Publications by authors named "Marcio A A Almeida"

9 Publications

  • Page 1 of 1

Genomic variations in patients with myelodysplastic syndrome and karyotypes without numerical or structural changes.

Sci Rep 2021 02 2;11(1):2783. Epub 2021 Feb 2.

Genetics Master's Program, Replicon Research Group, Department of Agricultural and Biological Sciences, Pontifical Catholic University of Goiás, Rua 235, n. 40, Bloco L, Área IV-S. Universitario, Setor Leste Universitario, Goiânia, GO, CEP 74605-050, Brazil.

Myelodysplastic syndrome (MDS) is an onco-hematologic disease with distinct levels of peripheral blood cytopenias, dysplasias in cell differentiation and various forms of chromosomal and cytogenomic alterations. In this study, the Chromosomal Microarray Analysis (CMA) was performed in patients with primary MDS without numerical and/or structural chromosomal alterations in karyotypes. A total of 17 patients was evaluated by GTG banding and eight patients showed no numerical and/or structural alterations. Then, the CMA was carried out and identified gains and losses CNVs and long continuous stretches of homozygosity (LCSHs). They were mapped on chromosomes 1, 2, 3, 4, 5, 6, 7, 9, 10, 12, 14, 16, 17, 18, 19, 20, 21, X, and Y. Ninety-one genes that have already been implicated in molecular pathways important for cell viability were selected and in-silico expression analyses demonstrated 28 genes differentially expressed in mesenchymal stromal cells of patients. Alterations in these genes may be related to the inactivation of suppressor genes or the activation of oncogenes contributing to the evolution and malignization of MDS. CMA provided additional information in patients without visible changes in the karyotype and our findings could contribute with additional information to improve the prognostic and personalized stratification for patients.
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http://dx.doi.org/10.1038/s41598-021-81467-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7854738PMC
February 2021

The genetic architecture of the human cerebral cortex.

Science 2020 03;367(6484)

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
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http://dx.doi.org/10.1126/science.aay6690DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295264PMC
March 2020

Genome-wide linkage on chromosome 10q26 for a dimensional scale of major depression.

J Affect Disord 2016 Feb 17;191:123-31. Epub 2015 Nov 17.

Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA.

Major depressive disorder (MDD) is a common and potentially life-threatening mood disorder. Identifying genetic markers for depression might provide reliable indicators of depression risk, which would, in turn, substantially improve detection, enabling earlier and more effective treatment. The aim of this study was to identify rare variants for depression, modeled as a continuous trait, using linkage and post-hoc association analysis. The sample comprised 1221 Mexican-American individuals from extended pedigrees. A single dimensional scale of MDD was derived using confirmatory factor analysis applied to all items from the Past Major Depressive Episode section of the Mini-International Neuropsychiatric Interview. Scores on this scale of depression were subjected to linkage analysis followed by QTL region-specific association analysis. Linkage analysis revealed a single genome-wide significant QTL (LOD=3.43) on 10q26.13, QTL-specific association analysis conducted in the entire sample revealed a suggestive variant within an intron of the gene LHPP (rs11245316, p=7.8×10(-04); LD-adjusted Bonferroni-corrected p=8.6×10(-05)). This region of the genome has previously been implicated in the etiology of MDD; the present study extends our understanding of the involvement of this region by highlighting a putative gene of interest (LHPP).
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http://dx.doi.org/10.1016/j.jad.2015.11.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4715913PMC
February 2016

Recurrent major depression and right hippocampal volume: A bivariate linkage and association study.

Hum Brain Mapp 2016 Jan 20;37(1):191-202. Epub 2015 Oct 20.

Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.

Previous work has shown that the hippocampus is smaller in the brains of individuals suffering from major depressive disorder (MDD) than those of healthy controls. Moreover, right hippocampal volume specifically has been found to predict the probability of subsequent depressive episodes. This study explored the utility of right hippocampal volume as an endophenotype of recurrent MDD (rMDD). We observed a significant genetic correlation between the two traits in a large sample of Mexican American individuals from extended pedigrees (ρg = -0.34, p = 0.013). A bivariate linkage scan revealed a significant pleiotropic quantitative trait locus on chromosome 18p11.31-32 (LOD = 3.61). Bivariate association analysis conducted under the linkage peak revealed a variant (rs574972) within an intron of the gene SMCHD1 meeting the corrected significance level (χ(2) = 19.0, p = 7.4 × 10(-5)). Univariate association analyses of each phenotype separately revealed that the same variant was significant for right hippocampal volume alone, and also revealed a suggestively significant variant (rs12455524) within the gene DLGAP1 for rMDD alone. The results implicate right-hemisphere hippocampal volume as a possible endophenotype of rMDD, and in so doing highlight a potential gene of interest for rMDD risk.
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http://dx.doi.org/10.1002/hbm.23025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981180PMC
January 2016

Pleiotropic locus for emotion recognition and amygdala volume identified using univariate and bivariate linkage.

Am J Psychiatry 2015 Feb 31;172(2):190-9. Epub 2014 Oct 31.

From the Department of Psychiatry, Yale University School of Medicine, New Haven, Conn.; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Conn.; the Department of Genetics, Texas Biomedical Research Institute, San Antonio; the Department of Psychiatry and the Research Imaging Institute, University of Texas Health Science Center at San Antonio; and South Texas Veterans Health System, San Antonio.

Objective: The role of the amygdala in emotion recognition is well established, and amygdala volume and emotion recognition performance have each been shown separately to be highly heritable traits, but the potential role of common genetic influences on both traits has not been explored. The authors investigated the pleiotropic influences of amygdala volume and emotion recognition performance.

Method: In a sample of randomly selected extended pedigrees (N=858), the authors used a combination of univariate and bivariate linkage to investigate pleiotropy between amygdala volume and emotion recognition performance and followed up with association analysis.

Results: The authors found a pleiotropic region for amygdala volume and emotion recognition performance on chromosome 4q26 (LOD score=4.40). Association analysis conducted in the region underlying the bivariate linkage peak revealed a variant meeting the corrected significance level (Bonferroni-corrected p=5.01×10(-5)) within an intron of PDE5A (rs2622497, p=4.4×10(-5)) as being jointly influential on both traits. PDE5A has been implicated previously in recognition-memory deficits and is expressed in subcortical structures that are thought to underlie memory ability, including the amygdala.

Conclusions: This study extends our understanding of the shared etiology between the amygdala and emotion recognition by showing that the overlap between amygdala volume and emotion recognition performance is due at least in part to common genetic influences. Moreover, this study identifies a pleiotropic locus for the two traits and an associated variant, which localizes the genetic signal even more precisely. These results, when taken in the context of previous research, highlight the potential utility of PDE5 inhibitors for ameliorating emotion recognition deficits in individuals suffering from mental or neurodegenerative illness.
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http://dx.doi.org/10.1176/appi.ajp.2014.14030311DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4314438PMC
February 2015

Genome-wide significant localization for working and spatial memory: Identifying genes for psychosis using models of cognition.

Am J Med Genet B Neuropsychiatr Genet 2014 Jan 14;165B(1):84-95. Epub 2013 Nov 14.

Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut and Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut.

It is well established that risk for developing psychosis is largely mediated by the influence of genes, but identifying precisely which genes underlie that risk has been problematic. Focusing on endophenotypes, rather than illness risk, is one solution to this problem. Impaired cognition is a well-established endophenotype of psychosis. Here we aimed to characterize the genetic architecture of cognition using phenotypically detailed models as opposed to relying on general IQ or individual neuropsychological measures. In so doing we hoped to identify genes that mediate cognitive ability, which might also contribute to psychosis risk. Hierarchical factor models of genetically clustered cognitive traits were subjected to linkage analysis followed by QTL region-specific association analyses in a sample of 1,269 Mexican American individuals from extended pedigrees. We identified four genome wide significant QTLs, two for working and two for spatial memory, and a number of plausible and interesting candidate genes. The creation of detailed models of cognition seemingly enhanced the power to detect genetic effects on cognition and provided a number of possible candidate genes for psychosis.
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http://dx.doi.org/10.1002/ajmg.b.32211DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4106137PMC
January 2014

Identification of common variants associated with human hippocampal and intracranial volumes.

Nat Genet 2012 Apr 15;44(5):552-61. Epub 2012 Apr 15.

Laboratory of Neuro Imaging, David Geffen School of Medicine, University of California, Los Angeles, California, USA.

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10(-7)).
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http://dx.doi.org/10.1038/ng.2250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3635491PMC
April 2012

Brazilian urban population genetic structure reveals a high degree of admixture.

Eur J Hum Genet 2012 Jan 24;20(1):111-6. Epub 2011 Aug 24.

Laboratory of Genetics and Molecular Cardiology, Heart Institute, Medical School of University of Sao Paulo, Sao Paulo, Brazil.

Advances in genotyping technologies have contributed to a better understanding of human population genetic structure and improved the analysis of association studies. To analyze patterns of human genetic variation in Brazil, we used SNP data from 1129 individuals--138 from the urban population of Sao Paulo, Brazil, and 991 from 11 populations of the HapMap Project. Principal components analysis was performed on the SNPs common to these populations, to identify the composition and the number of SNPs needed to capture the genetic variation of them. Both admixture and local ancestry inference were performed in individuals of the Brazilian sample. Individuals from the Brazilian sample fell between Europeans, Mexicans, and Africans. Brazilians are suggested to have the highest internal genetic variation of sampled populations. Our results indicate, as expected, that the Brazilian sample analyzed descend from Amerindians, African, and/or European ancestors, but intermarriage between individuals of different ethnic origin had an important role in generating the broad genetic variation observed in the present-day population. The data support the notion that the Brazilian population, due to its high degree of admixture, can provide a valuable resource for strategies aiming at using admixture as a tool for mapping complex traits in humans.
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http://dx.doi.org/10.1038/ejhg.2011.144DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3234512PMC
January 2012

An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations.

BMC Genet 2011 Jan 20;12:10. Epub 2011 Jan 20.

Laboratory of Genetics and Molecular Cardiology, Heart Institute, InCor, Hospital das Clínicas, FMUSP- Universidade de São Paulo, São Paulo, Brazil.

Background: Genome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases. The astonishing amount of generated data and the use of distinct genotyping platforms with variable genomic coverage are still analytical challenges. Imputation algorithms combine directly genotyped markers information with haplotypic structure for the population of interest for the inference of a badly genotyped or missing marker and are considered a near zero cost approach to allow the comparison and combination of data generated in different studies. Several reports stated that imputed markers have an overall acceptable accuracy but no published report has performed a pair wise comparison of imputed and empiric association statistics of a complete set of GWAS markers.

Results: In this report we identified a total of 73 imputed markers that yielded a nominally statistically significant association at P < 10 -5 for type 2 Diabetes Mellitus and compared them with results obtained based on empirical allelic frequencies. Interestingly, despite their overall high correlation, association statistics based on imputed frequencies were discordant in 35 of the 73 (47%) associated markers, considerably inflating the type I error rate of imputed markers. We comprehensively tested several quality thresholds, the haplotypic structure underlying imputed markers and the use of flanking markers as predictors of inaccurate association statistics derived from imputed markers.

Conclusions: Our results suggest that association statistics from imputed markers showing specific MAF (Minor Allele Frequencies) range, located in weak linkage disequilibrium blocks or strongly deviating from local patterns of association are prone to have inflated false positive association signals. The present study highlights the potential of imputation procedures and proposes simple procedures for selecting the best imputed markers for follow-up genotyping studies.
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http://dx.doi.org/10.1186/1471-2156-12-10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224203PMC
January 2011
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