Fine-Mapping Quantitative Trait Loci for Body Weight and Abdominal Fat Traits: Effects of Marker Density and Sample Size

Liu, Xianjing; Zhang, H.; Li, H.; Li, N.; Zhang, Y.; Zhang, Q.; Wang, Shunli; Wang, Q.; Wang, H.

doi:10.3382/ps.2007-00512

Cited by 46 publications

(39 citation statements)

References 35 publications

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“…A number of studies reported significant QTL effects in the 167–179 Mb region of GGA1. Uemoto et al also detected QTL affecting BW42 and BW63 in 165–172 Mb on GGA1 [11], and QTL effects were also reported in these regions were associated with BW28–BW84, average daily gain, WW or BMW [2], [6], [7], [10], [11], [15]. Besnier et al reported a QTL for BW56 in the 169–175 Mb region of GGA1 with the most significant QTL effect at position 173,709,609 bp on GGA1 [35].…”

Section: Discussionmentioning

confidence: 96%

Genome-Wide Association Study Identified a Narrow Chromosome 1 Region Associated with Chicken Growth Traits

et al. 2012

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Chicken growth traits are important economic traits in broilers. A large number of studies are available on finding genetic factors affecting chicken growth. However, most of these studies identified chromosome regions containing putative quantitative trait loci and finding causal mutations is still a challenge. In this genome-wide association study (GWAS), we identified a narrow 1.5 Mb region (173.5–175 Mb) of chicken (Gallus gallus) chromosome (GGA) 1 to be strongly associated with chicken growth using 47,678 SNPs and 489 F2 chickens. The growth traits included aggregate body weight (BW) at 0–90 d of age measured weekly, biweekly average daily gains (ADG) derived from weekly body weight, and breast muscle weight (BMW), leg muscle weight (LMW) and wing weight (WW) at 90 d of age. Five SNPs in the 1.5 Mb KPNA3-FOXO1A region at GGA1 had the highest significant effects for all growth traits in this study, including a SNP at 8.9 Kb upstream of FOXO1A for BW at 22–48 d and 70 d, a SNP at 1.9 Kb downstream of FOXO1A for WW, a SNP at 20.9 Kb downstream of ENSGALG00000022732 for ADG at 29–42 d, a SNP in INTS6 for BW at 90 d, and a SNP in KPNA3 for BMW and LMW. The 1.5 Mb KPNA3-FOXO1A region contained two microRNA genes that could bind to messenger ribonucleic acid (mRNA) of IGF1, FOXO1A and KPNA3. It was further indicated that the 1.5 Mb GGA1 region had the strongest effects on chicken growth during 22–42 d.

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Section: Discussionmentioning

confidence: 96%

Genome-Wide Association Study Identified a Narrow Chromosome 1 Region Associated with Chicken Growth Traits

et al. 2012

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“…Growth is a very complex trait that is regulated by a number of genes and pathways, and more than 1500 quantitative trait loci have been found to be associated with chicken growth traits (Liu et al, 2008;Uemoto et al, 2009;Wahlberg et al, 2009;Gu et al, 2011). In the present study, 15 previously identified SNPs in GGA1 were detected using a custom Beadchip analysis.…”

Section: Discussionmentioning

confidence: 99%

Single nucleotide polymorphisms associated with growth traits in Jinghai yellow chickens

et al. 2015

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ABSTRACT. Body weight is one of the most important economic traits in the poultry industry. In the present study, a custom SNP Beadchip was used to analyze the association between those 15 SNPs and 12 growth traits of Jinghai yellow chickens, and other important genetic parameters were also calculated and analyzed. The results indicated that nine of the 15 SNPs were associated with growth traits in Jinghai yellow chickens (P < 0.05), and the identified SNPs were also in linkage disequilibrium. Five of the nine identified SNPs were mainly associated with all of the growth traits, which indicated that those five SNPs might have significant influence on Jinghai yellow chicken growth traits. Polymorphism information content (PIC) analyses indicated that five of the nine SNPs exhibited moderate polymorphism (0.25 < PIC < 0.5), which reflected intermediate genetic diversity. Six candidate genes surrounding the significant SNPs were obtained and subjected to Gene Ontology annotation analyses and pathway analyses. The functions of six important candidate genes (SETDB2, ATP7B, INTS6, KPNA3, DLEU7, and FOXO1A) were discussed. The present study provided basic data for 16170 M.A. Abdalhag et al.

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“…Generally speaking, a gain of information by more markers density resulted in smaller intervals, therefore, the application of additional markers is an effective way to increase QTL mapping resolution (Liu et al, 2008). The traditional markers such as SSRs and restriction fragment length polymorphism (RFLP) are not suitable when their linkage distance are zero and cannot meet the requirements of the high QTL mapping resolution.…”

Section: Discussionmentioning

confidence: 99%

Mapping QTL for Seed Germinability under Low Temperature Using a New High-Density Genetic Map of Rice

Jiang

Shi

et al. 2017

Front. Plant Sci.

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Mapping major quantitative trait loci (QTL) responsible for rice seed germinability under low temperature (GULT) can provide valuable genetic source for improving cold tolerance in rice breeding. In this study, 124 rice backcross recombinant inbred lines (BRILs) derived from a cross indica cv. Changhui 891 and japonica cv. 02428 were genotyped through re-sequencing technology. A bin map was generated which includes 3057 bins covering distance of 1266.5 cM with an average of 0.41 cM between markers. On the basis of newly constructed high-density genetic map, six QTL were detected ranging from 40 to 140 kb on Nipponbare genome. Among these, two QTL qCGR8 and qGRR11 alleles shared by 02428 could increase GULT and seed germination recovery rate after cold stress, respectively. However, qNGR1 and qNGR4 may be two major QTL affecting indica Changhui 891germination under normal condition. QTL qGRR1 and qGRR8 affected the seed germination recovery rate after cold stress and the alleles with increasing effects were shared by the Changhui 891 could improve seed germination rate after cold stress dramatically. These QTL could be a highly valuable genetic factors for cold tolerance improvement in rice lines. Moreover, the BRILs developed in this study will serve as an appropriate choice for mapping and studying genetic basis of rice complex traits.

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Fine-Mapping Quantitative Trait Loci for Body Weight and Abdominal Fat Traits: Effects of Marker Density and Sample Size

Cited by 46 publications

References 35 publications

Genome-Wide Association Study Identified a Narrow Chromosome 1 Region Associated with Chicken Growth Traits

Genome-Wide Association Study Identified a Narrow Chromosome 1 Region Associated with Chicken Growth Traits

Single nucleotide polymorphisms associated with growth traits in Jinghai yellow chickens

Mapping QTL for Seed Germinability under Low Temperature Using a New High-Density Genetic Map of Rice

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