Integrating comparative genomics, QTL mapping and haplotype association revealed variation associated with wheat gluten quality. Structural variants may have larger effects and epistasis provided add...

SAIGE-QTL is a framework for eQTL mapping that directly models single-cell read counts using a Poisson mixed model, achieving improved statistical efficiency and power and enabling rare variant set-ba...

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Client for the 'OpenGWAS' genotype-phenotype map ('GPMap') API &lt;<a href="https://gpmap.opengwas.io" target="_top">https://gpmap.opengwas.io</a>&gt;, which brings together colocalisation, rare variant and molecular QTL results across thousands of traits. Functions retrieve traits, genes, variants and genomic regions with their colocalising studies, associations, linkage disequilibrium and pleiotropy summaries, search the map by name or variant, and upload GWAS summary statistics for analysis against it.

Photoperiodic flowering is well studied in facultative photoperiodism plants, but the genetic basis of obligate photoperiodism remains poorly understood. Using QTL mapping and RNA-seq in Mimulus gutt...

The SAIGE-QTL method contains three main steps for each gene (Extended Data Fig. 1 and Supplementary Note): (1) fitting the null Poisson mixed model, which includes individual-level covariates, such as sex, age and ancestry principal components for donors, and cell-specific covariates, such as expression-based PEER25 factors, as fixed effects and accounts for intra-individual cell-to-cell variability using a random effect; (2a) testing for the association between each common genetic variant (for example, MAF > 5%) and gene expression using score tests; (2b) conducting set-based tests for less frequent or rare variants with, for example, MAF ≤ 5%, including Burden tests, SKAT26 and ACAT-V27, each having superior power in various scenarios of effect directions and sparsity levels followed by an ACAT-O test27 to combine P values; (3) calculating a gene-level P value for each cis-eQTL region using the ACAT-V test27, which combines variant-level P values (from step 2a) using a Cauchy combination that is robust to the correlation among individual P values and the sparsity of effects. Full derivations of the null-model fitting algorithm, including the Sherman-Morrison-Woodbury (SMW) formula28,29 and preconditioned conjugate gradient (PCG30) approaches used to make step 1 efficient and scalable, the two-stage variance ratio and SPA24 used in step...

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