DataSheet1_Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption.pdf

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium’s Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and ‘omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.</p

DataSheet2_Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption.pdf

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium’s Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and ‘omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.</p

Descriptive characteristics^a of the PAGE and FBBP studies by race/ethnicity.

<p>Descriptive characteristics<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164132#t001fn002" target="_blank">^a</a> of the PAGE and FBBP studies by race/ethnicity.</p

Validation of the <i>KCNK3</i> rs2586886 associations with BP in independent multi-ethnic populations.

<p>Validation of the <i>KCNK3</i> rs2586886 associations with BP in independent multi-ethnic populations.</p

The genetic underpinnings of variation in ages at menarche and natural menopause among women from the multi-ethnic Population Architecture using Genomics and Epidemiology (PAGE) Study: A trans-ethnic meta-analysis

<div><p>Current knowledge of the genetic architecture of key reproductive events across the female life course is largely based on association studies of European descent women. The relevance of known loci for age at menarche (AAM) and age at natural menopause (ANM) in diverse populations remains unclear. We investigated 32 AAM and 14 ANM previously-identified loci and sought to identify novel loci in a trans-ethnic array-wide study of 196,483 SNPs on the MetaboChip (Illumina, Inc.). A total of 45,364 women of diverse ancestries (African, Hispanic/Latina, Asian American and American Indian/Alaskan Native) in the Population Architecture using Genomics and Epidemiology (PAGE) Study were included in cross-sectional analyses of AAM and ANM. Within each study we conducted a linear regression of SNP associations with self-reported or medical record-derived AAM or ANM (in years), adjusting for birth year, population stratification, and center/region, as appropriate, and meta-analyzed results across studies using multiple meta-analytic techniques. For both AAM and ANM, we observed more directionally consistent associations with the previously reported risk alleles than expected by chance (p-values_binomial≤0.01). Eight densely genotyped reproductive loci generalized significantly to at least one non-European population. We identified one trans-ethnic array-wide SNP association with AAM and two significant associations with ANM, which have not been described previously. Additionally, we observed evidence of independent secondary signals at three of six AAM trans-ethnic loci. Our findings support the transferability of reproductive trait loci discovered in European women to women of other race/ethnicities and indicate the presence of additional trans-ethnic associations both at both novel and established loci. These findings suggest the benefit of including diverse populations in future studies of the genetic architecture of female growth and development.</p></div

Generalization of five previously described age at menarche and natural menopause loci to multiple race/ethnic groups or trans-ethnically.

<p>Generalization of five previously described age at menarche and natural menopause loci to multiple race/ethnic groups or trans-ethnically.</p

Regional plots of the novel array-wide significant age at menarche (Panel A: <i>CUX2</i>) and natural menopause loci (Panels B,C: <i>FRMD5</i>, <i>GPRC5B</i>) using a modified random-effects trans-ethnic meta-analysis of more than 31,000 women, and showing independence from previously published cardiometabolic SNP associations (shown in gray if missing).

<p>Regional plots of the novel array-wide significant age at menarche (Panel A: <i>CUX2</i>) and natural menopause loci (Panels B,C: <i>FRMD5</i>, <i>GPRC5B</i>) using a modified random-effects trans-ethnic meta-analysis of more than 31,000 women, and showing independence from previously published cardiometabolic SNP associations (shown in gray if missing).</p

Regional plots for age at menarche Bonferroni-significant loci at <i>SEC16B</i> (Panel A), <i>BDNF</i> (Panel B) and <i>FTO</i> (Panel C), showing previously published body mass index (BMI) primary and secondary SNP associations, using a modified random-effects trans-ethnic meta-analysis of more than 31,000 women.

<p>Regional plots for age at menarche Bonferroni-significant loci at <i>SEC16B</i> (Panel A), <i>BDNF</i> (Panel B) and <i>FTO</i> (Panel C), showing previously published body mass index (BMI) primary and secondary SNP associations, using a modified random-effects trans-ethnic meta-analysis of more than 31,000 women.</p

Three loci with trans-ethnic array-wide significant modified random-effects associations^* at novel age at menarche or natural menopause loci.

<p>Three loci with trans-ethnic array-wide significant modified random-effects associations^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200486#t004fn001" target="_blank">*</a>} at novel age at menarche or natural menopause loci.</p

Descriptive statistics for the age at menarche (AAM, n = 44,367) and natural menopause (ANM, n = 17,100) analytic samples.

<p>Descriptive statistics for the age at menarche (AAM, n = 44,367) and natural menopause (ANM, n = 17,100) analytic samples.</p