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Creating a Kinship Matrix using
 Microsatellite Analyzer (MSA)

                        Zhifen Zhang
            The Ohio State University
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
Background
    Types of Populations for Association Mapping (Yu, et al. 2006):
Population Type              Pros                               Cons
“Ideal population”           Minimal population                 Difficult to collect,
                             structure and familial             small size
                             relatedness                        Narrow genetic basis
                             Gives the greatest statistical
                             power
Family-based population      Minimal population                 Limited sample size and
                             structure                          allelic diversity
Population with              Larger size and broader            False positives or loss in
population structure         allelic diversity                  power due to familial
                                                                relatedness.
Population with familial     Larger size and broader            Inadequate control for
relationships within         allelic diversity                  false positives due to
structured population                                           population structure
     The highlighted population needs a kinship matrix for analysis.
Scheme of Mapping
                   Germplasm


Phenotyping (Y)                      Genotyping

                  Genome-wide scan           Background
                                              markers

              Genome-wide              Population structure
            polymorphisms (G)            (Q), kinship (K)


Association analysis (General Mixed Model): Y=G+Q+K+e
                                                  Zhu et al. 2006
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
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
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
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)
Pipeline for Matrix Generation

   Genotyping with                    Data Entry to Create a
   Markers                            Spreadsheet


                  Arrange Data in a Format
                  Recognized by MSA



Import the .dat                    Run the Kinship
File into MSA                      Coefficient Analysis
A Case Study
Bacterial spot data provided by Dr. David Francis,
            The Ohio State University
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.
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.
Data Formatting
Population #
                                          Give a new value to each allele
       Mating scheme                      for each marker
                  Group name: default “1”




                             These 3 cells should
                                 be empty.




  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.
Data Formatting
Two-row entry for each individual (we are treating individuals as
populations) to specify heterozygotes and homozygotes
                       Homozygotes have the    Heterozygotes have
                       same value in both      different values in each
                       rows.                   row.
After Formatting
Save the file in a “Tab Delimited” text format, change the extension
name “.txt” into “.dat”.
MSA (Microsatellite Analyzer) is available for free,
provided by Daniel Dieringer.
MSA is compatible with different operation systems




                                    Choose the
                                    operating system
                                    you use and install it
                                    as other software
Install MSA
For Mac, a folder for MSA will be formed after you extract the
downloaded file
Use MSA




                 Manual for MSA is
                 available in the folder

Sample data is
available
Use MSA




                            Menu of
                            Commands




Enter command “i” to import file
Use MSA




Import your data file to MSA by dragging it to the MSA window
Use MSA
After the data file is imported,




                          Enter command “d” to access submenu of
                          “Distance setting”
Use MSA




Enter “4” to turn on kinship coefficient
module.
Use MSA
Each line will be treated as a population in this study




                      Input command “c” to turn on “Pair-wise
                      populations distances calc”
Use MSA




                              NOTE: Dkf in the
                              matrix will be 1-kf in
                              default.




After everything is set, input “!” command to
run the analysis.
Use MSA




After the analysis, a folder of results will be created
automatically in the same folder as your data file
Use MSA




The kinship matrix is in the folder of “Distance _data”
Use MSA
The Kinship matrix is saved as a file named “KSC_Pop.txt”




Open the file with Excel to read the the matrix
Number of
individuals                       Kinship Matrix
     The population (line) code
Number of
individuals                         Kinship Matrix
     The population (line) code   Please remember that the value in the
                                  matrix is “1-kinship coefficient”.
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.
The Mixed Model
Phenotype         Marker                Residual or
score             effect                Error



   Y= αX+ γM+ βQ + υK + e
                           Population
        Fixed Effect       structure
        rather than
        markers

 Yu et al. 2006
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).

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Creating a Kinship Matrix Using MSA

  • 1. Creating a Kinship Matrix using Microsatellite Analyzer (MSA) Zhifen Zhang The Ohio State University
  • 2. 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
  • 3. Background Types of Populations for Association Mapping (Yu, et al. 2006): Population Type Pros Cons “Ideal population” Minimal population Difficult to collect, structure and familial small size relatedness Narrow genetic basis Gives the greatest statistical power Family-based population Minimal population Limited sample size and structure allelic diversity Population with Larger size and broader False positives or loss in population structure allelic diversity power due to familial relatedness. Population with familial Larger size and broader Inadequate control for relationships within allelic diversity false positives due to structured population population structure The highlighted population needs a kinship matrix for analysis.
  • 4. Scheme of Mapping Germplasm Phenotyping (Y) Genotyping Genome-wide scan Background markers Genome-wide Population structure polymorphisms (G) (Q), kinship (K) Association analysis (General Mixed Model): Y=G+Q+K+e Zhu et al. 2006
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. 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)
  • 9. Pipeline for Matrix Generation Genotyping with Data Entry to Create a Markers Spreadsheet Arrange Data in a Format Recognized by MSA Import the .dat Run the Kinship File into MSA Coefficient Analysis
  • 10. A Case Study Bacterial spot data provided by Dr. David Francis, The Ohio State University
  • 11. 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.
  • 12. 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.
  • 13. Data Formatting Population # Give a new value to each allele Mating scheme for each marker Group name: default “1” These 3 cells should be empty. 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.
  • 14. Data Formatting Two-row entry for each individual (we are treating individuals as populations) to specify heterozygotes and homozygotes Homozygotes have the Heterozygotes have same value in both different values in each rows. row.
  • 15. After Formatting Save the file in a “Tab Delimited” text format, change the extension name “.txt” into “.dat”.
  • 16. MSA (Microsatellite Analyzer) is available for free, provided by Daniel Dieringer.
  • 17. MSA is compatible with different operation systems Choose the operating system you use and install it as other software
  • 18. Install MSA For Mac, a folder for MSA will be formed after you extract the downloaded file
  • 19. Use MSA Manual for MSA is available in the folder Sample data is available
  • 20. Use MSA Menu of Commands Enter command “i” to import file
  • 21. Use MSA Import your data file to MSA by dragging it to the MSA window
  • 22. Use MSA After the data file is imported, Enter command “d” to access submenu of “Distance setting”
  • 23. Use MSA Enter “4” to turn on kinship coefficient module.
  • 24. Use MSA Each line will be treated as a population in this study Input command “c” to turn on “Pair-wise populations distances calc”
  • 25. Use MSA NOTE: Dkf in the matrix will be 1-kf in default. After everything is set, input “!” command to run the analysis.
  • 26. Use MSA After the analysis, a folder of results will be created automatically in the same folder as your data file
  • 27. Use MSA The kinship matrix is in the folder of “Distance _data”
  • 28. Use MSA The Kinship matrix is saved as a file named “KSC_Pop.txt” Open the file with Excel to read the the matrix
  • 29. Number of individuals Kinship Matrix The population (line) code
  • 30. Number of individuals Kinship Matrix The population (line) code Please remember that the value in the matrix is “1-kinship coefficient”.
  • 31. 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.
  • 32. The Mixed Model Phenotype Marker Residual or score effect Error Y= αX+ γM+ βQ + υK + e Population Fixed Effect structure rather than markers Yu et al. 2006
  • 33. 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).