Modeling 3D Facial Shape from DNA

Claes, Peter; Liberton, Denise K.; Daniels, Katleen; Rosana, Kerri Matthes; Quillen, Ellen E.; Pearson, Laurel N.; McEvoy, Brian; Bauchet, Marc; Zaidi, Arslan; Yao, Wei; Tang, Hua; Barsh, Gregory S.; Absher, Devin; Puts, David A.; Rocha, Jorge; Beleza, Sandra; Pereira, Rinaldo Wellerson; Baynam, Gareth; Suetens, Paul; Vandermeulen, Dirk; Wagner, Jennifer K.; Boster, James S.; Shriver, Mark D.

doi:10.1371/journal.pgen.1004224

Cited by 204 publications

(226 citation statements)

References 35 publications

(44 reference statements)

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“…Psychology studies have reported that people find faces that are more similar to their own more attractive (25). In the GALAI dataset, we computed the correlation between partners at each SNP in a set of 49 genes previously curated to be related to human facial development (26). We pooled all 49 genes into one analysis to gain statistical power.…”

Section: Resultsmentioning

confidence: 99%

Genetic and socioeconomic study of mate choice in Latinos reveals novel assortment patterns

Zou

Park

Burchard

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Nonrandom mating in human populations has important implications for genetics and medicine as well as for economics and sociology. In this study, we performed an integrative analysis of a large cohort of Mexican and Puerto Rican couples using detailed socioeconomic attributes and genotypes. We found that in ethnically homogeneous Latino communities, partners are significantly more similar in their genomic ancestries than expected by chance. Consistent with this, we also found that partners are more closely related—equivalent to between third and fourth cousins in Mexicans and Puerto Ricans—than matched random male–female pairs. Our analysis showed that this genomic ancestry similarity cannot be explained by the standard socioeconomic measurables alone. Strikingly, the assortment of genomic ancestry in couples was consistently stronger than even the assortment of education. We found enriched correlation of partners’ genotypes at genes known to be involved in facial development. We replicated our results across multiple geographic locations. We discuss the implications of assortment and assortment-specific loci on disease dynamics and disease mapping methods in Latinos.

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Section: Resultsmentioning

confidence: 99%

Genetic and socioeconomic study of mate choice in Latinos reveals novel assortment patterns

Zou

Park

Burchard

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…Two studies show that age prediction is feasible from DNA information derived from blood samples [30,41]. Several genome-wide studies report the influence of genomic data on height [1], body mass index (BMI) [26], eye color [38], and facial shape [8,24]. Although variabilities of phenotypic traits is currently explained to a small extent by genomic differences, the aforementioned papers clearly demonstrate that genetic knowledge about the relationship between phenotype and genomic data is quickly expanding.…”

Section: Related Workmentioning

confidence: 99%

“…The attack relies upon the fact that our SNPs are intrinsically linked to our phenotypic traits (such as eye color, blood type, or genetic diseases) and that genomic research progress provides us with more information about these links. For instance, the relationship between SNPs and phenotypes is increasingly used in forensics for reconstructing facial composites from DNA information [7,8]. 5 Therefore,…”

Section: Introductionmentioning

confidence: 99%

De-anonymizing Genomic Databases Using Phenotypic Traits

Humbert

Huguenin

Hugonot

et al. 2015

Proceedings on Privacy Enhancing Technologies

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People increasingly have their genomes sequenced and some of them share their genomic data online. They do so for various purposes, including to find relatives and to help advance genomic research. An individual's genome carries very sensitive, private information such as its owner's susceptibility to diseases, which could be used for discrimination. Therefore, genomic databases are often anonymized. However, an individual's genotype is also linked to visible phenotypic traits, such as eye or hair color, which can be used to re-identify users in anonymized public genomic databases, thus raising severe privacy issues. For instance, an adversary can identify a target's genome using known her phenotypic traits and subsequently infer her susceptibility to Alzheimer's disease. In this paper, we quantify, based on various phenotypic traits, the extent of this threat in several scenarios by implementing de-anonymization attacks on a genomic database of OpenSNP users sequenced by 23andMe. Our experimental results show that the proportion of correct matches reaches 23% with a supervised approach in a database of 50 participants. Our approach outperforms the baseline by a factor of four, in terms of the proportion of correct matches, in most scenarios. We also evaluate the adversary's ability to predict individuals' predisposition to Alzheimer's disease, and we observe that the inference error can be halved compared to the baseline. We also analyze the effect of the number of known phenotypic traits on the success rate of the attack. As progress is made in genomic research, especially for genotype-phenotype associations, the threat presented in this paper will become more serious.

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“…Recently, researchers showed that it is even possible to model facial traits of an individual from his DNA [26]. Although such progress in human genetics is desirable for many applications (e.g., forensics), it can pose a threat to our proposed technique.…”

Section: Towards Phenotype-compatible Genoguardmentioning

confidence: 99%

GenoGuard: Protecting Genomic Data against Brute-Force Attacks

Huang

Ayday

Fellay

et al. 2015

2015 IEEE Symposium on Security and Privacy

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Abstract-Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term, but given the multidecade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise.We present a tool, called GenoGuard, for providing strong protection for genomic data both today and in the long term. GenoGuard incorporates a new theoretical framework for encryption called honey encryption (HE): it can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can be applied to messages from, unfortunately, a very restricted set of probability distributions. Therefore, GenoGuard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data.In GenoGuard, a potential adversary can attempt exhaustively to guess keys or passwords and decrypt via a bruteforce attack. We prove that decryption under any key will yield a plausible genome sequence, and that GenoGuard offers an information-theoretic security guarantee against messagerecovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of GenoGuard.

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Modeling 3D Facial Shape from DNA

Cited by 204 publications

References 35 publications

Genetic and socioeconomic study of mate choice in Latinos reveals novel assortment patterns

Genetic and socioeconomic study of mate choice in Latinos reveals novel assortment patterns

De-anonymizing Genomic Databases Using Phenotypic Traits

GenoGuard: Protecting Genomic Data against Brute-Force Attacks

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