Multitrait Genome-Wide Analysis - An Overview

Introduction:

Complex traits are associated with several genetic variations through gene expression. Some studies help in identifying genes associated with these traits. Genome-wide association study (GWAS) is a type of study that helps in identifying the genes that are associated with a particular disease and analyzing only one trait. Multi-trait genome analysis involves the identification of associated gene locus in complex diseases. With the help of deep phenotyping data, an adaptive multi-trait association test (aMAT) for multitrait genome-wide analysis can be done for multiple traits. This can control type 1 error rate and can identify multiple risk loci compared to GWAS.

What Is Multitrait Genome-Wide Analyses?

Multitrait genome-wide analyses is a study that involves an adaptive multi-trait association test (aMAT), which helps in detecting genetic variations in multiple traits. It explains the missing heritability, which cannot be done by GWAS, and enhances biological interpretation with increased statistical power. This uses deep phenotyping data from electronic health records and epidemiological studies.

What Are The Steps In Multitrait Genome-Wide Analyses?

This study uses an adaptive multi-trait association test (aMAT) which can analyze any number of traits (around hundreds) by taking the trait matrix’s individual potential and has high statistical power due to readily available data. Therefore, this method can identify additional associated genetic variants, which are ignored by other methods, including GWAS, by analyzing the phenotypes jointly. Using previous data to test the association of traits and giving Z scores, where Z is the single nucleotide polymorphisms (SNPs) and is done across all the genomes. Z scores are given until there is no association of SNPs with any trait.

This study has three steps, which are:

• The first step involves, linkage disequilibrium score regression (LDSR, a technique used to quantify the polygenic effects and other factors such as population stratification, which are taken from statistics of GWAS) which is applied to estimate the trait correlation matrix, and is denoted by R. To deal with the individual problem, modified pseudoinverse of R+ is used.

• The second step involves the implementation of multi-trait association tests (MTAs), and each trait should be powerful in certain scenarios. Therefore, multi-trait association tests are equal to:

MATs = Z R+ Z

Where Z (SNPs scores across the genome) is calculated as Z = (Z1 + Z2 + Z3 ……..ZP) and P is a number of traits of interest.

R is the trait correlation matrix.

• The third step involves combining the results of MAT tests to get adaptive MAT (aMAT). Gaussian copula approximation is used to calculate the P value of aMAT. aMAT is constructed with the help of:

  1. Estimation of the Trait Correlation Matrix (R): Bivariate LDSC (linkage disequilibrium score regression) is used to get the estimation of off-diagonal elements, and univariate LDSC is used for diagonal elements of R. R is the same across the genome’s SNPs and is estimated only once. The advantages of LDSC are:

     1. It avoids estimation errors.

     2. Avoids population stratification.

     3. There is no sample overlap.

     4. There are no technical artifacts.

     5. Avoids cryptic relatedness.

     6. It provides an accurate estimation of the trait correlation matrix.

  2. Construction of a Class of MATs: This includes the use of modified pseudoinverse R, which is denoted by R+ because R estimates a singular potential of trait matrix; when used in a large number of traits, it can show an error. To avoid this, modified pseudo inverse R+ is used.

  3. Construction of aMATs to Maintain High Power Over a Wide Range of Scenarios: MATs can be powerful when the principle component captures majority association signals across the number of traits of interest (P). Therefore, aMAT should use the smallest P-value to maintain high power.

What Are The Advantages Of Multitrait Genome-Wide Analyses?

The advantages of Multitrait genome-wide analyses include the following:

• aMats controls type 1 error rates.

• This can help in analyzing multiple numbers of traits with the help of data that are readily available.

• aMATs have high statistical power across a wide range of scenarios.

• This can identify associated gene locus in complex diseases.

• This can identify new genetic variations, therefore, provide additional biological interpretations.

• They can identify multiple risk loci.

• This study can explain the missing heritability.

• This study can analyze any number of traits.

How Are Multitrait Genome-Wide Analyses Useful In Studying Psychiatric Disorders?

The use of Genome-wide association studies in multiple disorders cannot identify contributing genetic loci. But, with the help of multi-trait analysis, joint analysis of multiple traits is possible and pathophysiological mechanisms and genetic structure can be understood. Psychiatric disorders, which are schizophrenia, bipolar disorder, major depressive disorder, attention deficit hyperactivity disorder, and autism spectrum disorder, have phenotypic associations related to mental illnesses such as hallucinations, disorders where clustered among families and have various degrees of genetic correlations among these five disorders. This study includes:

• Use of GWAS summary statistics.

• Using bivariate linkage disequilibrium score regression (LDSR).

• Using trait-specific effect.

• Using short computational time.

The results of the meta-analysis showed:

• Varied degrees of genetic associations among all the five major psychiatric disorders.

• There was a high genetic correlation between schizophrenia and bipolar disorder.

• There was a moderate genetic correlation between attention deficit hyperactivity disorder and major depressive disorder.

• These five disorders contain unique pathogenic genes along with shared genes.

• There is the presence of cross-trait genes.

• There were seven common genes present in four disorders which include schizophrenia, bipolar disorder, major depressive disorder, and an autism spectrum disorder.

• GABBR1 is a gene that encodes neurotransmitter receptors for GABA (gamma-aminobutyric acid). This is associated with schizophrenia and major depressive disorder.

• DCC is a gene for cognitive ability, intelligence, and educational attainment; it is associated with schizophrenia, major depressive disorder, autism spectrum disorder, and attention deficit hyperactivity disorder.

• Histone modifications act as epigenetic variations in these psychiatric disorders.

• The SORCS3 gene is associated with all these psychiatric disorders, which encodes type-1 receptor transmembrane protein and binds platelet-derived growth factor and nerve growth factor.

Conclusion:

A multi-trait association test is introduced to analyze any number of traits jointly. This is the first method developed to analyze a large number of traits with the help of deep phenotyping data, which is readily available. Therefore using this study can help in controlling type 1 errors, has high statistical power, can identify associated gene locus in complex diseases, new genetic variations, and multiple risk loci, and this study can analyze any number of traits.