GWAS and Fine-Mapping of Livability and Six Disease Traits in Holstein Cattle

Freebern, Ellen; Santos, D. R. dos; Fang, Lingzhao; Jiang, Jicai; Gaddis, Kristen L. Parker; Liu, George E.; VanRaden, P.M.; Maltecca, Christian; Cole, John B.; Ma, Li

doi:10.1101/775098

Cited by 14 publications

(31 citation statements)

References 45 publications

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“…We collected GWAS summary statistics of 45 complex traits in cattle [24,76]. We generally grouped these 45 complex traits into 5 categories, including reproduction (n = 12), production (milk-relevant; n = 6), body type (n = 17), health (immune-relevant; n = 9), and feed efficiency (residual feed intake, RFI; n = 1) traits.…”

Section: Gwas Enrichment Analysis Based On Tissue-specific Histone Marksmentioning

confidence: 99%

“…Details of the singlemarker GWAS for body type, reproduction, and production traits using 27,214 US Holstein bulls were presented previously [24]. GWAS for health traits using~10,000 bulls could be found in [76], while GWAS for feed efficiency using 3947 Holstein cows (i.e., RFI) were described by Li et al [77]. For human GWAS data, we obtained the summary statistics for 58 complex traits with an average sample size of 128,848 and an average SNP number of 5,905,874.…”

Section: Gwas Enrichment Analysis Based On Tissue-specific Histone Marksmentioning

confidence: 99%

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Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human

Liu

Zhang

et al. 2020

BMC Biol

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Background: Lack of comprehensive functional annotations across a wide range of tissues and cell types severely hinders the biological interpretations of phenotypic variation, adaptive evolution, and domestication in livestock. Here we used a combination of comparative epigenomics, genome-wide association study (GWAS), and selection signature analysis, to shed light on potential adaptive evolution in cattle. Results: We cross-mapped 8 histone marks of 1300 samples from human to cattle, covering 178 unique tissues/cell types. By uniformly analyzing 723 RNA-seq and 40 whole genome bisulfite sequencing (WGBS) datasets in cattle, we validated that cross-mapped histone marks captured tissue-specific expression and methylation, reflecting tissue-relevant biology. Through integrating cross-mapped tissue-specific histone marks with large-scale GWAS and selection signature results, we for the first time detected relevant tissues and cell types for 45 economically important traits and artificial selection in cattle. For instance, immune tissues are significantly associated with health and reproduction traits, multiple tissues for milk production and body conformation traits (reflecting their highly polygenic architecture), and thyroid for the different selection between beef and dairy cattle. Similarly, we detected relevant tissues for 58 complex traits and diseases in humans and observed that immune and fertility traits in humans significantly correlated with those in cattle in terms of relevant tissues, which facilitated the identification of causal genes for such traits. For instance, PIK3CG, a gene highly specifically expressed in mononuclear cells, was significantly associated with both age-at-menopause in human and daughter-still-birth in cattle. ICAM, a T cellspecific gene, was significantly associated with both allergic diseases in human and metritis in cattle.

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Section: Gwas Enrichment Analysis Based On Tissue-specific Histone Marksmentioning

confidence: 99%

Section: Gwas Enrichment Analysis Based On Tissue-specific Histone Marksmentioning

confidence: 99%

Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human

Liu

Zhang

et al. 2020

BMC Biol

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“…These genes were located within the top QTL of ketosis on BTA14 (Fig. 4 c) [ 10 ]. Furthermore, we found that these five candidate genes were also associated ( P < 0.05) with DSAB and livability (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…We obtained summary statistics of single-marker GWAS (n ≈ 10,000) for ketosis and five health traits in cattle, which was described previously [10]. Livability as a composite trait is correlated with disease traits, reflecting a cow's overall ability to stay alive in a milking herd by measuring the percentage of on-farm deaths per lactation [10]. DSAB, highly associated with KETO, occurs when the abomasum fills with gas and moves from the floor to the top of the abdomen.…”

Section: Single-maker Gwas and Gwas Signal Enrichment Analysismentioning

confidence: 99%

“…Ketosis is a complex trait controlled by both genetic and environmental factors, with the estimated heritability ranging from 0.01 to 0.16 [ 10 – 13 ]. Our previous large-scale ( n ≈ 10 K bulls) genome-wide association study (GWAS) of ketosis (the estimated heritability was 0.012) detected only a few significant loci on Bos Taurus autosome (BTA) 14 and BTA16 in Holstein cattle, which together explained a small proportion of its entire genetic variance [ 10 ]. This finding strongly suggests a highly polygenetic architecture underlying ketosis.…”

Section: Introductionmentioning

confidence: 99%

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Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle

et al. 2020

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Background: Ketosis is a common metabolic disease during the transition period in dairy cattle, resulting in longterm economic loss to the dairy industry worldwide. While genetic selection of resistance to ketosis has been adopted by many countries, the genetic and biological basis underlying ketosis is poorly understood. Results: We collected a total of 24 blood samples from 12 Holstein cows, including 4 healthy and 8 ketosisdiagnosed ones, before (2 weeks) and after (5 days) calving, respectively. We then generated RNA-Sequencing (RNA-Seq) data and seven blood biochemical indicators (bio-indicators) from leukocytes and plasma in each of these samples, respectively. By employing a weighted gene co-expression network analysis (WGCNA), we detected that 4 out of 16 gene-modules, which were significantly engaged in lipid metabolism and immune responses, were transcriptionally (FDR < 0.05) correlated with postpartum ketosis and several bio-indicators (e.g., high-density lipoprotein and low-density lipoprotein). By conducting genome-wide association signal (GWAS) enrichment analysis among six common health traits (ketosis, mastitis, displaced abomasum, metritis, hypocalcemia and livability), we found that 4 out of 16 modules were genetically (FDR < 0.05) associated with ketosis, among which three were correlated with postpartum ketosis based on WGCNA. We further identified five candidate genes for ketosis, including GRINA, MAF1, MAFA, C14H8orf82 and RECQL4. Our phenome-wide association analysis (Phe-WAS) demonstrated that human orthologues of these candidate genes were also significantly associated with many metabolic, endocrine, and immune traits in humans. For instance, MAFA, which is involved in insulin secretion, glucose response, and transcriptional regulation, showed a significantly higher association with metabolic and endocrine traits compared to other types of traits in humans.

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Machine learning for identifying resistance features of Klebsiella pneumoniae using whole-genome sequence single nucleotide polymorphisms

Liu

Ying

et al. 2021

Journal of Medical Microbiology

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Introduction. Klebsiella pneumoniae , a gram-negative bacterium, is a common pathogen causing nosocomial infection. The drug-resistance rate of K. pneumoniae is increasing year by year, posing a severe threat to public health worldwide. K. pneumoniae has been listed as one of the pathogens causing the global crisis of antimicrobial resistance in nosocomial infections. We need to explore the drug resistance of K. pneumoniae for clinical diagnosis. Single nucleotide polymorphisms (SNPs) are of high density and have rich genetic information in whole-genome sequencing (WGS), which can affect the structure or expression of proteins. SNPs can be used to explore mutation sites associated with bacterial resistance. Hypothesis/Gap Statement. Machine learning methods can detect genetic features associated with the drug resistance of K. pneumoniae from whole-genome SNP data. Aims. This work used Fast Feature Selection (FFS) and Codon Mutation Detection (CMD) machine learning methods to detect genetic features related to drug resistance of K. pneumoniae from whole-genome SNP data. Methods. WGS data on resistance of K. pneumoniae strains to four antibiotics (tetracycline, gentamicin, imipenem, amikacin) were downloaded from the European Nucleotide Archive (ENA). Sequence alignments were performed with MUMmer 3 to complete SNP calling using K. pneumoniae HS11286 chromosome as the reference genome. The FFS algorithm was applied to feature selection of the SNP dataset. The training set was constructed based on mutation sites with mutation frequency >0.995. Based on the original SNP training set, 70% of SNPs were randomly selected from each dataset as the test set to verify the accuracy of the training results. Finally, the resistance genes were obtained by the CMD algorithm and Venny. Results. The number of strains resistant to tetracycline, gentamicin, imipenem and amikacin was 931, 1048, 789 and 203, respectively. Machine learning algorithms were applied to the SNP training set and test set, and 28 and 23 resistance genes were predicted, respectively. The 28 resistance genes in the training set included 22 genes in the test set, which verified the accuracy of gene prediction. Among them, some genes (KPHS_35310, KPHS_18220, KPHS_35880, etc.) corresponded to known resistance genes (Eef2, lpxK, MdtC, etc). Logistic regression classifiers were established based on the identified SNPs in the training set. The area under the curves (AUCs) of the four antibiotics was 0.939, 0.950, 0.912 and 0.935, showing a strong ability to predict bacterial resistance. Conclusion. Machine learning methods can effectively be used to predict resistance genes and associated SNPs. The FFS and CMD algorithms have wide applicability. They can be used for the drug-resistance analysis of any microorganism with genomic variation and phenotypic data. This work lays a foundation for resistance research in clinical applications.

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GWAS and Fine-Mapping of Livability and Six Disease Traits in Holstein Cattle

Cited by 14 publications

References 45 publications

Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human

Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human

Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle

Machine learning for identifying resistance features of Klebsiella pneumoniae using whole-genome sequence single nucleotide polymorphisms

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