Pleiotropic Meta-Analysis of Age-Related Phenotypes Addressing Evolutionary Uncertainty in Their Molecular Mechanisms.

Age-related phenotypes are characterized by genetic heterogeneity attributed to an uncertain role of evolution in establishing their molecular mechanisms. Here, we performed univariate and pleiotropic meta-analyses of 24 age-related phenotypes dealing with such evolutionary uncertainty and leveraging longitudinal information. Our analysis identified 237 novel single nucleotide polymorphisms (SNPs) in 199 loci with phenotype-specific (61 SNPs) and pleiotropic (176 SNPs) associations and replicated associations for 160 SNPs in 68 loci in a modest sample of 26,371 individuals from five longitudinal studies. Most pleiotropic associations (65.3%, 115 of 176 SNPs) were impacted by heterogeneity, with the natural-selection-free genetic heterogeneity as its inevitable component. This pleiotropic heterogeneity was dominated (93%, 107 of 115 SNPs) by antagonistic genetic heterogeneity, a phenomenon that is characterized by antagonistic directions of genetic effects for directly correlated phenotypes. Genetic association studies of age-related phenotypes addressing the evolutionary uncertainty in establishing their molecular mechanisms have power to substantially improve the efficiency of the analyses. A dominant form of heterogeneous pleiotropy, antagonistic genetic heterogeneity, provides unprecedented insight into the genetic origin of age-related phenotypes and side effects in medical care that is counter-intuitive in medical genetics but naturally expected when molecular mechanisms of age-related phenotypes are not due to direct evolutionary selection.

Introduction

Large-scale genetic association studies including those called genome-wide association studies (GWAS) are a powerful tool for gaining insight into the genetics of human health span and life span. Historically, such studies were built in the framework of medical genetics, which is best adapted for Mendelian (hereditary) disorders. Major contributors to health span are common diseases (such as cardiovascular disease, stroke, cancer, Alzheimer's disease) occurring in late (i.e., post-reproductive) life and the risk factors that make individuals vulnerable to these diseases, collectively called age-related phenotypes. These are complex phenotypes of non-Mendelian type (Plomin et al., 2009). However, large-scale genetic association studies of such phenotypes utilize the same framework as for Mendelian disorders.

Biologists argue that gaining insight into biological processes requires evolutionary explanations (Dobzhansky, 1973). Because medical genetics historically focused on Mendelian-type hereditary disorders, such disorders are traditionally explained in terms of basic evolutionary forces such as mutation, natural selection, genetic drift, and gene flow. However, the sister discipline of evolutionary biology emphasizes fundamental difficulties in genetics of age-related phenotypes due to an uncertain role of evolution in establishing their molecular mechanisms (Kirkwood et al., 2011; Nesse et al., 2012). This problem is complicated by recent changes in human life expectancy (Oeppen and Vaupel, 2002) and the fitness landscape (Vijg and Suh, 2005; Crespi et al., 2010). Paradoxically, both medical genetics and evolutionary biology developed mostly independently, despite their reliance on the same forms of genomic data.

Evolutionary biology argues that mechanisms of age-related phenotypes are the results of indirect factors (“side-effects”) such as co-evolution with fast-evolving pathogens, mismatch with environments, reproductive success at the expense of health, trade-offs that leave every trait suboptimal, defenses and their special costs (Nesse and Williams, 1994; Nesse et al., 2012). The indirect role of evolution in mechanisms of age-related phenotypes, called in this paper evolutionary uncertainty, is the source of natural-selection–free genetic heterogeneity in predisposition to age-related phenotypes. This is an inevitable source of heterogeneity in the case of age-related phenotypes complementing the traditionally considered sources associated with complexity of phenotypes and their polygenicity. The inherent heterogeneity of age-related phenotypes can explain why the same allele can confer different, even antagonistic, risks to the same phenotype in different populations with the same ancestry (Day-Williams and Zeggini, 2011; Ukraintseva et al., 2016) and may result in complex forms of pleiotropy with seemingly related (Kulminski et al., 2016c, 2017) or unrelated (Goh et al., 2007; Barabasi et al., 2011; Kulminski et al., 2016a) phenotypes. This complexity implies that the role of the same genetic variant in the same phenotype can be naturally modified by the life course (Kulminski et al., 2013, 2017; Ukraintseva et al., 2016) regardless of heritability of such phenotypes because heritability concept for age-related phenotypes is notoriously problematic (Lewontin, 1974; Rose, 2006).

Here, we report the results of univariate and pleiotropic meta-analyses of genetic associations with multiple age-related phenotypes dealing with the evolutionary uncertainty in establishing their molecular mechanisms and leveraging longitudinal information. This approach follows the framework of evolutionary biology, which argues that genetic predisposition to age-related phenotypes is inherently heterogeneous, with the natural-selection–free genetic heterogeneity as its inevitable component. Thus, the key element of this biologically-motivated approach is addressing the inherent heterogeneity in genetic predisposition to such phenotypes. We identified 411 non-proxy single nucleotide polymorphisms (SNPs) (with linkage disequilibrium r2 < 70%) with genome-wide (GW) significance (p < p = 5 × 10−8) in a modest sample of 26,371 Caucasians from five longitudinal studies, including 237 novel SNPs (199 loci), 11 SNPs in the major histocompatibility complex (MHC), and 3 (3 loci) and 160 (68 loci) SNPs with p-values smaller and larger, respectively, in the current study than in previous studies. We show that the evolutionary uncertainty plays a dominant role in the associations with age-related phenotypes. We found that vast majority of pleiotropic associations with age-related phenotypes were affected by antagonistic genetic heterogeneity, a phenomenon not previously routinely recognized, that is characterized by antagonistic directions of genetic effects for directly correlated phenotypes.

Materials and Methods

Study Cohorts

Data were obtained from five longitudinal studies from the Candidate gene Association Resource, Atherosclerosis Risk in Communities (ARIC) study (Investigators, 1989), Cardiovascular Health Study (CHS) (Fried et al., 1991), Coronary Artery Risk Development in Young Adults (CARDIA) study (Hughes et al., 1987), Multi-Ethnic Study of Atherosclerosis (MESA) (Bild et al., 2002), and Framingham Heart Study (FHS) (Cupples et al., 2009). The FHS included three cohorts comprising parental (FHS_C1), offspring (FHS_C2), and grandchildren (FHS_C3) generations. Given this complex design, the FHS cohorts were examined separately.

Phenotypes

The analyses focused on 24 phenotypes (16 quantitative markers, 7 diseases, and death) listed in Table 1. Given the longitudinal design of the studied cohorts, the analyses leveraged repeated measurements of quantitative markers during follow-up and information about the timing of disease onset or death (Supplementary Table 1). These not strongly correlated phenotypes (Supplementary Figure 1) were available in the majority of selected cohorts. All studies collected information on diseases and death in population samples during follow-up.

Table 1

Basic characteristics of cohorts included in the analyses.

Cohort	Sample size	Number of visits	Age (SD), years	Birth dates (range)	Quantitative markers*	Risk outcomes**
ARIC	10,540	4	54.3 (5.7)	1920–1945	All except ADPN and AlbU	All except ND
CHS	4,174	10	72.8 (5.6)	1885–1925	All except IL6	All
FHS_C1	639	28	35.7 (4.3)	1895–1920	All except ADPN, AlbU, CRP, and IL6	All
FHS_C2	3,062	8	34.8 (9.8)	1910–1965	All	All
FHS_C3	3,960	2	40.2 (8.8)	1930–1980	All	Cancer only
MESA	2,474	5	62.7 (10.3)	1917–1957	All except HGB and AlbS	All except ND
CARDIA	1,522	6	25.6 (3.4)	1950–1965	All except ADPN and IL6	DM only

Quantitative markers are grouped in three domains: physiological (PHY): body mass index (BMI), diastolic blood pressure (DBP), forced expiratory volume in 1 second (FEV1), heart rate (HR), systolic blood pressure (SBP); blood (BLD): adiponectin (ADPN), albumin in serum (AlbS), blood glucose (BG), creatinine, hemoglobin (HGB), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), triglycerides (TG); and inflammation (INF): albumin in urine (AlbU), C-reactive protein (CRP), interleukin 6 (IL6).

Risk outcomes: atrial fibrillation (AF), cancer, coronary heart disease (CHD), diabetes mellitus (DM), death, heart failure (HF), dementia of Alzheimer type (ND), stroke.

Cohort: ARIC, Atherosclerosis Risk in Communities Study; CHS, Cardiovascular Health Study; FHS, Framingham Heart Study original cohort (FHS_C1), FHS_C2, FHS 2nd generation cohort; FHS_C3, FHS 3rd generation cohort; MESA, Multi-Ethnic Study of Atherosclerosis; CARDIA, Coronary Artery Risk Development in Young Adults.

Age is given at baseline; standard deviation (SD).

Additional details are provided in .

Genotypes

Genotyping in each study was performed using the same customized Illumina CVDSNP55v1_A chip with approximately 50,000 SNPs from more than 2,000 selected candidate genes. SNPs were included in the analyses after quality control in each study (call rate > 95%, Hardy-Weinberg disequilibrium p > 10−4, minor allele frequency [MAF] > 2%). In case of marginally smaller MAF for prioritized SNPs in a specific cohort compared with the MAF cut-off, these SNPs were used regardless of the MAF cut-off.

Genes and Loci

SNPs were mapped to genes using variant effect predictor from Ensembl and the NCBI SNP database (assembly GRCh38.p7). Because the customized Illumina CVDSNP55v1_A chip was enriched by genes, most SNPs identified in our analyses were within or near protein-coding genes. If an index SNP was not within a protein-coding gene, the closest gene was assigned. Multiple genes were assigned if they were at about the same distance up- and down-stream from the index SNP or if the index SNP was within the region of overlapping genes. Loci were naturally associated with genes.

Analyses were performed in two stages (Figure 1).

Figure 1

Scheme of univariate and pleiotropic meta-analyses in stage 2. (A) Statistics from stage 1 univariate genetic association study of 24 phenotypes in seven cohorts denoted PiCj, and . (B) Univariate statistics from the meta-analysis across cohorts using the fixed-effects meta-test (PiM) and Fisher test (PiFc). (C) Statistics from the pleiotropic meta-analysis across phenotypes in cohort j for: (i) omnibus tests with correlation matrix for phenotypes (OpCj) and effect statistics (ObCj) and (ii) Fisher test (FpCj). (D) Meta-statistics from Fisher test across cohorts for the results in (C) (OpFc, ObFc, and FpFc). (E) Meta-statistics across phenotypes for the results in (B) from (i) meta-test across cohorts and omnibus test across phenotypes with correlation matrix for phenotypes (MOp), (ii) meta-test across cohorts and omnibus test across phenotypes with correlation matrix for effect statistics (MOb), (iii) meta-test across cohorts and Fisher test across phenotypes (MFp), and (iv) Fisher test across cohorts and Fisher test across phenotypes (FcFp). Pathway 1: meta-analysis combining statistics across cohorts (pathway 1a) and pleiotropic meta-analysis across phenotypes (pathway 1b). Pathway 2: meta-analysis combining statistics across phenotypes (pathway 2a) and cohorts (pathway 2b).

Univariate Genetic Association Study in Stage 1

Genetic association study was performed for each phenotype in each cohort separately. This conventional approach was enhanced by leveraging longitudinal information. For quantitative markers, we used all measurements available during follow-up for the same individuals. Information on longitudinal measurements has multiple advantages including a potential gain in statistical power. To correct for repeated measurements correlations in the analyses of quantitative markers in all studies except the FHS, we used the generalized estimating equation (GEE) model with random effects for repeated measurements (gee package in R used with unstructured correlations). As the FHS included participants from large families, we used the linear mixed effects multilevel model (lme4 package in R) with random effects to correct for familial structure and repeated measurements correlations because the GEE model was not efficient due to memory constraints, particularly for variables with a large number of observations. Given gamma-like frequency distributions of ADPN, AlbU, CRP, and IL6, a GEE model with a gamma function and log-link was used in all studies. We evaluated associations for SNPs using the available measurements of quantitative markers for individuals of a given age at each examination.

Longitudinal information on time to events was implemented in the Cox proportional hazards mixed effects model (coxme package in R) that addressed familial relatedness. In the FHS, we used both prospective and retrospective onsets. The use of retrospective onsets in a failure-type model is justified by Prentice and Breslow (Prentice and Breslow, 1978). These analyses provide estimates of effects in a given population. The time variable in these analyses was the age at onset of an event or at right censoring.

The models were adjusted for: (all studies) age, sex, and the first five principal components; field center (ARIC, CARDIA, CHS, MESA); genotyping stage (CHS); and whether DNA samples had been subject to whole-genome amplification (FHS). Because the GEE model for AlbU with log-link gamma function adjusted for five principal components did not converge for CHS, this adjustment was disregarded in this analysis.

Principal component analysis (PCA) was performed on genotyping data after quality control using the smartpca program from the EIGENSOFT package.

Meta-Analyses in Stage 2

After stage 1, each SNP had a table with association statistics for up to 24 phenotypes in seven cohorts. These statistics were combined along two possible pathways (Figure 1): (i) across studies and then across phenotypes and (ii) across phenotypes and then across studies. These pathways can provide different results because of the inherent heterogeneity in genetic predisposition to age-related phenotypes (see the Significance). To address the impact such heterogeneity, we used four tests in pathway 1 and three tests in pathway 2 (Figure 1).

In pathway 1, univariate (phenotype-specific) meta-analysis combining stage 1 statistics across cohorts (pathway 1a) was performed using the Fisher test and conventional GWAS fixed-effects meta-test. Pleiotropic meta-analysis (pathway 1b) was performed by combining the univariate meta-statistics for the same SNPs across phenotypes. In pathway 1b, we used the Fisher test and two omnibus tests. The latter are traditionally used to address correlations between the effect statistics and phenotypes (details on all tests are below).

In pathway 2, we performed first pleiotropic meta-analysis by combining the stage 1 univariate statistics across phenotypes in each cohort separately (pathway 2a) using the Fisher test and the two omnibus tests as in pathway 1b. Then, we combined these pleiotropic meta-statistics across cohorts using the Fisher test (pathway 2b).

Fixed-Effects Meta-Test

We adopted a conventional GWAS meta-test using a fixed effects model with inverse-variance weighting [METAL software (Willer et al., 2010)]. The combined effect size was estimated as , and the variance of this effect-size was , where is the effect size in the study i and w is the reciprocal of the variance of .

Fisher Test

The Fisher test (Fisher, 1970) combines p-values assuming that there is no correlation between the tests that generated these p-values. In pathway 1a, this test has the power to reject the “null” hypothesis of no pooled effect regardless of the effect sizes and directions in the cohort-specific statistics. Accordingly, this test can indicate heterogeneity in genetic associations by providing smaller p-values than those from the fixed-effects meta-test. In pathways 1b and 2a, the Fisher test combines p-values across phenotypes assuming that these p-values are from independent associations. This test is often used for pleiotropic meta-analysis of modestly correlated phenotypes (Fortney et al., 2015). Because the Fisher test combines p-values from multiple statistics, it addresses the problem of multiple testing by increasing the number of degrees of freedom.

Omnibus Tests

The Fisher test is based on the assumption of independence of the combined statistics. The statistics for the associations of the same SNPs with different phenotypes may or may not be independent. It is therefore argued that tests penalizing for correlation of such statistics should be used to deflate the Fisher test estimates. A commonly adopted test in this case is an omnibus test (Xu et al., 2003; Bolormaa et al., 2014; Zhu et al., 2015).

Suppose that for a certain SNP we have an estimated effect size and its standard error for the phenotype in the cohort , where K is the number of phenotypes in a study j. A general omnibus test statistic can be constructed as , where is a z-score vector of associations of SNPs with phenotypes and Σ is the correlation matrix of the z-scores to be estimated (Xu et al., 2003; Bolormaa et al., 2014). Accordingly, this test takes into account the correlation of the effect statistics for different phenotypes. Under the null hypothesis (β = 0), the test statistic follows a chi-squared distribution with K degrees of freedom

from which we obtained a combined p-value p in the study j.

Correlation matrices Σ can be evaluated from the estimates of the effect statistics for each phenotype for independent SNPs; they are denoted Σ ≡ Σ. It has been argued that Σ can be also constructed by evaluating correlations between phenotypes (Zhu et al., 2015); these matrices are denoted Σ ≡ Σ. Correlation matrices were constructed for pathways 1 and 2 (Figure 1) separately. Cohort-specific matrices for pathway 2 were constructed by evaluating correlations of the effect statistics in each cohort using the results from the univariate genetic association study in stage 1 for each phenotype for independent SNPs. For pathway 1, matrix was constructed by evaluating correlations of the effect statistics from the fixed effect meta-test of all cohorts. The phenotype-based cohort-specific matrices for pathway 2, , were constructed using phenotype measurements in each cohort separately by evaluating correlations between vectors defined by person-observations of the selected phenotypes. In the case of no overlapping measurements of quantitative traits, the closest measurements to a given examination were used. Matrix for pathway 1 was constructed using a fixed effect meta-test applied to the correlation statistics of phenotypes in each cohort, which was evaluated using averaged values for quantitative traits measured longitudinally at different visits.

Genetic Heterogeneity

Evolutionary biology argues that genetic predisposition to age-related phenotypes is inherently heterogeneous due to the undefined role of evolution in establishing their molecular mechanisms. This implies that mechanisms driving genetic predisposition to age-related phenotypes and correlations between these phenotypes may have different origins.

Figure 2 illustrates four types of genetic heterogeneity in connections of SNPs with two partly correlated age-related phenotypes P1 and P2. Figures 2A,B illustrate commonly expected heterogeneities (of types 1 and 2) when vectors of the effects β and β are aligned with projections P1 and P2 of a vector of correlation, or, equivalently, a bivariate vector of the effects is aligned with a vector of correlation. Heterogeneity of type 1 (Figure 2A) refers to the situation when one observes perfect alignment of these vectors in some sub-samples. Although this situation resembles homogeneity, it actually may not be. To prove homogeneity, such perfect alignment should be in any sub-sample of a given sample. Type 2 (Figure 2B) is a commonly expected heterogeneity with a modest deviation from collinearity of the bivariate vector of the effects and the vector of correlation in subsamples and the entire sample. This heterogeneity, however, preserves alignment of these vectors in the same direction. Both types of heterogeneity can indicate shared mechanisms driving correlation between phenotypes and the effect statistics. Thus, either the Σ or Σ-based omnibus test can penalize for this correlation, deflating the estimates from the Fisher test. This is evident from the test statistics in the two-dimensional case,

Figure 2

Schematic illustration of 4 types of genetic heterogeneity. Heterogeneity of: (A) type 1, (B) type 2, (C) type 3, and (D) type 4 in the associations with two partly correlated age-related phenotypes P1 and P2. Small dots represent a sample of carriers of an effect allele A (red color) and those who do not carry this allele (no A; green color). Large ellipse shows correlation of P1 and P2 in this sample (r = 0.6). Small ellipses show correlation of P1 and P2 in subsamples of this sample. Red color denotes vector of correlation of P1 and P2 (thick diagonal vector) and its projections on P1 (horizontal) and P2 (vertical). Black vectors β1 and β2 denote the effects in the associations of allele A with P1 and P2. Sum of β1 and β2 represents bivariate vector (thick line) of the effects.

because ẑ1ẑ2Σ21, ẑ > 0 in this situation. Penalization of these statistics indicates overlap in the association signals, which is essential for identifying mediated pleiotropy (Solovieff et al., 2013) and, potentially, shared biological mechanisms in genetic predisposition to different phenotypes.

Figure 2C illustrates less commonly expected heterogeneity of type 3 by showing that despite virtually the same alignment as in Figure 2B of the bivariate vector of the effects with the vector of correlation in the entire sample, the mechanisms are different. The difference is that the bivariate vector in Figure 2C is a superposition of virtually independent (orthogonal) bivariate vectors of the effects in subsamples. The subsample-specific bivariate vectors can be strongly misaligned with the vector of the phenotype correlation. Then, the omnibus Σ-based test will be overly deflated compared to the Fisher and Σ-based omnibus tests. Type 3 heterogeneity indicates biological pleiotropy due to mechanisms other than those driving correlation between the studied phenotypes.

Figure 2D illustrates heterogeneity of type 4, which is counter-intuitive in medical genetics but naturally expected when molecular mechanisms of age-related phenotypes are not due to direct evolutionary selection. This heterogeneity implies (virtual) orthogonality of the bivariate vector of effects to the vector of the phenotype correlation, which is driven by opposite directions of the effect vector (β in this case) for one phenotype and projection of the vector of correlation on this phenotype (P2). Accordingly, this heterogeneity is called antagonistic heterogeneity, implying antagonistic directions of genetic effects for directly correlated phenotypes. In this case, the omnibus test provides smaller p-values than the Fisher test, as can be seen from (1) because ẑ1ẑ2Σ21, ẑ1ẑ2Σ12 < 0 in this case.

Genetic heterogeneity can be further complicated by subsamples with no effects (ellipse with no arrows in Figure 2C) and/or antagonistic effects for the same phenotypes at, e.g., different ages. This complication can make effects in the entire sample seemingly weak despite strong effects in subsamples.

Significance and Novelty

The fixed-effect, Fisher, and two omnibus meta-tests used in our analyses have power to identify associations that are adjusted or unadjusted for correlation and heterogeneity in genetic predisposition to age-related phenotypes. Accordingly, GW level (p = 5 × 10−8) attained in either of these tests was used as a cut-off for significance for a given SNP. The difference between p-values from these tests for a SNP was used to characterize the impact of correlation and heterogeneity on the association.

SNPs were considered novel if they attained GW significance in: (i) the univariate meta-analysis but were not reported in the GRASP catalog (Leslie et al., 2014) at p ≤ 5 × 10−8 or (ii) the pleiotropic meta-analysis but not in the pleiotropic analysis of results from GRASP. For univariate meta-analysis, we used evidence for the same (index) SNP in our study and GRASP. If no associations were reported in GRASP for the index SNP, we included SNPs with the smallest p-values within ± 1 Mb flanking region. For pleiotropic meta-analysis, we selected associations from GRASP for the index SNPs with 24 phenotypes used in our analysis (Table 1). Then we performed pleiotropic meta-analysis by applying the Fisher test to these GRASP results to present evidence for pleiotropy in prior studies. We used the Fisher test because the effect sizes were not reported in GRASP. We adopted a conservative approach by not penalizing the Fisher statistics for phenotypes with p-values not reported in GRASP.

Heterogeneity Coefficient

We used METAL software (Willer et al., 2010) to evaluate the heterogeneity coefficient I2. The I2 can be interpreted as the percentage of the total variability in a set of effect sizes due to between-sample variability.

Results

Study Overview

We used 24 weakly to moderately correlated (Supplementary Figure 1) age-related phenotypes (16 markers, seven diseases, and death) available from five longitudinal studies comprising seven cohorts (Table 1). Analyses were performed for 26,371 individuals of Caucasian ancestry, men, and women combined, in two stages using an additive genetic model with minor allele as an effect allele. In stage 1, we performed a genetic association study of each phenotype (one SNP—one phenotype) in each cohort separately (Figure 1A), following the traditional univariate GWAS design. This analysis leveraged longitudinal information from repeated measurements of quantitative markers and the timing of risk outcomes (Table 1 and Supplementary Table 1). The univariate statistics in each cohort for each phenotype (Figure 1A) can be combined via two pathways (Figures 1B–E). The first is the traditional pathway (pathway 1) meta-analyzing the results first across cohorts [as in most GWAS meta-analyses, e.g., (Willer et al., 2013)] and then across phenotypes (Visscher and Yang, 2016). The second pathway leverages the availability of the results from individual studies and, thus, it extends the methods based on the summary statistics (Visscher and Yang, 2016). Accordingly, for pathway 2, we first performed pleiotropic meta-analysis of the results across phenotypes in each cohort and then across cohorts. These analyses addressed the evolutionary uncertainty in establishing molecular mechanisms of age-related phenotypes by performing five meta-tests (Figure 1) (see “Materials and Methods”).

Univariate Meta-Analysis and Heterogeneity

Using a Fisher test and conventional GWAS fixed-effect meta-test (Figure 1, pathway 1a), we identified 61 novel SNPs (see “Materials and Methods”) and 3 SNPs with smaller p-values than previously reported (55 loci; counts of loci exclude the MHC) (Table 2 and Supplementary Table 2). One SNP (rs10160664) was associated with two phenotypes, albumin in urine (AlbU) and C-reactive protein (CRP), at the GW level. Of these 65 GW significant associations (for 64 SNPs), 23 were identified in the fixed-effects meta-test, 11 in the Fisher test, and 31 in both tests. The Fisher test outperformed the meta-test (p < p) for 18 of the 61 novel SNPs (29.5%). All associations were observed for quantitative markers: 52 for AlbU, 6 for total cholesterol (TC), 2 for heart rate (HR), 1 for high-density lipoprotein cholesterol (HDL-C), 1 for interleukin-6 (IL-6), 1 for albumin in serum (AlbS), 1 for adiponectin (ADPN), and 1 for CRP and AlbU. For 3 SNPs, GW significance in the current study was approximately the same as that in previous studies for the index SNPs (rs17645031, rs1784042) or a nearby SNP (rs1879266). The remaining 61 SNPs, or SNPs nearby to them, did not attain p in previous studies (Table 2).

Table 2

Novel SNPs, and SNPs with smaller p-values than previously reported, attained genome-wide significance (p < 5 × 10−8) in univariate meta-analysis of individual phenotypes.

ID	Locusa	SNPb	Chr	Location	EA	EAF	Trait	βmeta	Pmeta	PFisher	Ratio	I2 %	SNPSNPknownc	Pknown	LD %
1	TNFRSF1B	rs5745938	1	12,165,317	a	0.043	AlbU	−0.44	4.78E-09	2.05E-09	0.96	78	rs5746040	9.40E-03	0.2
2	PLA2G5	rs11573294	1	20,091,603	g	0.023	AlbU	−0.66	8.75E-17	5.01E-16	1.05	67	rs2236772	4.00E-02	0.2
3	TGFBR3	rs17131556	1	91,768,996	g	0.042	AlbU	−0.40	2.14E-08	4.11E-08	1.04	65	rs2296621	7.30E-03	1.1
4	MYBPHL	rs17645031	1	109,292,316	a	0.083	TC	−4.87	6.01E-23	4.51E-19	1.21	0	rs17645031	1.12E-22	S
5	F5	rs9332664	1	169,517,708	t	0.026	AlbU	−0.51	3.40E-09	5.75E-11	0.83	82	rs6011	1.90E-03	0.1
6	F5	rs9332684	1	169,582,297	g	0.026	AlbU	−0.45	1.85E-08	1.14E-06	1.30	0	rs6011	1.90E-03	0.0
7	RYR2	rs10157718	1	237,093,677	t	0.023	AlbU	−0.48	3.55E-08	2.20E-11	0.70	86	rs12041754	1.10E-06
8	ABCG5/DYNC2LI1	rs17031666	2	43,815,975	c	0.031	AlbU	−0.46	3.69E-09	3.64E-08	1.13	61	rs17031742	2.00E-02	0.5
9	EPAS1	rs17035091	2	46,383,270	g	0.037	AlbU	−0.49	1.24E-10	3.47E-09	1.17	47	rs7569138	6.30E-03	59
10	MEIS1	rs7563565	2	66,489,460	a	0.031	AlbU	−0.49	8.85E-10	2.52E-08	1.19	29	rs1055386	1.20E-04	1.5
11	FABP1	rs2970900	2	88,124,102	g	0.023	AlbU	−0.46	1.15E-08	1.15E-08	1.00	69	rs1545223	1.80E-02	0.1
12	DPP4	rs10490422	2	162,059,903	a	0.022	AlbU	−0.49	5.80E-08	8.13E-09	0.89	78	rs2892827	7.70E-03	0.8
13	CPS1	rs7558276	2	210,634,680	a	0.027	AlbU	−0.51	8.27E-10	6.22E-09	1.11	65	rs2371010	5.30E-04	0.0
14	VHL	rs17610448	3	10,153,488	c	0.024	AlbU	−0.49	2.66E-09	2.22E-07	1.29	4	rs9866514	3.30E-03
15	TGFBR2	rs3087463	3	30,605,604	a	0.023	AlbU	−0.52	3.21E-10	9.51E-09	1.18	39	rs17026647	2.20E-06
16	FOXP1	rs17747268	3	71,006,431	c	0.029	AlbU	−0.52	8.86E-12	3.87E-10	1.17	25	rs6549390	2.10E-03	0.8
17	SLC34A2	rs11731126	4	25,671,935	a	0.030	AlbU	−0.41	1.69E-07	3.67E-08	0.91	77	rs16876970	2.70E-04
18	TBC1D1	rs4832741	4	37,895,064	g	0.022	AlbU	−0.53	1.75E-11	2.42E-10	1.12	55	rs1435385	4.60E-04	1.1
19	FABP2	rs1511024	4	119,319,026	a	0.033	AlbU	−0.44	9.81E-08	1.32E-08	0.89	74	rs17050049	1.00E-03
20	SLC1A3*	rs2562544	5	36,524,655	g	0.021	AlbU	−0.56	1.42E-12	1.96E-11	1.11	56	rs298973	3.20E-03	0.8
21	ADGRV1	rs12522091	5	90,956,871	a	0.028	AlbU	−0.71	5.33E-13	1.18E-17	0.73	91	rs2935535	3.60E-04	2.8
22	GLRX*	rs17085170	5	95,836,627	a	0.051	AlbU	−0.40	2.78E-08	8.31E-07	1.24	32	rs10515242	3.60E-04	2.6
23	F13A1	rs3024471	6	6,319,302	g	0.028	AlbU	−0.48	2.17E-09	1.07E-08	1.09	56	rs4582427	9.90E-05
24	NFKBIL1	rs3130062	6	31,558,135	g	0.069	TC	−3.32	1.53E-09	6.17E-08	1.22	46	rs3130062	1.60E-02	S
25	MSH5	rs3131382	6	31,739,953	a	0.069	TC	−3.44	2.47E-10	6.61E-09	1.17	53	rs3131382	9.24E-04	S
26	RNF5/AGPAT1	rs3134943	6	32,179,984	a	0.137	TC	−2.21	2.12E-08	1.53E-07	1.13	58	rs3134943	1.16E-04	S
27	HLA-DRA*	rs3129868	6	32,436,600	a	0.141	TC	−2.30	5.08E-09	9.58E-09	1.03	67	rs3129868	7.27E-04	S
28	ESR1	rs17761320	6	151,830,830	a	0.043	AlbU	−0.46	3.75E-08	7.08E-08	1.04	62	rs17761320	3.00E-02	S
29	ESR1	rs6913408	6	152,056,977	a	0.029	AlbU	−0.33	1.37E-04	4.14E-10	0.41	90	rs17081526	7.10E-04	0.0
30	EGFR	rs6968014	7	55,179,749	t	0.026	AlbU	−0.35	5.19E-05	2.86E-08	0.57	86	rs4495378	5.30E-04	1.2
31	MLXIPL	rs17145750	7	73,612,048	a	0.153	HDLC	0.947	2.49E-08	2.21E-05	1.63	0	rs17145750	6.05E-08	S
32	HGF	rs5745660	7	81,745,771	a	0.032	AlbU	−0.44	4.41E-09	7.29E-08	1.17	50	rs5745660	2.20E-02	S
33	CDK5	rs2069454	7	151,055,895	g	0.048	AlbU	−0.36	1.22E-08	5.71E-07	1.27	46	rs1860742	8.40E-06
34	DEFA1*	rs12675298	8	6,971,291	a	0.023	AlbU	−0.42	1.12E-08	1.99E-08	1.03	70	rs7388463	2.70E-04	0.1
35	MMP16	rs11996222	8	88,204,251	a	0.061	AlbU	−0.38	1.22E-08	5.28E-07	1.26	0	rs7822721	4.40E-03
36	SQLE	rs16900175	8	125,011,901	c	0.022	AlbU	−0.56	3.02E-11	4.35E-10	1.12	40	rs16900770	2.70E-05
37	GPR20	rs36092215	8	141,357,146	a	0.040	AlbU	−0.46	2.94E-08	2.80E-06	1.36	0	rs10088001	2.30E-05
38	VLDLR	rs35845312	9	2,620,592	a	0.022	AlbU	−0.48	4.54E-08	3.01E-08	0.98	72	rs7856406	1.20E-03	0.8
39	CDKN2A	rs3731249	9	21,970,917	a	0.030	AlbU	−0.48	1.83E-10	8.67E-10	1.07	69	rs7024096	2.70E-03	2.2
40	ZFAND5	rs13292980	9	72,358,555	a	0.066	AlbU	−0.43	1.96E-10	1.16E-08	1.22	22	rs11788021	1.40E-04	1.0
41	SORCS1	rs822008	10	106,784,843	g	0.024	IL6	7.73	1.70E-04	3.49E-09	0.45	91
42	LRRC4C**	rs10160664	11	41,894,276	a	0.022	CRP	−0.18	1.40E-05	4.30E-09	0.58	86
							AlbU	−0.50	4.25E-09	3.07E-07	1.29	0	rs10837551	4.20E-03	1.4
43	GIF/TCN1*	rs17597065	11	59,848,727	g	0.041	AlbU	−0.46	1.22E-07	2.80E-08	0.92	74	rs11230503	9.1E-05	0.0
44	SIDT2	rs1784042	11	117,194,760	a	0.402	TC	−1.54	2.49E-08	5.35E-06	1.44	0	rs1784042	3.83E-08	S
45	CACNA1C	rs2238017	12	2,069,379	g	0.329	HR	−0.47	1.92E-08	5.73E-06	1.47	0	rs2238017	3.37E-05	S
46	CACNA1C	rs2238018	12	2,069,495	a	0.187	HR	−0.62	5.94E-10	8.03E-08	1.30	19	rs2238017	3.37E-05	47
47	BCAT1	rs4963823	12	24,929,329	a	0.022	AlbU	−0.50	1.10E-06	2.31E-08	0.78	82	rs6487431	6.90E-04
48	PRKAG1	rs1126930	12	49,005,349	c	0.032	AlbU	−0.44	1.73E-09	4.14E-08	1.19	29	rs11168354	3.50E-04	0.2
49	LRP1	rs1800137	12	57,154,683	a	0.022	AlbU	−0.59	2.69E-09	1.67E-08	1.10	51	rs11172106	1.8E-03
50	LRP1	rs34577247	12	57,184,890	a	0.021	AlbU	−0.55	1.36E-10	1.78E-08	1.27	0	rs11172106	1.8E-03	0.5
51	NTS	rs11117072	12	85,882,569	a	0.034	AlbU	−0.46	9.23E-10	1.34E-07	1.31	3	rs10863085	2.00E-04	7.0
52	DIABLO	rs7957117	12	122,209,055	c	0.021	ADPN	−0.16	7.72E-09	8.72E-08	1.15	39	rs7957117	5.05E-05	S
53	BNIP2	rs1057058	15	59,672,705	a	0.035	AlbU	−0.41	1.08E-08	1.19E-06	1.34	0	rs8032049	6.30E-05
54	AGRP*	rs13334182	16	67,490,121	a	0.022	AlbU	−0.44	1.69E-08	3.04E-08	1.03	66	rs8045523	2.70E-04	0.3
55	POLR2A	rs4265880	17	7,492,948	a	0.036	AlbU	−0.43	3.78E-08	5.22E-08	1.02	61	rs34780532	1.10E-03
56	GAS7	rs9894339	17	10,049,295	g	0.024	AlbU	−0.56	5.14E-09	4.24E-09	0.99	74	rs16959324	2.40E-04	0.1
57	EIF1/GAST*	rs12949732	17	41,701,367	g	0.024	AlbU	−0.41	8.96E-06	4.85E-11	0.49	90	rs730086	6.30E-04	3.7
58	YES1*	rs9945303	18	813,556	g	0.033	AlbU	−0.52	2.25E-09	1.98E-12	0.74	87	rs2846758	2.00E-03	0.8
59	BCL2	rs4941192	18	63,276,726	g	0.040	AlbU	−0.43	4.00E-08	7.93E-07	1.21	35	rs7238248	1.70E-03	0.4
60	BCL2	rs17070959	18	63,283,686	a	0.034	AlbU	−0.52	1.32E-09	5.84E-08	1.23	25	rs7238248	1.70E-03	0.2
61	NOTCH3	rs12082	19	15,159,825	a	0.029	AlbU	−0.41	3.45E-08	3.01E-06	1.35	0	rs7250903	1.10E-04	0.1
62	BAX	rs4645886	19	48,956,428	a	0.023	AlbU	−0.53	5.96E-11	1.91E-09	1.17	32	rs10401192	5.50E-05
63	FLT3LG/RPL13A	rs1879266	19	49,486,803	g	0.166	AlbS	−0.02	1.78E-08	4.11E-06	1.44	0	rs739347	3.20E-08	66
64	ERVV-1*	rs1650940	19	53,004,046	g	0.083	AlbU	−0.42	2.08E-07	1.39E-08	0.85	78	rs6509825	2.80E-04	0.5

Because the customized Illumina CVDSNP55v1_A array was gene enriched, loci were naturally associated with genes. If an index SNP was not within protein coding gene, the closest gene(s) were assigned. Multiple genes were selected if they were at about the same distance up- and downstream from the index SNP or the index SNP was within the region of overlapping genes.

genes within ± 100 Kb flanking region for the index SNP.

LRRC4C gene laid 435 Kb apart of rs10160664.

Proxy SNPs with linkage disequilibrium (LD) r.

Chr, chromosome; Location, location in base pairs; EA, effect allele; EAF, EA frequency.

Column “Trait” shows phenotype with GW significance in the fixed-effect meta-analysis (β.

Column “Ratio”: log (p.

.

The results for each cohort are provided in .

.

Out-performance of the meta-test by the Fisher test indicates a substantial role of heterogeneity in associations across cohorts (see “Materials and Methods”). This is evidenced by a strong and highly significant inverse correlation of the ratio of log-transformed p-values from these two tests, log10 (p)/log10 (p), with the heterogeneity coefficient I2 (Table 2; r = −0.874, p = 2.0 × 10−21). This correlation implies that the larger the p relative to the p, the more heterogeneous the association signals across cohorts. Forest plots in Supplementary Figure 2 illustrate homogeneous and heterogeneous associations for uncommon SNPs associated with AlbU across cohorts.

The univariate meta-analysis replicated 198 associations for 160 SNPs (68 loci) with p < p (Supplementary Table 3, Supplementary Material). For most of these SNPs, p-values were smaller for the meta-test than for the Fisher test (p < p) except for rs1260326, rs13333226, and rs2075650. As for the novel SNPs in Table 2, we observed a strong and highly significant inverse correlation of log10 (p)/log10 (p) with I2 (r = −0.603, p = 5.5 × 10−21). Most of these 198 associations (166 or 83.8%) were observed for lipids.

Univariate Meta-Analysis and Longitudinal Information

Longitudinal information from repeated measurements benefits univariate analyses for SNPs with relatively homogeneous associations across visits by decreasing standard errors (Supplementary Figure 3). For example, rs17645031 attained p = 6.01 × 10−23 in our modest sample (Table 2, SNP #4) and p = 1.12 × 10−22 in a previously described sample of 97,063 individuals (Teslovich et al., 2010) (Supplementary Figure 3C). This improvement substantially increases the efficiency of genetic association studies, which can be quantified by the ratio of the log-transformed p-value to the sample size (Kulminski et al., 2016b). For rs17645031, the efficiency can be approximated by the ratio of the sample sizes because the p-values in the different studies are virtually the same, yielding a 3.7-fold (= 97,063/26,371) larger efficiency in our study than in the previous study (Teslovich et al., 2010).

Pleiotropic Meta-Analysis

We used four tests in pathway 1 and three tests in pathway 2 (Figure 1) to examine pleiotropy in six domains: three domains of physiological, blood, and inflammation markers and three domains of all 16 quantitative markers, seven diseases and death, and all 24 phenotypes (Table 1; see “Materials and Methods”). Pleiotropic associations were defined as at least one test showing GW significance.

Our analysis identified 176 novel pleiotropic SNPs (152 loci) with p < p (Tables 3, 4 and Supplementary Tables 4, 5) by combining associations with multiple phenotypes that individually did not attain GW significance in univariate meta-analysis (p > p). Accordingly, this analysis identified SNPs for which associations may be considered “noise” in univariate analyses (Supplementary Figure 4). Of these, 79 (75 loci) and 97 (86 loci) SNPs attained GW significance in pathway 1 (Table 3 and Supplementary Table 4) and pathway 2 (Table 4 and Supplementary Table 5), respectively. Table 5 illustrates these associations for selected SNPs. Most SNPs in each pathway showed associations in the domain of 16 quantitative markers: 29 of 79 SNPs (pathway 1) and 77 of 97 SNPs (pathway 2). The remaining SNPs showed associations in the domains of: (i) 24 phenotypes (19 and 12 in pathways 1 and 2, respectively), (ii) inflammation markers (20 and 4), (iii) blood markers (4 and 4), and (iv) physiological markers (7 in pathway 1 only). No associations with p < p were identified in the disease/death domain. The 79 SNPs identified in pathway 1 were associated with up to 11 phenotypes at p < 0.05 (Table 3 and Supplementary Table 4), with most associations clustered in the domain of 16 quantitative markers. Figure 3 shows pleiotropic SNPs that attained GW significance in the domains of 16 quantitative markers or 24 phenotypes. Our analysis also identified 11 SNPs in the MHC (Supplementary Table 6).

Table 3

Novel pleiotropic SNPs attained genome-wide significance (p < 5 × 10−8) in pathway 1.

ID	Locusa	SNPb	Chr	Location, base pairs	EA	EAF	Domain	NP	Meta p-values				Group	PGRASP	NG
									PMOp	PMOb	PMFp	PFcFp
1	NPPB*	rs11801879	1	11,868,762	g	0.090	16P	5	6.33E-09	2.04E-07	6.49E-10	1.09E-06	M	5.00E-08	2
2	FAF1	rs17106235	1	50,477,698	c	0.098	16P	5	7.42E-09	2.50E-09	3.43E-08	2.13E-04	M	8.70E-03	2
3	GADD45A*	rs12097345	1	67,677,857	g	0.063	16P	5	1.23E-08	1.50E-06	2.26E-06	8.30E-05	HP
4	LMNA	rs6686943	1	156,123,200	g	0.024	INF	3	4.95E-07	7.70E-08	1.20E-09	8.47E-10	C
5	F5	rs6025	1	169,549,811	a	0.028	INF	3	2.01E-07	4.74E-08	2.17E-09	4.80E-07	M
6	PLA2G4A	rs10911944	1	186,881,788	g	0.054	24P	6	3.41E-08	3.82E-05	1.21E-05	1.90E-01	HP
7	ADAM17/IAH1	rs10495562	2	9,491,211	a	0.481	24P	7	2.69E-03	1.91E-04	2.35E-09	2.48E-06	D	4.04E-04	2
8	FOSL2	rs12624279	2	28,411,923	a	0.406	16P	6	3.59E-08	2.49E-06	8.22E-08	5.33E-04	M	3.41E-06	1
9	COL5A2	rs10191420	2	189,159,234	g	0.037	24P	7	8.70E-05	9.02E-03	3.75E-04	4.12E-09	H1a	1.62E-03	3
10	SDPR/TMEFF2*	rs11901744	2	191,888,309	g	0.045	24P	3	2.03E-08	1.37E-04	4.41E-04	7.21E-02	HP	2.16E-04	2
11	TRAK2	rs13022344	2	201,399,433	g	0.330	16P	4	4.01E-06	1.77E-05	4.69E-08	1.30E-06	D	4.25E-02	1
12	SSUH2	rs7647790	3	8,730,403	a	0.022	16P	5	1.83E-08	9.56E-06	8.19E-06	8.15E-04	HP	3.90E-02	1
13	IRAK2	rs1681663	3	10,178,045	a	0.233	INF	2	1.94E-07	1.94E-07	1.81E-08	7.68E-08	M
14	TFRC	rs3827556	3	196,069,703	t	0.074	24P	6	8.44E-05	6.83E-04	1.21E-08	1.15E-03	D
15	MTTP	rs2306986	4	99,583,418	g	0.036	16P	6	2.86E-03	3.41E-03	2.58E-02	3.68E-08	H1a	5.84E-06	3
16	CSF2	rs743564	5	132,075,186	g	0.393	24P	8	3.64E-08	2.36E-06	2.84E-05	3.71E-03	HP	3.06E-03	2
17	IL13*	rs2243297	5	132,663,479	a	0.040	INF	3	3.84E-06	4.74E-07	4.04E-09	2.04E-05	D
18	ADRB2*	rs33942282	5	148,829,255	a	0.028	24P	6	4.85E-04	3.64E-03	1.09E-04	9.05E-09	H1a
19	CDKAL1	rs4130302	6	21,103,928	a	0.022	16P	5	9.27E-04	5.37E-05	2.87E-08	3.12E-04	D	2.60E-02	1
20	UBD*	rs389419	6	29,553,668	a	0.088	16P	5	1.82E-09	4.53E-07	1.91E-05	3.43E-04	HP	2.35E-07	5
21	NRM	rs1075496	6	30,690,462	a	0.431	PHY	4	9.09E-08	4.88E-09	1.58E-05	2.29E-02	HB
22	HMGA1*	rs1776897	6	34,227,234	c	0.088	24P	4	8.67E-09	3.35E-05	5.56E-06	1.31E-04	HP	7.07E-03	2
23	MRPS18A	rs1334601	6	43,685,668	a	0.098	16P	6	6.48E-06	8.65E-05	4.11E-08	1.33E-02	D
24	CYP39A1	rs2277119	6	46,642,168	a	0.233	24P	10	5.57E-05	4.18E-04	7.01E-09	5.29E-03	D	6.68E-04	3
25	TNFAIP3**	rs6920220	6	137,685,367	a	0.207	16P	3	3.16E-09	5.38E-07	2.14E-05	3.81E-05	HP	3.80E-03	2
26	ESR1	rs2747648	6	152,101,200	g	0.037	INF	3	8.85E-06	1.64E-06	3.49E-08	6.95E-05	D
27	IL6	rs1548216	7	22,730,154	c	0.025	16P	6	1.38E-05	2.46E-05	2.32E-08	2.59E-04	D
28	IGFBP3*	rs1496499	7	45,939,424	c	0.465	PHY	1	1.24E-08	1.18E-07	5.28E-03	2.46E-03	HP	3.06E-02	1
29	MYH16	rs17161652	7	99,293,904	g	0.022	24P	9	1.01E-08	8.13E-07	5.72E-06	6.60E-04	HP
30	FOXP2	rs13227011	7	114,614,673	a	0.065	INF	2	5.78E-08	9.39E-08	1.23E-08	3.51E-08	M
31	GATA4	rs13250578	8	11,756,796	a	0.145	16P	4	3.73E-09	4.69E-07	7.99E-04	8.71E-02	HP	1.59E-05	3
32	FDFT1	rs1293328	8	11,837,551	a	0.267	16P	6	9.52E-09	2.46E-06	7.97E-07	2.07E-02	HP	4.13E-04	1
33	TNFRSF10A	rs13278062	8	23,225,458	c	0.479	24P	10	1.34E-04	2.19E-03	4.55E-10	3.07E-09	C
34	IKBKB	rs9785118	8	42,328,973	g	0.038	16P	6	2.05E-10	1.51E-07	4.78E-06	1.06E-02	HP	3.80E-03	1
35	PCSK5	rs7022503	9	76,127,438	g	0.491	BLD	4	1.17E-03	1.19E-04	3.73E-08	2.99E-04	D	2.75E-04	2
36	IKBKAP	rs4978374	9	108,884,703	a	0.239	PHY	3	1.38E-01	1.02E-01	2.51E-01	3.95E-08	H1a	2.53E-07	2
37	GATA3**	rs10508372	10	8,930,055	a	0.075	BLD	5	6.37E-05	3.75E-06	1.09E-09	3.80E-08	C	2.18E-05	3
38	CXCL12	rs266092	10	44,370,827	a	0.047	INF	3	1.07E-06	3.23E-07	1.03E-08	1.40E-06	D
39	NRG3	rs11195459	10	82,660,517	g	0.054	16P	3	1.48E-09	9.70E-06	5.99E-05	2.87E-04	HP	1.47E-02	1
40	LIPA	rs10509569	10	89,221,893	a	0.064	24P	11	3.29E-04	3.21E-04	6.44E-06	4.36E-08	H1a	1.83E-03	2
41	PKD2L1	rs735877	10	100,344,764	a	0.383	PHY	3	1.35E-05	2.05E-05	4.16E-08	1.04E-04	D	7.08E-04	3
42	CYP2E1	rs7081484	10	133,538,986	a	0.025	INF	3	1.84E-07	4.28E-08	2.00E-09	2.58E-08	M
43	LRRC4C**	rs7126989	11	42,152,027	a	0.027	16P	4	3.73E-06	7.74E-06	3.29E-08	3.77E-05	D
44	RAD9A	rs2255990	11	67,397,024	a	0.034	INF	2	4.55E-07	1.23E-07	5.48E-09	9.45E-08	M
45	CPT1A	rs11228373	11	68,829,752	g	0.128	BLD	3	1.50E-09	2.61E-05	5.47E-05	1.12E-02	HP	5.05E-03	2
46	SOX5	rs17402927	12	24,020,644	g	0.027	24P	4	1.01E-08	2.24E-05	2.30E-05	3.63E-02	HP
47	HDAC7	rs17122311	12	47,795,115	a	0.027	INF	2	5.30E-08	5.04E-08	8.54E-08	8.60E-07	M
48	APAF1	rs4319556	12	98,682,808	a	0.026	INF	3	9.52E-08	1.54E-08	2.41E-10	1.28E-07	M
49	HCAR1/DENR*	rs548349	12	122,740,362	a	0.065	24P	7	2.36E-09	2.24E-06	2.51E-06	2.01E-02	HP
50	BRCA2	rs11571590	13	32,320,347	a	0.029	INF	3	1.50E-06	4.89E-07	5.39E-08	8.84E-09	H1a
51	FOXO1	rs9603776	13	40,649,749	a	0.022	INF	2	6.61E-05	1.66E-04	3.15E-04	3.12E-08	H1a
52	ABCC4	rs1729741	13	95,131,902	g	0.116	16P	6	2.72E-06	1.85E-05	1.97E-08	1.74E-05	D	2.60E-02	1
53	IRS2	rs1044364	13	109,755,299	g	0.049	PHY	3	4.85E-05	1.07E-04	1.87E-08	2.05E-05	D	1.82E-02	1
54	TEP1	rs10083536	14	20,408,030	a	0.030	INF	2	2.89E-07	7.37E-08	4.66E-09	2.05E-10	C
55	MDGA2**	rs17117423	14	46,539,309	g	0.039	INF	3	5.84E-07	3.00E-07	6.78E-09	3.22E-07	D
56	FBN1	rs1848050	15	48,569,846	a	0.102	INF	3	2.80E-05	4.06E-06	4.59E-08	1.24E-05	D
57	CYP19A1	rs28757184	15	51,222,375	a	0.034	INF	2	4.05E-08	3.09E-07	4.42E-07	2.65E-06	HP
58	HCN4	rs11857639	15	73,345,431	a	0.092	16P	5	4.20E-06	5.52E-05	4.58E-08	3.17E-05	D	6.39E-03	1
59	HCN4*	rs7172808	15	73,371,291	g	0.161	PHY	2	1.68E-07	4.24E-08	2.40E-05	1.41E-04	HB	3.16E-02	1
60	BLM	rs2238335	15	90,747,136	c	0.024	24P	7	8.67E-09	7.30E-07	2.76E-06	2.98E-03	HP	4.40E-03	2
61	LMF1	rs577543	16	888,967	a	0.073	24P	7	3.03E-08	5.64E-06	5.95E-06	2.17E-01	HP	1.25E-04	3
62	NOD2	rs2076753	16	50,699,463	a	0.290	24P	5	3.99E-08	3.31E-04	6.42E-05	6.26E-05	HP	4.40E-02	1
63	MMP2/IRX6*	rs17232065	16	55,391,471	a	0.067	24P	9	9.60E-05	3.47E-03	9.92E-04	6.74E-09	H1a	2.14E-02	1
64	PYY	rs9895585	17	43,987,619	c	0.049	INF	3	2.28E-07	4.84E-08	1.62E-09	1.38E-07	M
65	HDAC5	rs228757	17	44,087,517	c	0.266	PHY	3	1.11E-05	1.59E-05	4.54E-08	2.27E-04	D	6.41E-04	2
66	HDAC5	rs8065686	17	44,116,529	a	0.093	16P	5	2.45E-04	7.70E-05	4.47E-09	8.62E-05	D	1.35E-03	1
67	SLC4A1	rs5035	17	44,261,630	c	0.040	16P	6	2.53E-05	2.02E-05	1.06E-08	3.03E-08	C	8.72E-03	2
68	OSBPL7	rs12602461	17	47,811,983	a	0.052	16P	4	6.57E-05	1.64E-06	3.63E-08	5.62E-08	C	9.14E-04	2
69	RPS6KB1	rs1292034	17	59,912,499	a	0.445	16P	5	1.16E-08	1.07E-05	9.55E-05	3.81E-03	HP
70	APOH	rs1801689	17	66,214,462	c	0.034	BLD	3	1.19E-09	3.26E-09	2.93E-04	3.87E-02	HP	4.92E-06	1
71	SYNGR2	rs4789546	17	78,171,312	a	0.045	16P	3	2.81E-08	1.02E-07	1.30E-07	1.78E-05	M
72	BCL2	rs10164240	18	63,277,305	g	0.022	INF	3	9.57E-07	3.38E-07	2.10E-08	2.51E-06	D
73	BCL2	rs4987719	18	63,293,077	a	0.032	24P	6	1.97E-08	1.51E-06	2.59E-08	3.48E-04	M	3.70E-03	1
74	LMNB2*	rs2392769	19	2,461,807	g	0.237	16P	5	3.56E-09	4.68E-06	5.15E-06	1.57E-03	HP
75	NKX2-2*	rs6035877	20	21,531,894	c	0.474	16P	6	2.28E-07	5.30E-05	1.66E-08	2.25E-04	D	1.72E-05	4
76	APP	rs17001660	21	26,051,722	g	0.051	INF	2	4.24E-04	2.96E-04	8.05E-05	2.22E-08	H1a
77	APP	rs12481729	21	26,148,455	c	0.025	16P	4	1.47E-11	4.61E-10	4.09E-09	4.79E-07	M
78	MMP11	rs738792	22	23,779,191	g	0.085	16P	7	1.51E-03	5.00E-03	6.14E-07	6.40E-09	H1a
79	HSCB	rs5752792	22	28,757,771	g	0.190	16P	6	1.23E-05	4.09E-04	3.73E-09	2.03E-05	D	4.80E-05	3

If an index SNP was not within protein coding gene, the closest gene was assigned. Multiple genes were assigned if they were at about the same distance up- and downstream from the index SNP or if the index SNP was within the region of overlapping genes. Loci were naturally associated with genes.

Protein coding genes within ±100 Kb flanking region for the index SNP.

Protein coding genes for four SNPs (#25, 37, 43, and 55) are within ± 1 Mb flanking region for the index SNP.

Proxy SNPs with linkage disequilibrium (LD) r.

Chr, chromosome; EA, effect allele; EAF, EA frequency.

Domain indicates the pleiotropic domain in which minimal GW significant p-values were attained in our pleiotropic meta-analysis examining: physiological (PHY), blood (BLD), inflammation (INF), 16 quantitative markers (16P), 7 diseases and death (8DD), and all 24 phenotypes (24P) domains. P-values are given for all four pleiotropic meta-tests (FcFp, MFp, MOp, and MOb) for a given domain (.

Group indicates the type of association defined in the “Genetic heterogeneity and correlation in pleiotropic meta-analysis” section.

P.

N.

More details on the associations for these pleiotropic SNPs are given in .

Table 4

Novel pleiotropic SNPs attained genome-wide significance (p < 5 × 10−8) in pathway 2.

ID	Locusa	SNPb	Chr	Location, base pairs	EA	EAF	Domain	Meta p-values			Group	PGRASP	NG
								POpFcc	PObFcc	PFpFcc
1	HTR6	rs3790756	1	19,669,739	a	0.136	16P	6.28E-09	6.30E-04	2.19E-04	HP	1.24E-03	2
2	PODN	rs17107806	1	53,066,863	c	0.023	16P	1.49E-08	6.51E-04	2.00E-03	HP
3	PDE4B	rs6667596	1	66,320,317	g	0.065	16P	8.94E-12	2.02E-06	8.76E-06	HP	4.92E-02	1
4	PDE4B	rs6696880	1	66,335,812	g	0.062	16P	4.46E-09	1.95E-05	7.82E-05	HP	1.19E-04	4
5	ABCA4	rs10874828	1	94,004,017	a	0.095	16P	3.20E-08	2.02E-04	4.89E-04	HP
6	VCAM1	rs3917011	1	100,726,586	g	0.027	16P	3.36E-08	2.80E-05	9.17E-07	M
7	ARNT	rs10305670	1	150,858,612	c	0.021	16P	1.63E-11	2.23E-05	1.27E-05	HP
8	SHC1	rs12076073	1	154,971,680	g	0.038	24P	1.24E-04	1.07E-04	3.30E-08	C
9	NOS1AP	rs2661808	1	162,302,581	c	0.022	16P	8.79E-09	1.75E-04	3.06E-03	HP	1.08E-02	2
10	PARP1	rs3219123	1	226,367,647	a	0.062	24P	3.24E-08	4.56E-04	6.75E-04	HP
11	NLRP3	rs4925659	1	247,440,161	a	0.384	24P	2.32E-08	2.03E-04	1.48E-05	HP	5.99E-03	2
12	CXCR4*	rs9973445	2	136,121,046	c	0.103	16P	3.36E-08	7.61E-05	3.05E-04	HP	1.22E-02	1
13	NEB/RIF1	rs1061305	2	151,490,465	g	0.410	16P	1.03E-08	5.40E-04	7.50E-03	HP	4.18E-04	4
14	TFPI	rs8176595	2	187,504,645	a	0.030	16P	2.54E-10	8.68E-06	3.06E-05	HP	3.14E-02	1
15	SLC39A10*	rs6748661	2	194,818,116	a	0.142	16P	7.91E-10	7.02E-06	1.56E-06	HP
16	INPP5D	rs6715810	2	233,164,597	a	0.172	16P	1.34E-09	5.68E-05	1.86E-03	HP	7.09E-05	2
17	INPP5D	rs6725722	2	233,164,999	a	0.038	16P	3.84E-08	1.06E-04	1.19E-05	HP	1.27E-02	1
18	CAV3/SSUH2	rs6764715	3	8,744,886	a	0.026	INF	3.05E-07	1.53E-07	1.31E-08	C
19	IRAK2	rs713016	3	10,237,012	a	0.126	16P	3.86E-08	5.72E-04	1.86E-04	HP
20	SCN5A	rs7624535	3	38,623,711	c	0.215	16P	1.92E-08	3.87E-03	1.20E-01	HP
21	GNAI2	rs762707	3	50,252,421	a	0.023	INF	2.44E-08	3.12E-08	5.11E-08	M
22	EVC	rs2291157	4	5,719,294	c	0.086	16P	4.31E-09	1.68E-05	1.52E-06	HP
23	EVC	rs7674034	4	5,741,326	g	0.338	16P	5.47E-08	9.88E-05	3.94E-08	CB	8.78E-04	2
24	IL21*	rs17005953	4	122,635,086	a	0.054	24P	3.34E-08	4.48E-05	1.76E-05	HP	2.72E-04	2
25	IL15	rs7698675	4	141,673,566	t	0.264	16P	6.77E-10	5.15E-06	1.05E-05	HP	3.62E-02	1
26	SLC9A3	rs6864158	5	505,936	g	0.423	16P	4.84E-08	2.54E-04	1.53E-03	HP	3.32E-03	2
27	SSBP2	rs6452419	5	81,575,583	a	0.323	16P	3.76E-08	1.01E-03	8.16E-04	HP	7.00E-03	1
28	ADGRV1	rs2438353	5	90,770,429	a	0.482	16P	5.58E-09	6.53E-04	7.42E-03	HP	1.29E-03	2
29	ADGRV1	rs7712313	5	90,932,581	g	0.309	24P	7.50E-08	2.40E-06	4.44E-08	CB	3.53E-05	4
30	ADGRV1	rs16869425	5	91,089,281	c	0.104	16P	2.29E-08	3.06E-04	6.93E-04	HP	2.69E-04	4
31	SLCO4C1	rs10066650	5	102,251,670	c	0.105	16P	4.12E-08	4.44E-05	3.12E-05	HP
32	FBN2	rs3805652	5	128,425,725	g	0.135	16P	8.16E-09	3.06E-04	7.68E-03	HP	5.03E-03	2
33	GMNN*	rs6904263	6	24,771,241	a	0.181	16P	4.40E-04	1.17E-02	2.45E-08	C	2.63E-03	2
34	TREM2*	rs7759295	6	41,168,112	a	0.124	16P	2.39E-08	1.60E-04	1.17E-03	HP	2.11E-03	2
35	PKHD1*	rs10948623	6	51,553,432	a	0.219	16P	2.55E-08	3.28E-03	1.24E-03	HP	5.28E-04	2
36	PDSS2	rs12199523	6	107,187,066	g	0.199	16P	2.46E-09	4.07E-05	1.18E-06	HP
37	ARG1/MED23	rs17788484	6	131,573,218	a	0.023	16P	7.35E-11	2.44E-06	9.60E-05	HP
38	MTHFD1L	rs2073188	6	150,937,175	a	0.063	16P	7.36E-09	1.01E-04	9.20E-05	HP
39	MTHFD1L	rs9478918	6	151,037,163	a	0.139	16P	2.36E-08	5.56E-05	3.73E-05	HP	2.47E-03	3
40	PLG	rs9456577	6	160,724,214	c	0.028	16P	2.68E-08	1.00E-04	3.11E-05	HP	2.06E-03	2
41	IGFBP3	rs3793345	7	45,918,079	g	0.204	16P	3.35E-08	1.15E-04	1.07E-02	HP
42	HGF	rs5745692	7	81,728,950	g	0.033	BLD	3.26E-08	4.30E-05	4.12E-04	HP
43	CDK6	rs17690388	7	92,716,043	a	0.052	16P	7.42E-10	1.44E-05	2.36E-05	HP	8.11E-03	2
44	MSRA	rs7840347	8	10,214,840	g	0.439	16P	1.15E-08	4.34E-05	2.03E-05	HP	3.24E-06	5
45	DLC1	rs1442534	8	13,117,098	a	0.168	16P	3.26E-08	3.39E-03	7.66E-04	HP
46	STMN4 112kb**	rs11135979	8	27,123,055	a	0.040	16P	6.30E-09	1.26E-05	1.35E-07	M	9.90E-03	1
47	PLAT*	rs2020918	8	42,214,920	a	0.341	24P	2.30E-08	4.15E-05	4.88E-09	CB	9.90E-03	1
48	TERF1	rs10104094	8	73,016,217	a	0.455	16P	1.14E-12	2.27E-06	7.81E-07	HP
49	MMP16	rs10103111	8	88,062,998	a	0.223	16P	3.99E-08	1.48E-04	1.74E-04	HP	9.22E-03	2
50	SDC2	rs2464474	8	96,585,444	g	0.110	16P	1.97E-11	1.83E-03	9.71E-03	HP	4.68E-02	1
51	SCRT1/TMEM249*	rs4333645	8	144,345,873	a	0.433	16P	2.31E-08	1.56E-04	1.19E-04	HP	4.96E-05	1
52	CA9	rs7858447	9	35,677,248	g	0.192	16P	1.62E-09	1.64E-05	1.23E-07	HP
53	C9orf114/ENDOG	rs2280845	9	128,820,891	a	0.249	24P	2.51E-09	1.83E-05	5.32E-04	HP	5.77E-07	2
54	PTGES	rs7872802	9	129,753,451	g	0.136	16P	3.34E-06	3.11E-05	4.37E-08	C	2.58E-02	1
55	RXRA*	rs36123461	9	134,441,548	g	0.020	16P	3.64E-09	8.30E-05	2.05E-04	HP
56	OIT3	rs2394931	10	72,906,307	a	0.055	16P	9.90E-09	2.03E-04	1.30E-04	HP	2.92E-02	1
57	VCL	rs2395075	10	74,008,422	g	0.251	16P	2.95E-08	1.20E-03	5.92E-04	HP	5.32E-03	1
58	NRG3	rs1649960	10	82,273,509	g	0.065	16P	3.15E-08	4.85E-05	6.36E-05	HP	2.57E-02	1
59	NRG3	rs10509455	10	82,507,966	c	0.101	24P	1.30E-08	4.67E-04	9.69E-04	HP
60	GRK5	rs2230349	10	119,436,823	a	0.100	16P	3.19E-09	3.58E-05	6.89E-05	HP
61	LSP1	rs2089910	11	1,853,174	a	0.235	INF	2.42E-08	1.72E-07	5.59E-07	M
62	LRRC4C 110 kb**	rs1457326	11	41,573,519	a	0.242	16P	1.40E-08	1.40E-03	5.79E-04	HP	1.68E-02	1
63	NOX4	rs553635	11	89,496,263	a	0.074	16P	1.90E-08	9.94E-06	2.00E-05	HP	6.43E-04	3
64	MMP10	rs17359286	11	102,772,987	a	0.037	24P	2.16E-08	4.35E-05	1.55E-06	HP	1.52E-02	2
65	BCAT1	rs7964239	12	24,920,751	g	0.082	16P	3.07E-08	5.54E-04	1.39E-03	HP	6.43E-04	3
66	HDAC7	rs2301783	12	47,788,458	c	0.123	16P	4.54E-09	4.64E-05	3.48E-06	HP	1.75E-02	1
67	ATF1	rs3742065	12	50,795,836	g	0.051	16P	4.39E-08	2.32E-05	1.76E-05	HP	1.90E-03	2
68	RARG	rs10082919	12	53,225,680	a	0.045	16P	1.25E-08	9.51E-05	2.81E-04	HP
69	IRS2	rs7999797	13	109,773,653	g	0.449	24P	4.83E-08	1.59E-05	9.82E-05	HP	7.64E-04	3
70	MTHFD1	rs17824591	14	64,420,993	a	0.213	16P	2.51E-08	5.13E-05	3.02E-07	M
71	SERPINA1	rs1980618	14	94,386,086	t	0.355	16P	3.37E-09	9.48E-04	9.92E-03	HP	3.72E-02	1
72	EXOC3L4/TNFAIP2*	rs7155575	14	103,116,465	a	0.1524	16P	3.02E-08	5.43E-04	5.25E-04	HP	4.13E-02	1
73	BAG5*	rs10129426	14	103,552,118	g	0.462	16P	8.47E-10	9.88E-04	2.40E-04	HP	3.88E-04	1
74	C15orf41	rs8039329	15	36,658,364	a	0.129	16P	3.14E-08	2.85E-04	2.37E-03	HP	9.69E-03	2
75	PCSK6	rs8042699	15	101,386,868	a	0.331	16P	1.11E-08	5.95E-05	2.32E-06	HP	3.21E-03	2
76	HBA2*	rs2974771	16	171,058	a	0.471	16P	1.89E-08	1.29E-03	3.53E-04	HP	3.90E-02	1
77	ACSM3/EXOD1	rs5716	16	20,785,065	c	0.084	16P	2.72E-08	1.79E-05	5.61E-05	HP
78	GAS7	rs9545	17	9,910,885	g	0.058	16P	1.14E-09	1.26E-04	5.90E-04	HP	2.54E-02	1
79	SEPT9 176kb**	rs7218173	17	77,676,394	c	0.075	16P	1.31E-08	6.95E-06	4.35E-07	HP	3.72E-02	1
80	SOCS3	rs4969168	17	78,357,712	a	0.140	16P	1.76E-09	3.29E-05	2.24E-03	HP	1.81E-05	4
81	BCL2	rs2046135	18	63,147,019	a	0.033	BLD	1.62E-08	7.42E-07	4.19E-06	HP
82	BCL2	rs17070739	18	63,152,150	c	0.038	16P	1.20E-09	2.86E-06	3.00E-05	HP	3.90E-02	1
83	BCL2	rs8097624	18	63,153,066	a	0.022	BLD	5.88E-09	2.87E-07	3.95E-05	HP
84	C3	rs11666133	19	6,686,410	a	0.043	16P	9.13E-06	8.29E-04	2.18E-08	C	4.31E-02	1
85	LRRC25*	rs8101249	19	18,391,020	c	0.176	16P	2.32E-09	1.98E-03	2.92E-04	HP	3.81E-03	2
86	DMPK	rs16939	19	45,772,798	a	0.459	16P	4.25E-08	4.29E-05	3.88E-05	HP	1.12E-02	1
87	IGFL4*	rs10500295	19	46,072,145	a	0.028	24P	1.61E-08	3.40E-04	2.46E-03	HP	4.44E-02	1
88	SLC8A2	rs11083878	19	47,436,586	g	0.083	16P	3.29E-09	2.17E-04	3.69E-03	HP
89	BPI	rs5741804	20	38,323,940	a	0.047	16P	1.81E-10	5.39E-05	5.77E-06	HP	4.20E-02	1
90	MMP9	rs9509	20	46,016,514	g	0.024	16P	1.37E-08	4.39E-05	1.67E-06	HP
91	NFATC2	rs6067766	20	51,423,549	a	0.115	16P	1.02E-08	2.82E-04	5.63E-04	HP
92	APP	rs743532	21	26,066,185	g	0.059	INF	1.33E-08	7.68E-08	8.31E-08	M
93	ABCG1*	rs4148082	21	42,199,305	a	0.090	24P	3.43E-05	5.83E-03	1.86E-09	C	1.27E-02	2
94	ABCG1	rs3788010	21	42,295,912	g	0.411	BLD	3.48E-08	8.45E-06	4.15E-07	M
95	VPREB3/C22orf15*	rs6003875	22	23,759,673	g	0.237	16P	1.92E-08	2.04E-05	1.44E-06	HP
96	PPARA	rs4253721	22	46,212,338	g	0.045	16P	1.25E-08	2.35E-04	1.43E-04	HP
97	PPARA	rs4253778	22	46,234,737	c	0.184	16P	7.10E-09	4.53E-04	3.71E-03	HP	1.65E-05	2

If an index SNP was not within protein coding gene, the closest gene were assigned. Multiple genes were assigned if they were at about the same distance up- and downstream from the index SNP or if the index SNP was within the region of overlapping genes. Loci were naturally associated with genes.

Protein coding genes within ± 100 Kb flanking region for the index SNP.

Protein coding genes for three SNPs (#46, 62, and 79) are within ±200 Kb flanking region for the index SNP.

Proxy SNPs with linkage disequilibrium (LD) r.

Chr, chromosome; EA, effect allele; EAF, EA frequency.

Domain indicates the pleiotropic domain in which minimal GW significant p-values were attained in our pleiotropic meta-analysis examining: physiological (PHY), blood (BLD), inflammation (INF), 16 quantitative markers (16P), 7 diseases and death (8DD), and all 24 phenotypes (24P) domains. P-values are given for all three pleiotropic meta-tests (FpFc, OpFc, and ObFc) for a given domain (.

Group indicates the type of association defined in the “Genetic heterogeneity and correlation in pleiotropic meta-analysis” section.

P.

N.

More details on the associations for these pleiotropic SNPs are given in .

Table 5

Selected novel pleiotropic genome-wide significant SNPs.

ID	Gene	SNP	Chr	Location base pairs	EA	EAF	Domain	NP	Meta p-values				Group	PGRASP	NG
									MOp/OpFc	MOb/ObFc	MFp	FcFp/FpFc
PATHWAY 1
9	COL5A2	rs10191420	2	189,159,234	g	0.037	24P	7	8.70E-05	9.02E-03	3.75E-04	4.12E-09	H1a	1.62E-03	3
21	NRM	rs1075496	6	30,690,462	a	0.431	PHY	4	9.09E-08	4.88E-09	1.58E-05	2.29E-02	HB
24	CYP39A1	rs2277119	6	46,642,168	a	0.233	24P	10	5.57E-05	4.18E-04	7.01E-09	5.29E-03	D	6.68E-04	3
33	TNFRSF10A	rs13278062	8	23,225,458	c	0.479	24P	10	1.34E-04	2.19E-03	4.55E-10	3.07E-09	C
70	APOH	rs1801689	17	66,214,462	c	0.034	BIO	3	1.19E-09	3.26E-09	2.93E-04	3.87E-02	HP	4.92E-06	1
73	BCL2	rs4987719	18	63,293,077	a	0.032	24P	6	1.97E-08	1.51E-06	2.59E-08	3.48E-04	M	3.70E-03	1
PATHWAY 2
29	ADGRV1	rs7712313	5	90,932,581	g	0.309	24P		7.50E-08	2.40E-06		4.44E-08	CB	3.53E-05	4
54	PTGES	rs7872802	9	129,753,451	g	0.136	16P		3.34E-06	3.11E-05		4.37E-08	C	2.58E-02	1
59	NRG3	rs10509455	10	82,507,966	c	0.101	24P		1.30E-08	4.67E-04		9.69E-04	HP
92	APP	rs743532	21	26,066,185	g	0.059	INF		1.33E-08	7.68E-08		8.31E-08	M

Chr, chromosome; EA, effect allele; EAF, EA frequency.

Domain indicates the pleiotropic domain in which minimal GW significant p-values were attained examining: physiological (PHY), blood (BLD), inflammation (INF), 16 quantitative markers (16P), 7 diseases and death (8DD), and all 24 phenotypes (24P) domains. P-values are given for all pleiotropic meta-tests for a given domain (.

Group indicates the type of association defined in the “Genetic heterogeneity and correlation in pleiotropic meta-analysis” section.

P.

N.

ID numbers correspond to those in .

Figure 3

Heat map of phenotype-specific associations for selected pleiotropic SNPs. Data are for SNPs with pleiotropic associations in the domains of 16 quantitative markers (16P) and all 24 phenotypes (24P) from Table 3. Phenotypes are defined in Table 1. FcFp, MFp, MOp, and MOb denote pleiotropic meta-tests (Figure 1). Colors code -log10 (p-value) trimmed at GW level -log10 (5 × 10−8) = 7.3 for better resolution.

Because p < p < p for pleiotropic SNPs, this inequality automatically validates pleiotropy for 79 SNPs in pathway 1, as it implies that pleiotropic statistics improved to attain p < p by pooling contributions from multiple phenotypes with p > p (Supplementary Tables 4B–D). Because combining statistics in pathway 2 may yield p < p driven by strong pleiotropy in one cohort only, the set of 97 SNPs did not include SNPs with p < p in one cohort that did not attain nominal significance (adjusted for six tests, p > 0.05/6 = 8.3 × 10−3) in at least one additional cohort (Supplementary Table 7).

Genetic Heterogeneity and Correlation in Pleiotropic Meta-Analysis

The Fisher test in pathways 1b and 2a (Figure 1) combined p-values across phenotypes assuming that they were from independent associations. Given the evolutionary uncertainty in establishing molecular mechanisms of age-related phenotypes, these associations may or may not be independent, even for correlated phenotypes, because the mechanisms driving genetic associations with these phenotypes and the correlations among them may have different origins (see “Materials and Methods”). Then, differences in p-values from the Fisher and omnibus tests (Figures 1D,E) reflect the impacts of heterogeneity and/or correlation in genetic associations. Below, we characterize these impacts. We used an ad-hoc cut-off for the difference in p-values between these tests of ≥1.5 orders of magnitude to characterize a strong impact.

Our analysis identified conventional and unconventional sets of associations in each pathway. The conventional set, represented by three groups of SNPs in pathways 1 (D, C, and M) and 2 (C, CB, and M) (Table 5), was characterized by associations commonly expected in GWAS. It included 43 SNPs from pathway 1 (Table 3) and 16 SNPs from pathway 2 (Table 4), covering 33.5% (59 of 176) of SNPs. The C (6 SNPs in each pathway) and CB (3 SNPs in pathway 2 only) groups included 15 SNPs with p