Inferring population structure in biobank-scale genomic data

Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. While a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.

Medical, Genomic, and Evolutionary Aspects of the Peptide Sharing between Pathogens, Primates, and Humans

Comparing mammalian proteomes for molecular mimicry with infectious pathogens highlights the highest levels of heptapeptide sharing between pathogens and human, murine, and rat proteomes, while the peptide sharing level is minimal (or absent) with proteomes from nonhuman primates such as gorilla, chimpanzee, and rhesus macaque. From the medical point of view, the data might be useful to clinicians and vaccinologists to develop and evaluate immunomodulatory and immunotherapeutic approaches. As a matter of fact, primates seem to be unreliable animal models for revealing potential autoimmune events in preclinical testing of immunotherapies. In terms of genomics, the scarce or absent peptide sharing between pathogens and primates versus the massive peptide sharing existing between pathogens and humans lets foresee mechanisms of pathogen sequence insertion/deletion/alteration that have differently operated in mammals over evolutionary timescales. Why and how the human genome has been colonized by pathogen sequences and why and how primates escaped such a colonization appears to be the new scientific challenge in our efforts to understand not only the origin of Homo sapiens but also his autoimmune diseasome.

Tractor: A framework allowing for improved inclusion of admixed individuals in large-scale association studies

Admixed populations are routinely excluded from medical genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulations and empirical data focused on admixed African-European individuals. Tractor generates ancestryspecific effect size estimates, can boost GWAS power, and improves the resolution of association signals. Using a local ancestry aware regression model, we replicate known hits for blood lipids in admixed populations, discover novel hits missed by standard GWAS procedures, and localize signals closer to putative causal variants.

Mexican Samples, Latino DNA: The Trajectory of a National Genome in Transnational Science

Experts have widely promoted developing country investment in national genome projects in order to ensure their inclusion in medical genomic advances, to protect their genomes from foreign exploitation, and to foster their participation in a future genomics-based bioeconomy. In this context, the Mexican federal government’s investments to establish the National Institute of Genomic Medicine in 2004, that institute’s subsequent efforts to map the “Mexican genome” between 2004 and 2009, and the passage of legislation in 2008 to protect Mexico’s “genomic sovereignty” drew attention as the most comprehensive national genomics program among the world’s emerging economies. Given the prominence of Mexico’s decision to pursue its “national genome” and to understand how this approach to science policy has unfolded with time, we track major developments in the field of genomic medicine in Mexico and the trajectory of the “Mexican genome” over the last decade. Rather than the nation-state bound “Mexican genome,” we show that flexibility and ambiguity with regard to genomic identity has been instrumental amid the increasingly transnational and public-private nature of this scientific field. Over the last decade, Mexican samples have frequently been re-branded as the source of flexible, panethnic “Latino” or “Latin American” DNA.