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How to analyze the application of SSGWAS, in view of this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

Note: there seems to be something wrong with this new feature of blupf90. Several people have tested SSGWAS results and P-value does not show them.

For a long time, GWAS and GS have always been separate, separate, and rarely crossed. Both of them are based on statistical analysis, and GWAS focuses on finding significant SNP loci and genes associated with traits. The focus of GS is to calculate the breeding value of an individual and make a ranking selection.

The BLUP-based method (GBLUP,SSGBLUP) omits the estimation of SNP effect and directly calculates the breeding value (BLUP). With the wide implementation of genome selection in breeding, the combination of GS and GWAS becomes more meaningful:

The significant loci obtained by GWAS, the structure of different populations and the size of effects all affect the application of MAS (molecular marker-assisted breeding).

In GWAS analysis, individuals need to have genotype information, but in actual breeding, there are a large number of phenotypic data only related to pedigree, which can be applied to genome selection by one-step method (single-step), but GWAS analysis has always been a difficulty.

In GWAS analysis, although PCA or population structure can be used as a fixed factor correction model, there are many mature models in animal breeding, such as maternal effect and permanent environmental effect, which can be put into random effects.

If we can dig out the loci of a specific population and apply it to the MAS of a specific population in one-step method, it is of great practical significance.

Blupf90 software proposed SSGWAS in 2014, but it can only calculate the effect value of SNP, but there is no way to calculate the P-value of SNP. The following article introduces their team to upgrade the software so that the preGSf90 module can calculate the P-value of SNP. In this way, it can be used in animal breeding, especially in breeding populations with a lot of pedigree information, part of genotype information and a lot of phenotypic information. MAS based on GS and SSGWAS is more promising in specific population selection.

1. Literature

two。 Abstract

Background

One-step method (SSGBLUP) is widely used in genomic breeding. There is no significance test for SSGWAS in this framework. Our purpose is to develop significance test (p-value) for SSGWAS and test it in real data.

Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for

Genomic prediction. Point estimates of marker efects from SSGBLUP are often used for genome-wide association

Studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for singlemarker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and

Procedures, and by applying these to a large beef cattle population

Method

P-values were obtained based on the prediction error (co) variances for single nucleotide polymorphisms

(SNPs), which were obtained from the prediction error (co) variances of genomic predictions based on the inverse of

The coefcient matrix and formulas to estimate SNP efects.

Result

Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree

And 1424 genotyped sires, and no infation of statistics was observed. The SNPs that passed the Bonferroni threshold

Of 10 − 5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the

Same signifcance levels and efects, some of them explained less genetic variance due to lower allele frequency

Conclusion

The use of a p-value for SSGWAS is a very general and efcient strategy to identify quantitative trait

Loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the

Pedigreed animals are genotyped.

3. `EMMAX` VS `SSGWAS`

EMMAX framework:

The mixed linear model framework, which relies on single marker as a fixed factor regression analysis, represents the software EMMAX, which requires all individuals to have genotypes and phenotypes, which is not realistic in animal breeding.

Some traits are limited, cannot be directly used, and need to be adjusted, such as bull milk yield, using De-regression value, using it for GWAS analysis, loss of information, inaccuracy, especially if the sequencing individual includes the bull and its offspring, it will be repeated (double-counting).

In single SNP regression analysis, if the variance components are all estimated, the REML used will be very slow.

SSGWAS framework:

Variance components were estimated and population structure was considered at the same time (SSGBLUP method). Pedigree data, genotype data and phenotypic data were used.

The breeding value and SNP effect value can be calculated simultaneously by using SSGBLUP, and it has been proved that GBLUP and single locus GWAS (EMMAX) are equivalent.

The SSGWAS framework can combine sequenced and non-sequenced individuals for analysis, which is of great application value.

4. SSGWAS algorithm framework

Step 1: build H-matrix

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Step 2: build mixed linear equations

Step 3: calculate the sparse inverse matrix of the coefficient matrix

Step 4: solve the system of equations

Step 5: calculate the breeding value

Step 6: calculate the SNP effect value

Step 7: calculate the standard error

Step 8: calculate the P-value of SNP

5. Result

This is the answer to the question on how to analyze the application of SSGWAS. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel to learn more about it.

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