Although quantitative trait locus (QTL) associations have been identified for many molecular traits such as gene expression, it remains challenging to distinguish the causal nucleotide from nearby variants. In addition to traditional QTLs by association, allele-specific (AS) QTLs are a powerful measure of cis-regulation that are concordant with traditional QTLs but typically less susceptible to technical/environmental noise. However, existing methods for estimating causal variant probabilities (i.e., fine mapping) cannot produce valid estimates from asQTL signals due to complexities in linkage disequilibrium (LD). We introduce PLASMA (Population Allele-Specific Mapping), a fine-mapping method that integrates QTL and asQTL information to improve accuracy. In simulations, PLASMA accurately prioritizes causal variants over a wide range of genetic architectures. Applied to RNA-seq data from 524 kidney tumor samples, PLASMA achieves a greater power at 50 samples than conventional QTL-based fine mapping at 500 samples, with more than 17% of loci fine mapped to within five causal variants, compared to 2% by QTL-based fine mapping, and a 6.9-fold overall reduction in median credible set size compared to QTL-based fine mapping when applied to H3K27AC ChIP-seq from just 28 prostate tumor/normal samples. Variants in the PLASMA credible sets for RNA-seq and ChIP-seq were enriched for open chromatin and chromatin looping, respectively, at a comparable or greater degree than credible variants from existing methods while containing far fewer markers. Our results demonstrate how integrating AS activity can substantially improve the detection of causal variants from existing molecular data.

1Although quantitative trait locus (QTL) associations have been identified for many molecular 2 traits such as gene expression, it remains challenging to distinguish the causal nucleotide from 3 nearby variants. In addition to traditional QTLs by association, allele-specific (AS) QTLs are 4 a powerful measure of cis-regulation that are largely concordant with traditional QTLs, and 5 can be less susceptible to technical/environmental noise. However, existing asQTL analysis 6 methods do not produce probabilities of causality for each marker, and do not take into account 7 correlations among markers at a locus in linkage disequilibrium (LD). We introduce PLASMA 8 (PopuLation Allele-Specific MApping), a novel, LD-aware method that integrates QTL and 9 asQTL information to fine-map causal regulatory variants while drawing power from both the 10 number of individuals and the number of allelic reads per individual. We demonstrate through 11 simulations that PLASMA successfully detects causal variants over a wide range of genetic 12 architectures. We apply PLASMA to RNA-Seq data from 524 kidney tumor samples and show 13 that over 17 percent of loci can be fine-mapped to within 5 causal variants, compared less than 2 14 percent of loci using existing QTL-based fine-mapping. PLASMA furthermore achieves a greater 15 power at 50 samples than conventional QTL fine-mapping does at over 500 samples. Overall, 16 PLASMA achieves a 6.9-fold reduction in median 95% credible set size compared to existing 17 QTL-based fine-mapping. We additionally apply PLASMA to H3K27AC ChIP-Seq from 28 18 prostate tumor/normal samples and demonstrate that PLASMA is able to prioritize markers 19 even at small samples, with PLASMA achieving a 1.3-fold reduction in median 95% credible set 20 sizes over existing QTL-based fine-mapping. Variants in the PLASMA credible sets for RNA-21 Seq and ChIP-Seq were enriched for open chromatin and chromatin looping (respectively) at a 22 comparable or greater degree than credible variants from existing methods, while containing far 23 fewer markers. Our results demonstrate how integrating AS activity can substantially improve 24 the detection of causal variants from existing molecular data and at low sample size. 25 1 Introduction 26 A major open problem in genetics is understanding the biological mechanisms underlying complex 27traits, which are largely driven by non-coding variants. A widely adopted approach for elucidating 28 1 these regulatory patterns is the identification of disease variants that also modify molecular pheno-29 types (such as gene expression) [1][2][3][4]. These variants, known as quantitative trait loci (QTLs), are 30 typically single nucleotide polymorphisms (SNPs) that exhibit a statistical association with overall 31 gene expression abundance [5][6][7][8]. Although QTL association analysis is now mature, it remains 32 challenging to identify the precise variants that causally influence the molecular trait (as opposed 33 to variants in linkage disequilibrium (LD) with causal variants...