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Genetic Analysis Reveals Relationship Between Clinical Measurements, Disease Traits

NEW YORK (GenomeWeb) – A genome-wide association study has examined the genetic links that tie clinical laboratory measurements and complex disease together.

Researchers led by the Riken Center for Integrative Medical Science's Yoichiro Kamatani analyzed the relationship between five dozen quantitative traits and genetic variants among individuals from the BioBank Japan Project. They identified more than 1,400 loci associated with different quantitative traits, as they reported today in Nature Genetics, and further traced relationships between different loci, multiple diseases, and different cell types.

"Our findings demonstrate that even without prior biological knowledge of cross-phenotype relationships, genetics corresponding to clinical measurements successfully recapture those measurements’ relevance to diseases, and thus can contribute to the elucidation of unknown etiology and pathogenesis," Kamatani and his colleagues wrote in their paper.

The researchers conducted a GWAS of 58 quantitative traits, examining whether they had links to 5.9 million autosomal and nearly 150,000 X chromosomal variants within their cohort of 162,255 Japanese individuals. Those quantitative traits spanned nine categories — such as metabolic or hematological traits — and included measurements of potassium levels, triglycerides, blood pressure, and more.

They uncovered 1,407 trait-associated loci for 53 quantitative traits that reached genome-wide significance, 679 of which were novel. When the researchers compared the allele frequencies of the loci they uncovered within East Asian and European populations, they found that the novel loci tended to be more common among East Asians.

Kamatani and his colleagues examined pleiotropy, or the sharing of risk alleles by multiple traits, at 763 loci. Of these, they found 313 loci with pleiotropy, and saw that another 88 loci exhibited pleiotropy in traits across different categories. ALDH2, for instance, was associated 21 traits in seven categories. There, the most significant associations were at rs79105258, which the researchers noted has a high minor allele frequency among East Asians.

At the same time, they looked at genetic overlap between traits. Through this, they found 173 significant correlations, nearly 60 percent of which were intercategorical. The highest number of intercategorical genetic correlations was with BMI, which was linked to 22 quantitative traits in seven trait categories.

By folding in data from an additional 30 GWAS of complex diseases and traits that were performed on Japanese individuals, Kamatani and his colleagues conducted a pairwise analysis of 59 quantitative traits and 30 diseases to uncover 68 significant genetic correlations. About three-quarters of these correlations involved cardiometabolic diseases, linking them with traits across seven categories. Type 2 diabetes, for instance, was linked to 15 quantitative traits, most significantly hemoglobin A1c.

When the researchers constructed a network detailing these relationships, they noted clusters of biologically similar phenotypes, such as of autoimmune diseases and chronic inflammatory diseases.

Using 220 cell-type-specific annotations, the researchers stratified their GWAS results by cell type. They found 72 heritability enrichments within these cell-type groups for 44 traits and diseases. They uncovered, for instance, an enrichment in immune or hematopoietic cell types for rheumatoid arthritis and Grave's disease, as would be expected.

They further clustered their heritability enrichments to note that immune or hematopoietic cell clusters are enriched for hematological traits, as well as for autoimmune, allergic, and infectious deceases.

A network of these interactions also uncovered clusters, including three — WBCs, lymphocyte count, and height — what were connected by an enrichment of adipose nuclei.

"Our findings suggest that there are complex interrelations between clinical measurements and diseases, demonstrating the value of GWASs for a variety of traits in a single large-scale cohort with detailed clinical information," Kamatani and his colleagues wrote.

They noted that their study has a few limitations, such as a lack of a validation cohort.

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