Additive coding format

Use the following commands to transform binary genotype into additive coding format:

./hiblup --trans-geno
         --bfile demo
         --add
         --out add_coding

A file named “add_coding.geno.A.txt” will be generated in the work directory, overview of the file:

ID M1 M2 M3 M4 M5
IND0701 2 1 1 1 0
IND0702 1 0 1 1 0
IND0703 0 2 0 0 0
IND0704 1 1 1 1 0
IND0705 1 0 0 1 0

Dominance coding format

Use the following commands to transform binary genotype into dominance coding format:

./hiblup --trans-geno
         --bfile demo
         --dom
         --out dom_coding

A file named “dom_coding.geno.D.txt” will be generated in the work directory, overview of the file:

ID M1 M2 M3 M4 M5
IND0701 0 1 1 1 0
IND0702 1 0 1 1 0
IND0703 0 0 0 0 0
IND0704 1 1 1 1 0
IND0705 1 0 0 1 0

BLUPF90 acceptable genotype format

Use the following commands to transform binary genotype into the acceptable genotype coding format of BLUPF90:

./hiblup --trans-geno
         --bfile demo
         --blupf90
         --add         # or '--dom' for dominance effect
         --out bf90

A file named “bf90.geno.A.txt” will be generated in the work directory, overview of the file:

IND0701 211100111001
IND0702 101101011001
IND0703 020000001000
IND0704 111100001000
IND0705 100100111000

• --keep: to specify a file listing the individuals which will be used in analysis;
• --remove: to specify a file listing the individuals which will be removed in analysis;
• --extract: to specify a file listing the SNPs which will be used in analysis;
• --exclude: to specify a file listing the SNPs which will be removed in analysis.

Please see file format here.

The command to do prediction is as follows:

./hiblup --pred
         --bfile demo        #the binary genotype data
         --score demo.snpeff #the pre-computed SNP effects
         --threads 10
         --out demo

Note that the genotype should be in PLINK binary file, and at least 5 columns should be included in SNP effects file (see detailed format here). By default, HIBLUP codes the genotype in additive genetic effect (i.e., 0 1 2 for AA Aa aa), user can add a flag “--dom” to code the genotype in dominant effect (i.e., 0 1 0 for AA Aa aa) if the provided SNPs effects are all in dominant. But if both “--add” and “--dom” are specified in commands, the SNP effect file must have 6 columns, of which the 5th and 6th are the additive and dominant SNP effects, respectively. Thus, if there are additive and dominant effects in file, please remember to add flag “--add --dom” in the commands.

A file named “demo.bv” will be generated in the work directory, overview of this file:

id	trait1	trait2
Ind2	-0.305403	2.6644
Ind5	0.00897198	-1.36166
Ind11	0.392148	-0.653216
Ind17	0.00232218	-0.213599
Ind22	-0.359507	-2.12692
Ind45	-0.232806	0.269005

As shown above, the first column is the individual id, and the rest of columns are the predicted GEBV or GPRS, the number of columns depends on the number of effects provided in SNP effects file.

./hiblup --sblup
         --sumstat demo1.ma demo2.ma demo3.ma demo4.ma   #the summary data, use space as separator
         --ldm demo_ldm      #the pre-computed LD correlation matrix
         --h2 0.3234 0.1256 0.6345 0.3536
         --rg 0.1336 0.5567 0.2345 0.8454 0.3446 0.4633
       # --pcg               #use PCG for fast computing
         --threads 10
         --out demo

Note that the number of summary data of traits in analysis is not limited. The summary data should be prepared in COJO format, as described here, and the LD correlation matrix is stored in binary file, which can be output by HIBLUP (see here). The heritability of trait and the genetic correlation among traits must be specified, these genetic parameters can be estimated from REML if the individual-level data are available or from LD score regression using the summary data. The input order of genetic correlations is the lower triangle of matrix (take the above command for example, the input order should be: 1-2 1-3 1-4 2-3 2-4 3-4).

After running successfully, a file named “demo.snpeff” will be generated in the work directory as follows:

id a1 a2 freq_a1 demo1 demo2 demo3 demo4
M1 A G 0.1285 -0.000963937 -0.000577569 -0.000792698 0.000175215
M2 T C 0.1285 -0.00108931 -0.000597102 -0.000825137 0.000177501
M3 A G 0.1062 0.00588629 0.00155157 0.00270818 0.000154987
M4 G A 0.1285 -0.00164344 -0.000557257 -0.000874613 0.000155528
M5 A C 0.2459 -0.00100206 -0.000456737 -0.000855748 -0.000422206

As shown above, the estimated SNP effects are listed in the last several columns by traits. To obtain the predicted GEBV or GPRS of individuals, we recommend using HIBLUP to implement it (see here), since we tested that it is several times faster than the ‘--score‘ function in PLINK.

1. Heritability estimation

./hiblup --ldreg
         --sumstat demo.ma   #the summary data
         --lds demo.ldsc     #the pre-computed LD scores
         --out demo

As shown above, the summary data file and LD scores file should be provided, the file format of summary data and how to calculate/make LD scores can be found at other tutorial chapters (i.e., summary data and LD scores). Please always remember not to delete SNPs in LD scores file to keep it consistent with that in summary data file, just leave it as it is, because the total number of SNPs used to calculate this LD scores is quite crucial for LD score regression.

The estimated results are stored in a file named “demo.ldsr.h2“, overview of this file:

Item	Intercept	Intercept_SE	h2	h2_SE	h2_Pval
demo	1.08285	0.011433	0.122826	0.00393122	2.71554e-214

The ‘Intercept‘ is associated with the population structure, the closer it is to 1, the less stratified of the population is. The ‘h2‘ is the estimated heritability of the trait, and ‘h2_Pval‘ is the p-value of chi-square testing significance.

2. Genetic correlation estimation

The usage of genetic correlation estimation is quite similar with heritability estimation, if HIBLUP detected more than one summary data file in the command, it will estimate heritability and genetic correlation automatically:

./hiblup --ldreg
         --sumstat demo1.ma demo2.ma demo3.ma   #the summary data of multiple traits, use space as separator
         --lds demo.ldsc     #the pre-computed LD scores
         --out demo

Note that the number of summary data of traits is not limited. Two files will be generated in the work directory: the file “test.ldsr.h2” recorded the estimated heritability of trait as described before; and the file “test.ldsr.rg” stores the genetic correlation of pairs of traits, overview of this file:

Item	CovG	CovG_SE	Intercept	Intercept_SE	rG	rG_SE	rG_Pval
demo1:demo2	0.0252414	0.00716841	0.0166108	0.00819384	0.141956	0.0403148	0.000429607
demo1:demo3	0.0744506	0.00892225	0.102821	0.00671784	0.296832	0.0355727	7.15917e-17
demo2:demo3	0.262124	0.0340515	0.315166	0.0112682	0.608769	0.0790827	1.38348e-14

The ‘CovG‘ is the genetic covariance, ‘rG‘ is the estimated genetic correlation between traits, and ‘rG_Pval‘ is the p-value of chi-square testing significance.

The relevant options can be specified by users for LD score regression:

• --M: to specify the number of SNPs in LD score regression. By default, HIBLUP use the number of SNPs in the LD score file with MAF between 5% and 50% as it is suggested by Bulik-Sullivan.
• --chisq-max: to specify the maximum threshold of X² for the first step estimator of intercept, the default is 30.
• --intercept-h2: to constrain the intercept with a constant rather than estimating it from data for heritability estimation.
• --intercept-gencov: to constrain the intercept with a constant rather than estimating it from data for genetic correlation estimation.

./hiblup --ldscore
         --bfile demo   
         --window-bp 1000000
         --threads 10
         --out test

There are several options to set the window size:

• --window-bp: to specify the size of non-overlapped window (default 1Mb, i.e., --window-bp 1000000), in which the number of SNPs is not fixed;
• --window-num: to specify a fixed number of SNPs in a window (e.g., --window-num 500), the size of window is not constant in this case;
• --window-geno: to define all SNPs across entire genome as one window, note that it will take a long time and huge memory cost if there are large number of SNPs;
• --window-file: to specify a text file in which the windows are pre-defined by users, see the file format here.

A file named “test.ldsc” will be generated in the work directory, overview of this file:

id	maf	ldscore
M1	0.481875	1
M2	0.145	1
M3	0.320625	1
.	.	.
.	.	.
.	.	.
M991	0.089375	1.044
M992	0.11375	1.33231
M993	0.31875	1.2885
M994	0.103125	1
M995	0.115625	1

As shown above, the first column is the vector of SNP names, the second is minor allele frequency, the third is the calculated LD scores.

Users can also use the publicly available LD scores file to run HIBLUP, but sometimes the column “maf” is missing. Since HIBLUP uses the “maf” column to set the options “--M” by counting the number of markers with MAF >= 0.05, thus if the option “--M” is specified when running LD score regression (e.g., the value in the file “XXX.M_5_50“), the “maf” column will be useless, users can assign any dummy values to this column.

chr	start	stop
1	10583	1577084
1	1577084	2364990
1	2364990	3150345
1	3150345	4284187
1	4284187	4854314
2	10133	341834
2	341834	1161563
2	1161563	1688845
2	1688845	2829810
2	2829810	3389305

Note that the windows should be non-overlapped.

This file should be assigned to the parametric option --window-file.

ID Breed
Ind1 DD
Ind2 YY
Ind3 LL
Ind4 YY
Ind5 LL

The file should be assigned to parametric option --pop-class.

./hiblup --ld
         --bfile demo 
         --window-bp 1e6
         --threads 16
         --out demo_ldm

Two files (*.info, *.bin) will be generated in the folder. Since the binary file (*.bin) cannot be opened directly, users can add a flag --write-txt in the above commands to output a readable “.txt” file.

SNP A1 A2 FREQ BETA SE P NMISS
M1 G T 0.5181 -1.565 1.155 0.1762 500
M2 A G 0.145 -1.77 1.519 0.2445 500
M3 G A 0.3206 1.498 1.583 0.3445 500
M4 C G 0.5356 0.3366 1.003 0.7374 500
M5 C G 0.0975 1.27 1.755 0.4695 500

Columns are SNP, the effect allele, the other allele, frequency of the effect allele, effect size, standard error, p-value and sample size. Note that “A1” needs to be the effect allele with “A2” being the other allele, and “FREQ” should be the frequency of “A1”.

The file should be assigned to parametric option --sumstat.