Joni Coleman

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Genome-wide association study of major anxiety disorders in 122,341 European-ancestry cases identifies 58 loci and highlights GABAergic signaling - Nature Genetics Genome-wide association meta-analysis identifies 58 independent risk loci for major anxiety disorders among individuals of European ancestry and implicates GABAergic signaling as a potential mechanism...

Out earlier this week in @natgenet.nature.com: GWAS of major anxiety disorders in 122,341 European-ancestry cases identifying 58 loci (www.nature.com/articles/s41...)

Awesome work, and great news for the anx genetics community — each @pgcgenetics.bsky.social paper seeds 100s more papers!

Genome-wide association study of major anxiety disorders in 122,341 European-ancestry cases identifies 58 loci and highlights GABAergic signaling - Nature Genetics Genome-wide association meta-analysis identifies 58 independent risk loci for major anxiety disorders among individuals of European ancestry and implicates GABAergic signaling as a potential mechanism...

www.nature.com/articles/s41... 🧪. Impressive GWAS meta-analysis from the Anxiety Disorders Working Group of the Psychiatric Genomics Consortium

Developmental Psychology & Psychopathology | King's College London This course will develop your knowledge about genetic and environmental causes of developmental psychopathology across the lifespan.

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If your background is in psychology, biosciences, or a related field, our MSc in Developmental Psychology & Psychopathology at @kingsioppn.bsky.social could be your next step: www.kcl.ac.uk/study/postgr...

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Latent factors are orthogonal, so fine-mapping is more straightforward because there is no trait trait correlation.

Application to blood cell traits allows identification of relevant biology and causal variants

Cell Press: STAR Protocols STAR Protocols is an open access, peer-reviewed journal from Cell Press. We offer structured, transparent, accessible, and repeatable step-by-step experimental and computational protocols from all are...

STAR protocol paper: star-protocols.cell.com/protocols/4581

Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits - PubMed Genome-wide association studies (GWASs) of high-dimensional traits, such as blood cell or metabolic traits, often use univariate approaches, ignoring trait relationships. Biological mechanisms generating variation in high-dimensional traits can be captured parsimoniously through a GWAS of latent fac …

Latent factor GWAS - flashFMZero. Common underlying biological mechanisms.

pubmed.ncbi.nlm.nih.gov/40220762/

See improved results on lipid analysis using MGflashFM compared to original analysis by relaxing previous assumptions and limitations.

MGflashFM, Multi-group multi-trait fine-mapping. Flexible framework , simple interpretation: www.nature.com/articles/s41...

Multi ancestry methods also allow for improved fine mapping (again, assuming shared causal variants). Older methods exclude non shared variants, but this results in a lot of loss with more ancestries and more diverse ancestries.

Combining functional annotation and multi-trait fine-mapping methods improves fine-mapping resolution at glycaemic trait loci - PubMed The Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) identified 242 loci associated with glycaemic traits fasting insulin (FI), fasting glucose (FG), 2 h-Glucose (2hGlu), and glycated haemoglobin (HbA1c). However, for the majority, the causal variant(s) remain(s) unknown. Model …

Also see resolution improvement on glycaemic traits, even more so when combined with function annotations, as well as higher posterior probabilities for top variants: pubmed.ncbi.nlm.nih.gov/41251494/

Applied to lipid traits in the Ugandan Genome Resource. Can resolve a locus for HDL and LDL in APOE in 6407 participants. See different variant in flashFM versus single trait methods for HDL. In a bigger AFR meta-analysis, the flashFM variant is then supported by single trait fine-mapping.

FlashFM - allows multiple causal variants per trait accounting for correlations. Flexible framework, shared information (without bias — if no shared effects, results are as single trait mapping), simple interpretation.

www.nature.com/articles/s41...

Joint analysis is computationally burdensome - intersections of phenotypes rapidly increase the search space. Need to account for trait selection and define sensible priors.

Fine-mapping is the process of trying to find causal variants from associated ones, a specific example of identifying true effects among correlated variables

Why joint mapping? Uses the proposed shared effects to gain power without needing new recruitment, and allows pleiotropy to be studied.

Finally, Jennifer Asimit will discuss leveraging phenotypic similarity and ancestry diversity in cardiometabolic genetics.

Multi trait and multi ancestry methods for fine-mapping are emerging but challenging to implement

Also coverage issues - short read sequencing leaves gaps, which if not accounted for can be misunderstood. [Maybe long read from UK Biobank will help with this?]

UK Biobank allow you to explore mechanisms - can show proteomic and other omic effects for associations seen at organismal level.

But caveat emptor - interesting results can turn out to be explainable by longer-distant haplotype effects (especially indexed by D' and not by r²) of known effects

Porting methods into multiple biobanks. Adjust each strata of the meta-analysis for the overall top variant, then re-meta-analyse. Result is robust to haplotype effects.

Very important for multiple ancestry analysis. Most effects are suppressed. Still ID interesting results.

Heritability probably the wrong metric for rare variants - not impactful on population level, but probably very important in carriers

Very hard to estimate heritability to very rare variants - confounding via population stratification causes inflation.

Saturate heritability at about MAC>10 (ignoring pop strat)

Substantial aggregation of UTR variants in height for FGF5[?]

Yengo et al saturated the common variant space in height - does rare variation lie near to common variants? Yes, mostly. Independent, colocated signals. For height, rare heritability is located near these loci. Not true for BMI,WHRadjBMI

E.g. IGF2BP2 variants - rare regulatory variants upstream of the gene associated with WHRadjBMI. Gene has been previously associated with this. But profile with other phenotypes is different - regulatory variants don't need to do the same thing as nearby regulatory variants!

Proximal Vs intergenic Vs sliding window annotations of regulatory effects. Integrated with further annotations and conditional on nearby coding variants.

GH developed a framework for single variant association analysis for MAC>5 and genomic aggregate testing. Latter is more straightforward for coding - most affect alleles are going to be deleterious. Non-coding is harder - less of a prior for same effects. Examined different aggregation categories .

Whole-genome sequencing analysis of anthropometric traits in 672,976 individuals reveals convergence between rare and common genetic associations Genetic association studies have mostly focussed on common variants from genotyping arrays or rare protein-coding variants from exome sequencing. Here, we used whole-genome sequence (WGS) data in 672,...

Final session - Gareth Hawkes talks about using WGS to examine the convergence of rare and common traits.

Preprint: www.biorxiv.org/content/10.1...

Glad to see this out!

Finally Georgios Kalantzis on a recessive meta-analysis across six biobanks, including diverse samples and multiple phenotypes.

58 sig associations. 17 better fit recessive than additive models. See HBB associations that are partly (but not fully) explained by anaemias.

Examined PIEZO1 variant in Genes and Health GWAS. MAF 3.9% in CSA, negligible elsewhere.

Clinical implications of variation - increased delayed diagnosis, complications.