2024-11-18
093_ Investigating the association of mitochondria-related gene variants with lithium response in individuals with bipolar disorder
Research Question and Aims
After 75 years, lithium (Li) is still the primary medication of choice for treating bipolar disorder (BD) (1). Over half of the individuals with BD do not respond completely to Li and therefore, identifying biomarkers to assess Li response might be helpful in guiding therapeutic plans for these individuals (2). Accordingly, determining the genetic markers that may influence the Li response could provide insight into the underlying molecular mechanisms (1, 2).
Mitochondria are membrane-bound cell organelles, which are enriched in active tissues like muscle and brain (3). Numerous cell functions, such as cellular metabolism, energy production and ion homeostasis are adjusted by mitochondria (3, 4). In the nervous system, neural activity including synaptic connection, axon formation, and neuronal plasticity, can be significantly harmed by mitochondria malfunction (5). Previous studies showed that variations in the mitochondria-related genes could modify the activity of critical proteins related to mitochondria function, and can contribute to vulnerability to mental illnesses such as BD, and also affect responding to Li (1, 3, 5, 6). For instance, while it has been reported that mitochondria are essential for intracellular calcium signaling pathways, Li is also known to affect phosphoinositide pathways, which are linked to calcium mobilization (1, 7). Thus, mitochondria-related gene polymorphisms that could result in amino acid substitution in mitochondria-protein complexes may influence Li response by changing intracellular calcium dynamics (1). In addition, it has been suggested that in individuals with BD, reduced intracellular pH, which may be brought on by mitochondrial DNA polymorphisms, is associated with Li response (8, 9); as a result, mitochondria-related gene variants may be helpful in predicting Li response.
In this vein, we hypothesized that single nucleotide polymorphisms (SNPs) in mitochondria-related genes could be associated with Li response. Therefore, the aim of this study is to look for association of SNPs in mitochondria-related genes with response to Li (dichotomous and continuous phenotypes) in individuals with BD from the international consortium on lithium genetic (ConLi+Gen) as a discovery dataset and from the PsyCourse Study as a replication dataset.
Analytic Plan
Analysis will use the ConLi+Gen samples (n = 2374; as a discovery dataset) and the PsyCourse BD samples with Alda score (n = 124 as a replication dataset) genotyped by the GSA. Among the whole genome SNP array dataset, only the mitochondria-related gene set variants will be extracted. The mitochondria-related gene set includes both mitochondria-encoded genes (37 genes from mtDNA [2 rRNAs, 22 tRNAs, and 13 polypeptides]) and nuclear-encoded genes with mitochondria function (∼1,500 genes from the nuclear genome) (5). We use the gene list of “MitoCarta3.0” that includes 1136 nuclear and mtDNA human genes, which express proteins and have substantial evidence for mitochondrial localization (https://www.broadinstitute.org/mitocarta). Using mitochondria-related gene variants data and Li-response phenotypic data (based on the Retrospective Assessment of the Li Response Phenotype Scale [Alda scale]: individuals with scores ≥ 7 as “responder” and those with scores < 7 as “non-responder”) (10), we will investigate the association of mitochondria-related gene variants with response to Li (dichotomous and continuous response phenotypes) in a cross-sectional approach. The analyses will be adjusted for age, sex, medications, ancestry principal components, and other relevant covariates in the context of logistic/linear regression models.
Resources needed
v1_sex
v1_stat
v4_age
v4_bmi
V4_cur_psy_trm
V1_dur_illness
v1_scid_dsm_dx_cat
v1_lith
v1_lith_prd
v2_lith
v2_lith_prd
v3_lith
v3_lith_prd
v4_lith
v4_lith_prd
v4_alda_A
v4_alda_B1
v4_alda_B2
v4_alda_B3
v4_alda_B4
v4_alda_B5
v4_alda_tot
v4_panss_sum_pos
v4_panss_sum_neg
v4_panss_sum_gen
v4_ymrs_sum
v4_idsc_sum
v4_bdi2_sum
v4_asrm_sum
v4_mss_sum
Raw genotypes (GSA chip) and imputed data from PsyCourse patients and healthy controls.