Michel Guillaume Nivard

The paper introduces an ultra-fast analytic solution for multivariate GWAS in Genomic SEM, reducing computation time by over 800 times and enabling efficient analysis on standard hardware.

LDSC++ improves heritability and genetic correlation estimation in multivariate GWAS by enhancing LD score regression with segmented regression and better handling of shared genetic variants and imputation quality.

The paper compares the multivariate genetic architecture of eight psychiatric disorders between males and females using Genomic Structural Invariance, finding similarities and sex-specific differences in genetic factor loadings.

The paper introduces a statistical method using trigonometry to detect nonlinear genetic relationships between traits, finding nonlinear associations between BMI and psychiatric traits but not between height and psychiatric traits.

This study performs the first GWAS on a latent partner choice index using 206,617 individuals, identifying genetic correlates and solutions to reduce assortative mating biases in genetic studies.

This study investigates the shared genetic architecture between rhythm and dyslexia using large-scale genome-wide association studies.

This study identifies a common variant in DNASE1L3 that significantly affects cfDNA properties and impacts the accuracy of non-invasive prenatal testing.

The paper investigates genetic influences on inter-individual differences in the sensorimotor-association axis of cortical organisation using twin data from the Human Connectome Project.

This study identifies genetic subtypes of major depressive symptoms by analyzing data from clinical and community cohorts, finding distinct factors for appetite/weight symptoms and missing data patterns.

This study shows noncognitive skills become more predictive of academic achievement over time and their genetic contribution increases with age.

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

The paper shows that standard methods for liability-scale heritability estimation in GWAS meta-analysis can be downwardly biased and proposes a simple correction based on effective sample size summation.

The paper estimates how parents' cognitive and non-cognitive skills affect offspring education through environmental effects using polygenic scores and multiple study designs.

This study controls for geographic confounding in GWASs of 56 traits, showing that regional differences in SES reduce genetic signals for traits like education and income.

This study integrates GWAS and exome data with single-cell gene expression to show common and ultra-rare schizophrenia variants are enriched in brain-expressed, selection-sensitive genes.

This study identifies four genetic factors underlying 11 psychiatric disorders and finds limited evidence for a single dimension of genetic risk across them.

The paper identifies genetic loci associated with survey nonresponse behaviors and shows their genetic correlation with education and health.

The paper compares multivariate methods to estimate direct and indirect genetic effects on offspring phenotypes using GWAS summary data, finding Genomic SEM most accurate and identifying novel fertility loci.

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

This paper conducts a multivariate GWAS of AUDIT items to correct biases and identify novel genetic associations between alcohol consumption, misuse, and health outcomes.

The paper combines GWAS and GWAX data using a multivariate method to estimate SNP heritability of Alzheimer's Disease, finding higher heritability than previous SNP-based estimates.

The paper introduces T-SEM to analyze gene expression's role in cognitive traits, identifying 184 genes linked to general cognitive function and enrichments in specific genomic categories.

The paper identifies sex-differential participation bias in genetic studies by performing GWAS on sex in 3.3 million individuals, finding 158 loci associated with sex and highlighting traits like BMI that may influence participation.

The paper links genotype data from the Netherlands Twin Register to health care expenditure data and performs genome-wide association analyses, finding moderate heritability but no significant SNPs associated with expenditure.

The paper investigates how genetic correlates of socio-economic status affect shared heritability across mental health traits using genome-wide association summary statistics.

In 159,701 siblings from 17 cohorts, population GWAS overstate direct effects on height, education, smoking, cognition, depressive symptoms and age at first birth; clinical traits mostly agree across models.

This study identifies 157 genome-wide significant loci associated with non-cognitive contributions to educational attainment, explaining 57% of genetic variance.

This paper introduces within-family Mendelian randomization methods to reduce biases from family structure and shows they yield different results compared to population-based analyses.

This study identifies traits associated with polygenic risk for depression using UK Biobank data and finds causal links between depression risk and brain structure, as well as environmental interactions.

The paper uses polygenic scores to show that students from advantaged schools are more likely to take advanced math courses and persist in math, while disadvantaged schools track fewer high-potential students into advanced math.