qgg: an R package for large-scale quantitative genetic analyses

Rohde, Palle Duun; Sørensen, Izel Fourie; Sørensen, Peter

doi:10.1101/503631

Cited by 4 publications

(4 citation statements)

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“…Then, the enrichment of GWA signals in each term or pathway was tested using the effects of the SNP associated with the term or pathway. A marker-set test method was applied to the enrichment analyses (Rohde et al, 2016;Fang et al, 2017), implemented by the R package for Quantitative Genetic and Genomic analyses (Rohde et al, 2018). The summary statistics (T sum ) for each term or pathway was calculated as follows using the SNP effects associated with each term or pathway:…”

Section: Gwa Signal Enrichment Analysesmentioning

confidence: 99%

High-density genome-wide association study for residual feed intake in Holstein dairy cattle

Fang

Null

et al. 2019

Journal of Dairy Science

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Improving feed efficiency (FE) of dairy cattle may boost farm profitability and reduce the environmental footprint of the dairy industry. Residual feed intake (RFI), a candidate FE trait in dairy cattle, can be defined to be genetically uncorrelated with major energy sink traits (e.g., milk production, body weight) by including genomic predicted transmitting ability of such traits in genetic analyses for RFI. We examined the genetic basis of RFI through genome-wide association (GWA) analyses and post-GWA enrichment analyses and identified candidate genes and biological pathways associated with RFI in dairy cattle. Data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States. Of these cows, 3,555 were genotyped and were imputed to a high-density list of 312,614 SNP. We used a single-step GWA method to combine information from genotyped and nongenotyped animals with phenotypes as well as their ancestors' information. The estimated genomic breeding values from a single-step genomic BLUP were back-solved to obtain the individual SNP effects for RFI. The proportion of genetic variance explained by each 5-SNP sliding window was also calculated for RFI. Our GWA analyses suggested that RFI is a highly polygenic trait regulated by many genes with small effects. The closest genes to the top SNP and sliding windows were associated with dry matter intake (DMI), RFI, energy homeostasis and energy balance regulation, digestion and metabolism of carbohydrates and proteins, immune regulation, leptin signaling, mitochondrial ATP activities, rumen development, skeletal muscle development, and spermatogenesis. The region of 40.7 to 41.5 Mb on BTA25 (UMD3.1 reference genome) was the top associated region for RFI. The closest genes to this region, CARD11 and EIF3B, were previously shown to be related to RFI of dairy cattle and FE of broilers, respectively. Another candidate region, 57.7 to 58.2 Mb on BTA18, which is associated with DMI and leptin signaling, was also associated with RFI in this study. Post-GWA enrichment analyses used a sumbased marker-set test based on 4 public annotation databases: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and medical subject heading (MeSH) terms. Results of these analyses were consistent with those from the top GWA signals. Across the 4 databases, GWA signals for RFI were highly enriched in the biosynthesis and metabolism of amino acids and proteins, digestion and metabolism of carbohydrates, skeletal development, mitochondrial electron transport, immunity, rumen bacteria activities, and sperm motility. Our findings offer novel insight into the genetic basis of RFI and identify candidate regions and biological pathways associated with RFI in dairy cattle.

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Section: Gwa Signal Enrichment Analysesmentioning

confidence: 99%

High-density genome-wide association study for residual feed intake in Holstein dairy cattle

Fang

Null

et al. 2019

Journal of Dairy Science

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“…To investigate the genetic basis underlying treatment response, we applied an integrative quantitative genomic approach using the genomic feature models (GFM) (Edwards et al 2016;Rohde et al 2016aRohde et al , 2017Rohde et al , 2018Rohde et al , 2019Sarup et al 2016;Fang et al 2017). The GFM approach combines genetic data with other types of biological data, for example known pathways or molecular phenotypes such as gene expression data, to infer the contribution of SNPs located within sets of SNPs defined using the external data-genomic features-on the phenotype.…”

Section: Quantitative Genomic Analysesmentioning

confidence: 99%

“…Gene set enrichment analyses: KEGG pathways and gene networks were tested for enrichment of transcripts with extreme expression profiles. We used a standard enrichment analysis implemented in the qgg package (Rohde et al 2019), where for each gene set we computed a summary statistic, T sum ¼ P n t i¼1 jt i j, where n t is the number of transcripts within each gene set and t i represents the ith t-test statistic from the gene expression analysis (Ackermann and Strimmer 2009;Maciejewski 2014). Then, for each gene set, the summary statistic was compared to a random set of transcripts to assess statistical significance.…”

Section: Genomic Feature Setsmentioning

confidence: 99%

“…To investigate if the set of differentially expressed transcripts identified in the WT mass population was important in forming the DGRP behavioral response to MPH, we used an integrative genomic prediction approach: the GFM (Edwards et al 2016;Sarup et al 2016;Fang et al 2017;Rohde et al 2017Rohde et al , 2018Rohde et al , 2019. This enabled us to predict activity levels after MPH treatment using the genomic data of the DGRP (Figure S1B in File S1).…”

Section: Genotype-specific Response To Mphmentioning

confidence: 99%

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Genetic Signatures of Drug Response Variability inDrosophila melanogaster

et al. 2019

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Knowledge of the genetic basis underlying variation in response to environmental exposures or treatments is important in many research areas. For example, knowing the set of causal genetic variants for drug responses could revolutionize personalized medicine. We used Drosophila melanogaster to investigate the genetic signature underlying behavioral variability in response to methylphenidate (MPH), a drug used in the treatment of attention-deficit/hyperactivity disorder. We exposed a wild-type D. melanogaster population to MPH and a control treatment, and observed an increase in locomotor activity in MPH-exposed individuals. Whole-genome transcriptomic analyses revealed that the behavioral response to MPH was associated with abundant gene expression alterations. To confirm these patterns in a different genetic background and to further advance knowledge on the genetic signature of drug response variability, we used a system of inbred lines, the Drosophila Genetic Reference Panel (DGRP). Based on the DGRP, we showed that the behavioral response to MPH was strongly genotype-dependent. Using an integrative genomic approach, we incorporated known gene interactions into the genomic analyses of the DGRP, and identified putative candidate genes for variability in drug response. We successfully validated 71% of the investigated candidate genes by gene expression knockdown. Furthermore, we showed that MPH has cross-generational behavioral and transcriptomic effects. Our findings establish a foundation for understanding the genetic mechanisms driving genotype-specific responses to medical treatment, and highlight the opportunities that integrative genomic approaches have in optimizing medical treatment of complex diseases. KEYWORDS Ritalin; ADHD; Drosophila Genetic Reference Panel; cross-generational effects; locomotor activity U NDERSTANDING individual variability in response to treatment is one of the fundamental challenges in biological sciences. Treatment can be defined as any expected or unanticipated biotic or abiotic exposure; that is, any change in the environment of the organism, whether in the surrounding environment, such as temperature changes, or in the intrinsic environment, such as metabolic alterations caused by ingestion of chemicals. For many scientific fields-including medicine, animal and plant breeding, and evolutionary biology-knowledge of the mechanisms that underlie variability in how an individual responds to such treatment is important. This could, for example, involve predicting the consequences of climate change on species distribution, obtaining consistent output in livestock and plant breeding across heterogeneous production systems, or providing optimal treatment of conditions that require medical attention. The latter example was specifically investigated in the present study. Responses to pharmacological interventions are highly variable for many human diseases (Roden and George 2002). Personalized medicine has been proposed as a strategy to provide more accurate and effective medical treatm...

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Untangling the Genetic Basis of Drug Response

Rohde

Kristensen

2019

Pharmacogenomics

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qgg: an R package for large-scale quantitative genetic analyses

Cited by 4 publications

References 14 publications

High-density genome-wide association study for residual feed intake in Holstein dairy cattle

High-density genome-wide association study for residual feed intake in Holstein dairy cattle

Genetic Signatures of Drug Response Variability inDrosophila melanogaster

Untangling the Genetic Basis of Drug Response

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