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ASHG Talk Describes Ability to Detect Detailed Ancestry Data for Biobank Participants Across NYC

BALTIMORE (GenomeWeb) – A Mount Sinai-led team is mapping fine-scale population patterns in New York City to not only understand the city's past demographic dynamics, but also to design more precise population-specific disease studies and to tease apart genetic and environmental contributors to disease.

The researchers used identity-by-descent patterns to uncover hidden relationships between individuals enrolled in the Icahn School of Medicine at Mount Sinai BioMe Biobank.

The biobank contains more than 31,500 samples and is linked to electronic health records and neighborhood data for the individuals enrolled, explained Gillian Belbin, a graduate student in population geneticist Eimear Kenny's Icahn School of Medicine lab.

Belbin, who presented the work at a session on reconstructing human history on Friday, noted that the team has access to detailed survey data for individuals who joined the biobank since 2010, including self-reported ancestry, country of birth, grandparents' birthplaces, and so on.

So far, she and her colleagues have genotyped more than 11,300 individuals at some 600,000 SNPs using Illumina OmniExpress arrays. They combined that information with available self-reported ancestry data and family histories to inform their analyses of samples from participants who did not provide the same level of detailed survey data.

When they considered the identity-by-descent patterns in the participants assessed so far, who reside in neighborhoods from across New York City's five boroughs, the researchers saw community clusters that closely coincided with self-reported ancestry patterns for several neighborhoods.

For example, the team saw clusters of Ashkenazi Jewish communities on the Upper West Side and parts of Brooklyn, whereas many individuals with ancestry from the Dominican Republic mapped to the Manhattan neighborhood of Washington Heights, known for its strong Dominican Republic presence.

Yet another cluster of individuals shared ancestry from the so-called Garifuna population, a group that arose in South and Central Africa via Venezuela when a slave ship from Africa ran into trouble on Venezuela's coast. Individuals from the population typically have high proportions of Native American and African ancestry, with little to no European ancestry.

In a still-to-be-published analysis, the researchers detected a group of individuals of Puerto Rican ancestry who were all afflicted with Steel syndrome, a condition characterized by short stature, skeletal malformations, and muscular problems. With the available genetic data, they were able to narrow in on a genetic variation that was present in affected individuals and some other Puerto Ricans, but absent from all of the individuals from other population backgrounds who have been tested so far.

As more and more community clusters are linked to health record data, Kenny noted, it might be possible to harness results to design clinical trials for such conditions, come up with targeted treatments, and/or develop more precise diagnostic tests for rare conditions that are more prevalent in particular populations.

And by bringing together data for tens of thousands of individuals, the team believes that it should be possible to increase its power to detect associations involving rare variants, even in minority populations.

The researchers are continuing to genotype and analyze samples from the biobank and eventually hopes to do whole-genome sequencing on as many of the Mount Sinai biobank samples as possible with an eye to designing more precise clinical studies within clusters of individuals who share ancestral backgrounds.

"We want to develop better methods to home in on populations of people who might share risk," Kenny said during a meeting with reporters at the conference.

And because the participants' zip code data is available, it may also be possible to compare disease-susceptibility in individuals who have similar ancestral patterns but live in different parts of the city, Kenny and Belbin explained, potentially providing a means to help distinguish genetic from environmental disease contributors.