Haseman Elston Regression - gc5k/GEAR GitHub Wiki

The Haseman-Elston regression for GWAS

It provides a procedure that estimates the additive variance components for GWAS data.

Citation:

Chen, G.B., Estimating heritability of complex traits from genome-wide association studies using IBS-based Haseman-Elston regression, 2015, Front Genet:5:107

Chen, G.B., On the reconciliation of missing heritability for genome-wide association studies, 2016, Euro J Hum Genet, 2016, 24:1810:6


Master command: he

There are three kinds of Haseman-Elston regressions implemented. The three options are

  1. --sd: it models the phenotype as (y-y)2

  2. --ss: it models the phenotype as (y+y)2

  3. --cp: it models the phenotype as y*y

--grm

This option will read a pair of files: file.grm.gz, file.grm.id. The grm files can be generated with grm option.

--grm-list

This option provides a file which contains a list of grm file pairs. For example, if the file reads as:

file1

file2

...

fileN

then GEAR will search for pairs fileX.grm.gz and fileX.grm.id.

--grm-bin

This option provides a pair of files: file.grm.bin (binary format), file.grm.id. The grm binary files can be generated with GCTA.

--grm-bin-list

Analogous to grm-list, but in this case GEAR searches for fileX.grm.bin and fileX.grm.id.

--grm-cutoff

This option specifies the cut-off for grm. Any grm scores greater than the cutoff will be discarded.

Notes: neither grm-cutoff nor grm-abs-cutoff works for grm-list and grm-bin-list options.

--pheno

This option specifies the phenotype file, in which the first column is family IDs, the second column is individual IDs, and the rest are phenotype columns. Missing values were "-9", "NA", "na", "-Inf", and "Inf".

--mpheno

This option specifies the target trait or phenotype column that is used in Haseman-Elston regression. By default, mpheno=1, meaning the first phenotype column (i.e. the third column in the phenotype file).

--scale

This option will standardize the phenotype.

--keep

If specifies the individuals should be kept in the analysis.

--jackknife/--jk

Jackknife for evaluating the sampling variance for HE regression.

Examples

gear he --sd --grm test --pheno test.phe --out he-test --scale
gear he --sd --grm test --pheno test.phe --jk --out he-test --scale

gear he --sd --grm test --pheno test.phe --grm-cutoff 0.1 --out he-test
gear he --sd --grm test --scale --pheno test.phe --mpheno 1 --out he-test

gear he --cp --grm test --pheno test.phe --mpheno 1  --covar test.cov --covar-number 1 3 --keep keep.txt --out he-test
gear he -ss --grm-list mgrm.txt --pheno test.phe --covar test.cov --covar-number 1 2 3 --keep keep.txt --out mgrm

This work has been cited by


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Implications of simplified linkage equilibrium SNP simulation, PNAS, 2015, 112:e5449-e5451

Reply to Lee: Downward bias in heritability estimation is not due to simplified linkage equilibrium SNP simulation, PNAS, 2015, 112:e5452-e5453

Relationship between LD Score and Haseman-Elston Regression, bioRxiv, 2015

Mixed Model with Correction for Case-Control Ascertainment Increases Association Power, Am J Hum Genet, 2015, 96:720-730

DNA methylation in Arabidopsis has a genetic basis and shows evidence of local adaptation, eLife, 2015, e05255

Mixed Model with Correction for Case-Control Ascertainment Increases Association Power, Am J Hum Genet, 2015, 96:720-730

Disease and Polygenic Architecture: Avoid Trio Design and Appropriately Account for Unscreened Control Subjects for Common Disease, Am J Hum Genet, 2016, 98:382-391

An efficient method to handle the ‘large p, small n’ problem for genomewide association studies using Haseman–Elston regression, J Genetics, 2016, 95:847-852

EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations, Heredity, 2016, 117:51-61

On the reconciliation of missing heritability for genome-wide association studies, Euro J Hum Genet, 2016, 24:1810-1816

Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation, Nat Genet, 2016, 49:54-64

Reproduction and In-Depth Evaluation of Genome-Wide Association Studies and Genome-Wide Meta-analyses Using Summary Statistics, G3, 2017, 7:943-952

Partitioning Phenotypic Variance Due to Parent-of-Origin Effects Using Genomic Relatedness Matrices, Behavior Genetics, 2017, 48:67-79

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A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci, Genetics, 2018, 210:499-515

The Relationship Between Population Attributable Fraction and Heritability in Genetic Studies, Front Genet, 2018, 9:352

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A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits, Am J Hum Genet, 2020, 106:71-91

A rapid genomic selection method combining Haseman-Elston (HE) model and algorithm for proven and young (APY), Mol Breed, 2020, 40:12

Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models, J Comput Biol, 2020, 27:599-612

Heritability jointly Explained by Host Genotype and Microbiome:Will Improve Traits Prediction?, bioRxiv, 2020

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Dissecting the heritable risk of breast cancer: from statistical methods to susceptibility genes, Seminars in Cancer Biology, 2020, 72:175-184

Estimating SNP heritability in presence of population substructure in biobank-scale datasets, bioRxiv, 2020

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Exploring the Link Between Additive Heritability and Prediction Accuracy From a Ridge Regression Perspective, Frontiers in Genetics, 2021, 11:581594

Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals, Am J Hum Genet, 2021, 108:786-798

Subsampling Technique to Estimate Variance Component for UK-Biobank Traits, Frontiers in Genetics, 2021, 12:612045

Efficient Genomic Control for Mixed Model Associations in Large-scale Population, bioRxiv, 2021

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