Inference of kinship coefficients from Korean SNP genotyping data

Park, Seong Jin; Yang, Jin Ok; Kim, Sang Cheol; Kwon, Jek Eun; Lee, Sang Hyuk; Lee, Byung Wook

doi:10.5483/bmbrep.2013.46.6.177

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…Note that this is a valid assumption, because quite a few works have shown that kinship or familial relationships from SNP genotyping data can be inferred with very high accuracy for small and medium size groups [e.g. dozens or hundreds of individuals ( Goudet et al , 2018 ; Park et al , 2013 ), large size populations ( Wang et al , 2017 ), or even worldwide ( Li et al , 2008 )] or such information can be obtained directly from the metadata.…”

Section: System Threat Model and Success Metricsmentioning

confidence: 99%

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Robust fingerprinting of genomic databases

Ayday

Yılmaz

et al. 2022

Bioinformatics

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Motivation Database fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint (distort the steganographic marks, i.e. the embedded fingerprint bit-string) by launching effective correlation attacks, which leverage the intrinsic correlations among genomic data (e.g. Mendel’s law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks. Results Via experiments using a real-world genomic database, we first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g. database accuracy and consistency of SNP–phenotype associations measured via P-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP–phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases, e.g. the mitigation techniques only lead to around 3% loss in accuracy. Availability and implementation https://github.com/xiutianxi/robust-genomic-fp-github.

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Section: System Threat Model and Success Metricsmentioning

confidence: 99%

“…the allele shared distance (i.e. ASD) ( Park et al , 2013 ), which is also related to the considered inner product]. We denote the SNP sequence of an individual i after the mitigation as

.…”

Section: Consolidating the Foundation: Making The Vanilla Genomic Fin...mentioning

confidence: 99%

Robust fingerprinting of genomic databases

Ayday

Yılmaz

et al. 2022

Bioinformatics

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“…Thus, we assume that the malicious SP has access to the sets of families in the database as well as the genome similarities (denoted as S) among family members in each set. Note that this is a valid assumption, because quite a few works have shown that kinship or familial relationships from SNP genotyping data can be inferred with very high accuracy for small and medium size groups (e.g., dozens or hundreds of individuals [10], [26], large size populations [29], or even worldwide [18]) or such information can be obtained directly from the metadata.…”

Section: B Threat Modelmentioning

confidence: 99%

“…In this paper, the similarity between two individuals is defined as the inner product between their SNP sequences. One can also define the similarity using a common metric adopted in biology, e.g., the allele shared distance (i.e., ASD) (page 308[26]), which is also related to the considered inner product.…”

mentioning

confidence: 99%

Robust Fingerprinting of Genomic Databases

Ji¹,

Ayday²,

Yılmaz³

et al. 2022

Preprint

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Relational database fingerprinting (a steganography technique that randomly changes database entries) has been widely used to discourage unauthorized redistribution of databases by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint (distort the steganographic marks, i.e., the embedded fingerprint bit string) by launching effective correlation attacks which leverage the intrinsic correlations among genomic data (e.g., Mendel's law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks.Via experiments using a real-world genomic database, we first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g., database accuracy and consistency of SNP-phenotype associations measured via p-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP-phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases, e.g., the mitigation techniques only lead to around 3% loss in accuracy. 1

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Inference of kinship using spatial distributions of SNPs for genome-wide association studies

Lee

Chen

2016

BMC Genomics

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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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2696-0) contains supplementary material, which is available to authorized users.

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Inference of kinship coefficients from Korean SNP genotyping data

Cited by 3 publications

References 16 publications

Robust fingerprinting of genomic databases

Robust fingerprinting of genomic databases

Robust Fingerprinting of Genomic Databases

Inference of kinship using spatial distributions of SNPs for genome-wide association studies

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