Consumer genomics databases have reached the scale of millions of individuals. Recently, law enforcement authorities have exploited some of these databases to identify suspects via distant familial relatives. Using genomic data of 1.28 million individuals tested with consumer genomics, we investigated the power of this technique. We project that about 60% of the searches for individuals of European descent will result in a third-cousin or closer match, which theoretically allows their identification using demographic identifiers. Moreover, the technique could implicate nearly any U.S. individual of European descent in the near future. We demonstrate that the technique can also identify research participants of a public sequencing project. On the basis of these results, we propose a potential mitigation strategy and policy implications for human subject research.

Family trees have vast applications in fields as diverse as genetics, anthropology, and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. We collected 86 million profiles from publicly available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of human longevity and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other data sets with our resource.

The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals. Such pedigrees provide the opportunity to answer genetic and epidemiological questions in scales much larger than previously possible. Linear mixed models (LMMs) are often used for analysis of pedigree data. However, LMMs cannot naturally scale to large pedigrees spanning millions of individuals, owing to their steep computational and storage requirements. Here we propose a novel modeling framework called Sparse Cholesky factorIzation LMM (SciLMM), that alleviates these difficulties by exploiting the sparsity patterns found in large pedigree data. The proposed framework can construct a matrix of genetic relationships between trillions of pairs of individuals in several hours, and can fit the corresponding LMM in several days. We demonstrate the capabilities of SciLMM via simulation studies and by estimating the heritability of longevity in a very large pedigree spanning millions of individuals and over five centuries of human history. The SciLMM framework enables the analysis of extremely large pedigrees that was not previously possible. SciLMM

Consumer genomics databases reached the scale of millions of individuals. Recently, law enforcement investigators have started to exploit some of these databases to find distant familial relatives, which can lead to a complete re-identification. Here, we leveraged genomic data of 600,000 individuals tested with consumer genomics to investigate the power of such long-range familial searches. We project that half of the searches with European-descent individuals will result with a third cousin or closer match and will provide a search space small enough to permit re-identification using common demographic identifiers. Moreover, in the near future, virtually any European-descent US person could be implicated by this technique. We propose a potential mitigation strategy based on cryptographic signature that can resolve the issue and discuss policy implications to human subject research.. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/350231 doi: bioRxiv preprint first posted online Jun. 18, 2018; 2 Main TextConsumer genomics has gained tremendous popularity in the last few years 1 . As of today, more than 15 million people have taken direct-to-consumer (DTC) autosomal genetic tests for self-curiosity, with about 7 million kits sold in 2017 alone 2 . Nearly all major DTC providers use dense genotyping arrays that probe ~700,000 SNPs in the genome of each participant and most DTC providers allow participants to download their raw genotype files in a textual format. This option has led to the advent of third-party services, such as DNA.Land and GEDmatch that allow participants to upload their raw genetic data in order to get further analysis (Table 1) 3 .DNA matching is one of the most popular features in consumer genomics. This feature harnesses the dense autosomal genotypes to find identity-by-descent (IBD) segments, which are indicative of a shared ancestor. Previous studies have shown that this technique has virtually perfect accuracy to find close relatives and good accuracy to find distant relatives, such as 2 nd or 3 rd cousins 4-6 , providing the option for long range familial searches. This feature has led to many "success stories" by the genetic genealogy community, including the reunions of Holocaust survivors with relatives when they thought they had no living family left, reunions of adoptees with their biological families, and investigations of potential abductions of babies 7 .From a technical and regulatory perspective, the consumer genomics tools are far more powerful for familial searches than traditional forensic techniques. Forensic familial searches use a small set of ~20 autosomal STR regions that were standardized for traditional fingerprinting 8,9 . This set is not sufficient to detect IBD matches and instead forensic techniques have to rely on partial allelic matches as a mean...