ArticleBMC genomics2016
Inference of kinship using spatial distributions of SNPs for genome-wide association studies.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.Briefings in bioinformatics · 2022Pooled it
- Genome-wide association study reveals the QTLs and candidate genes associated with seed longevity in soybean (Glycine max (L.) Merrill).BMC plant biology · 2025Article
- A novel approach of kinship determination based on the physical length of genetically shared regions of chromosomes.Genes & genomics · 2024Article
- From Samples to Germline and Somatic Sequence Variation: A Focus on Next-Generation Sequencing in Melanoma Research.Life (Basel, Switzerland) · 2022Review
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2 authors.
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Abstract
backgroundGenome-wide association studies (GWASs) are powerful in identifying genetic loci which cause complex traits of common diseases. However, it is well known that inappropriately accounting for pedigree or population structure leads to spurious associations. GWASs have often encountered increased type I error rates due to the correlated genotypes of cryptically related individuals or subgroups. Therefore, accurate pedigree information is crucial for successful GWASs.
resultsWe propose a distance-based method KIND to estimate kinship coefficients among individuals. Our method utilizes the spatial distribution of SNPs in the genome that represents how far each minor-allele variant is located from its neighboring minor-allele variants. The SNP distribution of each individual was presented in a feature vector in Euclidean space, and then the kinship coefficient was inferred from the two vectors of each individual pair. We demonstrate that the distance information can measure the similarity of genetic variants of individuals accurately and efficiently. We applied our method to a synthetic data set and two real data sets (i.e. the HapMap phase III and the 1000 genomes data). We investigated the estimation accuracy of kinship coefficients not only within homogeneous populations but also for a population with extreme stratification.
conclusionsOur method KIND usually produces more accurate and more robust kinship coefficient estimates than existing methods especially for populations with extreme stratification. It can serve as an important and very efficient tool for GWASs.
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