Ranks of genuine associations in whole-genome scans.
- Authors
- Zaykin, Dmitri V; Zhivotovsky, Lev A
- Year
- 2005
- Journal
- Genetics
- PMID
- 16020784
- DOI
- 10.1534/genetics.105.044206
- PMCID
- PMC1456777
With the recent advances in high-throughput genotyping techniques, it is now possible to perform whole-genome association studies to fine map causal polymorphisms underlying important traits that influence susceptibility to human diseases and efficacy of drugs. Once a genome scan is completed the results can be sorted by the association statistic value. What is the probability that true positives will be encountered among the first most associated markers? When a particular polymorphism is found associated with the trait, there is a chance that it represents either a "true" or a "false" association (TA vs. FA). Setting appropriate significance thresholds has been considered to provide assurance of sufficient odds that the associations found to be significant are genuine. However, the problem with genome scans involving thousands of markers is that the statistic values of FAs can reach quite extreme magnitudes. In such situations, the distributions corresponding to TAs and the most extreme FAs become comparable and significance thresholds tend to penalize TAs and FAs in a similar fashion. When sorting between true and false associations, the "typical" place (i.e., rank) of TAs among the most significant outcomes becomes important, ordered by the association statistic value. The distribution of ranks that we study here allows calculation of several useful quantities. In particular, it gives the number of most significant markers needed for a follow-up study to guarantee that a true association is included with certain probability. This can be calculated conditionally on having applied a multiple-testing correction. Effects of multilocus (e.g., haplotype association) tests and impact of linkage disequilibrium on the distribution of ranks associated with TAs are evaluated and can be taken into account.
No figures extracted from this document.
No chunks — full text not yet ingested.
No entities extracted from this document yet.
No uploaded files.
No citations found.
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Late-onset neonatal sepsis: genetic differences by sex and involvement of the NOTCH pathway. | Ciesielski TH et al. | — | 2023 | → |
| Association of polygenic risk scores, traumatic life events and coping strategies with war-related PTSD diagnosis and symptom severity in the South Eastern Europe (SEE)-PTSD cohort. | Weber H et al. | — | 2022 | → |
| Genetics of osteoarthritis. | Aubourg G et al. | — | 2022 | → |
| TreeMap: a structured approach to fine mapping of eQTL variants. | Liu L et al. | — | 2021 | → |
| Critical Issues in the Inclusion of Genetic and Epigenetic Information in Prevention and Intervention Trials. | Latendresse SJ et al. | — | 2018 | → |
| From genome-wide associations to candidate causal variants by statistical fine-mapping. | Schaid DJ et al. | — | 2018 | → |
| Informing Prevention and Intervention Policy Using Genetic Studies of Resistance. | Maher BS et al. | — | 2018 | → |
| Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation. | Szefer E et al. | — | 2017 | → |
| Convergent evidence for the molecular basis of musical traits. | Oikkonen J et al. | — | 2016 | → |
| Polygenic Scores in Epidemiology: Risk Prediction, Etiology, and Clinical Utility. | Maher BS | — | 2015 | → |
| A novel method, the Variant Impact On Linkage Effect Test (VIOLET), leads to improved identification of causal variants in linkage regions. | Martin LJ et al. | — | 2014 | → |
| Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors. | Ciesielski TH et al. | — | 2014 | → |
| Overrepresentation of glutamate signaling in Alzheimer's disease: network-based pathway enrichment using meta-analysis of genome-wide association studies. | Pérez-Palma E et al. | — | 2014 | → |
| Genotype to phenotype maps: multiple input abiotic signals combine to produce growth effects via attenuating signaling interactions in maize. | Makumburage GB et al. | — | 2013 | → |
| The ranking probability approach and its usage in design and analysis of large-scale studies. | Kuo CL et al. | — | 2013 | → |
| An international collaborative family-based whole genome quantitative trait linkage scan for myopic refractive error. | Abbott D et al. | — | 2012 | → |
| A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder. | Casey JP et al. | — | 2012 | → |
| A latent model for prioritization of SNPs for functional studies. | Fridley BL et al. | — | 2011 | → |
| Cathepsin D gene and the risk of Alzheimer's disease: a population-based study and meta-analysis. | Schuur M et al. | — | 2011 | → |
| Correcting away the hidden heritability. | Williams SM et al. | — | 2011 | → |
| Database of genetic studies of bipolar disorder. | Piletz JE et al. | — | 2011 | → |
| Novel rank-based approaches for discovery and replication in genome-wide association studies. | Kuo CL et al. | — | 2011 | → |
| Phenotype Uniformity in Combined-Stress Environments has a Different Genetic Architecture than in Single-Stress Treatments. | Makumburage GB et al. | — | 2011 | → |
| Sample size and power analysis for sparse signal recovery in genome-wide association studies. | Xie J et al. | — | 2011 | → |
| The AVPR1A gene and substance use disorders: association, replication, and functional evidence. | Maher BS et al. | — | 2011 | → |
| Allelic heterogeneity in genetic association meta-analysis: an application to DTNBP1 and schizophrenia. | Maher BS et al. | — | 2010 | → |
| Efficiency robust statistics for genetic linkage and association studies under genetic model uncertainty. | Joo J et al. | — | 2010 | → |
| Maize leaf epiphytic bacteria diversity patterns are genetically correlated with resistance to fungal pathogen infection. | Balint-Kurti P et al. | — | 2010 | → |
| Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies. | Miclaus K et al. | — | 2010 | → |
| What's the best statistic for a simple test of genetic association in a case-control study? | Kuo CL et al. | — | 2010 | → |
| An open access database of genome-wide association results. | Johnson AD et al. | — | 2009 | → |
| A pilot genome-wide association study of early-onset breast cancer. | Kibriya MG et al. | — | 2009 | → |
| A robust genome-wide scan statistic of the Wellcome Trust Case-Control Consortium. | Joo J et al. | — | 2009 | → |
| Estimating the posterior probability that genome-wide association findings are true or false. | Bukszár J et al. | — | 2009 | → |
| Genome-wide association study implicates chromosome 9q21.31 as a susceptibility locus for asthma in mexican children. | Hancock DB et al. | — | 2009 | → |
| Methodological Issues in Multistage Genome-wide Association Studies. | Thomas DC et al. | — | 2009 | → |
| Methods for analysis in pharmacogenomics: lessons from the Pharmacogenetics Research Network Analysis Group. | Srinivasan BS et al. | — | 2009 | → |
| Robust physical methods that enrich genomic regions identical by descent for linkage studies: confirmation of a locus for osteogenesis imperfecta. | Brooks P et al. | — | 2009 | → |
| Robust Tests in Genome-Wide Scans under Incomplete Linkage Disequilibrium. | Zheng G et al. | — | 2009 | → |
| SNP selection and multidimensional scaling to quantify population structure. | Miclaus K et al. | — | 2009 | → |
| The genetics of bipolar disorder. | Barnett JH et al. | — | 2009 | → |
| Comparison of two-phase analyses for case-control genetic association studies. | Zheng G et al. | — | 2008 | → |
| MAX-rank: a simple and robust genome-wide scan for case-control association studies. | Li Q et al. | — | 2008 | → |
| Meta-analysis of two genome-wide association studies of bipolar disorder reveals important points of agreement. | Baum AE et al. | — | 2008 | → |
| Probability that a two-stage genome-wide association study will detect a disease-associated snp and implications for multistage designs. | Gail MH et al. | — | 2008 | → |
| The future is now - will the real disease gene please stand up? | Martin ER et al. | — | 2008 | → |
| The success of the genome-wide association approach: a brief story of a long struggle. | Seng KC et al. | — | 2008 | → |
| Combining p-values in large-scale genomics experiments. | Zaykin DV et al. | — | 2007 | → |
| Effect of linkage disequilibrium between markers in linkage and association analyses. | Dupuis J et al. | — | 2007 | → |
| Flexible design for following up positive findings. | Yu K et al. | — | 2007 | → |
| Large-scale pathways-based association study in amyotrophic lateral sclerosis. | Kasperaviciute D et al. | — | 2007 | → |
| Multiple testing in the genomics era: findings from Genetic Analysis Workshop 15, Group 15. | Martin LJ et al. | — | 2007 | → |
| Multistage sampling for genetic studies. | Elston RC et al. | — | 2007 | → |
| Pathway-based approaches for analysis of genomewide association studies. | Wang K et al. | — | 2007 | → |
| Robust ranks of true associations in genome-wide case-control association studies. | Zheng G et al. | — | 2007 | → |
| Are we ready for genome-wide association studies? | Thomas DC | — | 2006 | → |
| A scan statistic for identifying chromosomal patterns of SNP association. | Sun YV et al. | — | 2006 | → |
| Family-based designs in the age of large-scale gene-association studies. | Laird NM et al. | — | 2006 | → |
| Incorporating genetic analyses into birth defects cluster investigations: strategies for identifying candidate genes. | Green RF et al. | — | 2006 | → |