Abstract Background Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative—an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients ( N =36,736). Methods We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes. Results We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals’ SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p -value=2.32×10 −16 , EAA p -value=6.73×10 −11 ). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group. Conclusions Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.
more »
« less
What can one chromosome tell us about human biogeographical ancestry?
We study the problem of predicting human
biogeographical ancestry using genomic data. While continental
level ancestry is relatively simple using genomic information,
distinguishing between individuals from closely associated subpopulations
(e.g., from the same continent) is still a difficult
challenge. In particular, we focus on the case where the analysis
is constrained to using single nucleotide polymorphisms (SNPs)
from just one chromosome. We thus propose methods to
construct such ancestry informative SNP panels, and access the
performance of such SNP panels from just one chromosome, for
both continental-level and sub-population level ancestry
prediction. We include results that demonstrate the performance
of the proposed methods, including comparison with other
recently published related methods.
more »
« less
- NSF-PAR ID:
- 10053534
- Date Published:
- Journal Name:
- Proc. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Page Range / eLocation ID:
- 188 to 193
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Background Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.
Results Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352.
Conclusions Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations.
Short Abstract Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.
-
Background While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party. Objective In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. Methods Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority. Results We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model. Conclusions Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security.more » « less
-
more » « less
Abstract Objectives Crown and root traits, like those in the Arizona State University Dental Anthropology System (ASUDAS), are seemingly useful as genetic proxies. However, recent studies report mixed results concerning their heritability, and ability to assess variation to the level of genomic data. The aim is to test further if such traits can approximate genetic relatedness, among continental and global samples.
Materials and Methods First, for 12 African populations, Mantel correlations were calculated between mean measure of divergence (MMD) distances from up to 36 ASUDAS traits, and
F STdistances from >350,000 single nucleotide polymorphisms (SNPs) among matched dental and genetic samples. Second, among 32 global samples, MMD andF STdistances were again compared. Correlations were also calculated between them and inter‐sample geographic distances to further evaluate correspondence.Results A close ASUDAS/SNP association, based on MMD and
F ST correlations, is evident, withr m ‐ values between .72 globally and .84 in Africa. The same is true concerning their association with geographic distances, from .68 for a 36‐trait African MMD to .77 forF ST globally; one exception isF ST and African geographic distances,r m = 0.49. Partial MMD/F ST correlations controlling for geographic distances are strong for Africa (.78) and moderate globally (.4).Discussion Relative to prior studies, MMD/
F ST correlations imply greater dental and genetic correspondence; for studies allowing direct comparison, the present correlations are markedly stronger. The implication is that ASUDAS traits are reliable proxies for genetic data—a positive conclusion, meaning they can be used with or instead of genomic markers when the latter are unavailable. -
more » « less
Abstract Understanding how genetic diversity is distributed across spatiotemporal scales in species of conservation or management concern is critical for identifying large‐scale mechanisms affecting local conservation status and implementing large‐scale biodiversity monitoring programmes. However, cross‐scale surveys of genetic diversity are often impractical within single studies, and combining datasets to increase spatiotemporal coverage is frequently impeded by using different sets of molecular markers. Recently developed molecular tools make surveys based on standardized single‐nucleotide polymorphism (SNP) panels more feasible than ever, but require existing genomic information. Here, we conduct the first survey of genome‐wide SNPs across the native range of brook trout (
Salvelinus fontinalis ), a cold‐adapted species that has been the focus of considerable conservation and management effort across eastern North America. Our dataset can be leveraged to easily design SNP panels that allow datasets to be combined for large‐scale analyses. We performed restriction site‐associated DNA sequencing for wild brook trout from 82 locations spanning much of the native range and domestic brook trout from 24 hatchery strains used in stocking efforts. We identified over 24,000 SNPs distributed throughout the brook trout genome. We explored the ability of these SNPs to resolve relationships across spatial scales, including population structure and hatchery admixture. Our dataset captures a wide spectrum of genetic diversity in native brook trout, offering a valuable resource for developing SNP panels. We highlight potential applications of this resource with the goal of increasing the integration of genomic information into decision‐making for brook trout and other species of conservation or management concern.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BIBM.2017.8217648
