Dataset Information

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

ABSTRACT: A hundred years of data collection in dairy cattle can facilitate powerful studies of complex traits. Cattle GWAS have identified many associated genomic regions. With increasing numbers of cattle sequenced, fine-mapping of causal variants is becoming possible. Here we imputed selected sequence variants to 27,214 Holstein bulls that have highly reliable phenotypes for 35 production, reproduction, and body conformation traits. We performed single-marker scans for the 35 traits and multi-trait tests of the three trait groups, revealing 282 candidate QTL for fine-mapping. We developed a Bayesian Fine-MAPping approach (BFMAP) to integrate fine-mapping with functional enrichment analysis. Our fine-mapping identified 69 promising candidate genes, including ABCC9, VPS13B, MGST1, SCD, MKL1, CSN1S1 for production, CHEK2, GC, KALRN for reproduction, and TMTC2, ARRDC3, ZNF613, CCND2, FGF6 for conformation traits. Collectively, these results demonstrated the utility of BFMAP, identified candidate genes, and enhanced our understanding of the genetic basis of cattle complex traits.

SUBMITTER: Jiang J

PROVIDER: S-EPMC6582147 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Jiang Jicai J Cole John B JB Freebern Ellen E Da Yang Y VanRaden Paul M PM Ma Li L

Communications biology 20190618

A hundred years of data collection in dairy cattle can facilitate powerful studies of complex traits. Cattle GWAS have identified many associated genomic regions. With increasing numbers of cattle sequenced, fine-mapping of causal variants is becoming possible. Here we imputed selected sequence variants to 27,214 Holstein bulls that have highly reliable phenotypes for 35 production, reproduction, and body conformation traits. We performed single-marker scans for the 35 traits and multi-trait tes ...[more]

PMID: 31240250

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Project description: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 disease-related 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.

Project description:Carthamus tinctorius L. (safflower) is an important oilseed crop producing seed oil rich in unsaturated fatty acids. Scarcity of identified marker-trait associations is a major limitation toward development of successful marker-assisted breeding programs in safflower. In the present study, a safflower panel (CartAP) comprising 124 accessions derived from two core collections was assayed for its suitability for association mapping. Genotyping of CartAP using microsatellite markers revealed significant genetic diversity indicated by Shannon information index (H = 0.7537) and Nei's expected heterozygosity (I = 0.4432). In Principal Coordinate Analysis, the CartAP accessions were distributed homogeneously in all quadrants indicating their diverse nature. Distance-based Neighbor Joining analysis did not delineate the CartAP accessions in consonance with their geographical origin. Bayesian analysis of population structure of CartAP demonstrated the unstructured nature of the association panel. Kinship analysis at population (Gij ) and individual level (Fij ) revealed absence of or weak relatedness between the CartAP accessions. The above parameters established the suitability of CartAP for association mapping. We performed association mapping using phenotypic data for eight traits of agronomic value (viz., seed oil content, oleic acid, linoleic acid, plant height, number of primary branches, number of capitula per plant, 100-seed weight and days to 50% flowering) available for two growing seasons (2011-2012 and 2012-2013) through General Linear Model and Mixed Linear Model. Our study identified ninety-six significant marker-trait associations (MTAs; P < 0.05) of which, several MTAs with correlation coefficient (R2) > 10% were consistently represented in both models and in both seasons for traits viz., oil content, oleic acid content, linoleic acid content and number of primary branches. Several MTAs with high R2-values were detected either in a majority or in some environments (models and/or seasons). Many MTAs were also common between traits (viz., oleic/linoleic acid content; plant height/days to 50% flowering; number of primary branches/number of capitula per plant) that showed positive or negative correlation in their phenotypic values. The marker-trait associations identified in this study will facilitate marker-assisted breeding and identification of genetic determinants of trait variability.

Dataset Information

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

Publications

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

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