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Verhulst, B.

Publications and source records attributed to Verhulst, B..

5 recordsLinked to original sources

Gene-drug interactions identify genomic loci that enhance statin effectiveness in lowering LDL cholesterol.

Hyperlipidemia, and high low-density lipoprotein cholesterol (LDL-c) in particular, is a risk factor for cardiovascular disease, including atherosclerosis, myocardial infarction, and stroke. Nearly 200 million people worldwide take HMG-CoA reductase inhibitors, commonly known as statins, to lower their LDL-c. If statins interfere with the genetic pathways that endogenously increase the risk for hyperlipidemia, gene-statin interactions may identify genomic variants, and thereby individuals with those genotypes, that are particularly sensitive to these medications. We performed a series of genome-wide gene-statin interaction analyses in the UK Biobank for LDL-c and two related lipids: high-density lipoprotein cholesterol (HDL-c) and triglycerides (TG). We identified five genome-wide significant gene-statin interactions for LDL-c, two interactions for HDL-c, and four interactions for TG. Importantly, only the SNP-based heritability of LDL-c was reduced by statin use. Using data from All of Us, we replicated all five significant gene-statin interaction loci for LDL-c in the European-like ancestry sample, two loci in the Americas-like ancestry sample, and one locus in the African-like ancestry sample. We also identified fifteen loci that remained associated with LDL-c despite statin treatment, highlighting potential additional genetic targets for drug development, enhancement, and repurposing. These loci include gene-targets for the recently developed hyperlipidemia drug class (PCSK9 inhibitors) validating our approach to finding new treatments. These results are an important step towards personalized medicine for patients with hyperlipidemia. Author SummaryHigh cholesterol raises the risk of heart attacks and strokes and nearly 200 million people worldwide take statins to lower it. While statins work for nearly everyone, they work better for some people than others. We examined how genetic differences enhance the effectiveness of statin medication as a step toward enhancing personalized medicine for those with high cholesterol. By analyzing genetic and health data from about 390,000 people, we found that statins primarily disrupt the link between genes and LDL or "bad" cholesterol levels. Across the genome, statins reduce the impact of genes on LDL-c levels, but not other blood lipids like HDL-c or triglycerides. For LDL-c specifically, we found five regions where genetic differences increase the effectiveness of statins. People with the protective genetic variants are expected to see greater cholesterol reduction with statin use. We confirmed four of the five findings in European-like ancestry samples, with partial replication in Americas-like and African-like ancestry samples (two variants and one variant, respectively). We also found 15 genomic regions where cholesterol stays high despite statin treatment. These genomic regions could be targets for new or enhanced cholesterol medications. In fact, a newer drug class targets one of these regions supporting our approach to finding new treatments.

genetics↗

Moderating heritability with genomic data

Environmental moderators may amplify or suppress the heritability (i.e., the proportion of genetic variation) of a phenotype. This genetic sensitivity to the environment is called gene-environment interaction (GxE). Existing GxE methods struggle to identify replicable interactions because they focus on the interaction coefficients. We propose a novel method for estimating GxE heritability using genetic marginal effects from GxE genome-wide analyses and LD Score Regression (LDSC). We demonstrate the effectiveness of our method for body mass index (BMI) treating biological sex (binary) and age (continuous) as moderators. We find robust, interpretable evidence for GxE that is not detected by existing methods.

genomics↗

MR-DoC2: bidirectional causal modeling with instrumental variables and data from relatives

Establishing causality is an essential step towards developing interventions for psychiatric disorders, substance use and many other conditions. While randomized controlled trials (RCTs) are considered the gold standard for causal inference, they are unethical in many scenarios. Mendelian randomization (MR) can be used in such cases, but importantly both RCTs and MR assume unidirectional causality. In this paper, we developed a new model, MRDoC2, that can be used to identify bidirectional causation in the presence of confounding due to both familial and non- familial sources. Our model extends the MRDoC model (Minic[a] et al 2018), by simultaneously including risk scores for each trait. Furthermore, the power to detect causal effects in MRDoC2 does not require the phenotypes to have different additive genetic or shared environmental sources of variance, as is the case in the direction of causation twin model (Heath et al., 1993).

genetics↗

The Multivariate Genetic Architecture of Literacy-, Language- and Working Memory-related Abilities as Captured by Genome-wide Variation

There is genetic overlap between many measures of literacy, language and phonological working memory (PWM) though our knowledge of multivariate genetic architectures is incomplete. Here, we directly modeled genetic trait interrelationships in unrelated UK youth (8-13 years, N=6,453), as captured by genome-wide relationship matrices, using novel structural equation modeling techniques. We identified, besides shared genetic factors across different domains (explaining 91-97% genetic variance in literacy-related measures such as passage reading fluency, spelling, phonemic awareness, 44% in oral language and 53% in PWM), evidence for distinct cognitive abilities; trait-specific genetic influences ranged between 47% for PWM to 56% for oral language. Among reading fluency measures (non-word, word and passage reading), single-word reading was genetically most diverse. Multivariate genetic and residual covariance patterns showed concordant effect directionality, except for near-zero residual correlations between oral language and literacy-related abilities. These findings suggest differences in etiological mechanisms and trait modifiability even among genetically highly correlated skills.

genetics↗

Twin Study of Early-Onset Major Depression Finds DNA Methylation Enrichment for Neurodevelopmental Genes

Major depression (MD) is a debilitating mental health condition with peak prevalence occurring early in life. Genome-wide examination of DNA methylation (DNAm) offers an attractive comple ment to studies of allelic risk given it can reflect the combined influence of genes and environment. The current study used a co-twin control design to identify differentially and variably methylated regions of the genome that distinguish monozygotic (MZ) twins with and without a lifetime history of early-onset MD. The sample included 150 Caucasian monozygotic twins (73% female; Mage=17.52 SD=1.28) assessed during a developmental stage characterized by relatively dis tinct neurophysiological changes. All twins were generally healthy and currently free of medica tions with psychotropic effects. DNAm was measured in peripheral blood cells using the Infinium Human BeadChip 450K Array. MD associations were detected at 760 differentially and variably methylated probes/regions that mapped to 428 genes. Results indicated an association between early-onset MD and many genes and genomic regions involved in neural circuitry formation, pro jection, functioning, and plasticity. Gene enrichment analyses implicated genes related to neuron structures and neurodevelopmental processes including cell-cell adhesion genes (e.g., CDHs, PCDHAs, PCDHA1C/2C). Genes previously implicated in mood and psychiatric disorders as well as chronic stress (e.g., HDAC4, NRG1) also were identified. DNAm regions associated with MD where found to overlap genetic loci observed in the latest Psychiatric Genomics Consortium meta- analysis of depression. Understanding the time course of epigenetic influences during emerging adulthood may clarify developmental phases where genes modulate individual differences in MD risk.

genetics↗