Christelle

Christelle 
Belmont High School
Belmont, MA

Mentor: 
Maheak Vora
Mercader Lab

 

Christelle spent her summer exploring a major challenge in human genetics: a genetic variant associated with disease in one population may not have exactly the same frequency or effect in another. Understanding this ancestry-related heterogeneity is particularly important for diseases such as type 2 diabetes, which affects hundreds of millions of people worldwide.

Working with Samyak, Christelle examined multi-ancestry GWAS summary statistics and asked whether variants with ancestry-related differences also tend to show stronger disease associations under a recessive genetic model. Unlike the additive model commonly used in GWAS, a recessive model can identify variants whose effects become apparent primarily when two copies of a risk allele are inherited.

Christelle developed computational and quantitative skills while working with large genetic datasets. The project involved matching variants across datasets using genomic coordinates and alleles, comparing heterogeneity P-values, examining ancestry-specific allele frequencies, prioritizing candidate variants, and interpreting forest plots showing effect sizes and confidence intervals across populations and cohorts.

The analysis identified a modest positive relationship between ancestry-related heterogeneity and recessive heterogeneity among 1,244 shared variants. One particularly informative example was a variant in the PAX4 region, which is enriched in East Asian populations and demonstrated its strongest recessive association in East Asian ancestry groups. Results across several East Asian cohorts supported the reproducibility of this pattern.

Christelle learned how the choice of genetic model and the representation of different ancestry groups can shape what researchers discover from large human genetic datasets.

Her work illustrates why genetic research cannot assume that one population tells the whole story. Her project contributes to efforts to understand population-specific genetic architecture and to make discoveries from GWAS more informative across diverse populations.

The experience also reshaped Christelle’s confidence in what she can take on. She said BSSP taught her “to never underestimate [herself]” and to believe that she can fully pursue challenging opportunities, jobs, and research - a lesson that mirrors the independence and persistence required by her computational genetics project.