gepi.20202] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Linkage analyses of complex, multifactorial traits and diseases, such as essential hypertension, have been difficult to interpret and reconcile. Many published studies provide evidence suggesting that different genes and genomic regions influence hypertension, but knowing which of these studies reflect true positive results is challenging. The reasons for this include the diversity of analytical methods used across these studies, the different samples and sample sizes in each study, and the complicated biological underpinnings of hypertension. We have undertaken a comprehensive linkage analysis of 371 autosomal microsatellite markers genotyped on 4,334 sibling pairs affected with hypertension from five ethnic groups sampled from 13 different field centers associated with the Family Blood Pressure Program (FBPP). We used a single analytical technique known to be robust to interpretive problems associated with a lack of completely informative markers to assess evidence for linkage to hypertension both within and across the ethnic groups and field centers. We find evidence for linkage to a number of genomic regions, with the most compelling evidence from analyses that combine data across field center and ethnic groups (e.g., chromosomes 2 and 9). We also pursued linkage analyses that accommodate locus heterogeneity, which is known to plague the identification of disease susceptibility loci in linkage studies of complex diseases. We find evidence for linkage heterogeneity on chromosomes 2 and 17. Ultimately our results suggest that evidence for linkage heterogeneity can only be detected with large sample sizes, such as the FBPP, which is consistent with theoretical sample size calculations.
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Affiliation(s)
- Tiffany A Greenwood
- Polymorphism Research Laboratory, Department of Psychiatry, University of California, San Diego, La Jolla, California 92093-0603, USA
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Chang YPC, Liu X, Kim JDO, Ikeda MA, Layton MR, Weder AB, Cooper RS, Kardia SLR, Rao DC, Hunt SC, Luke A, Boerwinkle E, Chakravarti A. Multiple genes for essential-hypertension susceptibility on chromosome 1q. Am J Hum Genet 2007; 80:253-64. [PMID: 17236131 PMCID: PMC1785356 DOI: 10.1086/510918] [Citation(s) in RCA: 89] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2006] [Accepted: 11/16/2006] [Indexed: 11/03/2022] Open
Abstract
Essential hypertension, defined as elevated levels of blood pressure (BP) without any obvious cause, is a major risk factor for coronary heart disease, stroke, and renal disease. BP levels and susceptibility to development of essential hypertension are partially determined by genetic factors that are poorly understood. Similar to other efforts to understand complex, non-Mendelian phenotypes, genetic dissection of hypertension-related traits employs genomewide linkage analyses of families and association studies of patient cohorts, to uncover rare and common disease alleles, respectively. Family-based mapping studies of elevated BP cover the large intermediate ground for identification of genes with common variants of significant effect. Our genomewide linkage and candidate-gene-based association studies demonstrate that a replicated linkage peak for BP regulation on human chromosome 1q, homologous to mouse and rat quantitative trait loci for BP, contains at least three genes associated with BP levels in multiple samples: ATP1B1, RGS5, and SELE. Individual variants in these three genes account for 2-5-mm Hg differences in mean systolic BP levels, and the cumulative effect reaches 8-10 mm Hg. Because the associated alleles in these genes are relatively common (frequency >5%), these three genes are important contributors to elevated BP in the population at large.
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Affiliation(s)
- Yen-Pei Christy Chang
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Van Steen K, Laird NM, Markel P, Molenberghs G. Approaches to handling incomplete data in family-based association testing. Ann Hum Genet 2006; 71:141-51. [PMID: 17096676 DOI: 10.1111/j.1469-1809.2006.00325.x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The high throughput of data arising from the complete sequence of the human genome has left statistical geneticists with a rich and extensive information source. The wide availability of software and the increase in computing power has improved the possibilities to access and process such data. One problem is incompleteness of the data: unobserved or partially observed data points due to technical reasons or reasons associated with the patient's status or erroneous measurements of phenotype or genotype, to name a few. When not properly accounted for, these sources of incompleteness may seriously jeopardize the credibility of results from analyses. In this paper we provide some perspectives on the occurrence and analysis of different forms of incomplete data in family-based genetic association testing.
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Affiliation(s)
- K Van Steen
- Department of Applied Mathematics and Computer Science, Ghent University, Ghent, Belgium
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12
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Gene mapping: Balance among quality, quantity and cost of data in the era of whole-genome mapping for complex disease. Eur J Hum Genet 2006. [DOI: 10.1038/sj.ejhg.5201693] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Elevated prevalence of arterial hypertension amongst Belgian taxi drivers during the World Hypertension Day campaign 2006. J Hypertens 2006. [DOI: 10.1097/01.hjh.0000249714.74510.e0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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