In genomics, nearby SNPs are in LD (correlated) with each other. It violates the independence assumption in linear models and are treated as random effect in linear mixed model in a method estimating the degree that phenotype is influenced by genotype (i.e., estimating heritability in GCTA). Random effect is a grouping variable, hence can only be integer. This is possible in genotypic estimation because there are only three genotypes considered (e.g., AA, AT, TT).

But what if they are correlated continuous variables? They are correlated so linear regression can't be used. They are not integer so can't be treated as random effect in linear mixed model.

In genomics, nearby SNPs are in LD (correlated) with each other. It violates the independence assumption in linear models and are being treated as random effect in linear mixed model in a method estimating the degree that phenotype is influenced by genotype (i.e., estimating heritability in GCTA). Random effect is a grouping variable, hence can only be integer. This is possible in genotypic estimation because there are only three genotypes considered (e.g., AA, AT, TT).

But what if they are correlated continuous variables? They are correlated so linear regression can't be used. They are not integer so can't be treated as random effect in linear mixed model.

In genomics, nearby SNPs are in LD (correlated) with each other. It violates the independence assumption in linear models and are treated as random effect in linear mixed model in a method estimating the degree that phenotype is influenced by genotype (i.e., estimating heritability in GCTA). Random effect is a grouping variable, hence can only be integer. This is possible in genotypic estimation because there are only three genotypes considered (e.g., AA, AT, TT).

But what if they are correlated continuous variables? They are correlated so linear regression can't be used. They are not integer so can't be treated as random effect in mixed models.

In genomics, nearby SNPs are in LD (correlated) with each other. It violates the independence assumption in linear models and are treated as random effect in linear mixed model in a method estimating the degree that phenotype is influenced by genotype (i.e., estimating heritability in GCTA). Random effect is a grouping variable, hence can only be integer. This is possible in genotypic estimation because there are only three genotypes considered (e.g., AA, AT, TT).

But what if they are correlated continuous variables? They are correlated so linear regression can't be used. They are not integer so can't be treated as random effect in linear mixed model.

In genomics, nearby SNPs are in LD (correlated) with each other. It violates the independence assumption in linear models and are treated as random effect in linear mixed model in a method estimating the degree that phenotype is influenced by genotype (i.e., estimating heritability in GCTA). Random effect is a grouping variable, hence can only be integer. This is possible in genotypic estimation because there are only three genotypes considered (e.g., AA, AT, TT).

But what if they are correlated continuous variable? They are correlated so linear regression can't be used. They are not integer so linear mixed models can't be used.

In genomics, nearby SNPs are in LD (correlated) with each other. It violates the independence assumption in linear models and are treated as random effect in linear mixed model in a method estimating the degree that phenotype is influenced by genotype (i.e., estimating heritability in GCTA). Random effect is a grouping variable, hence can only be integer. This is possible in genotypic estimation because there are only three genotypes considered (e.g., AA, AT, TT).

But what if they are correlated continuous variables? They are correlated so linear regression can't be used. They are not integer so can't be treated as random effect in mixed models.