Functional annotation and Bayesian fine-mapping reveals candidate genes for important agronomic traits in Holstein bulls

Jiang, Jicai; Cole, J.B.; Freebern, Ellen; Da, Yang; VanRaden, P.M.; Ma, Li

doi:10.1038/s42003-019-0454-y

Cited by 66 publications

(108 citation statements)

References 49 publications

(73 reference statements)

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“…Genome-wide association studies (GWAS) have been successful at interrogating the genetic basis of complex traits and diseases in cattle [7][8][9][10]. Because complex traits are influenced by many genes, their interactions, and environment and due to the high level of linkage disequilibrium (LD) between genomic variants, pinpointing causal variants of complex traits has been challenging [11].…”

Section: Introductionmentioning

confidence: 99%

“…The utility of finemapping in cattle studies, however, has been limited by data availability and the high levels of LD present in cattle populations [12][13][14]. To circumvent this challenge, a recent study developed a fast Bayesian Fine-MAPping method (BFMAP), which performs fine-mapping by integrating various functional annotation data [10]. Additionally, this method can be exploited to identify biologically meaningful information from candidate genes to enhance the understanding of complex traits [15].…”

Section: Introductionmentioning

confidence: 99%

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GWAS and fine-mapping of livability and six disease traits in Holstein cattle

et al. 2020

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Background: Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data. Results: We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in diseaserelated tissues and relevant biological explanations such as the expression of Group-specific Component (GC) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C (CCDC88C) expression in CD8 cells and association with cow livability. Conclusions: Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GWAS and fine-mapping of livability and six disease traits in Holstein cattle

et al. 2020

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“…For the associated variant at 56,645,629 – 56,773,438 bp on BTA21, it is located close to the CCDC88C gene (Table 4). In addition to our detection of tissue-specific expression with the CD8 cell, this gene has been associated with traits such as dairy form and days to first breeding in cattle [19]. Interestingly, in humans, mutation of the CCDC88C gene caused a recessive form of congenital hydrocephalus, which presents symptoms of an elevated CD4/CD8 ratio [35, 36].…”

Section: Discussionmentioning

confidence: 99%

“…In order to identify potential candidate genes and their causal variants, GWAS signals were investigated through a fine-mapping procedure using a Bayesian approach with the software BFMAP v.1 [19]. Independent association signals within the same QTL region as well as a posterior probability of causality (PPC) to each variant and its causality p-value were assigned.…”

Section: Methodsmentioning

confidence: 99%

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

Freebern

Santos

Fang

et al. 2019

Preprint

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Background: Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multitissue transcriptome data. Results:We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, phenotypes for 11,880 to 24,699 Holstein bulls were included in the analyses of the seven traits. GWAS was performed using a mixed-model association test, and a Bayesian finemapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS results detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine MHC region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health.Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of GC in the liver and association with mastitis as well as the CCDC88C expression in CD8 cells and association with cow livability.

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“…Currently, several fine-mapping studies are based on imputation strategies or the integration of results with functional enrichment analysis to identify promising candidate genes and QTNs (e.g., Jiang et al ., 2019; Cai et al ., 2019; Liu et al ., 2019). These approaches largely depend on imputation accuracy and the status of genome annotation, thus limiting the ability to detect causal variants, especially those with a low allele frequency (Dadaev et al ., 2018).…”

Section: Discussionmentioning

confidence: 99%

Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations

Wittenburg

Bonk

Doschoris

et al. 2019

Preprint

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22Single nucleotide polymorphisms (SNPs) which capture a significant impact on 23 a trait can be identified with genome-wide association studies. High linkage disequi-24 librium (LD) among SNPs makes it difficult to identify causative variants correctly. 25Thus, often target regions instead of single SNPs are reported. Sample size has not 26 only a crucial impact on the precision of parameter estimates, it also ensures that a 27 desired level of statistical power can be reached. We study the design of experiments 28 for fine-mapping of signals of a quantitative trait locus in such a target region. 29A multi-locus model allows to identify causative variants simultaneously, to state 30 their positions more precisely and to account for existing dependencies. Based on 31 the commonly applied SNP-BLUP approach, we determine the z-score statistic for 32 locally testing non-zero SNP effects and investigate its distribution under the al-33 ternative hypothesis. This quantity employs the theoretical instead of observed 34 dependence between SNPs; it can be set up as a function of paternal and maternal 35 LD for any given population structure. 36 We simulated multiple paternal half-sib families and considered a target region of 37 1 Mbp. A bimodal distribution of estimated sample size was observed, particu-38 larly if more than two causative variants were assumed. The median of estimates 39 constituted the final proposal of optimal sample size; it was consistently less than 40 sample size estimated from single-SNP investigations which was used as a baseline 41 approach. The second mode pointed to inflated sample sizes and could be explained 42 by blocks of varying linkage phases leading to negative correlations between SNPs. 43Optimal sample size increased almost linearly with number of signals to be identified 44 but depended much stronger on the assumption on heritability. For instance, three 45 times as many samples were required if heritability was 0.1 compared to 0.3. These 46 results enable the resource-saving design of future experiments for fine-mapping of 47

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Functional annotation and Bayesian fine-mapping reveals candidate genes for important agronomic traits in Holstein bulls

Cited by 66 publications

References 49 publications

GWAS and fine-mapping of livability and six disease traits in Holstein cattle

GWAS and fine-mapping of livability and six disease traits in Holstein cattle

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

Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations

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