Launch the Marker Relatedness Platform

To estimate a square matrix of genetic relationships between pairs of individuals in the data table, launch the Marker Relatedness platform by selecting Analyze > Life Sciences > Marker Relatedness.

Figure 5.4 Marker Relatedness Launch Window 

Marker

Select the marker columns that you want and click Marker to specify the markers that you want to analyze.

Sample ID

Use this option to specify one variable whose values can provide a unique identifier for each row.

Note: Only one variable is allowed.

By

Produces a separate report for each level of the By variable. If more than one By variable is assigned, a separate report is produced for each possible combination of the levels of the By variables.

Ploidy

Enables you to specify the ploidy level of the experimental organism under investigation. Note that this must be an even number

Set Random Seed

Use this option to specify a nonnegative integer to start the random number stream. Different values produce different outcomes of the algorithm.

Kinship Type

Use this option to specify the type of measurement to use to assess the relationship between samples (rows).

• Select Identical by State to examine the effects of marker identity on relatedness of the individuals.

• Select Additive to examine the additive marker effects on relatedness of the individuals.

• Select Dominance to examine the marker dominance effects on relatedness of the individuals.

• Select Epistasis to examine the effects of marker interaction on relatedness of the individuals.

Additive Type

Use this option to specify a method to assess the additive marker effects on relatedness of the individuals.

• Select Diploid Method 1 to compute the relationship matrix by using Van Raden’s first method (Van Raden et al. 2008). (P.M. Van Raden, “Efficient Methods to Compute Genomic Predictions“. J.Dairy Sci. 91:4414-4423, 2008 )

• Select Diploid Method 2 to use the second method of VanRaden’s second method (Van Raden et al. 2008).

• Select Polyploid to use the method of Batista et al. (2022). This method builds a covariance matrix of additive effects by using estimates of ploidy and allele dosage.

Note: This option is available only when you select Additive as the Kinship Type or when you select Epistasis AND either Additive by Additive or Additive by Dominance as the Epistasis Type.

Dominance Type

Use this option to specify a method to assess the marker dominance effects on relatedness of the individuals.

• Select Diploid Method 1 to use the method of Su et al. (2012).

• Select Diploid Method 2 to use the Vitezica method (Vitezica et al. 2013). This method uses a matrix of dominant genomic relationships across individuals. This matrix can be used in a mixed-model context to estimate dominant variances in the population.

• Select Polyploid to use the method of Batista et al. (2022). This method builds a covariance matrix of dominance effects by using estimates of ploidy and allele dosage.

Note: This option is available only when you choose Dominance as the Kinship Type or when you select Epistasis and either Additive by Dominance or Dominance by Dominance as the Epistasis Type.

Epistasis Type

Use this method to specify a method to assess the effects of marker interaction on relatedness of the individuals.

• Select Additive by Additive to compute the interaction between the Additive Type that is specified in the window with itself. For example, when the Additive Type is Diploid Method 1, it computes the interaction between this relationship type with itself.

• Select Additive by Dominance to compute the interaction between the Additive and Dominance Types that are specified in the window. For example, when the Additive Type is Diploid Method 1 and the Dominance Type is Diploid Method 1, it computes the interaction between these two relationship types.

• Select Dominance by Dominance to compute the interaction between the Dominance Type that is specified in the window with itself. For example, when the Dominance Type is Diploid Method 1, it computes the interaction between this relationship type with itself.

Note: This option is available only when you choose Epistasis as the Kinship Type

Missing Marker Imputation Method

Use this option to specify how missing marker values are to be imputed. Because this platform does not run when your data is missing marker data, you must impute any missing data.

– Select HWE Off to impute the missing genotypes with random draws from a multinomial distribution in which the frequency of each genotype class is set to be the observed frequency from the data.

– Select HWE On to impute the missing genotypes with random draws from a multinomial distribution in which the frequency of each genotype class is set to be the expected frequency under the assumption of the Hardy-Weinberg equilibrium (HWE).

– Select Random to randomly assign one of the acceptable values (0, 1, 2, ..., K (where K is the ploidy level)).

– Select Specified to impute the missing genotypes with a specified integer between zero and the ploidy number.

Imputation Value

Specify a value between 0 and the ploidy number.

The values listed here assume diploid organisms. Adjust values accordingly for organisms with higher ploidy.

Note: This option is available only when you select Specified as the Missing Marker Imputation Method.

Principal Components

Select this box to compute and plot principal components on the square matrix.

Clustering

Select this box to cluster the individuals by marker relatedness.

Unthreaded

Use this option to suppress multi-threading. Deselect this option for improved computational speed.

Required Data Format for the Marker Relatedness Platform

Most of the processes in JMP assume that the input table has a particular data structure. JMP distinguishes between tall and wide data sets. A tall data table has samples as columns and molecular entity (for example, marker, gene, clone, protein, or metabolite) as rows, whereas a wide data table is the transpose of the tall data table, having the samples as rows and molecular entity as columns.

When specifying the input data set for a process, it is important to know the required form. Marker Relatedness requires a wide data table. The Transpose platform under the Tables menu enables you to transform your data tables between tall and wide forms.

Marker data must be encoded in the one-column, numeric format. Typically, in this format, diploid individuals homozygous for the least common, or minor allele, are represented in the table by a 2, whereas the heterozygotes are represented by a 1. Homozygotes for the most common allele are represented by a 0.