PLIGHT: A tool to assess privacy risk by inferring identifying characteristics from sparse, noisy genotypes

The leakage of identifying information in genetic and omics data has been established in many studies, with single nucleotide polymorphisms (SNPs) shown to carry a strong risk of reidentification for individuals and their genetic relatives. While the ability of thousands or hundreds of thousands of SNPs (especially rare ones) to identify individuals has been demonstrated, here we sought to measure the informativeness of even a sparse set of tens of noisy, common SNPs from an individual, by putting the genotype-based privacy leakage from an individual on quantitative footing. We present a computational tool, PLIGHT ("Privacy Leakage by Inference across Genotypic HMM Trajectories"), that employs a population-genetics-based Hidden Markov Model of recombination and mutation to find piecewise matches of a sparse query set of SNPs to a reference genotype panel. Given the ready availability of auxiliary sources of noisy genotype data -- such as acquiring small samples of environmental DNA or learning about someone's Mendelian diseases and physical characteristics -- inference on sparse data becomes a genuine concern. We explore cases where query individuals are either known to be in databases or not, and consider both simulated "mosaics" of genotypes (i.e. genotypes stitched together from diploid segments sampled from two or more source individuals) and actual genotype data obtained from swabs of coffee cups used by a known individual. Our findings are as follows: (1) Even 10 common SNPs (minor allele frequency > 0.05) often are sufficient to identify individuals in conventional genomic databases. (2) We are able to identify first-order relatives (parents, children and siblings) of query individuals with 20-30 common SNPs. (3) We find some potential for leakage of phenotypic information, based on a simulated attack by combining polygenic risk scores (PRSs) of the piecewise genotypic matches. We also found, for simulated mosaics of two individuals, that 20 common SNPs were often sufficient to find the correct identities of both component individuals. Finally, applying PLIGHT to coffee-cup-derived SNPs, we find that our tool is able identify the individual (when present in the reference database) using as little as 30 SNPs; alternatively, when the individual is not present in the reference database, we reconstruct possible genomes for the individual based on just 30-90 query SNPs by piecewise matching to the reference haplotype database. In this way, we are able to perform a small degree of imputation of unobserved query SNPs. Overall, the tool could be used to determine the value of selectively masking released SNPs, in a way that is agnostic to any explicit assumptions about underlying population membership or allele frequencies.

SSADH Variants Increase Susceptibility of U87 Cells to Mitochondrial Pro-Oxidant Insult

Succinate semialdehyde dehydrogenase (SSADH) is a mitochondrial enzyme, encoded by ALDH5A1, mainly involved in γ-aminobutyric acid (GABA) catabolism and energy supply of neuronal cells, possibly contributing to antioxidant defense. This study aimed to further investigate the antioxidant role of SSADH, and to verify if common SNPs of ALDH5A1 may affect SSADH activity, stability, and mitochondrial function. In this study, we used U87 glioblastoma cells as they represent a glial cell line. These cells were transiently transfected with a cDNA construct simultaneously harboring three SNPs encoding for a triple mutant (TM) SSADH protein (p.G36R/p.H180Y/p.P182L) or with wild type (WT) cDNA. SSADH activity and protein level were measured. Cell viability, lipid peroxidation, mitochondrial morphology, membrane potential (ΔΨ), and protein markers of mitochondrial stress were evaluated upon Paraquat treatment, in TM and WT transfected cells. TM transfected cells show lower SSADH protein content and activity, fragmented mitochondria, higher levels of peroxidized lipids, and altered ΔΨ than WT transfected cells. Upon Paraquat treatment, TM cells show higher cell death, lipid peroxidation, 4-HNE protein adducts, and lower ΔΨ, than WT transfected cells. These results reinforce the hypothesis that SSADH contributes to cellular antioxidant defense; furthermore, common SNPs may produce unstable, less active SSADH, which could per se negatively affect mitochondrial function and, under oxidative stress conditions, fail to protect mitochondria.

Using synthetic datasets to bridge the gap between the promise and reality of basing health-related decisions on common single nucleotide polymorphisms

Background: While the academic genetic literature has clearly shown that common genetic single nucleotide polymorphisms (SNPs), and even large polygenic SNP risk scores, cannot reliably be used to determine risk of disease or to personalize interventions, a significant industry of companies providing SNP-based recommendations still exists. Healthcare practitioners must therefore be able to navigate between the promise and reality of these tools, including being able to interpret the literature that is associated with a given risk or suggested intervention. One significant hurdle to this process is the fact that most population studies of common SNPs only provide average (+/- error) phenotypic or risk descriptions for a given genotype, which hides the true heterogeneity of the population and reduces the ability of an individual to determine how they themselves or their patients might truly be affected. Methods: We generated synthetic datasets generated from descriptive phenotypic data published on common SNPs associated with obesity, elevated fasting blood glucose, and methylation status. Using simple statistical theory and full graphical representation of the generated data, we developed a method by which anybody can better understand phenotypic heterogeneity in a population, as well as the degree to which common SNPs truly drive disease risk. Results: Individual risk SNPs had a <10% likelihood of effecting the associated phenotype (bodyweight, fasting glucose, or homocysteine levels). Example polygenic risk scores including the SNPs most associated with obesity and type 2 diabetes only explained 2% and 5% of the final phenotype, respectively. Conclusions: The data suggest that most disease risk is dominated by the effect of the modern environment, providing further evidence to support the pursuit of lifestyle-based interventions that are likely to be beneficial regardless of genetics.

Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

Despite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze 9 complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8x enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWAS due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

Evaluation of imputation accuracy using the combination of two high-density panels in Nelore beef cattle

This study compared imputation from lower-density commercial and customized panels to high-density panels and a combined panel (Illumina and Affymetrix) in Nelore beef cattle. Additionally, linkage disequilibrium and haplotype block conformation were estimated in individual high-density panels and compared with corresponding values in the combined panel after imputation. Overall, 814 animals were genotyped using BovineHD BeadChip (IllumHD), and 93 of these animals were also genotyped using the Axion Genome-Wide BOS 1 Array Plate (AffyHD). In general, customization considering linkage disequilibrium and minor allele frequency had the highest accuracies. The IllumHD panel had higher values of linkage disequilibrium for short distances between SNPs than AffyHD and the combined panel. The combined panel had an increased number of small haplotype blocks. The use of a combined panel is recommended due to its increased density and number of haplotype blocks, which in turn increase the probability of a marker being close to a quantitative trait locus of interest. Considering common SNPs between IllumHD and AffyHD for the customization of a low-density panel increases the imputation accuracy for IllumHD, AffyHD and the combined panel.

Using synthetic datasets to better understand and explain health outcomes associated with common single nucleotide polymorphisms

ABSTRACTDue to decreasing costs and a move towards “personalised medicine”, the use of direct-to-consumer genetic analyses is increasing. Both consumers and healthcare practitioners must therefore be able to understand the true disease risks associated with common genetic single nucleotide polymorphisms (SNPs). However, most population studies of common SNPs only provide average (+/−error) phenotypic or risk descriptions for a given genotype, which hides the true heterogeneity of the population and reduces the ability of an individual to determine how they themselves might truly be effected. Here, we describe the use of synthetic datasets generated from descriptive phenotypic data published on common SNPs associated with obesity, elevated fasting blood glucose, and methylation status. Using both simple statistical theory and full graphical representation of the generated data, we show that single common SNPs are associated with a less than 10% likelihood of effecting final phenotype, even in homozygotes. The significant heterogeneity in the data, as well as the baseline disease risk of Western populations suggests that most disease risk is dominated by the effect of the modern environment.

Recovery of trait heritability from whole genome sequence data

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1, but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2–5. It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as over-estimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be fully recovered from whole-genome sequence (WGS) data on 21,620 unrelated individuals of European ancestry. We assigned 47.1 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned variation accordingly. The estimated heritability was 0.79 (SE 0.09) for height and 0.40 (SE 0.09) for BMI, consistent with pedigree estimates. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein altering variants, consistent with negative selection thereon. Cumulatively variants in the MAF range of 0.0001 to 0.1 explained 0.54 (SE 0.05) and 0.51 (SE 0.11) of heritability for height and BMI, respectively. Our results imply that the still missing heritability of complex traits and disease is accounted for by rare variants, in particular those in regions of low LD.