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Anema, J. A.

Publications and source records attributed to Anema, J. A..

2 recordsLinked to original sources

Identification of novel protective loci for executive function using the trail making test part B in the Long Life Family Study

The Trail Making Test (TMT) Part B (TMT-B), a well-established assessment of cognitive function, is a frequent component of diagnostic assessments for Mild Cognitive Impairment and dementia in older adults. Identifying the genetic variants associated with the TMT-B will not only gain insights of genetic determinants of cognitive function, but also the molecular mechanisms for dementia. Published GWAS to date for TMT-B suffer from relatively low power due to the use of population level data and imputation methods. To address these deficits, we used a family-based study design to identify the genetic variants associated with the TMT-B incorporating both genome-wide linkage analysis (GWLS) and whole genome sequencing (WGS). As such, we examined the sequenced genetic determinants of TMT-B using GWLS in over 2000 participants from Long Life Family Study (LLFS). In GWLS, the estimated heritability of TMT-B was 0.29. We detected one significant linkage peak at 15q25 (LOD>3.0). Statistical fine-mapping nominated five variants including three SNPs (NTRK3-rs74031103, protective CEMIP-rs2271159, and protective AGBL1-rs4134376) and two INDELs (protective KLHL25-15:85882445:IND, and protective CEMIP-15:80893381:IND) contributing to the linkage peak. Four out of these five variants are protective for TMT-B. The rs2271159 SNP influences CEMIP expression in cerebellum and hippocampus, while the 15:80893381:IND modulates CEMIP expression in blood. Additionally, the variant rs4134376 is a basal ganglia-specific eQTL for AGBL1. In conclusion, we utilized GWLS, leveraged multi-omics data (whole genome sequence genomic data, transcriptomic data, and lipidomic data), and identified novel protective variants and genes for TMT-B performance.

genomics↗

A Paradigm For Calling Sequence In Families: The Long Life Family Study

Over Several years, we have developed a system for assuring the quality of whole genome sequence (WGS) data in the LLFS families. We have focused on providing data to identify germline genetic variants with the aim of releasing as many variants on as many individuals as possible. We aim to assure the quality of the individual calls. The availability of family data has enabled us to use and validate some filters not commonly used in population-based studies. We developed slightly different procedures for the autosomal, X, Y, and Mitochondrial (MT) chromosomes. Some of these filters are specific to family data, but some can be used with any WGS data set. We also describe the procedure we use to construct linkage markers from the SNP sequence data and how we compute IBD values for use in linkage analysis.

genetics↗