Publications by authors named "Gabriel Cuellar-Partida"

Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.

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Perinatal traits are influenced by fetal and maternal genomes. We investigate the performance of three strategies to detect loci in maternal and fetal genome-wide association studies (GWASs) of the same quantitative trait: (i) the traditional strategy of analysing maternal and fetal GWASs separately; (ii) a two-degree-of-freedom test which combines information from maternal and fetal GWASs; and (iii) a one-degree-of-freedom test where signals from maternal and fetal GWASs are meta-analysed together conditional on estimated sample overlap. We demonstrate that the optimal strategy depends on the extent of sample overlap, correlation between phenotypes, whether loci exhibit fetal and/or maternal effects, and whether these effects are directionally concordant.

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Objectives: We sought to assess whether genetic associations with metabolite concentrations in septic shock patients could be used to identify pathways of potential importance for understanding sepsis pathophysiology.

Design: Retrospective multicenter cohort studies of septic shock patients.

Setting: All participants who were admitted to 27 participating hospital sites in three countries (Australia, New Zealand, and the United Kingdom) were eligible for inclusion.

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Article Synopsis
  • Perinatal traits, such as birth weight, are influenced by genetic variants from both mothers and their fetuses, and traditional methods analyze these separately, which may miss important information.
  • This study explores three strategies for analyzing maternal and fetal genetic data together: a traditional separate analysis, a new two degree of freedom test that combines the data, and a one degree of freedom test that meta-analyzes the data based on sample overlap.
  • The findings suggest that combining these analyses can significantly increase the number of identified genetic loci associated with birth weight, revealing new biological pathways and improving research methods, which are available in the DINGO software package.
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  • * Using large genome-wide association studies, researchers found that higher levels of CRP are genetically correlated with these chronic pain types, indicating a possible shared genetic background.
  • * The findings suggest that higher CRP levels may causally contribute to chronic pain, highlighting the need for more extensive research to discover new treatment targets for these conditions.
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Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.

Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches.

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Study Objectives: Despite its association with severe health conditions, the etiology of sleep apnea (SA) remains understudied. This study sought to identify genetic variants robustly associated with SA risk.

Methods: We performed a genome-wide association study (GWAS) meta-analysis of SA across five cohorts (NTotal = 523 366), followed by a multi-trait analysis of GWAS (multi-trait analysis of genome-wide association summary statistics [MTAG]) to boost power, leveraging the high genetic correlation between SA and snoring.

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Understanding how variations in the plasma and brain proteome contribute to multiple sclerosis susceptibility can provide important insights to guide drug repurposing and therapeutic development for the disease. However, the role of genetically predicted protein abundance in multiple sclerosis remains largely unknown. Integrating plasma proteomics (n = 3301) and brain proteomics (n = 376 discovery; n = 152 replication) into multiple sclerosis genome-wide association studies (n = 14 802 cases and 26 703 controls), we employed summary-based methods to identify candidate proteins involved in multiple sclerosis susceptibility.

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The multiple comorbidities & dimensions of chronic pain present a formidable challenge in disentangling its aetiology. Here, we performed genome-wide association studies of 8 chronic pain types using UK Biobank data (N =4,037-79,089 cases; N = 239,125 controls), followed by bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine (respectively) their genetic correlations and genetic causal proportion (GCP) parameters with 1,492 other complex traits. We report evidence of a shared genetic signature across chronic pain types as their genetic correlations and GCP directions were broadly consistent across an array of biopsychosocial traits.

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  • Common SNPs may account for 40-50% of human height variation, and this study identifies 12,111 SNPs linked to height from a large sample of 5.4 million individuals.
  • These SNPs cluster in 7,209 genomic segments, encompassing about 21% of the genome and showing varying densities enriched in relevant genes.
  • While these SNPs explain a substantial portion of height variance in European populations (40-45%), their predictive power is lower (10-24%) in other ancestries, suggesting a need for more research to enhance understanding in diverse populations.
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Background: Venous thromboembolism (VTE) is a life-threatening vascular event with environmental and genetic determinants. Recent VTE genome-wide association studies (GWAS) meta-analyses involved nearly 30 000 VTE cases and identified up to 40 genetic loci associated with VTE risk, including loci not previously suspected to play a role in hemostasis. The aim of our research was to expand discovery of new genetic loci associated with VTE by using cross-ancestry genomic resources.

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  • Researchers studied the genetic connections to blood fats using data from 1.6 million people from different backgrounds to understand why certain fats are higher or lower in the body.
  • They looked at special genes and how they interact in the liver and fat cells, finding that the liver plays a big part in controlling fat levels.
  • Two specific genes, CREBRF and RRBP1, were highlighted as important in understanding how our bodies manage fats due to strong supporting evidence.
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Migraine is a highly common and debilitating disorder that often affects individuals in their most productive years of life. Previous studies have identified both genetic variants and brain morphometry differences associated with migraine risk. However, the relationship between migraine and brain morphometry has not been examined on a genetic level, and the causal nature of the association between brain structure and migraine risk has not been determined.

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Genome wide association studies provide statistical measures of gene-trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organizes 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for protein-protein interactions and highly distinct biological processes governing development, signalling, disease, and homeostasis.

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  • Depression is a leading mental health disorder and a major cause of disability globally, prompting a study to explore genetic links between depression and various human traits.
  • Researchers analyzed genetic data and identified 559 traits that have a genetic correlation with depression, with 46 traits potentially causing depression, including factors like lifestyle habits, certain diseases, and obesity.
  • The study suggests that genetic predisposition toward certain conditions, such as pain and inflammation, may increase the risk of depression, offering new areas for future research and prevention strategies.
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Handedness has low heritability and epigenetic mechanisms have been proposed as an etiological mechanism. To examine this hypothesis, we performed an epigenome-wide association study of left-handedness. In a meta-analysis of 3914 adults of whole-blood DNA methylation, we observed that CpG sites located in proximity of handedness-associated genetic variants were more strongly associated with left-handedness than other CpG sites (P = 0.

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Background: Greater maternal adiposity before or during pregnancy is associated with greater offspring adiposity throughout childhood, but the extent to which this is due to causal intrauterine or periconceptional mechanisms remains unclear. Here, we use Mendelian randomisation (MR) with polygenic risk scores (PRS) to investigate whether associations between maternal pre-/early pregnancy body mass index (BMI) and offspring adiposity from birth to adolescence are causal.

Methods: We undertook confounder adjusted multivariable (MV) regression and MR using mother-offspring pairs from two UK cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) and Born in Bradford (BiB).

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Structural neuroimaging studies of individuals with chronic pain conditions have often observed decreased regional grey matter at a phenotypic level. However, it is not known if this association can be attributed to genetic factors. Here we employed a novel integrative data-driven and hypothesis-testing approach to determine whether there is a genetic basis to grey matter morphology differences in chronic pain.

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  • Increased blood lipid levels are linked to cardiovascular disease and are influenced by genetics, diet, and medication use globally.
  • Despite advancements in treatment, heart disease remains the top cause of death, highlighting the need for continued research.
  • A recent multi-ancestry GWAS encompassing 1.65 million individuals, including 350,000 from non-European backgrounds, emphasizes the importance of diverse genetic studies for better understanding lipid levels and improving clinical applications of genetic findings.
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  • ADHD is a complex disorder that starts in childhood and can continue into adulthood, with its causes and risk factors still not fully understood.
  • Using genetic data from GWAS, researchers identified 37 potential causal links between ADHD and various traits like obesity, iron deficiency anemia, and substance use, indicating a complex interplay of genetics.
  • The study suggests that genetic predispositions related to physical health conditions and social participation may influence the risk of developing ADHD, which could inform future clinical research and treatment approaches.
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Genome-wide association studies (GWAS) have revealed sixteen risk loci for endoemtrial cancer but the identification of candidate susceptibility genes remains challenging. Here, we perform transcriptome-wide association study (TWAS) analyses using the largest endometrial cancer GWAS and gene expression from six relevant tissues, prioritizing eight candidate endometrial cancer susceptibility genes, one of which (EEFSEC) is located at a potentially novel endometrial cancer risk locus. We also show evidence of biologically relevant tissue-specific expression associations for CYP19A1 (adipose), HEY2 (ovary) and SKAP1 (whole blood).

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Genome-wide association studies (GWAS) have identified genetic variants associated with brain morphology and substance use behaviors (SUB). However, the genetic overlap between brain structure and SUB has not been well characterized. We leveraged GWAS summary data of 71 brain imaging measures and alcohol, tobacco, and cannabis use to investigate their genetic overlap using linkage disequilibrium score regression.

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  • Pneumonia shows varying susceptibility and symptoms among individuals, with host genetic factors playing a significant role according to a large meta-analysis of data from UK Biobank and FinnGen.
  • The study identified two key genetic loci on chromosome 15 linked to pneumonia, along with 18 associated genes across chromosomes 15, 16, and 9, highlighting connections to smoking and cardiovascular health.
  • Findings indicate that genetic variations can predict pneumonia risk, though the effect size is relatively small, suggesting more research is needed to fully understand the genetic factors influencing this respiratory condition.
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