PMID- 30356716
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220410
IS  - 1664-8021 (Print)
IS  - 1664-8021 (Electronic)
IS  - 1664-8021 (Linking)
VI  - 9
DP  - 2018
TI  - Including Phenotypic Causal Networks in Genome-Wide Association Studies Using 
      Mixed Effects Structural Equation Models.
PG  - 455
LID - 10.3389/fgene.2018.00455 [doi]
LID - 455
AB  - Network based statistical models accounting for putative causal relationships 
      among multiple phenotypes can be used to infer single-nucleotide polymorphism 
      (SNP) effect which transmitting through a given causal path in genome-wide 
      association studies (GWAS). In GWAS with multiple phenotypes, reconstructing 
      underlying causal structures among traits and SNPs using a single statistical 
      framework is essential for understanding the entirety of genotype-phenotype maps. 
      A structural equation model (SEM) can be used for such purposes. We applied SEM 
      to GWAS (SEM-GWAS) in chickens, taking into account putative causal relationships 
      among breast meat (BM), body weight (BW), hen-house production (HHP), and SNPs. 
      We assessed the performance of SEM-GWAS by comparing the model results with those 
      obtained from traditional multi-trait association analyses (MTM-GWAS). Three 
      different putative causal path diagrams were inferred from highest posterior 
      density (HPD) intervals of 0.75, 0.85, and 0.95 using the inductive causation 
      algorithm. A positive path coefficient was estimated for BM --> BW, and negative 
      values were obtained for BM --> HHP and BW --> HHP in all implemented scenarios. 
      Further, the application of SEM-GWAS enabled the decomposition of SNP effects 
      into direct, indirect, and total effects, identifying whether a SNP effect is 
      acting directly or indirectly on a given trait. In contrast, MTM-GWAS only 
      captured overall genetic effects on traits, which is equivalent to combining the 
      direct and indirect SNP effects from SEM-GWAS. Although MTM-GWAS and SEM-GWAS use 
      the similar probabilistic models, we provide evidence that SEM-GWAS captures 
      complex relationships in terms of causal meaning and mediation and delivers a 
      more comprehensive understanding of SNP effects compared to MTM-GWAS. Our results 
      showed that SEM-GWAS provides important insight regarding the mechanism by which 
      identified SNPs control traits by partitioning them into direct, indirect, and 
      total SNP effects.
FAU - Momen, Mehdi
AU  - Momen M
AD  - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University 
      of Kerman, Kerman, Iran.
FAU - Ayatollahi Mehrgardi, Ahmad
AU  - Ayatollahi Mehrgardi A
AD  - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University 
      of Kerman, Kerman, Iran.
FAU - Amiri Roudbar, Mahmoud
AU  - Amiri Roudbar M
AD  - Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University 
      of Kerman, Kerman, Iran.
FAU - Kranis, Andreas
AU  - Kranis A
AD  - Roslin Institute, University of Edinburgh, Midlothian, United Kingdom.
FAU - Mercuri Pinto, Renan
AU  - Mercuri Pinto R
AD  - Department of Exact Sciences, University of Sao Paulo-Escola Superior de 
      Agricultura Luiz de Queiroz, Piracicaba, Brazil.
AD  - Department of Animal Sciences, University of Wisconsin, Madison, WI, United 
      States.
FAU - Valente, Bruno D
AU  - Valente BD
AD  - Department of Animal Sciences, University of Wisconsin, Madison, WI, United 
      States.
FAU - Morota, Gota
AU  - Morota G
AD  - Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and 
      State University, Blacksburg, VA, United States.
FAU - Rosa, Guilherme J M
AU  - Rosa GJM
AD  - Department of Animal Sciences, University of Wisconsin, Madison, WI, United 
      States.
AD  - Department of Biostatistics and Medical Informatics, University of Wisconsin, 
      Madison, WI, United States.
FAU - Gianola, Daniel
AU  - Gianola D
AD  - Department of Animal Sciences, University of Wisconsin, Madison, WI, United 
      States.
AD  - Department of Biostatistics and Medical Informatics, University of Wisconsin, 
      Madison, WI, United States.
AD  - Department of Dairy Science, University of Wisconsin, Madison, WI, United States.
LA  - eng
PT  - Journal Article
DEP - 20181009
PL  - Switzerland
TA  - Front Genet
JT  - Frontiers in genetics
JID - 101560621
PMC - PMC6189326
OTO - NOTNLM
OT  - GWAS
OT  - SEM
OT  - SNP effect
OT  - causal structure
OT  - multiple traits
OT  - path analysis
EDAT- 2018/10/26 06:00
MHDA- 2018/10/26 06:01
PMCR- 2018/10/09
CRDT- 2018/10/26 06:00
PHST- 2018/06/21 00:00 [received]
PHST- 2018/09/18 00:00 [accepted]
PHST- 2018/10/26 06:00 [entrez]
PHST- 2018/10/26 06:00 [pubmed]
PHST- 2018/10/26 06:01 [medline]
PHST- 2018/10/09 00:00 [pmc-release]
AID - 10.3389/fgene.2018.00455 [doi]
PST - epublish
SO  - Front Genet. 2018 Oct 9;9:455. doi: 10.3389/fgene.2018.00455. eCollection 2018.