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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

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