Convergence of recent GWAS data for suicidality with previous blood biomarkers: independent reproducibility using independent methodologies in independent cohorts

Niculescu, Alexander B.; Le-Niculescu, Helen

doi:10.1038/s41380-019-0465-6

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Since ML uses mathematical rather than statistical methods to learn and predict class, it is interesting ML independently identified many chromosome #21 and differentially expressed RNAs as important predictors. In the future, it might be valuable to prioritize markers by clustering via gene ontology, pathway, or Bayesian-like convergent functional genomics approach [ 76 , 77 ].…”

Section: Discussionmentioning

confidence: 99%

Detection of Embryonic Trisomy 21 in the First Trimester Using Maternal Plasma Cell-Free RNA

et al. 2022

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Prenatal trisomy 21 (T21) screening commonly involves testing a maternal blood sample for fetal DNA aneuploidy. It is reliable but poses a cost barrier to universal screening. We hypothesized maternal plasma RNA screening might provide similar reliability but at a lower cost. Discovery experiments used plasma cell-free RNA from 20 women 11–13 weeks tested by RNA and miRNA microarrays followed by qRT-PCR. Thirty-six mRNAs and 18 small RNAs of the discovery cDNA were identified by qPCR as potential markers of embryonic T21. The second objective was validation of the RNA predictors in 998 independent pregnancies at 11–13 weeks including 50 T21. Initial analyses identified 9–15 differentially expressed RNA with modest predictive power (AUC < 0.70). The 54 RNAs were then subjected to machine learning. Eleven algorithms were trained on one partition and tested on an independent partition. The three best algorithms were identified by Kappa score and the effects of training/testing partition size and dataset class imbalance on prediction were evaluated. Six to ten RNAs predicted T21 with AUCs up to 1.00. The findings suggest that maternal plasma collected at 11–13 weeks, tested by qRT-PCR, and classified by machine learning, may accurately predict T21 for a lower cost than plasma DNA, thus opening the door to universal screening.

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Section: Discussionmentioning

confidence: 99%

Detection of Embryonic Trisomy 21 in the First Trimester Using Maternal Plasma Cell-Free RNA

et al. 2022

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“…Notably, there was a single-base difference between Japonica rice (base T at Oscd1 V449) and Indica rice (base A at Oscd1 D449). GWAS technology has reduced the time and labor required for gene mapping and cloning, improved efficiency in these processes, and facilitated direct access to gene information . However, difficulties may arise in breeding low-Cd cultivars if undesirable traits are linked to low-Cd-related genes.…”

Section: Molecular-assisted Breeding Of Cd-pscsmentioning

confidence: 99%

“…GWAS has been applied to identify single nucleotide polymorphisms (SNPs) in the whole genome. , The technology is first applied in the field of human medical health and has achieved breakthroughs in exploring important complicated diseases. , Nowadays, GWAS has also been widely used in plants and crops, such as rice and wheat . GWAS was developed based on linkage disequilibrium mapping, which aims to identify the relationship between target traits and genetic markers or candidate genes by leveraging the linkage disequilibrium.…”

Section: Molecular-assisted Breeding Of Cd-pscsmentioning

confidence: 99%

Molecular-Assisted Breeding of Cadmium Pollution-Safe Cultivars

Ni,

Mubeen,

Leng

et al. 2023

J. Agric. Food Chem.

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Cadmium (Cd) contamination in edible agricultural products, especially in crops intended for consumption, has raised worldwide concerns regarding food safety. Breeding of Cd pollution-safe cultivars (Cd-PSCs) is an effective solution to preventing the entry of Cd into the food chain from contaminated agricultural soil. Molecular-assisted breeding methods, based on molecular mechanisms for cultivar-dependent Cd accumulation and bioinformatic tools, have been developed to accelerate and facilitate the breeding of Cd-PSCs. This review summarizes the recent progress in the research of the low Cd accumulation traits of Cd-PSCs in different crops. Furthermore, the application of molecular-assisted breeding methods, including transgenic approaches, genome editing, marker-assisted selection, whole genome-wide association analysis, and transcriptome, has been highlighted to outline the breeding of Cd-PSCs by identifying critical genes and molecular biomarkers. This review provides a comprehensive overview of the development of Cd-PSCs and the potential future for breeding Cd-PSC using modern molecular technologies.

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“…Recent work by our group has identified blood gene expression biomarkers that track suicidality, a tragic outcome of mood disorders, using a new powerful withinsubject longitudinal stepwise approach [4,5,11]. These studies show good reproducibility and provide a Rosetta Stone for recent multiple genetic studies of suicide (GWAS, family based) [7]. More recently, we have conducted such studies for pain [8], for stress disorders [9], and for memory/ Alzheimer's Disease [10].…”

Section: Introductionmentioning

confidence: 99%

“…Recent work by us has identified blood gene expression biomarkers that track suicidality, a tragic behavioral outcome of mood disorders, using powerful longitudinal within-subject designs, validated them in suicide completers, and tested them in independent cohorts for ability to assess state (suicidal ideation), and ability to predict trait (future hospitalizations for suicidality) [3][4][5][6]. These studies showed good reproducibility with subsequent independent genetic studies [7]. More recently, we have conducted such studies also for pain [8], for stress disorders [9], and for memory/Alzheimer's Disease [10].…”

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confidence: 96%

Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

et al. 2021

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Mood disorders (depression, bipolar disorders) are prevalent and disabling. They are also highly co-morbid with other psychiatric disorders. Currently there are no objective measures, such as blood tests, used in clinical practice, and available treatments do not work in everybody. The development of blood tests, as well as matching of patients with existing and new treatments, in a precise, personalized and preventive fashion, would make a significant difference at an individual and societal level. Early pilot studies by us to discover blood biomarkers for mood state were promising [1], and validated by others [2]. Recent work by us has identified blood gene expression biomarkers that track suicidality, a tragic behavioral outcome of mood disorders, using powerful longitudinal within-subject designs, validated them in suicide completers, and tested them in independent cohorts for ability to assess state (suicidal ideation), and ability to predict trait (future hospitalizations for suicidality) [3–6]. These studies showed good reproducibility with subsequent independent genetic studies [7]. More recently, we have conducted such studies also for pain [8], for stress disorders [9], and for memory/Alzheimer’s Disease [10]. We endeavored to use a similar comprehensive approach to identify more definitive biomarkers for mood disorders, that are transdiagnostic, by studying mood in psychiatric disorders patients. First, we used a longitudinal within-subject design and whole-genome gene expression approach to discover biomarkers which track mood state in subjects who had diametric changes in mood state from low to high, from visit to visit, as measured by a simple visual analog scale that we had previously developed (SMS-7). Second, we prioritized these biomarkers using a convergent functional genomics (CFG) approach encompassing in a comprehensive fashion prior published evidence in the field. Third, we validated the biomarkers in an independent cohort of subjects with clinically severe depression (as measured by Hamilton Depression Scale, (HAMD)) and with clinically severe mania (as measured by the Young Mania Rating Scale (YMRS)). Adding the scores from the first three steps into an overall convergent functional evidence (CFE) score, we ended up with 26 top candidate blood gene expression biomarkers that had a CFE score as good as or better than SLC6A4, an empirical finding which we used as a de facto positive control and cutoff. Notably, there was among them an enrichment in genes involved in circadian mechanisms. We further analyzed the biological pathways and networks for the top candidate biomarkers, showing that circadian, neurotrophic, and cell differentiation functions are involved, along with serotonergic and glutamatergic signaling, supporting a view of mood as reflecting energy, activity and growth. Fourth, we tested in independent cohorts of psychiatric patients the ability of each of these 26 top candidate biomarkers to assess state (mood (SMS-7), depression (HAMD), mania (YMRS)), and to predict clinical course (future hospitalizations for depression, future hospitalizations for mania). We conducted our analyses across all patients, as well as personalized by gender and diagnosis, showing increased accuracy with the personalized approach, particularly in women. Again, using SLC6A4 as the cutoff, twelve top biomarkers had the strongest overall evidence for tracking and predicting depression after all four steps: NRG1, DOCK10, GLS, PRPS1, TMEM161B, GLO1, FANCF, HNRNPDL, CD47, OLFM1, SMAD7, and SLC6A4. Of them, six had the strongest overall evidence for tracking and predicting both depression and mania, hence bipolar mood disorders. There were also two biomarkers (RLP3 and SLC6A4) with the strongest overall evidence for mania. These panels of biomarkers have practical implications for distinguishing between depression and bipolar disorder. Next, we evaluated the evidence for our top biomarkers being targets of existing psychiatric drugs, which permits matching patients to medications in a targeted fashion, and the measuring of response to treatment. We also used the biomarker signatures to bioinformatically identify new/repurposed candidate drugs. Top drugs of interest as potential new antidepressants were pindolol, ciprofibrate, pioglitazone and adiphenine, as well as the natural compounds asiaticoside and chlorogenic acid. The last 3 had also been identified by our previous suicidality studies. Finally, we provide an example of how a report to doctors would look for a patient with depression, based on the panel of top biomarkers (12 for depression and bipolar, one for mania), with an objective depression score, risk for future depression, and risk for bipolar switching, as well as personalized lists of targeted prioritized existing psychiatric medications and new potential medications. Overall, our studies provide objective assessments, targeted therapeutics, and monitoring of response to treatment, that enable precision medicine for mood disorders.

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Convergence of recent GWAS data for suicidality with previous blood biomarkers: independent reproducibility using independent methodologies in independent cohorts

Cited by 10 publications

References 12 publications

Detection of Embryonic Trisomy 21 in the First Trimester Using Maternal Plasma Cell-Free RNA

Detection of Embryonic Trisomy 21 in the First Trimester Using Maternal Plasma Cell-Free RNA

Molecular-Assisted Breeding of Cadmium Pollution-Safe Cultivars

Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

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