Quantitative analysis of population-scale family trees using millions of relatives

Abstract

Family trees have vast applications in multiple fields from genetics to 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. Here, we collected 86 million profiles from publicly-available online data from 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 longevity by inspecting millions of relative pairs and to provide insights to population genetics theories on the dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data. One Sentence SummaryUsing massive crowd-sourced genealogy data, we created a population-scale family tree resource for scientific studies.. CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/106427 doi: bioRxiv preprint first posted online Feb. 7, 2017; 2 Main TextFamily trees are mathematical graph structures that capture two fundamental processes: mating and parenthood. As such, the edges of the trees represent potential transmission lines for a wide variety of genetic, cultural, socio-demographic, and economic factors in the human population. The foundation of quantitative genetics is built on dissecting the interplay of these factors by overlaying data on family trees and analyzing the correlation of various classes of relatives (1-3). In addition, family trees can serve as a multiplier for genetic information by using study designs that leverage genotype or phenotype data from relatives (4-7), analyzing parent-of-origin effects (8), refining heritability measures (9, 10), or improving individual risk assessment (11)(12)(13). Beyond classical genetic applications, large-scale family trees have played an important role in a wide array of disciplines including human evolution (14,15), anthropology (16), and economics (17,18).Despite the range of applications, constructing population-scale family trees has been a labor-intensive process. So far, the leading approach has mainly relied on local data record repositories such as churches or vital record offices (19)(20)(21). While playing an important role in previous studies, this approach has several limitations (22,23): first, it requires non-trivial resources to digitize the records and organize the data. Second, the resulting trees are usually limited in scope to the specific geographical area of the repository, which precludes comparisons between regions. Finally, vital record data is usually subject to various privacy protections and regulations. This reduces its accessibility and complicates its fusion with other sources such of information such as genomic or health data.Her...

Indirect assortative mating for human disease and longevity

“…The results suggest that partner correlations for lifespan, after adjusting for mating structure due to year and place of birth, are in the region of 0.1 -0.12. Estimates of heritability for longevity in the FamiLinx cohort imply a phenotypic correlation between 1 st degree relatives of 0.06 (KAPLANIS et al 2017), while previous estimates of heritability suggest higher correlations of 0.13 (HERSKIND et al 1996). Our estimates of SNP heritability for longevity of an individual's parents suggest a phenotypic correlation between 1 st degree relatives of 0.03 or 0.04.…”

“…However, this approach is limited by how well genetic values predict phenotype, i.e., the heritability, and the precision with which genetic values can be estimated. The heritabilities of longevity and many late onset diseases are medium to low (CANELA-XANDRI et al 2017), with estimates for SNP heritability of longevity ranging from 0.12 to 0.3 (KAPLANIS et al 2017).…”

Big Data and Population Processes: A Revolution?

“…For example, Fire and Elovici [8] use data collected from the WikiTree website to study correlations in lifespans among parents and children, as well as spouses. Similarly, Kaplanis et al (2017) [16] leverage the data produced by enthusiasts of genealogy to evaluate population genetics theories on the dispersion of families. The key here is that (a) there are digital records that are left behind by people or institutions and (b) there is a critical mass of people who organize the data in meaningful ways for their own purposes and common goals.…”

Indirect assortative mating for human disease and longevity

“…We furthermore removed individuals with lifespans below 30 or above 130 and those born before 1600, due to the sparsity and lower reliability of data before that date, and after 1910, due to the bias towards individuals with reduced lifespan after that date. Finally, we removed individuals who died during the American Civil War (year of death 1861 to 1865), the 1 st World War (year of death 1914 to 1918) and the 2 nd World War (year of death 1939 to 1945) due to the previously reported excess number of early death in these periods (15). This resulted in a dataset of 3,445,971 individuals containing 323,155 couples, 97,223 sets of fathers in law and 66,077 sets of mothers in law with lifespan information.…”