The age effect on the genetic architecture of the WM microstructure as measured by FA

1206 

Abstract Submission 

Long Wei¹, xin xu¹, Suyu Zhong²

¹shandong jianzhu university, jinan, shandong, ²Beijing university of posts and telecommunications, Beijing, Beijing

Long Wei  
shandong jianzhu university
jinan, shandong

xin xu  
shandong jianzhu university
jinan, shandong

Suyu Zhong  
Beijing university of posts and telecommunications
Beijing, Beijing

Using diffusion MRI, a number of studies have explored white matter microstructure highly relevant to ageing [1]. It has been shown that the white matter microstructure may be influenced by genetic variants [2-3]. However, how the genetic contributions affect brain microstructural aging and particularly if existing homogeneous heritability aging pattern and distinct genetic architecture remain largely unknown. Here, we aim to identify the underlying genetic aging pattern and genetic variants of the white matter microstructure measured by fractional anisotropy (FA), which explains the mechanisms of aging changes.

We used dMRI and imputed SNP data from 22936 UKB individuals. We firstly performed the following SNP data quality controls using PLINK [4], excluding subjects with >2% missing genotypes, only including SNPs with MAF > 0.01, INFO score >0.7, and passing Hardy–Weinberg test (p value > 1 × 10−7) [5]. This resulted in 15847 individuals and 8063,552 biallelic variants. We further used a sliding window approach of every 16 years range to examine genetic contributions vary with age (i.e., 40–55 years, 41–56 years, ...,55–70 years). Specifically, we estimated the proportion of variation explained by all autosomal SNPs with using GCTA-GREML analysis [6] to estimate the SNP-based heritability for FA of each tract. Then we assessed whether heritability estimates exhibited a homogeneous trend with age and explored heritability aging patterns. To capture the distinct, significantly SNP association, and independent lead SNPs with FA of each age range, we finally used FUMA (versionv1.4.0) [7] applying to the results from GWAS. In addition, we re-ran the above analyses with the second sliding window strategies for all subjects to further assess the genetic influence reproducibility.

Fig. 1 showed FA heritability varying pattern of left and right tract with age. As shown in Fig.1, there were 12 tracts exhibiting a significant hemisphere × age interaction effect on the heritability, suggesting a significant hemisphere difference in the association between heritability and age. Specifically, there are three pattern types of these tracts. Fig.1A illustrated 7 tracts showing a significant reduced trajectory for left tract heritability as age increased. Fig. 1B illustrated 4 tracts showing a significant trajectory of first decreasing and then increasing for left tract heritability as age increased. And Fig.1C exhibited 1 tract of a significant increased trajectory for left tract heritability. Mean age of each age range was shown in Fig.1D. Based on the heritability trajectory pattern of left tract, we summarized them into 3 categories (Fig. 2). As illustrated in Fig. 2, the number of significant SNPs discovered for tracts with decreased heritability in the left hemisphere (Fig. 2A), increased heritability in the left hemisphere (Fig. 2B) and first decreasing and then increasing heritability in the left hemisphere (Fig. 2C) were exhibited. And we annotated SNPs to genes by three strategies: physical position, expression quantitative trait locus (eQTL) information, and chromatin interactions. The numbers of annotated genes were shown in Fig. 2D-E.

Our study has demonstrated that genetic contributions varied with brain microstructural aging. Particularly, three types of genetic influences were explored based that some tracts exhibited relatively consistent heritability trajectory pattern. In addition, our genetic variants analysis revealed the significant genetic effects on each type tracts as well as the gene mapping for the three patterns. Taken together, the present study explored the age effects on genetic architecture of white matter microstructure and identify some genetic age_varying patterns and variants, which may provide valuable implications for understanding observed white matter microstructure aging mechanisms.

Genetic Association Studies ²

Aging ¹

Aging

MRI

White Matter

Other - genetic architecture