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Minkyoung Cha

Mary Gates Research Scholar
Winter 2025
Major: Psychology
Mentor: Jennifer Forsyth, Psychology

Project

Impact of Genetic Risk Factors on Within-Family IQ Differences in Schizophrenia: A Focus on Copy Number Variations and Loss-of-Function Mutations

Schizophrenia is a complex disorder influenced by genetic, environmental, and neurodevelopmental factors. Cognitive impairment is a core feature and a key predictor of long-term outcomes. Current literature suggests that damaging genetic risk factors, such as copy number variations (CNVs) and loss of function (LOF) mutations, are important contributors to schizophrenia etiology and may influence cognitive functioning among patients. However, isolating genetic effects is complicated by environmental and familial confounds. This study analyzed data from 355 subjects enrolled in the UCLA Family Study, involving 71 schizophrenia and 47 control families by using a within-family design to control for shared background factors and examine how these rare genetic variants relate to cognitive impairment in schizophrenia individuals. Mean IQ across four groups (probands and relatives in schizophrenia and control families) and within-family IQ differences (schizophrenia vs. control) were compared using ANOVA, controlling for age, sex, and genetic ancestry (PC1–PC10). Among schizophrenia probands, linear mixed-effects models tested associations between rare damaging variants (LOF mutations and/or risk CNVs) and both raw IQ and within-family IQ differences. Schizophrenia probands who carried LOF mutations in NDD-/SSD-associated genes, or both LOF mutations and NDD/SSD risk CNVs, showed significantly lower IQ scores and greater within-family IQ differences compared to non-carriers. However, effect sizes were slightly smaller in within-family models, likely due to measurement error or limited statistical power from the small number of carriers. These findings suggest that rare damaging variants are strongly linked to cognitive impairment in schizophrenia, even after accounting for shared environmental factors. Slightly reduced effect sizes in within-family models highlight the need for larger carrier samples and refined within-family designs, such as sibling-only comparisons, to enhance signal clarity.

What I learned

Through my research experience, I developed proficiency in R and gained hands-on experience managing and analyzing data from over 400 subjects—merging demographic, cognitive, and genetic information, and conducting statistical analyses such as ANOVA and linear regression. With no prior background in psychiatric genetics, I took the initiative to independently learn complex concepts and navigate the literature. Balancing this work with full-time coursework was challenging, especially when troubleshooting code or interpreting dense scientific texts. But those moments taught me resilience, self-direction, and that research isn’t about having perfect answers but it’s about asking better questions and embracing uncertainty with curiosity.

Advice for future applicants

My biggest advice is—don’t count yourself out just because you’re not an expert yet. I often felt overwhelmed by dense literature or error messages in R, but I was deeply curious about the research questions and determined to figure things out, one step at a time. The Mary Gates Research Scholarship isn’t about having it all figured out, it’s about showing initiative, asking meaningful questions, and being open to learning. Also, take advantage of the scholarship workshops and advising sessions! They helped me better articulate not just what I was doing, but why it mattered and how I was growing. Whether you’re in research or leadership, your application should reflect both your project and your personal journey such as what excites you, what challenges you’ve faced, and how you’re developing through the process.
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