ArticleGenetic epidemiology2014
Combining family- and population-based imputation data for association analysis of rare and common variants in large pedigrees.
Article in Genetic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family-Based Association Testing.Genetic epidemiology · 2025Article
- The sequence kernel association test for the proportional odds model.Bioinformatics (Oxford, England) · 2025Article
- Genomic Exploration of Essential Hypertension in African-Brazilian Quilombo Populations: A Comprehensive Approach With Pedigree Analysis and Family-Based Association Studies.Journal of the American Heart Association · 2025Article
- How local reference panels improve imputation in French populations.Scientific reports · 2024Article
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- A joint use of pooling and imputation for genotyping SNPs.BMC bioinformatics · 2022Article
- Burden of Type 2 Diabetes and Associated Cardiometabolic Traits and Their Heritability Estimates in Endogamous Ethnic Groups of India: Findings From the INDIGENIUS Consortium.Frontiers in endocrinology · 2022Article
- Alternative Applications of Genotyping Array Data Using Multivariant Methods.Trends in genetics : TIG · 2020Review
- Kinpute: using identity by descent to improve genotype imputation.Bioinformatics (Oxford, England) · 2019Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- Revisit Population-based and Family-based Genotype Imputation.Scientific reports · 2019Article
- Association score testing for rare variants and binary traits in family data with shared controls.Briefings in bioinformatics · 2019Article
- Comparison and assessment of family- and population-based genotype imputation methods in large pedigrees.Genome research · 2019Article
- The Mega2R package: R tools for accessing and processing genetic data in common formats.F1000Research · 2018Article
- PADRE: Pedigree-Aware Distant-Relationship Estimation.American journal of human genetics · 2016Article
- Family-based approaches: design, imputation, analysis, and beyond.BMC genetics · 2016Article
- A combined association test for rare variants using family and case-control data.BMC proceedings · 2016Article
- Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data.BMC proceedings · 2016Article
- Imputing rare variants in families using a two-stage approach.BMC proceedings · 2016Article
- Linkage Analysis of Urine Arsenic Species Patterns in the Strong Heart Family Study.Toxicological sciences : an official journal of the Society of Toxicology · 2015Article
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2 authors.
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Abstract
In the last two decades, complex traits have become the main focus of genetic studies. The hypothesis that both rare and common variants are associated with complex traits is increasingly being discussed. Family-based association studies using relatively large pedigrees are suitable for both rare and common variant identification. Because of the high cost of sequencing technologies, imputation methods are important for increasing the amount of information at low cost. A recent family-based imputation method, Genotype Imputation Given Inheritance (GIGI), is able to handle large pedigrees and accurately impute rare variants, but does less well for common variants where population-based methods perform better. Here, we propose a flexible approach to combine imputation data from both family- and population-based methods. We also extend the Sequence Kernel Association Test for Rare and Common variants (SKAT-RC), originally proposed for data from unrelated subjects, to family data in order to make use of such imputed data. We call this extension "famSKAT-RC." We compare the performance of famSKAT-RC and several other existing burden and kernel association tests. In simulated pedigree sequence data, our results show an increase of imputation accuracy from use of our combining approach. Also, they show an increase of power of the association tests with this approach over the use of either family- or population-based imputation methods alone, in the context of rare and common variants. Moreover, our results show better performance of famSKAT-RC compared to the other considered tests, in most scenarios investigated here.
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