ONETOOL for the analysis of family-based big data

Song, Yeunjoo E.; Lee, Sung Young; Park, Kyungtaek; Elston, Robert C.; Yang, Hyeon Jong; Won, Sungho

doi:10.1093/bioinformatics/bty180

Cited by 37 publications

(30 citation statements)

References 5 publications

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“…In the linear model of association using QFAM, the adjusted-outcome was regressed on allele count and the family structure was corrected using 20,000 permutations. Second, a family-based association analysis was performed using a generalized score test [113]. This test accounts for familial correlation using a kinship matrix and allows for multiple covariates.…”

Section: Single Marker Gwa Analysismentioning

confidence: 99%

Genome-wide identification of loci associated with growth in rainbow trout

et al. 2020

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Background: Growth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout. Results: A previously developed 50 K gene-transcribed SNP chip, containing~21 K SNPs showing allelic imbalances potentially associated with important aquaculture production traits including body weight, muscle yield, was used for genotyping a total of 789 fish with available phenotypic data for bodyweight gain. Genotyped fish were obtained from two consecutive generations produced in the NCCCWA growth-selection breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform a genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with bodyweight gain. Using genomic sliding windows of 50 adjacent SNPs, 247 SNPs associated with bodyweight gain were identified. SNP-harboring genes were involved in cell growth, cell proliferation, cell cycle, lipid metabolism, proteolytic activities, chromatin modification, and developmental processes. Chromosome 14 harbored the highest number of SNPs (n = 50). An SNP window explaining the highest additive genetic variance for bodyweight gain (~6.4%) included a nonsynonymous SNP in a gene encoding inositol polyphosphate 5-phosphatase OCRL-1. Additionally, based on a single-marker GWA analysis, 33 SNPs were identified in association with bodyweight gain. The highest SNP explaining variation in bodyweight gain was identified in a gene coding for thrombospondin-1 (THBS1) (R 2 = 0.09). Conclusion: The majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs.

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Section: Single Marker Gwa Analysismentioning

confidence: 99%

Genome-wide identification of loci associated with growth in rainbow trout

et al. 2020

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“…were observed, respectively. Heritability was estimated for fat and moisture content using genomic relationship matrix (GRM) calculated from the variant data [100].…”

Section: Fish Population Tissue Sampling and Phenotypic Traitsmentioning

confidence: 99%

“…where SSregression is the sum of squares due to regression, and SStotal is the total sum of squares [132]. A generalized test score in ONETOOL [100] was used to perform familybased association analysis. This test allows for multiple covariates and accounts for family structure using a kinship matrix.…”

Section: Fifty-snp Window Gwa Analysismentioning

confidence: 99%

Genome-Wide Scan for Common Variants Associated With Intramuscular Fat and Moisture Content in Rainbow Trout

Ali

Al-Tobasei

Lourenço³

et al. 2020

Preprint

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Background Genetic improvement of fillet quality attributes is a priority of the aquaculture industry. Muscle composition impacts quality attributes such as flavor, appearance, texture, and juiciness. Fat and moisture make up about ~80% of the tissue weight. The genetic architecture underlying the fat and moisture content of the muscle is still to be fully explored in fish. A 50K gene transcribed SNP chip was used for genotyping 789 fish with available phenotypic data for fat and moisture content. Genotyped fish were obtained from two consecutive generations produced in the National Center for Cool and Cold Water Aquaculture (NCCCWA) growth-selective breeding program. Estimates of SNP effects from weighted single-step GBLUP (WssGBLUP) were used to perform genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with the studied traits. Results Using genomic sliding windows of 50 adjacent SNPs, 137 and 178 SNPs were identified as associated with fat and moisture content, respectively. Chromosomes 19 and 29 harbored the highest number of SNPs explaining at least 2% of the genetic variation in fat and moisture content. A total of 61 common SNPs on chromosomes 19 and 29 affected the aforementioned traits; this association suggests common mechanisms underlying intramuscular fat and moisture content. Additionally, based on single-marker GWA analyses, 8 and 24 SNPs were identified in association with fat and moisture content, respectively. Conclusion SNP-harboring genes were primarily involved in lipid metabolism, cytoskeleton remodeling, and protein turnover. This work provides putative SNP markers that could be prioritized and used for genomic selection in breeding programs.

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“…In the linear model of association using QFAM, the adjusted-outcome was regressed on allele count and the family structure was corrected using 20,000 permutations. Second, a family-based association analysis was performed using a generalized score test [110]. This test accounts for familial correlation using a kinship matrix and allows for multiple covariates.…”

Section: Single Marker Gwa Analysismentioning

confidence: 99%

Genome-wide identification of loci associated with growth in rainbow trout

Ali

Al-Tobasei

Lourenço

et al. 2019

Preprint

View full text Add to dashboard Cite

Background Growth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout.Results A previously developed 50K gene-transcribed SNP chip, containing ~21K SNPs showing allelic imbalances potentially associated with important aquaculture production traits including body weight, muscle yield, was used for genotyping a total of 789 fish with available phenotypic data for bodyweight gain. Genotyped fish were obtained from two consecutive generations produced in the NCCCWA growth-selection breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform a genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with bodyweight gain. Using genomic sliding windows of 50 adjacent SNPs, 247 SNPs associated with bodyweight gain were identified. SNP-harboring genes were involved in cell growth, cell proliferation, cell cycle, lipid metabolism, proteolytic activities, chromatin modification, and developmental processes. Chromosome 14 harbored the highest number of SNPs (n = 50). An SNP window explaining the highest additive genetic variance for bodyweight gain (~6.4%) included a nonsynonymous SNP in a gene encoding inositol polyphosphate 5-phosphatase OCRL-1. Additionally, based on a singlemarker GWA analysis, 46 SNPs were identified in association with bodyweight gain. The highest SNP associated with this trait was identified in a gene coding for thrombospondin-1 (THBS1) (R 2 = 0.09). ConclusionThe majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs.Biol Chem 2008, 283(9):5760-5768. Rep 2014, 9(5):1723-1728. -11 and Ceap-16, two novel splicing-variantproteins, associated with centrosome, microtubule aggregation and cell proliferation. J Mol Biol 2004, 343(1):71-82. 31. Tobias ES, Hurlstone AF, MacKenzie E, McFarlane R, Black DM: The TES gene at 7q31.1 is methylated in tumours and encodes a novel growth-suppressing LIM domain protein. Oncogene 2001, 20(22):2844-2853. 32. Ramirez-Valle F, Braunstein S, Zavadil J, Formenti SC, Schneider RJ: eIF4GI links nutrient sensing by mTOR to cell proliferation and inhibition of autophagy. J Cell Biol 2008, 181(2):293-307. 33. Uhl GR: Involvement of the neutral amino acid transporter SLC6A15 and leucine in obesity-related phenotypes. PLoS One 2013, 8(9):e68245. 34. Fernandez AI, Perez-Montarelo D, Barragan C, Ramayo-Caldas Y, Ibanez-Escriche N, Castello A, Noguera JL, Silio L, Folch JM, Rodriguez MC: Genome-wide linkage analysis of QTL for growth and body composition employing the PorcineSNP60 BeadChip. BMC Genet 2012, 13:41. 35. Yang K, Hitomi M, Stacey DW: Variations in cyclin D1 levels through the cell cycle determine the proliferative fate of a cell. Cell Div 2006,...

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ONETOOL for the analysis of family-based big data

Cited by 37 publications

References 5 publications

Genome-wide identification of loci associated with growth in rainbow trout

Genome-wide identification of loci associated with growth in rainbow trout

Genome-Wide Scan for Common Variants Associated With Intramuscular Fat and Moisture Content in Rainbow Trout

Genome-wide identification of loci associated with growth in rainbow trout

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