scholarly journals Identifying SNPs and candidate genes for three litter traits using single-step GWAS across six parities in Landrace and Large White pigs

2018 ◽  
Vol 50 (12) ◽  
pp. 1026-1035 ◽  
Author(s):  
Pingxian Wu ◽  
Kai Wang ◽  
Qiang Yang ◽  
Jie Zhou ◽  
Dejuan Chen ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Major Effect ◽  
Single Step ◽  
Nucleotide Polymorphisms ◽  
Large White ◽  
Gene Effects ◽  
Single Nucleotide ◽  
Litter Traits ◽  
Interesting Candidate ◽  
Major Factors

Total number born (TNB), number born alive (NBA), and litter weight born alive (LWB) are critically important traits in pig production. The sow’s parity is one of the major factors influencing litter traits. Because of monogenic or polygenic contributions and the presence of temporal gene effects in different sows’ parities, it is difficult to clarify the biological and genetic background. To systematically explore the genetic mechanism of litter traits, we conducted 18 GWASs using single-step GWAS (ssGWAS) based on two breeds (908 Landrace and 1,130 Large White sow litter records) for each litter trait in different parities. A total of 300 Landrace and 300 Large White sows were genotyped by sequencing (GBS). ssGWAS was performed separately for each breed and each parity due to population stratification and temporal gene effect. In summary, we identified 80 (15 for Landrace and 65 for Large White), 227 (52 for Landrace, 175 for Large White), and 187 (34 for Landrace, 153 for Large White) single nucleotide polymorphisms (SNPs) affecting TNB, NBA, and LWB, respectively. Of them, we suggest that a total of 22 loci (SSC1: 125098202, SSC1: 117560058, SSC14: 147794697, SSC8: 84823302, SSC9: 143554876, and SSC9: 138766097 for Landrace; SSC1: 4023577, SSC1: 3859573, SSC1: 4891063, SSC16: 5197665, SSC10: 32050819, SSC13: 13552924, SSC13: 92819, SSC17: 3579607, SSC13: 196698221, SSC7: 30918403, SSC16: 46221484, SSC16: 46169204, SSC2: 41988642, SSC2: 44475457, SSC2: 42521875, and SSC7: 58411951 for Large White) are shared by TNB, NBA, and LWB. These results indicate the existence of gene temporal effect in each parity. Furthermore, our findings suggest four interesting candidate genes (FBXL7, ALDH1A2, LEPR, and DDX1) associated with litter traits in different parities that have a major effect on embryonic development progression. In conclusion, 22 crucial SNPs and four interesting candidate genes were identified for three litter traits across six parities. These findings advance our understanding of the genetic architecture of litter traits and confirm the presence of temporal gene effects in different parities. Importantly, functional validation studies for findings of particular interest are recommended in litter traits.

scholarly journals Associations of fifty single nucleotide polymorphisms within candidate genes with meatness in pigs

2014 ◽  
Vol 59 (No. 5) ◽  
pp. 227-237 ◽  
Author(s):  
A. Borowska ◽  
T. Szwaczkowski ◽  
M. Koćwin-Podsiadła ◽  
S. Kamiński ◽  
A. Ruść ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Candidate Genes ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Continuous Variable ◽  
Chromosome 1 ◽  
Nucleotide Polymorphisms ◽  
Entropy Analysis ◽  
Large White ◽  
Single Nucleotide

The objective of the paper was to classify 50 SNPs (from 17 chromosomes) according to their contribution to the meatness of 293 boars of two breeds (Polish Landrace and Polish Large White) using entropy analysis and standard association analysis. The collected data were classified into two groups (according to the official EUROP procedure) and used for entropy analysis. Associations of single genotypes versus their groups (located at single chromosomes) with the trait studied were estimated by the use of the Generalized Linear Model (GLM). Thus meatness was included as a continuous variable. The most important contributions have been estimated by both approaches for the following SNPs: SULT1A1:g.76G>A (SSC3), PKLR:g.384C>T (SSC4), MYOD1:c.566G>C (SSC2), TNNT3:g.153T>C (SSC2), GAA:g.38T>C (SSC12), LDLRR1:c.459A>G (SSC8), MYF6:g.255T>C (SSC5), CAS:g.499A>C (SSC2), PPARGC:c.678T>A (SSC15). Moreover, interactions among some studied loci are suggested, especially for the loci at chromosome 1.  

Dementia key gene identification with multi-layered SNP-gene-disease network

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i831-i839
Author(s):  
Dong-gi Lee ◽  
Myungjun Kim ◽  
Sang Joon Son ◽  
Chang Hyung Hong ◽  
Hyunjung Shin
Keyword(s):  
Candidate Genes ◽  
Learning Algorithm ◽  
Search Space ◽  
Supplementary Information ◽  
Gene Identification ◽  
Nucleotide Polymorphisms ◽  
Disease Network ◽  
Single Nucleotide ◽  
Key Genes ◽  
Significant Attention

Abstract Motivation Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted. In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures. Results The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes. Availability and implementation The code for the framework will be available at http://www.alphaminers.net/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Transcriptomic profiling of wheat near-isogenic lines reveals candidate genes on chromosome 3A for pre-harvest sprouting resistance

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xingyi Wang ◽  
Hui Liu ◽  
Kadambot H. M. Siddique ◽  
Guijun Yan
Keyword(s):  
Candidate Genes ◽  
Quantitative Trait ◽  
Genomic Region ◽  
Nucleotide Polymorphisms ◽  
Near Isogenic Lines ◽  
Gene Effects ◽  
Isogenic Lines ◽  
Trait Locus ◽  
Grain Yield And Quality ◽  
Sprouting Resistance

Abstract Background Pre-harvest sprouting (PHS) in wheat can cause severe damage to both grain yield and quality. Resistance to PHS is a quantitative trait controlled by many genes located across all 21 wheat chromosomes. The study targeted a large-effect quantitative trait locus (QTL) QPhs.ccsu-3A.1 for PHS resistance using several sets previously developed near-isogenic lines (NILs). Two pairs of NILs with highly significant phenotypic differences between the isolines were examined by RNA sequencing for their transcriptomic profiles on developing seeds at 15, 25 and 35 days after pollination (DAP) to identify candidate genes underlying the QTL and elucidate gene effects on PHS resistance. At each DAP, differentially expressed genes (DEGs) between the isolines were investigated. Results Gene ontology and KEGG pathway enrichment analyses of key DEGs suggested that six candidate genes underlie QPhs.ccsu-3A.1 responsible for PHS resistance in wheat. Candidate gene expression was further validated by quantitative RT-PCR. Within the targeted QTL interval, 16 genetic variants including five single nucleotide polymorphisms (SNPs) and 11 indels showed consistent polymorphism between resistant and susceptible isolines. Conclusions The targeted QTL is confirmed to harbor core genes related to hormone signaling pathways that can be exploited as a key genomic region for marker-assisted selection. The candidate genes and SNP/indel markers detected in this study are valuable resources for understanding the mechanism of PHS resistance and for marker-assisted breeding of the trait in wheat.

149 Integration of Selection Signatures Analyses and Weighted Single-step GWAS to Prioritize Candidate Genes for Body Conformation Traits in Pigs

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 76-77
Author(s):  
Seyed Milad Vahedi ◽  
Siavash Salek Ardestani ◽  
Duy Ngoc Do ◽  
Karim Karimi ◽  
Younes Miar
Keyword(s):  
Candidate Genes ◽  
Complex Traits ◽  
Body Height ◽  
Single Step ◽  
Fixation Index ◽  
Nucleotide Polymorphisms ◽  
Selection Signatures ◽  
Body Conformation ◽  
Yorkshire Pigs ◽  
Estimated Breeding Values

Abstract Body conformation traits such as body height (BH) and body length (BL) have been included in the swine industry’s selection criteria. The objective of this study was to identify the quantitative trait loci (QTLs) and candidate genes for pig conformation traits using an integration of selection signatures analyses and weighted single-step GWAS (WssGWAS). Body measurement records of 5,593 Yorkshire pigs of which 598 animals were genotyped with Illumina 50K panel were used. Estimated breeding values (EBVs) for BH and BL were computed using univariate animal models. Genotyped animals were grouped into top 5% and bottom 5% based on their EBVs, and selection signatures analyses were performed using fixation index (Fst), FLK, hapFLK, and Rsb statistics, which were then combined as a Mahalanobis distance (Md) framework. The WssGWAS was conducted to detect the single nucleotide polymorphisms (SNPs) associated with the studied traits. The top 1% SNPs (n=530) from Md distribution that overlapped with the top 1% SNPs from WssGWAS (n = 530) were used to detect the candidate genes. A total of 31 and six overlapped SNPs were found to be associated with BH and BL, respectively. Several candidate genes were identified for BH (PARVA, DCDC1, SYT1, CASTOR2, RGSL1, RGS8, RBMS3, TGFBR2, and HS6ST1) and BL (SNTB1, AK7, PAPOLA, KSR1, CHODL, and BMP2), explaining 2.58% and 0.42% of the trait’s genetic variation, respectively. Our results indicated that integrating data from the signatures of selection tests with WssGWAS could help elucidate genomic regions underlying complex traits.

scholarly journals Association of non-synonymous SNPs of <i>OPN</i> gene with litter size traits in pigs

2015 ◽  
Vol 58 (2) ◽  
pp. 317-323 ◽  
Author(s):  
T. Kumchoo ◽  
S. Mekchay
Keyword(s):  
Litter Size ◽  
Reproductive Traits ◽  
Snp Markers ◽  
Strong Linkage Disequilibrium ◽  
Nucleotide Polymorphisms ◽  
Large White ◽  
Single Nucleotide ◽  
Pcr Rflp ◽  
Polymerase Chain ◽  
Acid Exchange

Abstract. Osteopontin (OPN) gene is a secreted phosphoprotein which appears to play a key function in the conceptus implantation, placentation and maintenance of pregnancy in pigs. The objectives of this study were to verify the non-synonymous single nucleotide polymorphisms (SNPs) and their association with litter size traits in commercial Thai Large White pigs. A total of 320 Thai Large White sows were genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Three SNPs at c.425G> A, c.573T> C and c.881C> T revealed amino acid exchange rates of p.110Ala> Thr, p.159Val> Ala and p.262Pro> Ser, respectively, and were then segregated. These three SNPs were significantly associated with total number born (TNB) and number born alive (NBA) traits. No polymorphisms of the two SNP markers (c.278A> G and c.452T> G) were observed in this study. Moreover, the SNPs at c.425G> A and c.573T> C were found to be in strong linkage disequilibrium. The association of OPN with litter size emphasizes the importance of porcine OPN as a candidate gene for reproductive traits in pig breeding.

scholarly journals Haplotypes within tandemly duplicated candidate genes at BnaA9.MRP5 modulate phytate concentration in canola (Brassica napus L.)

2021 ◽  
Author(s):  
Haijiang Liu ◽  
xiaojuan Li ◽  
Qianwen Zhang ◽  
pan yuan ◽  
Lei Liu ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Oilseed Rape ◽  
Genome Wide Association Study ◽  
Mineral Elements ◽  
Nucleotide Polymorphisms ◽  
Brassica Napus L ◽  
Single Nucleotide ◽  
Genome Wide ◽  
A Genome ◽  
Phytate Content

Phytate is the storage form of phosphorus in angiosperm seeds and plays vitally important roles during seed development. However, in crop plants phytate decreases bioavailability of seed-sourced mineral elements for humans, livestock and poultry, and contributes to phosphate-related water pollution. However, there is little knowledge about this trait in oilseed rape B. napus (oilseed rape). Here, a panel of 505 diverse B. napus accessions was screened in a genome-wide association study (GWAS) using 3.28 x 106 single nucleotide polymorphisms (SNPs). This identified 119 SNPs significantly associated with phytate concentration (PA_Conc) and phytate content (PA_Cont) and six candidate genes were identified. Of these, BnaA9.MRP5 represented the candidate gene for the significant SNP chrA09_5198034 (27kb) for both PA_Cont and PA_Conc. Transcription of BnaA9.MRP5 in a low -phytate variety (LPA20) was significantly elevated compared with a high -phytate variety (HPA972). Association and haplotype analysis indicated that inbred lines carrying specific SNP haplotypes within BnaA9.MRP5 were associated with high- and low-phytate phenotypes. No significant differences in seed germination and seed yield were detected between low and high phytate cultivars examined. Candidate genes, favorable haplotypes and the low phytate varieties identified in this study will be useful for low-phytate breeding of B. napus.

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