applications of technology molecular markers in aquaculture genetics

Applications of Technology Molecular Markers in Aquaculture Genetics

©Robi Binur [21110003]

ITB SITH Bioteknologi

September 2011

© Robi Binur 2011

Introduction

• Molecular markers are classified into two categories (Liu ZJ & Cordes JF, 2004):

1. Type I are markers associated with genes of known function.

2. Type II markers are associated with anonymous genomic segments.

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• Type I markers are very important for studies genetic linkage and QTL mapping. Type I markers have utility in studies comparative genomics, genome evolution, and candidate gene identification.

• Type II markers useful in species, strain, and hybrid identification in breeding studies and more recently as markers linked to QTL.

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Trends in marker usage

Allozyme 1973; RFLP 1980; RAPD 1990; AFLP 1995; Microsatellites 1989; SNP 1996 (Liu ZJ & Cordes JF, 2004)

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Choice of marker systems

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Applications of Molecular Markers in Aquaculture Genetics

1. Species, strain, and hybrid identification

2. Genetic diversity and resource analysis of aquaculture stocks

3. Parentage assignments and reproductive contribution

4. Mapping quantitative trait loci (QTL) and marker-assisted selection (MAS)

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Species, strain, and hybrid identification

• Genetic identification of species or strains is required in an aquaculture setting. Their identification using DNA markers is relatively straightforward. RFLP, RAPD, AFLP, and microsatellite markers are all applicable.

• Species identification is often required for determining whether fish stocks are pure species or hybrids. Both RAPD and AFLP analyses can provide rapid solutions.

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• The amount of genetic variation among strains may be limited and require DNA markers and techniques with higher resolution. Microsatellites and AFLPs provide sufficient power for the determination of strains in aquaculture fish species.

• The AFLP approach has been used to identify strains of common carp and catfish.

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Genetic diversity and resource analysis of aquaculture stocks

• Long aquaculture history is not well for characterized genetically of broodstocks. Many strains of aquaculture species may be used and genetic relationship among strains is most often unknown.

• AFLPs and microsatellites should be highly powerful in genetic diversities. AFLP markers very useful for genetic diversity analyses because of the large number of loci that can be screened simultaneously.

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Parentage assignments and reproductive contribution

• Analyse Parentage assignments and reproductive contribution are required for basic research, different types of aquaculture operations, and control the trade in aquatic animals and products.

• For parental assignments, microsatellites provide the best results because among individuals have high microsatellites, while polymorphism in other markers is generally low among individuals of the same strain.

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Analysis quantitative trait loci (QTL), and marker-assisted selection (MAS)

• Performance and production are controlled by multiple genes and quantitative traits. Analysis associated quantitative trait loci (QTL) very important for aquaculture genetics/genomics.

• QTL are largely unidentified genes that affect performance traits (such as growth rate and disease resistance) that are important to breeders.

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• QTL mapping in a species genome can be identified begins by constructing genetic linkage map. Genetic linkage maps are constructed by polymorphic DNA markers (microsatellites, SNP, or AFLPs).

• This information can be used to help aquaculture in efficiently crossing different strains of cultured species to maximize growth, disease resistance.

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• MAS (marker-assisted selection) refers to a selection process in which future breeders are chosen based on genotypes using molecular markers.

• MAS need to produce high-resolution linkage maps, understand the number of QTL affecting performance or production trait, determine the linkage and potential interactions of different QTL for the trait and for other traits, and estimate the economic importance of each trait.

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Microsatellites

• Microsatellites consist of multiple copies of tandemly simple sequence repeats (SSRs) that range in size from 1 to 6 base pairs. Example: mono- AAAAAAAAAAAAAAA noted as (A)15, di-(TG)19, tri- (AGC)13, tetra-(GATA)8, penta-(ATCGC)4 and hexa-(ATTGCC)5.

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• Repeats sequence of microsatellites classified are:

1. Perfect (un-interrupted run of repeats); e.g. (GTG)15

2. Imperfect (interrupted with base substitutions); e.g. (GTG)7CTCTG(GTG)8

3. Compound (two or more runs of different repeat units); e.g. (GTG)8(AT)16

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• Microsatellites are inherited in a Mendelian fashion as co-dominant markers. This is another strength of microsatellite markers: their abundance, even genomic distribution, small locus size, and high polymorphism (PIC value).

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Co-dominant markers

• The ability to distinguish between homozygotes and heterozygotes.

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Polymorphic Information Content (PIC)

• PIC refers to the value of a marker for detecting polymorphism in a population.

• PIC depends on number of alleles and distribution of their frequencies. The equals: 1 minus the sum of the square of all allele frequencies.

• For example: the PIC of a microsatellite marker with two alleles of frequency 0.5 so PIC value 1-[(0.5)2+(0.5)2] = 0.5

• The greater the number of alleles and allele frequencies, the greater the PIC.

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• Used to three primer: forward, riverse, universal primer (M13) and fluorescent dyes.

• These fluorescent dyes are 6-carboxy-fluorescine (FAM), hexachloro-6-carboxy-fluorescine (HEX), 6-carboxy-X-rhodamine (ROX), or tetrachloro-6-carboxy-fluorescine (TET)2.

PCR method

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Schuelke,M. (2000)

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Result

• Size of allel product about 130 bp

• Electropherogram results was analyzed using Genemarker 1.85

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Analysis of allelic variation

• PIC value (using Cervus 3.0)

where k is the number of alleles and pi and pj are the population frequencies of the i and j alleles.

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• Heterozygosity (Ho) and expected heterozygosity (He) value (using GenAlEx 6.3)

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• The allelic pattern among the haploid, diploid or combined population is based on the number of alleles (Na) and number of effective alleles (Ne) (using GenAlEx 6.3)

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Genetic diversity analysis

• Genetic diversity analysis is based on calculating genetic distance. The analysis with Principal Coordinate Analysis (PCO) (using GenAlEx 6.3 or other program e.g SPSS, NTSYS, XLSTAT).

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Source data Hutama TC. (2010)

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References

• Chauhan, T. & Rajiv, K. 2010. Molecular markers and their applications in fisheries and aquaculture. Advances in Bioscience and Biotechnology (1): 281-291

• Freelang, J.R. 2007. Molecular Ecology. John Wiley & Sons ltd. Chichester England

• Goldstein, D.B. and Schlotterer C. 1999. Microsatellites Evolution and Application. Oxford University Press inc. New York

• Liu, Z.J., Cordes, J.F. 2004. DNA marker technologies and their applicationsin aquaculture genetics (Review). Aquaculture Journal 238 : 1–37

• Okumu I. & Ciftci Y. 2003. Fish Population Genetics and Molecular Markers: II- Molecular Markers and Their Applications in Fisheries and Aquaculture. Turkish Journal of Fisheries and Aquatic Sciences 3: 51-79

• Schuelke,M. 2000. An economic method for the fluorescent labeling of PCR fragments. Nature Biotechnology 18 (2) : 233–234

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