create a kinship matrix by using microsatellite anaylzer · creating a kinship matrix using...
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Creating a Kinship Matrix using Microsatellite Analyzer (MSA)
Zhifen Zhang
The Ohio State University
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Why create a kinship matrix?
• Visualize family relatedness
• Use in association mapping
The Objective of this Tutorial: To introduce one approach to generate a kinship matrix for a population
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Types of Populations for Association Mapping (Yu, et al. 2006):
Background
Population Type Pros Cons
“Ideal population” Minimal population structure and familial relatedness Gives the greatest statistical power
Difficult to collect, small size Narrow genetic basis
Family-based population Minimal population structure
Limited sample size and allelic diversity
Population with population structure
Larger size and broader allelic diversity
False positives or loss in power due to familial relatedness.
Population with familial relationships within structured population
Larger size and broader allelic diversity
Inadequate control for false positives due to population structure
The highlighted population needs a kinship matrix for analysis.
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Germplasm
Genotyping Phenotyping (Y)
Background markers
Genome-wide scan
Genome-wide polymorphisms (G)
Population structure (Q), kinship (K)
Association analysis (General Mixed Model): Y=G+Q+K+e
Scheme of Mapping
Zhu et al. 2006
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Coefficient of Kinship
Coefficient of kinship is used to measure relatedness
DEFINITION: Coefficient of kinship is the probability that the alleles of a particular locus chosen randomly from two individuals are identical by descent (Lange, 2002)
* Identical by descent: the identical alleles from two individuals arise from the same allele in an earlier generation
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Kinship Matrix Calculation
The kinship coefficient is the probability that an allele (a) taken randomly from population i (at a given locus) will be identical by descent to an allele taken randomly from population j at the same locus Kf= ∑k∑a (fai * faj)/D Where ∑k∑a (fai * faj)/D is the sum of all loci and all alleles; fai is the frequency of allele a in population i, faj is the frequency of allele a in population j, D is the number of loci.
Cavalli-Sforza and Bodmer, 1971.
MSA manual page 23
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An Example
Population Locus A Locus B Locus C
611R2 A b c
A b c
611R3 A B c
A B c
Coefficient of kinship for populations 611R2 and 611R3: Kf= ∑k∑a (fai * faj)/D = (1x1+1x0 + 0x1+1x1)/3=2/3 The frequencies of alleles A and c in populations 611R2 and 611R3 are 1; the frequency of allele b in population 611R2 is 1 and 0 while in population 611R3 is 0
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Kinship Matrix
In the unified mixed model (Yu et al. 2006), marker-based kinship coefficient matrices are included
A marker-based kinship coefficient matrix can be generated using Microsatellite Analyzer (MSA)
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Pipeline for Matrix Generation
Genotyping with Markers
Data Entry to Create a Spreadsheet
Arrange Data in a Format Recognized by MSA
Import the .dat File into MSA
Run the Kinship Coefficient Analysis
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A Case Study
Bacterial spot data provided by Dr. David Francis, The Ohio State University
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Data Preparation
For genotyping, molecular markers can be: Microsatellite (Simple Sequence Repeat, SSR) SNP (Single Nucleotide Polymorphism) RFLP (Restriction Fragment Length Polymorphism) AFLP (Amplified Fragment Length Polymorphism) Other sequencing markers MSA was designed for SSR analysis For other marker types, a value is designated for each
allele, e.g. For SNPs, A can be 11, C can be 12, G can be 13, and T can be 14.
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Data Entry
After genotyping, a spreadsheet of genotypic data can be generated as shown below.
Marker Population Genotype Score
Genotype Score: 0 is homozygous for allele a, 1 is homozygous for allele b, 2 is heterozygous.
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Data Formatting Population #
Mating scheme
Group name: default “1”
Give a new value to each allele for each marker
Row 1 can be left empty except for columns a, b, and c, which have to be filled. In cell A1, enter ‘1’. Row 2 is marker name. Genotype data start in row 3.
These 3 cells should be empty.
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Data Formatting Two-row entry for each individual (we are treating individuals as populations) to specify heterozygotes and homozygotes
Homozygotes have the same value in both rows.
Heterozygotes have different values in each row.
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After Formatting
Save the file in a “Tab Delimited” text format, change the extension name “.txt” into “.dat”.
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MSA (Microsatellite Analyzer) is available for free, provided by Daniel Dieringer.
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MSA is compatible with different operation systems
Choose the operating system you use and install it as other software
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Install MSA
For Mac, a folder for MSA will be formed after you extract the downloaded file
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Use MSA
Manual for MSA is available in the folder
Sample data is available
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Use MSA
Menu of Commands
Enter command “i” to import file
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Use MSA
Import your data file to MSA by dragging it to the MSA window
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Use MSA
Enter command “d” to access submenu of “Distance setting”
After the data file is imported,
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Use MSA
Enter “4” to turn on kinship coefficient module.
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Use MSA
Each line will be treated as a population in this study
Input command “c” to turn on “Pair-wise populations distances calc”
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Use MSA
After everything is set, input “!” command to run the analysis.
NOTE: Dkf in the matrix will be 1-kf in default.
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Use MSA
After the analysis, a folder of results will be created automatically in the same folder as your data file
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Use MSA
The kinship matrix is in the folder of “Distance _data”
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Use MSA
The Kinship matrix is saved as a file named “KSC_Pop.txt”
Open the file with Excel to read the the matrix
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Kinship Matrix
The population (line) code
Number of individuals
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Kinship Matrix
The population (line) code
Number of individuals
Please remember that the value in the matrix is “1-kinship coefficient”.
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Final Matrix Use “1-X” (X is the value of each cell in the matrix) to generate a new matrix that will be the kinship matrix in Excel.
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The Mixed Model
Y= αX+ γM+ βQ + υK + e
Population structure
Marker effect
Fixed Effect rather than markers
Residual or Error
Phenotype score
Yu et al. 2006
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References Cited & External Link References Cited
• Cavalli-Sforza, L. L., and W. F. Bodmer. 1971. The genetics of human populations, W.H. Freeman and Company, NY.
• Lange, K. 2002. Mathematical and statistical methods for genetic analysis, 2nd edition. Springer-Verlag, NY.
• Yu, J. G. Pressoir, W. H. Briggs, I. V. Bi, M. Yamasaki, J. F. Doebley, M. D. McMullen, B. S. Gaut, D. M. Nielsen, J. B. Holland, S. Kresovich, and E. S. Buckler. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics 38: 203-208. (Available online at: http://dx.doi.org/10.1038/ng1702) (verified 8 July 2011).
• Zhu, C. M. Gore, E. S. Buckler, and J. Yu. 2008. Status and prospects of association mapping in plants. The Plant Genome 1: 5-20. (Available online at: http://dx.doi.org/10.3835/plantgenome2008.02.0089) (verified 8 July 2011).
External Link
• Dieringer, D. Microsatellite analyzer (MSA) [Online]. Available at: http://i122server.vu-wien.ac.at/MSA/MSA_download.html (verified 8 July 2011).