Functionally informed fine-mapping and polygenic localization of complex trait heritability.
Functionally informed fine-mapping and polygenic localization of complex trait heritability.
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在功能上了解了复杂性状遗传力的精细图和多基因定位。
DOI：
10.1038/s41588-020-00735-5
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发表时间：
2020-12
期刊：
影响因子：
30.8
通讯作者：
Price AL
中科院分区：
文献类型：
--
作者：
Weissbrod O;Hormozdiari F;Benner C;Cui R;Ulirsch J;Gazal S;Schoech AP;van de Geijn B;Reshef Y;Márquez-Luna C;O'Connor L;Pirinen M;Finucane HK;Price AL
精细定位旨在识别影响复杂性状的因果变异。我们提出PolyFun，一个计算可扩展的框架，以提高精细映射的准确性，利用整个基因组的功能注释，而不仅仅是基因组范围内的重要位置，以指定先验概率精细映射方法，如SuSiE或FINEMAP。在模拟中，PolyFun+SuSiE和PolyFun+FINEMAP经过良好校准，比非功能性知情的同行多识别出>20%的后验因果概率>0.95的变体。在对49个英国生物库性状（平均N= 318 K）的分析中，PolyFun+SuSiE确定了3，025个精细映射的变异性状对，后验因果概率>0.95，与SuSiE相比提高了32%。我们使用PolyFun+SuSiE的后验平均单核苷酸多态性遗传力进行多基因定位，构建最小的常见SNP集合，因果解释50%的常见SNP遗传力;这些集合的大小从28（头发颜色）到3，400（身高）到200万（儿童数量）不等。总之，PolyFun优先考虑功能后续的变体，并提供对复杂性状结构的见解。
Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome—not just genome-wide significant loci—to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun+SuSiE and PolyFun+FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts. In analyses of 49 UK Biobank traits (average N=318K), PolyFun+SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE. We used posterior mean per-SNP heritabilities from PolyFun+SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
影响因子：
30.8
作者：
Mahajan A;Taliun D;Thurner M;Robertson NR;Torres JM;Rayner NW;Payne AJ;Steinthorsdottir V;Scott RA;Grarup N;Cook JP;Schmidt EM;Wuttke M;Sarnowski C;Mägi R;Nano J;Gieger C;Trompet S;Lecoeur C;Preuss MH;Prins BP;Guo X;Bielak LF;Below JE;Bowden DW;Chambers JC;Kim YJ;Ng MCY;Petty LE;Sim X;Zhang W;Bennett AJ;Bork-Jensen J;Brummett CM;Canouil M;Ec Kardt KU;Fischer K;Kardia SLR;Kronenberg F;Läll K;Liu CT;Locke AE;Luan J;Ntalla I;Nylander V;Schönherr S;Schurmann C;Yengo L;Bottinger EP;Brandslund I;Christensen C;Dedoussis G;Florez JC;Ford I;Franco OH;Frayling TM;Giedraitis V;Hackinger S;Hattersley AT;Herder C;Ikram MA;Ingelsson M;Jørgensen ME;Jørgensen T;Kriebel J;Kuusisto J;Ligthart S;Lindgren CM;Linneberg A;Lyssenko V;Mamakou V;Meitinger T;Mohlke KL;Morris AD;Nadkarni G;Pankow JS;Peters A;Sattar N;Stančáková A;Strauch K;Taylor KD;Thorand B;Thorleifsson G;Thorsteinsdottir U;Tuomilehto J;Witte DR;Dupuis J;Peyser PA;Zeggini E;Loos RJF;Froguel P;Ingelsson E;Lind L;Groop L;Laakso M;Collins FS;Jukema JW;Palmer CNA;Grallert H;Metspalu A;Dehghan A;Köttgen A;Abecasis GR;Meigs JB;Rotter JI;Marchini J;Pedersen O;Hansen T;Langenberg C;Wareham NJ;Stefansson K;Gloyn AL;Morris AP;Boehnke M;McCarthy MI
通讯作者：
McCarthy MI
影响因子：
30.8
作者：
Finucane HK;Bulik-Sullivan B;Gusev A;Trynka G;Reshef Y;Loh PR;Anttila V;Xu H;Zang C;Farh K;Ripke S;Day FR;ReproGen Consortium;Schizophrenia Working Group of the Psychiatric Genomics Consortium;RACI Consortium;Purcell S;Stahl E;Lindstrom S;Perry JR;Okada Y;Raychaudhuri S;Daly MJ;Patterson N;Neale BM;Price AL
通讯作者：
Price AL
影响因子：
64.5
作者：
Boyle EA;Li YI;Pritchard JK
通讯作者：
Pritchard JK
影响因子：
16.6
作者：
Haworth, Simon;Mitchell, Ruth;Timpson, Nicholas J.
通讯作者：
Timpson, Nicholas J.