Gender-dependent multiple cross-phenotype association of interferon lambda genetic variants with peripheral blood profiles in healthy individuals

Abstract

Background: Type III interferons (IFN), also called as lambda IFNs (IFN-λs), are antiviral and immunomodulatory cytokines that are evolutionarily important in humans. Given their central roles in innate immunity, they could be influencing other aspects of human biology. This study aimed to examine the association of genetic variants that control the expression and/or activity of IFN-λ3 and IFN-λ4 with multiple phenotypes in blood profiles of healthy individuals.

Methods: In a cohort of about 550 self-declared healthy individuals, after applying several exclusion criteria to determine their health status, we measured 30 blood parameters, including cellular, biochemical, and metabolic profiles. We genotyped them at rs12979860 and rs28416813 using competitive allele-specific PCR assays and tested their association with the blood profiles under dominant and recessive models for the minor allele. IFN-λ4 variants rs368234815 and rs117648444 were also genotyped or inferred.

Results: We saw no association in the combined cohort under either of the models for any of the phenotypes. When we stratified the cohort based on gender, we saw a significant association only in males with monocyte (p = 1 × 10^-3 ) and SGOT (p = 7 × 10^-3 ) levels under the dominant model and with uric acid levels (p = 0.01) under the recessive model. When we tested the IFN-λ4 activity modifying variant within groupings based on absence or presence of one or two copies of IFN-λ4 and on different activity levels of IFN-λ4, we found significant (p < 0.05) association with several phenotypes like monocyte, triglyceride, VLDL, ALP, and uric acid levels, only in males. All the above significant associations did not show any confounding when we tested for the same with up to ten different demographic and lifestyle variables.

Conclusions: These results show that lambda interferons can have pleiotropic effects. However, gender seems to be an effect modifier, with males being more sensitive than females to the effect.

Keywords: IFNL; IFNL3; IFNL4; PheWAS; cross-phenotype association; interferon lambda; rs117648444; rs12979860; rs28416813; rs368234815; rs4803217.

FIGURE 1

The human IFNL locus and their features in the study cohort. (a) Schematic of the human IFN lambda locus on chr. 19. The four genetic variants that control the expression and activity of IFN‐λ3 (rs4803217 and rs28416813) and IFN‐λ4 (rs368234815 and rs117648444) are shown along with the GWAS hit SNP rs12979860 (Ge et al., 2009). (b, c) The minor allele frequency (MAF) of the three variants, p‐value that shows their HWE status and their LD plot. The dinucleotide variant rs368234815 was in perfect LD with rs12979860 in line with our previous results (Bhushan et al., 2017) and hence its genotype status was inferred from the latter to determine the IFN‐λ4 status in a given individual along with the genotype information at rs117648444.

FIGURE 2

Non‐random distribution of variance according to IFNL SNPs and gender in multiple phenotypes in the study cohort. Heat map shows the p‐value distribution in the combined cohort and the gender sub‐cohorts after applying Bonferroni correction for multiple testing as described in the Results section; the darker color is suggestive of highly significant association of the variance between the major homozygotes group and the group with the remaining individuals (dominant model for the minor allele). The p‐values were derived from the F‐statistic. The results from only the dominant model are shown here. *Triglyceride levels qualified for the criteria by showing significant p‐values for both the SNPs under the recessive model even though the dominant model showed insignificant p‐values in the combined cohort; however, they showed highly significant p‐values for both the SNPs under the dominant model only when the cohort was stratified on gender and interestingly the association was in opposite directions in males vs. females. Abbreviations: VLDL‐cholesterol: Very Low‐Density Lipoprotein‐cholesterol, SGOT: Serum Glutamic Oxaloacetic Transaminase, GGT: Gamma Glutamyl Transferase, TSH: Thyroid Stimulating Hormone.

FIGURE 3

Multiple phenotypes significantly associate with IFNL SNPs only in males. (a–d) Violin plots showing the distribution of data around the median (thick dashed line shows median and the two thin lines separate the quartiles in each half) for the phenotypes shown according to the IFNL SNPs under the dominant or recessive models (for a, b and c) or additive model (for d) for the minor allele as shown. All the p‐values shown were for the effect of the individual SNPs derived from multiple linear regression analysis with age as the covariate (fully described in Table S4). No significant effect of the SNPs was seen in the female sub‐cohort.

FIGURE 4

Multiple phenotypes significantly associate with groupings based on IFN‐λ4 copy number and status. (a–d) The IFN‐λ4 status was inferred from the genotype information at rs12979860 and rs117648444 for each individual before grouping them in to one of the four groups‐ no‐IFN‐λ4, P70‐1 (having a single copy of the high activity P70 variant of IFN‐λ4), P70‐2 (having two copies of the high activity P70 variant of IFN‐λ4) and S70‐1 (while the large majority of the individuals in this group had one copy of the low‐activity S70 variant of IFN‐λ4, a minority of them had diplotypes, i.e. had one copy of the high activity P70 variant as well, as explained in e). (e) The IFN‐λ4 status based on copy number and activity level explained. A minority (10 in the combined, 07 in the males and 03 in the female sub‐cohort) of individuals had both a high activity and low‐activity IFN‐λ4 variant in them; this grouping strategy allowed us to have more power in testing the S70 variant due to the low MAF of rs117648444 SNP (see Figure 1b). All the p‐values shown were for the effect of the individual SNPs/variants derived from multiple linear regression analysis with age and gender (for the combined cohort) or age only (for the individual gender sub‐cohorts) as the covariate(s) (fully described in Table S4).

FIGURE 5

Summary statistics of PheWAS data derived from the UK biobank showing the multiple cross‐phenotype association of IFNL SNPs. The data was accessed from Global Biobank Engine (GBE), Stanford, CA (URL: http://gbe.stanford.edu) [June–July 2023]. MAF‐minor allele frequency.

FIGURE 6

The effect sizes of IFNL SNPs from our study for male‐specific monocyte counts and SGOT levels are better than that for many of the top genes/SNPs for the respective phenotypes represented in the GBE. The summary statistics (beta values, p‐values and MAFs) for the top genes/SNPs in terms of effect sizes (red balls) or significance (gray balls) were obtained from the GBE PheWAS database. The MAFs are represented by showing the gene/SNP IDs in different colors, as explained at the bottom of the figure. Note that the majority of the genes/SNPs that have the best effect and significance on the two phenotypes have low MAFs. The beta coefficient for the SNPs derived from our study (only from male sub‐cohort), shown as purple balls, were calculated in a multiple linear regression model after standardization with age as a covariate. GBE, global biobank engine; MAF, minor allele frequency.