Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method

Area of Science:

• Neurogenetics
• Quantitative Trait Loci (QTL) analysis
• Brain Imaging Genetics

Background:

• Regional human brain volumes are heritable and linked to neurological disorders.
• The genetic underpinnings of brain structure and function remain largely unexplored.
• The Pediatric Imaging, Neurocognition, and Genetics (PING) dataset offers valuable genome-wide genetic and neuroimaging data.

Purpose of the Study:

• To explore the genetic architecture of brain structure using genome-wide association studies (GWAS).
• To identify genetic risk variants influencing regional brain volumes.
• To evaluate a novel nonparametric test for association analysis in large-scale datasets.

Main Methods:

• Genome-wide association studies (GWAS) were performed on 315 brain volumetric phenotypes from the PING dataset (1036 samples, 539,865 SNPs).
• A nonparametric K-sample Ball Divergence (KBD) test was introduced and applied to identify significant single-nucleotide polymorphisms (SNPs).
• Simulations were conducted to assess the power and type I error rate control of the KBD test.

Main Results:

• The KBD test demonstrated power in identifying significant SNPs associated with multivariate phenotypes while controlling the type I error rate.
• Nine SNPs were identified with a significance level below 5 × 10^-5 in the PING dataset.
• Two identified SNPs (rs486179 and rs562110) are located in the ADRA1A gene, a known risk factor for mental illnesses like schizophrenia and ADHD.

Conclusions:

• The nonparametric KBD test is an effective method for identifying genetic variants associated with complex diseases in large-scale GWAS.
• The identified genetic variants, particularly those in ADRA1A, provide insights into the genetic basis of brain structure and its relation to neurological and psychiatric disorders.