Dataset Information

Quantitative analysis of population-scale family trees with millions of relatives.

ABSTRACT: 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.

SUBMITTER: Kaplanis J

PROVIDER: S-EPMC6593158 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantitative analysis of population-scale family trees with millions of relatives.

Kaplanis Joanna J Gordon Assaf A Shor Tal T Weissbrod Omer O Geiger Dan D Wahl Mary M Gershovits Michael M Markus Barak B Sheikh Mona M Gymrek Melissa M Bhatia Gaurav G MacArthur Daniel G DG Price Alkes L AL Erlich Yaniv Y

Science (New York, N.Y.) 20180301 6385

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. ...[more]

PMID: 29496957

Similar Datasets

Project description:BackgroundIt has become increasingly apparent that a comprehensive database of RNA motifs is essential in order to achieve new goals in genomic and proteomic research. Secondary RNA structures have frequently been represented by various modeling methods as graph-theoretic trees. Using graph theory as a modeling tool allows the vast resources of graphical invariants to be utilized to numerically identify secondary RNA motifs. The domination number of a graph is a graphical invariant that is sensitive to even a slight change in the structure of a tree. The invariants selected in this study are variations of the domination number of a graph. These graphical invariants are partitioned into two classes, and we define two parameters based on each of these classes. These parameters are calculated for all small order trees and a statistical analysis of the resulting data is conducted to determine if the values of these parameters can be utilized to identify which trees of orders seven and eight are RNA-like in structure.ResultsThe statistical analysis shows that the domination based parameters correctly distinguish between the trees that represent native structures and those that are not likely candidates to represent RNA. Some of the trees previously identified as candidate structures are found to be "very" RNA like, while others are not, thereby refining the space of structures likely to be found as representing secondary RNA structure.ConclusionSearch algorithms are available that mine nucleotide sequence databases. However, the number of motifs identified can be quite large, making a further search for similar motif computationally difficult. Much of the work in the bioinformatics arena is toward the development of better algorithms to address the computational problem. This work, on the other hand, uses mathematical descriptors to more clearly characterize the RNA motifs and thereby reduce the corresponding search space. These preliminary findings demonstrate that graph-theoretic quantifiers utilized in fields such as computer network design hold significant promise as an added tool for genomics and proteomics.

Dataset Information

Quantitative analysis of population-scale family trees with millions of relatives.

Publications

Quantitative analysis of population-scale family trees with millions of relatives.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets