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Genetic Analysis Reveals Risk of Diseases Among Ethnic Groups


The frequency of genomic variants associated with specific diseases can differ from one population to another.


Researchers identified 27 new genomic variants associated with blood pressure, Type 2 diabetes, cigarette use and chronic kidney disease in diverse populations, according to the findings of a genome-wide association analysis published in the journal Nature.

The research team also assessed whether known disease associations with nearly 9,000 established genomics variants and specific diseases in European ancestry populations could be detected in other populations: African-American, Hispanic/Latino, Asian, Native Hawaiian and Native American. They found that the frequency of genomic variants associated with specific diseases can differ from one population to another. Other genomic variants, like ones related to the function of hemoglobin, are found in multiple groups.

“There are scientific benefits to including people from different ethnic groups in research studies,” said co-author Lucia Hindorff, Ph.D., program director in the genomic medicine division at the National Human Genome Research Institute. “This paper gives us a glimpse of how ethnic diversity can be harnessed to better understand disease biology and clinical interpretations.”

Hindorff and the research team conducted the genome-wide association analysis to better understand how genomic variants influence the risk of forming certain diseases in people of different ethnic groups. The researchers looked for genomic variants in DNA associated with measures of health and disease.

The researchers drew information from three population-based cohorts:

  • Hispanic Community Health Study/Study of Latinos
  • Women’s Health Initiative and Multiethnic Cohort
  • The Icahn School of Medicine and Mount Sinai BioMe biobank in New York City

Nearly 50,000 individuals were genotyped on the Multi-Ethnic Genotyping Array, which the researchers developed to impartially capture global genetic variation.

The researchers then performed a genome-wide association analysis on 26 traits across the four studies, adjusted for the top 10 principal components, indicators for study and self-identified race and ethnicity. Analytical tools helped model population structure, relatedness between individuals and population-specific genetic heterogeneity.

Then the research team conducted an analysis stratified by self-identified race and ethnicity and combined the analyses in a meta-analysis.

Researchers identified 16 novel genome-wide significant trait-variant associations and 11 low-frequency loci with suggestive associations. The research team also identified 32 significant trait-variant associations after conditioning on all trait-specific known variants in an ‘adjusted’ model.

Correlations between the risk allele genotype and each of the top 10 principal components revealed a population structure that underlies many identified trait-variant associations.

Most notably, the researchers identified a single-nucleotide polymorphism that is associated with the number of cigarettes smoked per day among smokers, along with PC4, which represents the gradient of Native Hawaiian ancestry. This association was rare in other populations.

“Ultimately, the (Population Architecture using Genomics and Epidemiology) study underscores the value of studying diverse populations, because only with a full understanding of genomic variations across population can researchers comprehend the full potential of the human genome,” said Hindorff.

The study addresses the need for new methods and tools for collecting varied amounts of genomic data, according to the researchers.

Researchers could use the Population Architecture using Genomics and Epidemiology study to identify variants associated with diseases and to understand how associations differ across race and ethnicity.

“We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities,” the authors wrote.

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