Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs.
Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs.
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DOI：
10.1371/journal.pgen.1009154
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发表时间：
2020-10
期刊：
影响因子：
4.5
通讯作者：
Evans DM
中科院分区：
文献类型：
--
作者：
Hwang LD;Tubbs JD;Luong J;Lundberg M;Moen GH;Wang G;Warrington NM;Sham PC;Cuellar-Partida G;Evans DM
间接亲本遗传效应可以定义为亲本基因型对后代表型的影响超过了基因从亲本传递给后代的影响。然而，由于世界各地大规模的基于家庭的队列相对缺乏，很难证明父母对人类特征的遗传效应，特别是在个别基因座上。在这篇手稿中，我们说明了如何父母的遗传效应对后代的表型，包括迟发性疾病，可以估计在原则上使用大规模的全基因组关联研究（GWAS）的数据，即使在父母的基因型的情况下，在个别位点。我们的策略包括通过使用来自亲属对的物理基因型数据来估计亲本基因型的基因型剂量，从而创建“虚拟”母亲和父亲。然后，我们利用父母的预期剂量，和后代相对对的实际基因型，进行条件遗传关联分析，以获得母亲，父亲和后代的遗传效应的渐近无偏估计。我们将我们的方法应用于19066同胞对从英国生物银行，并表明，多基因得分包括插补父母的教育程度SNP剂量是密切相关的后代的教育程度，即使在同一位点的后代基因型校正。我们开发了一个免费的Web应用程序，使用封闭形式的渐近解来量化我们的方法的力量。我们在一个用户友好的软件包IMPISH（在similar和Half similar中输入亲本基因型）中实现了我们的方法，该软件包允许用户快速有效地在大型全基因组数据集中跨基因组输入亲本基因型，然后在下游线性混合模型关联分析中使用这些估计的剂量。我们的结论是，估算父母的基因型，从相对对可能提供一个有用的辅助现有的大规模遗传研究的父母和他们的后代。间接亲本遗传效应可以定义为亲本基因型对后代表型的影响超过了基因从亲本传递给后代的影响。在基因型水平上估计亲本对后代结果的间接遗传效应一直具有挑战性，因为它需要来自亲本及其后代的大规模个体水平基因型，并且世界各地缺乏具有此信息的队列。在这里，我们提出了一种新的方法来估计间接的父母的遗传效应，而不需要物理基因型的父母。我们的方法创建虚拟亲本基因型的基础上的后代对的基因型，然后使用这些虚拟基因型在下游的遗传关联分析。我们开发了一个软件包“IMPISH”，允许用户在自己的全基因组数据集中估算虚拟亲本基因型，然后在下游全基因组关联分析中使用这些基因型，以及一系列功效计算器来估计检测亲本对后代表型的间接遗传效应的功效。我们将我们的方法应用于英国生物银行的教育程度数据，并表明，即使在同一位点校正后代基因型后，间接的父母遗传效应与后代的教育程度有关。
Indirect parental genetic effects may be defined as the influence of parental genotypes on offspring phenotypes over and above that which results from the transmission of genes from parents to their children. However, given the relative paucity of large-scale family-based cohorts around the world, it is difficult to demonstrate parental genetic effects on human traits, particularly at individual loci. In this manuscript, we illustrate how parental genetic effects on offspring phenotypes, including late onset conditions, can be estimated at individual loci in principle using large-scale genome-wide association study (GWAS) data, even in the absence of parental genotypes. Our strategy involves creating “virtual” mothers and fathers by estimating the genotypic dosages of parental genotypes using physically genotyped data from relative pairs. We then utilize the expected dosages of the parents, and the actual genotypes of the offspring relative pairs, to perform conditional genetic association analyses to obtain asymptotically unbiased estimates of maternal, paternal and offspring genetic effects. We apply our approach to 19066 sibling pairs from the UK Biobank and show that a polygenic score consisting of imputed parental educational attainment SNP dosages is strongly related to offspring educational attainment even after correcting for offspring genotype at the same loci. We develop a freely available web application that quantifies the power of our approach using closed form asymptotic solutions. We implement our methods in a user-friendly software package IMPISH (IMputing Parental genotypes In Siblings and Half Siblings) which allows users to quickly and efficiently impute parental genotypes across the genome in large genome-wide datasets, and then use these estimated dosages in downstream linear mixed model association analyses. We conclude that imputing parental genotypes from relative pairs may provide a useful adjunct to existing large-scale genetic studies of parents and their offspring. Indirect parental genetic effects may be defined as the influence of parental genotypes on offspring phenotypes over and above that which results from the transmission of genes from parents to children. Estimating indirect parental genetic effects on offspring outcomes at the genotype level has been challenging because it requires large-scale, individual level genotypes from both parents and their offspring, and there is a paucity of cohorts around the world with this information. Here we present a new approach to estimate indirect parental genetic effects without the requirement of physically genotyped parents. Our method creates virtual parental genotypes based on the genotypes of offspring pairs, and then uses these virtual genotypes in downstream genetic association analyses. We developed a software package “IMPISH” that allows users to impute virtual parental genotypes in their own genome-wide datasets and then use these in downstream genome-wide association analyses, as well a series of power calculators to estimate the power to detect indirect parental genetic effects on offspring phenotypes. We apply our method to educational attainment data from the UK Biobank and show that indirect parental genetic effects are related to offspring educational attainment even after correcting for offspring genotype at the same loci.
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影响因子：
2.6
作者：
Eaves, Lindon J.;St Pourcain, Beate;Smith, George Davey;York, Timothy P.;Evans, David M.
通讯作者：
Evans, David M.
影响因子：
3.5
作者：
Beaumont RN;Warrington NM;Cavadino A;Tyrrell J;Nodzenski M;Horikoshi M;Geller F;Myhre R;Richmond RC;Paternoster L;Bradfield JP;Kreiner-Møller E;Huikari V;Metrustry S;Lunetta KL;Painter JN;Hottenga JJ;Allard C;Barton SJ;Espinosa A;Marsh JA;Potter C;Zhang G;Ang W;Berry DJ;Bouchard L;Das S;Early Growth Genetics (EGG) Consortium;Hakonarson H;Heikkinen J;Helgeland Ø;Hocher B;Hofman A;Inskip HM;Jones SE;Kogevinas M;Lind PA;Marullo L;Medland SE;Murray A;Murray JC;Njølstad PR;Nohr EA;Reichetzeder C;Ring SM;Ruth KS;Santa-Marina L;Scholtens DM;Sebert S;Sengpiel V;Tuke MA;Vaudel M;Weedon MN;Willemsen G;Wood AR;Yaghootkar H;Muglia LJ;Bartels M;Relton CL;Pennell CE;Chatzi L;Estivill X;Holloway JW;Boomsma DI;Montgomery GW;Murabito JM;Spector TD;Power C;Järvelin MR;Bisgaard H;Grant SFA;Sørensen TIA;Jaddoe VW;Jacobsson B;Melbye M;McCarthy MI;Hattersley AT;Hayes MG;Frayling TM;Hivert MF;Felix JF;Hyppönen E;Lowe WL Jr;Evans DM;Lawlor DA;Feenstra B;Freathy RM
通讯作者：
Freathy RM
基因发现和多基因预测，从基因组全基因组协会的教育程度研究中，有110万个人。
影响因子：
30.8
作者：
Lee JJ;Wedow R;Okbay A;Kong E;Maghzian O;Zacher M;Nguyen-Viet TA;Bowers P;Sidorenko J;Karlsson Linnér R;Fontana MA;Kundu T;Lee C;Li H;Li R;Royer R;Timshel PN;Walters RK;Willoughby EA;Yengo L;23andMe Research Team;COGENT (Cognitive Genomics Consortium);Social Science Genetic Association Consortium;Alver M;Bao Y;Clark DW;Day FR;Furlotte NA;Joshi PK;Kemper KE;Kleinman A;Langenberg C;Mägi R;Trampush JW;Verma SS;Wu Y;Lam M;Zhao JH;Zheng Z;Boardman JD;Campbell H;Freese J;Harris KM;Hayward C;Herd P;Kumari M;Lencz T;Luan J;Malhotra AK;Metspalu A;Milani L;Ong KK;Perry JRB;Porteous DJ;Ritchie MD;Smart MC;Smith BH;Tung JY;Wareham NJ;Wilson JF;Beauchamp JP;Conley DC;Esko T;Lehrer SF;Magnusson PKE;Oskarsson S;Pers TH;Robinson MR;Thom K;Watson C;Chabris CF;Meyer MN;Laibson DI;Yang J;Johannesson M;Koellinger PD;Turley P;Visscher PM;Benjamin DJ;Cesarini D
通讯作者：
Cesarini D
影响因子：
7
作者：
Gusev, Alexander;Lowe, Jennifer K.;Pe'er, Itsik
通讯作者：
Pe'er, Itsik
影响因子：
64.8
作者：
Okbay A;Beauchamp JP;Fontana MA;Lee JJ;Pers TH;Rietveld CA;Turley P;Chen GB;Emilsson V;Meddens SF;Oskarsson S;Pickrell JK;Thom K;Timshel P;de Vlaming R;Abdellaoui A;Ahluwalia TS;Bacelis J;Baumbach C;Bjornsdottir G;Brandsma JH;Pina Concas M;Derringer J;Furlotte NA;Galesloot TE;Girotto G;Gupta R;Hall LM;Harris SE;Hofer E;Horikoshi M;Huffman JE;Kaasik K;Kalafati IP;Karlsson R;Kong A;Lahti J;van der Lee SJ;deLeeuw C;Lind PA;Lindgren KO;Liu T;Mangino M;Marten J;Mihailov E;Miller MB;van der Most PJ;Oldmeadow C;Payton A;Pervjakova N;Peyrot WJ;Qian Y;Raitakari O;Rueedi R;Salvi E;Schmidt B;Schraut KE;Shi J;Smith AV;Poot RA;St Pourcain B;Teumer A;Thorleifsson G;Verweij N;Vuckovic D;Wellmann J;Westra HJ;Yang J;Zhao W;Zhu Z;Alizadeh BZ;Amin N;Bakshi A;Baumeister SE;Biino G;Bønnelykke K;Boyle PA;Campbell H;Cappuccio FP;Davies G;De Neve JE;Deloukas P;Demuth I;Ding J;Eibich P;Eisele L;Eklund N;Evans DM;Faul JD;Feitosa MF;Forstner AJ;Gandin I;Gunnarsson B;Halldórsson BV;Harris TB;Heath AC;Hocking LJ;Holliday EG;Homuth G;Horan MA;Hottenga JJ;de Jager PL;Joshi PK;Jugessur A;Kaakinen MA;Kähönen M;Kanoni S;Keltigangas-Järvinen L;Kiemeney LA;Kolcic I;Koskinen S;Kraja AT;Kroh M;Kutalik Z;Latvala A;Launer LJ;Lebreton MP;Levinson DF;Lichtenstein P;Lichtner P;Liewald DC;LifeLines Cohort Study;Loukola A;Madden PA;Mägi R;Mäki-Opas T;Marioni RE;Marques-Vidal P;Meddens GA;McMahon G;Meisinger C;Meitinger T;Milaneschi Y;Milani L;Montgomery GW;Myhre R;Nelson CP;Nyholt DR;Ollier WE;Palotie A;Paternoster L;Pedersen NL;Petrovic KE;Porteous DJ;Räikkönen K;Ring SM;Robino A;Rostapshova O;Rudan I;Rustichini A;Salomaa V;Sanders AR;Sarin AP;Schmidt H;Scott RJ;Smith BH;Smith JA;Staessen JA;Steinhagen-Thiessen E;Strauch K;Terracciano A;Tobin MD;Ulivi S;Vaccargiu S;Quaye L;van Rooij FJ;Venturini C;Vinkhuyzen AA;Völker U;Völzke H;Vonk JM;Vozzi D;Waage J;Ware EB;Willemsen G;Attia JR;Bennett DA;Berger K;Bertram L;Bisgaard H;Boomsma DI;Borecki IB;Bültmann U;Chabris CF;Cucca F;Cusi D;Deary IJ;Dedoussis GV;van Duijn CM;Eriksson JG;Franke B;Franke L;Gasparini P;Gejman PV;Gieger C;Grabe HJ;Gratten J;Groenen PJ;Gudnason V;van der Harst P;Hayward C;Hinds DA;Hoffmann W;Hyppönen E;Iacono WG;Jacobsson B;Järvelin MR;Jöckel KH;Kaprio J;Kardia SL;Lehtimäki T;Lehrer SF;Magnusson PK;Martin NG;McGue M;Metspalu A;Pendleton N;Penninx BW;Perola M;Pirastu N;Pirastu M;Polasek O;Posthuma D;Power C;Province MA;Samani NJ;Schlessinger D;Schmidt R;Sørensen TI;Spector TD;Stefansson K;Thorsteinsdottir U;Thurik AR;Timpson NJ;Tiemeier H;Tung JY;Uitterlinden AG;Vitart V;Vollenweider P;Weir DR;Wilson JF;Wright AF;Conley DC;Krueger RF;Davey Smith G;Hofman A;Laibson DI;Medland SE;Meyer MN;Yang J;Johannesson M;Visscher PM;Esko T;Koellinger PD;Cesarini D;Benjamin DJ
通讯作者：
Benjamin DJ