A fast linkage method for population GWAS cohorts with related individuals

Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional linkage methods are computationally inefficient for larger datasets. Here, we describe Population Linkage, a novel application of Haseman-Elston regression as a method of moments estimator of variance components and their standard errors. We achieve additional computational efficiency by using modern methods for detection of IBD segments and variance component estimation, efficient preprocessing of input data, and minimizing redundant numerical calculations. We also refined variance component models to account for the biases in population-scale methods for IBD segment detection. We ran Population Linkage on four blood lipid traits in over 70,000 individuals from the HUNT and SardiNIA studies, successfully detecting 25 known genetic signals. One notable linkage signal that appeared in both was for low-density lipoprotein (LDL) cholesterol levels in the region near the gene APOE (LOD = 29.3, variance explained = 4.1%). This is the region where the missense variants rs7412 and rs429358, which together make up the ε2, ε3, and ε4 alleles each account for 2.4% and 0.8% of variation in circulating LDL cholesterol. Our results show the potential for linkage analysis and other large-scale applications of method of moments variance components estimation.

Use of molecular data in zoo and aquarium collection management: Benefits, challenges, and best practices

The global zoo and aquarium community widely recognizes that its animal collections and cooperative breeding programs are facing a sustainability crisis. It has become commonly accepted that numerous priority species cannot be maintained unless new management strategies are adopted. While molecular data have the potential to greatly improve management across a range of scenarios, they have been generally underutilized by the zoo and aquarium community. This failure to effectively apply molecular data to collection management has been due, in part, to a paucity of resources within the community on which to base informed decisions about when the use of such data is appropriate and what steps are necessary to successfully integrate data into management. Here, we identify three broad areas of inquiry where molecular data can inform management: 1) taxonomic identification; 2) incomplete or unknown pedigrees; and 3) hereditary disease. Across these topics, we offer a discussion of the advantages, limitations, and considerations for applying molecular data to ex situ animal populations in a style accessible to zoo and aquarium professionals. Ultimately, we intend for this compiled information to serve as a resource for the community to help ensure that molecular projects directly and effectively benefit the long-term persistence of ex situ populations.