CNVRanger: association analysis of CNVs with gene expression and quantitative phenotypes

Silva, Vinícius da; Ramos, Marcel; Groenen, M.A.M.; Crooijmans, R.P.M.A.; Johansson, Anna M.; Regitano, Luciana Correia de Almeida; Coutinho, Luiz Lehmann; Zimmer, Ralf

doi:10.1093/bioinformatics/btz632

Cited by 25 publications

(27 citation statements)

References 11 publications

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“…Individual CNV calls filtered by PennCNV overlapping at least one base pair were concatenated into CNV regions (CNVRs) using the populationRanges (grl, density = 0.1) function from the CNVRanger R/Bioconductor package [74]. Genomic areas with density < 10% were deleted to avoid false positive predictions.…”

Section: Cnvr Compilationmentioning

confidence: 99%

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Genome-wide detection of CNVs and their association with performance traits in broilers

et al. 2021

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Background Copy number variations (CNVs) are a major type of structural genomic variants that underlie genetic architecture and phenotypic variation of complex traits, not only in humans, but also in livestock animals. We identified CNVs along the chicken genome and analyzed their association with performance traits. Genome-wide CNVs were inferred from Affymetrix® high density SNP-chip data for a broiler population. CNVs were concatenated into segments and association analyses were performed with linear mixed models considering a genomic relationship matrix, for birth weight, body weight at 21, 35, 41 and 42 days, feed intake from 35 to 41 days, feed conversion ratio from 35 to 41 days and, body weight gain from 35 to 41 days of age. Results We identified 23,214 autosomal CNVs, merged into 5042 distinct CNV regions (CNVRs), covering 12.84% of the chicken autosomal genome. One significant CNV segment was associated with BWG on GGA3 (q-value = 0.00443); one significant CNV segment was associated with BW35 (q-value = 0.00571), BW41 (q-value = 0.00180) and BW42 (q-value = 0.00130) on GGA3, and one significant CNV segment was associated with BW on GGA5 (q-value = 0.00432). All significant CNV segments were verified by qPCR, and a validation rate of 92.59% was observed. These CNV segments are located nearby genes, such as KCNJ11, MyoD1 and SOX6, known to underlie growth and development. Moreover, gene-set analyses revealed terms linked with muscle physiology, cellular processes regulation and potassium channels. Conclusions Overall, this CNV-based GWAS study unravels potential candidate genes that may regulate performance traits in chickens. Our findings provide a foundation for future functional studies on the role of specific genes in regulating performance in chickens.

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Section: Cnvr Compilationmentioning

confidence: 99%

“…Genome-wide association analyses between performance traits and CNV segments were carried out using the CNVRanger R/Bioconductor package [74]. This procedure was originally proposed by da Silva et al [23] .…”

Section: Genome-wide Association Analysesmentioning

confidence: 99%

Genome-wide detection of CNVs and their association with performance traits in broilers

et al. 2021

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“…Individual CNV calls filtered by PennCNV overlapping at least one base pair were concatenated into CNV regions (CNVRs) using the populationRanges(grl, density=0.1) function from the CNVRanger R/Bioconductor package [90]. Genomic areas with density <10% were deleted to avoid false positive predictions.…”

Section: Cnvr Compilationmentioning

confidence: 99%

“…Genome-wide association analyses between performance traits and CNV segments were carried out using the CNVRanger R/Bioconductor package [90]. This procedure was originally proposed by da Silva et al [51].…”

Section: Genome-wide Association Analysesmentioning

confidence: 99%

“…For that, a state was assigned for each of the SNP probes overlapping a CNV call. Then, we estimated the CNV frequency in each probe and selected only those with frequency above 5% [90]. Finally, selected probes were used to construct the CNV segments based on a CNV-genotype similarity, in which subsequent probes with identical genotype in ≥95% of our population were concatenated to CNV segments.…”

Section: Genome-wide Association Analysesmentioning

confidence: 99%

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CNV-based genome-wide association studies with performance traits in broilers

Fernandes¹

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First and foremost, thank you, God, for always being there for me. To my parents, Anna and Douglas, for your truly selfless and unconditional love. Thank you both for always giving me strength, for reminding me every day that I can do this and for being a constant source of support in my many many moments of crisis, during the challenges of graduate school and life. You are my world and my everything this life can bring. I could not have done it without you. My little brother André, aunt Kátia and uncle Luciano deserve my wholehearted thanks and appreciation as well. To my beloved grandpa Pasquale and great-aunts Lola and Beta. You are loved beyond words and missed beyond measures. I will always have this piece of my heart that smiles whenever I think about you. You were a priceless gift in my life and I will be forever grateful for the love, knowledge and values you instilled in me. I would like to sincerely express my gratitude and appreciation to my academic advisor, Dr. Luiz Coutinho, for his guidance, help and support, and especially for his confidence in me. You have set an example of excellence as a researcher and mentor. I would also like to thank my LBA team, in particular Dr. Gabriel and Dr. Vinicius, for guiding me throughout this study; your discussion, ideas, and feedback have been absolutely invaluable. To all my friends, in particular Pedro and Thais. You are the people who make me smile brighter, laugh louder and live better. True friendship isn't about being inseparable. It's being separated and nothing changes. Your friendship makes my life a better experience! Finally, I wish to express my deepest gratitude to University of São Paulo-"Luiz de Queiroz" College of Agriculture (USP/ESALQ) for the privilege granted and for aggregating so much knowledge in my academic history. You challenged me to be fearless in the pursuit of all opportunities and taught me that learning knows no boundaries. A special acknowledgment to CNPq for supporting me with a scholarship (process 134211/2019-7).

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Pituitary neuroendocrine tumors with PIT1/SF1 co-expression show distinct clinicopathological and molecular features

Dottermusch,

Ryba,

Ricklefs

et al. 2024

Acta Neuropathol

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Pituitary neuroendocrine tumors (PitNETs) are classified according to cell lineage, which requires immunohistochemistry for adenohypophyseal hormones and the transcription factors (TFs) PIT1, SF1, and TPIT. According to the current WHO 2022 classification, PitNETs with co-expression of multiple TFs are termed “plurihormonal”. Previously, PIT1/SF1 co-expression was prevailingly reported in PitNETs, which otherwise correspond to the somatotroph lineage. However, little is known about such tumors and the WHO classification has not recognized their significance. We compiled an in-house case series of 100 tumors, previously diagnosed as somatotroph PitNETs. Following TF staining, histopathological features associated with PIT1/SF1 co-expression were assessed. Integration of in-house and publicly available sample data allowed for a meta-analysis of SF1-associated clinicopathological and molecular features across a total of 270 somatotroph PitNETs. The majority (74%, 52/70) of our densely granulated somatotroph PitNETs (DGST) unequivocally co-expressed PIT1 and SF1 (DGST-PIT1/SF1). None (0%, 0/30) of our sparsely granulated somatotroph PitNETs (SGST) stained positive for SF1 (SGST-PIT1). Among DGST, PIT1/SF1 co-expression was significantly associated with scarce FSH/LH expression and fewer fibrous bodies compared to DGST-PIT1. Integrated molecular analyses including publicly available samples confirmed that DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1 represent distinct tumor subtypes. Clinicopathological meta-analyses indicated that DGST-PIT1 respond more favorably towards treatment with somatostatin analogs compared to DGST-PIT1/SF1, while both these subtypes show an overall less aggressive clinical course than SGST-PIT1. In this study, we spotlight that DGST with co-expression of PIT1 and SF1 represent a common, yet underrecognized, distinct PitNET subtype. Our study questions the rationale of generally classifying such tumors as “plurihormonal”, and calls for a refinement of the WHO classification. We propose the term “somatogonadotroph PitNET”.

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CNVRanger: association analysis of CNVs with gene expression and quantitative phenotypes

Cited by 25 publications

References 11 publications

Genome-wide detection of CNVs and their association with performance traits in broilers

Genome-wide detection of CNVs and their association with performance traits in broilers

CNV-based genome-wide association studies with performance traits in broilers

Pituitary neuroendocrine tumors with PIT1/SF1 co-expression show distinct clinicopathological and molecular features

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