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1.  A Scallop IGF Binding Protein Gene: Molecular Characterization and Association of Variants with Growth Traits 
PLoS ONE  2014;9(2):e89039.
Scallops represent economically important aquaculture shellfish. The identification of genes and genetic variants related to scallop growth could benefit high-yielding scallop breeding. The insulin-like growth factor (IGF) system is essential for growth and development, with IGF binding proteins (IGFBPs) serving as the major regulators of IGF actions. Although an effect of IGF on growth was detected in bivalve, IGFBP has not been reported, and members of the IGF system have not been characterized in scallop.
We cloned and characterized an IGFBP (PyIGFBP) gene from the aquaculture bivalve species, Yesso scallop (Patinopecten yessoensis, Jay, 1857). Its full-length cDNA sequence was 1,445 bp, with an open reading frame of 378 bp, encoding 125 amino acids, and its genomic sequence was 10,193 bp, consisting of three exons and two introns. The amino acid sequence exhibited the characteristics of IGFBPs, including multiple cysteine residues and relatively conserved motifs in the N-terminal and C-terminal domains. Expression analysis indicated that PyIGFBP was expressed in all the tissues and developmental stages examined, with a significantly higher level in the mantle than in other tissues and a significantly higher level in gastrulae and trochophore larvae than in other stages. Furthermore, three single nucleotide polymorphisms (SNPs) were identified in this gene. SNP c.1054A>G was significantly associated with both shell and soft body traits in two populations, with the highest trait values in GG type scallops and lowest in AG type ones.
We cloned and characterized an IGFBP gene in a bivalve, and this report also represents the first characterizing an IGF system gene in scallops. A SNP associated with scallop growth for both the shell and soft body was identified in this gene. In addition to providing a candidate marker for scallop breeding, our results also suggest the role of PyIGFBP in scallop growth.
PMCID: PMC3929647  PMID: 24586493
2.  RADtyping: An Integrated Package for Accurate De Novo Codominant and Dominant RAD Genotyping in Mapping Populations 
PLoS ONE  2013;8(11):e79960.
Genetic linkage maps are indispensable tools in genetic, genomic and breeding studies. As one of genotyping-by-sequencing methods, RAD-Seq (restriction-site associated DNA sequencing) has gained particular popularity for construction of high-density linkage maps. Current RAD analytical tools are being predominantly used for typing codominant markers. However, no genotyping algorithm has been developed for dominant markers (resulting from recognition site disruption). Given their abundance in eukaryotic genomes, utilization of dominant markers would greatly diminish the extensive sequencing effort required for large-scale marker development. In this study, we established, for the first time, a novel statistical framework for de novo dominant genotyping in mapping populations. An integrated package called RADtyping was developed by incorporating both de novo codominant and dominant genotyping algorithms. We demonstrated the superb performance of RADtyping in achieving remarkably high genotyping accuracy based on simulated and real mapping datasets. The RADtyping package is freely available at detailen.asp?id=727.
PMCID: PMC3836964  PMID: 24278224
3.  High-Resolution Linkage and Quantitative Trait Locus Mapping Aided by Genome Survey Sequencing: Building Up An Integrative Genomic Framework for a Bivalve Mollusc 
Genetic linkage maps are indispensable tools in genetic and genomic studies. Recent development of genotyping-by-sequencing (GBS) methods holds great promise for constructing high-resolution linkage maps in organisms lacking extensive genomic resources. In the present study, linkage mapping was conducted for a bivalve mollusc (Chlamys farreri) using a newly developed GBS method—2b-restriction site-associated DNA (2b-RAD). Genome survey sequencing was performed to generate a preliminary reference genome that was utilized to facilitate linkage and quantitative trait locus (QTL) mapping in C. farreri. A high-resolution linkage map was constructed with a marker density (3806) that has, to our knowledge, never been achieved in any other molluscs. The linkage map covered nearly the whole genome (99.5%) with a resolution of 0.41 cM. QTL mapping and association analysis congruously revealed two growth-related QTLs and one potential sex-determination region. An important candidate QTL gene named PROP1, which functions in the regulation of growth hormone production in vertebrates, was identified from the growth-related QTL region detected on the linkage group LG3. We demonstrate that this linkage map can serve as an important platform for improving genome assembly and unifying multiple genomic resources. Our study, therefore, exemplifies how to build up an integrative genomic framework in a non-model organism.
PMCID: PMC3925396  PMID: 24107803
bivalve; genome sequencing; 2b-RAD genotyping; linkage mapping; quantitative trait locus mapping
4.  Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions 
Biology Direct  2012;7:17.
Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome.
Here we describe an improved maximum likelihood (ML) algorithm called iML, which can achieve high genotyping accuracy for SNP calling in the non-model organisms without a reference genome. The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions. Through analysis of simulation and real sequencing datasets, we demonstrate that in comparison with ML or a threshold approach, iML can remarkably improve the accuracy of de novo SNP genotyping and is especially powerful for the reference-free genotyping in diploid genomes with high repeat contents.
The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy. Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.
This article was reviewed by Dr. Richard Durbin, Dr. Liliana Florea (nominated by Dr. Steven Salzberg) and Dr. Arcady Mushegian.
PMCID: PMC3472322  PMID: 22682067
Next-generation sequencing; single nucleotide polymorphism; genotyping; maximum likelihood; mixed Poisson/normal model

Results 1-4 (4)