PMC:5118429 / 13768-17308
Annnotations
{"target":"http://pubannotation.org/docs/sourcedb/PMC/sourceid/5118429","sourcedb":"PMC","sourceid":"5118429","source_url":"https://www.ncbi.nlm.nih.gov/pmc/5118429","text":"Genome-wide association study (GWAS)\nGWAS was carried out using the weighted single-step GBLUP approach (wssGWAS, Wang et al., 2012). This method combines phenotype, genotype, and pedigree data in a joint analysis and is implemented in the BLUPF90 software (Misztal et al., 2002; Aguilar et al., 2010; Wang et al., 2012). The wssGWAS avoids spurious solutions of SNPs, uses phenotypes from non-genotyped individuals included in the pedigree, and allows multi-trait analyses (Fragomeni et al., 2014; Wang et al., 2014). However, multi-trait analysis was not performed in this study due to convergence issues associated with the balance of the information. Thus, a single trait analysis was performed using the following model: y=Xb+Z1a+Z2w+e where y is the vector of the phenotypes, b is the vector of fixed effects including hatch year (all traits), harvest group (except BW10 and BW13), and contemporary group (only for BW10 and BW13), a is the vector of additive genetic effects or SNP effects, w is the vector of the common environment effect, and e is the residual error. Two co-variables were included: post-hatch age for BW10 and BW13 and harvest weight for CAR, FW, and FY. X, Z1, and Z2 are incidence matrices relating a record to fixed effects in b and random animal and common environment effects in a and w, respectively. The genomic (G) relationship matrix was created according to VanRaden (2008) and combined with the numerator (A) relationship matrix into a realized (H) relationship matrix to estimate additive genetic relationships among all individuals (Aguilar et al., 2010). The number of phenotypic records used in each single trait analysis is given in Table 1, and all analyses were conducted using complete pedigree data (17,706 fish) dating back to initial development of the population (8 generations). A total of 875 fish from 196 full-sib families used in the GWAS analyses had genotypic data from the SNP array and phenotypic data for all traits in Table 1.\nThe ssGWAS2 scenario described by Wang et al. (2014) was used to estimate genomic breeding values and iteratively estimate and weight SNP effects. Windows of 20 adjacent SNPs based on the new genetic linkage map were created using POSTGSF90 (Aguilar et al., 2014). Creation of windows of consecutive SNPs can capture the effect of the quantitative trait loci (QTL) better than a single SNP (Habier et al., 2011) due to signal concentration (Sun et al., 2011). Similar to previous reports (Dikmen et al., 2013; Fragomeni et al., 2014; Wang et al., 2014; Zhang et al., 2016), we found that the use of windows with a smaller number of SNPs resulted in a decreased signal-to-noise ratio compared to windows with 20 or more SNPs (data not shown). Therefore, exclusive windows (non-overlapping) of 20 consecutive SNPs were used in the GWAS analyses and the Manhattan plots with GWAS results were created using the R package qqman (Turner, 2014). Due to the quantitative nature of the traits used in this study, five iterations (wssGWAS) were implemented to reduce the background noise of SNP windows and differentiate the windows accounting for the largest proportion of variance. However, the differences in the results beyond 3 iterations were minimal (results not presented). Rather than using P-values from classical hypothesis tests to declare regions as significantly associated with the trait (Dikmen et al., 2013), here we identified genomic regions (windows) that explained the highest proportion (around 1%) of genetic variances (Wang et al., 2014).","divisions":[{"label":"title","span":{"begin":0,"end":36}},{"label":"p","span":{"begin":37,"end":1986}}],"tracks":[{"project":"2_test","denotations":[{"id":"27920797-22624567-33226434","span":{"begin":127,"end":131},"obj":"22624567"},{"id":"27920797-20105546-33226435","span":{"begin":296,"end":300},"obj":"20105546"},{"id":"27920797-22624567-33226436","span":{"begin":315,"end":319},"obj":"22624567"},{"id":"27920797-25324857-33226437","span":{"begin":493,"end":497},"obj":"25324857"},{"id":"27920797-24904635-33226438","span":{"begin":512,"end":516},"obj":"24904635"},{"id":"27920797-18946147-33226439","span":{"begin":1404,"end":1408},"obj":"18946147"},{"id":"27920797-20105546-33226440","span":{"begin":1588,"end":1592},"obj":"20105546"},{"id":"27920797-24904635-33226441","span":{"begin":2034,"end":2038},"obj":"24904635"},{"id":"27920797-21605355-33226442","span":{"begin":2393,"end":2397},"obj":"21605355"},{"id":"27920797-21624169-33226443","span":{"begin":2440,"end":2444},"obj":"21624169"},{"id":"27920797-23935954-33226444","span":{"begin":2491,"end":2495},"obj":"23935954"},{"id":"27920797-25324857-33226445","span":{"begin":2515,"end":2519},"obj":"25324857"},{"id":"27920797-24904635-33226446","span":{"begin":2534,"end":2538},"obj":"24904635"},{"id":"27920797-27594861-33226447","span":{"begin":2554,"end":2558},"obj":"27594861"},{"id":"27920797-23935954-33226448","span":{"begin":3397,"end":3401},"obj":"23935954"},{"id":"27920797-24904635-33226449","span":{"begin":3534,"end":3538},"obj":"24904635"}],"attributes":[{"subj":"27920797-22624567-33226434","pred":"source","obj":"2_test"},{"subj":"27920797-20105546-33226435","pred":"source","obj":"2_test"},{"subj":"27920797-22624567-33226436","pred":"source","obj":"2_test"},{"subj":"27920797-25324857-33226437","pred":"source","obj":"2_test"},{"subj":"27920797-24904635-33226438","pred":"source","obj":"2_test"},{"subj":"27920797-18946147-33226439","pred":"source","obj":"2_test"},{"subj":"27920797-20105546-33226440","pred":"source","obj":"2_test"},{"subj":"27920797-24904635-33226441","pred":"source","obj":"2_test"},{"subj":"27920797-21605355-33226442","pred":"source","obj":"2_test"},{"subj":"27920797-21624169-33226443","pred":"source","obj":"2_test"},{"subj":"27920797-23935954-33226444","pred":"source","obj":"2_test"},{"subj":"27920797-25324857-33226445","pred":"source","obj":"2_test"},{"subj":"27920797-24904635-33226446","pred":"source","obj":"2_test"},{"subj":"27920797-27594861-33226447","pred":"source","obj":"2_test"},{"subj":"27920797-23935954-33226448","pred":"source","obj":"2_test"},{"subj":"27920797-24904635-33226449","pred":"source","obj":"2_test"}]},{"project":"MyTest","denotations":[{"id":"27920797-22624567-33226434","span":{"begin":127,"end":131},"obj":"22624567"},{"id":"27920797-20105546-33226435","span":{"begin":296,"end":300},"obj":"20105546"},{"id":"27920797-22624567-33226436","span":{"begin":315,"end":319},"obj":"22624567"},{"id":"27920797-25324857-33226437","span":{"begin":493,"end":497},"obj":"25324857"},{"id":"27920797-24904635-33226438","span":{"begin":512,"end":516},"obj":"24904635"},{"id":"27920797-18946147-33226439","span":{"begin":1404,"end":1408},"obj":"18946147"},{"id":"27920797-20105546-33226440","span":{"begin":1588,"end":1592},"obj":"20105546"},{"id":"27920797-24904635-33226441","span":{"begin":2034,"end":2038},"obj":"24904635"},{"id":"27920797-21605355-33226442","span":{"begin":2393,"end":2397},"obj":"21605355"},{"id":"27920797-21624169-33226443","span":{"begin":2440,"end":2444},"obj":"21624169"},{"id":"27920797-23935954-33226444","span":{"begin":2491,"end":2495},"obj":"23935954"},{"id":"27920797-25324857-33226445","span":{"begin":2515,"end":2519},"obj":"25324857"},{"id":"27920797-24904635-33226446","span":{"begin":2534,"end":2538},"obj":"24904635"},{"id":"27920797-27594861-33226447","span":{"begin":2554,"end":2558},"obj":"27594861"},{"id":"27920797-23935954-33226448","span":{"begin":3397,"end":3401},"obj":"23935954"},{"id":"27920797-24904635-33226449","span":{"begin":3534,"end":3538},"obj":"24904635"}],"namespaces":[{"prefix":"_base","uri":"https://www.uniprot.org/uniprot/testbase"},{"prefix":"UniProtKB","uri":"https://www.uniprot.org/uniprot/"},{"prefix":"uniprot","uri":"https://www.uniprot.org/uniprotkb/"}],"attributes":[{"subj":"27920797-22624567-33226434","pred":"source","obj":"MyTest"},{"subj":"27920797-20105546-33226435","pred":"source","obj":"MyTest"},{"subj":"27920797-22624567-33226436","pred":"source","obj":"MyTest"},{"subj":"27920797-25324857-33226437","pred":"source","obj":"MyTest"},{"subj":"27920797-24904635-33226438","pred":"source","obj":"MyTest"},{"subj":"27920797-18946147-33226439","pred":"source","obj":"MyTest"},{"subj":"27920797-20105546-33226440","pred":"source","obj":"MyTest"},{"subj":"27920797-24904635-33226441","pred":"source","obj":"MyTest"},{"subj":"27920797-21605355-33226442","pred":"source","obj":"MyTest"},{"subj":"27920797-21624169-33226443","pred":"source","obj":"MyTest"},{"subj":"27920797-23935954-33226444","pred":"source","obj":"MyTest"},{"subj":"27920797-25324857-33226445","pred":"source","obj":"MyTest"},{"subj":"27920797-24904635-33226446","pred":"source","obj":"MyTest"},{"subj":"27920797-27594861-33226447","pred":"source","obj":"MyTest"},{"subj":"27920797-23935954-33226448","pred":"source","obj":"MyTest"},{"subj":"27920797-24904635-33226449","pred":"source","obj":"MyTest"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#ec93de","default":true},{"id":"MyTest","color":"#93ece0"}]}]}}