Genetic Architecture of Complex Traits and Disease Risk Predictors

bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Soke Yuen Yong ∗1, Timothy G. Raben †1, Louis Lello ‡1,2, and Stephen D.H. Hsu §1,2 1Department of Physics and Astronomy, Michigan State University 2Genomic Prediction, North Brunswick, NJ

January 2020

Abstract

Genomic prediction of complex human traits (e.g., height, cognitive ability, bone density) and disease risks (e.g., breast cancer, diabetes, heart disease, atrial fibrillation) has advanced considerably in recent years. Predictors have been constructed using penalized algorithms that favor sparsity: i.e., which use as few genetic variants as possible. We analyze the specific genetic variants (SNPs) utilized in these predictors, which can vary from dozens to as many as thirty thousand. We find that the fraction of SNPs in or near genic regions varies widely by phenotype. For the majority of disease conditions studied, a large amount of the variance is accounted for by SNPs outside of coding regions. The state of these SNPs cannot be determined from exome- sequencing data. This suggests that exome data alone will miss much of the heritability for these traits – i.e., existing PRS cannot be computed from exome data alone. We also study the fraction of SNPs and of variance that is in common between pairs of predictors. The DNA regions used in disease risk predictors so far constructed seem to be largely disjoint (with a few interesting exceptions), suggesting that individual genetic disease risks are largely uncorrelated. It seems possible in theory for an individual to be a low-risk outlier in all conditions simultaneously.

Introduction

Genomic prediction of complex traits and disease risks has advanced considerably thanks to the recent advent of large data sets and improved algorithms. These algorithms range from simple regression, applied to one SNP at a time to estimate statistical significance and effect size (e.g., as used in GWAS), to high dimensional optimization methods such as compressed sensing or sparse learning [1–4]. They produce Polygenic Risk Scores (PRS) or Polygenic Scores (PGS): functions that map the state of an individual’s DNA at specific locations (SNPs), to a risk score or predicted quantitative trait value. Predictors (PGS or PRS) now exist for a number of important traits and risks, many of which have undergone out of sample testing (i.e., validation in groups of individuals not used in training and from other data sets or from separate ancestries.)[5–7]. The genetic architectures uncovered vary significantly: the number of SNPs required to capture most of the predictor variance ranges from a few dozen to many thousands. In contrast, traditional Genome Wide Association studies (GWAS) can implicate the entire genome [8, 9], making them unwieldy to analyze.

∗Corresponding Author: [email protected] †[email protected] ‡[email protected] §[email protected]

1 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

In the case of disease risk, the predictors are already good enough to identify risk outliers. That is, individuals with unusually high (or low) risk of a specific condition. There are many clinical applications for such predictors [5, 10–19] (although there is still much work to be done to overcome sampling and algorithmic biases and disparity [20, 21]). Below we mention two examples. Breast Cancer: Certain variants in the BRCA1 and BRCA2 genes are known to elevate Breast Cancer risk significantly [22, 23]. However, these mutations affect no more than a few women per thousand in the general population[24–26]. By contrast, PRS using thousands of common SNP variants can now identify an order of ten times as many women who are in the high-risk category [5, 7, 10, 27, 28]. Standard of Care for high-risk women typically includes additional screening, such as mammograms beginning a decade earlier than for normal risk women. Early detection can also lead to significant cost savings [29]. What can we say about the thousands of common SNPs used in the PRS? Do they overlap with SNPs used in PRS for other conditions (e.g., other cancers)? Height: Idiopathic Short Stature (ISS) refers to extreme short stature that does not have a diagnostic explanation (e.g., height below 5 foot 2 inches in adult males)[30]. Growth hormone treatment is sometimes prescribed for children who are at risk for this condition, at a cost in the $100k range. Typically, these would be children in the bottom percentiles for height within their age group [31, 32]. However, it is difficult for pediatric endocrinologists, whose responsibility it is to prescribe HGH for these children, to know whether the child is simply passing through a temporary phase of slow growth (and will, by adulthood, reach normal height)[33, 34]. Adult height prediction from DNA (with 95 percent confidence interval roughly ±2 inches) will allow physicians to avoid expensive HGH treatment (with significant potential side-effects) for children who are merely short for their age (late-developing) and are likely to be in the normal range in adulthood.

For the first time, we can begin to address some general questions concerning the genetic architectures of complex traits. In this paper we address the following questions: 1. What is the (qualitative) genetic architecture of specific disease risks? How many SNPs, where are they, how many genes? 2. How much of the total risk is controlled by loci in coding vs non-coding regions? 3. Is exome sequencing data sufficient for computation of Polygenic Risk Scores (PRS)? 4. How much genetic overlap exists between different disease architectures? With millions of SNPs in the genome it is entirely possible that different diseases have nearly disjoint genetic architectures – i.e., risk is mostly controlled by distinct regions of DNA. On the other hand, we might uncover overlap regions of DNA which affect multiple disease risks simultaneously.

In this paper we consider predictors for a selection of disease conditions/traits: asthma, atrial fibrillation, basal cell carcinoma, breast cancer, coronary artery disease, type-1 diabetes, type-2 diabetes, diastolic blood pressure, educational years, gallstones, glaucoma, gout, heart attack, height, high cholesterol, hypertension, hypothyroidism, malignant melanoma, menopause, pulse rate, and systolic blood pressure. All predictors, except the coronary artery disease predictor, were built by training on case-control phenotype data from the UK Biobank [35, 36] that relied on custom array genotyping (see Appendix C for details). This array was designed to have detailed coverage in areas known to be associated with certain phenotypes, and to contain a wide sampling of the entire human genome. More details of the array design can be found on the UK Biobank website https://www.ukbiobank.ac.uk/scientists-3/uk-biobank-axiom-array/ and in Appendix C. These predictors were derived using the L1 penalized regression (sparse learning) methods found in [5, 6]. Recent algorithmic benchmarking for complex trait prediction in plants and animals has shown that linear methods work as well if not better than non-linear, Bayesian, or deep learning approaches for current data set sizes [37]. Predictors for diastolic blood pressure, systolic blood pressure, and pulse rate are reported for the first time here, but were designed as described in [5]. The coronary artery predictor originated from [7], and the associated minor allele frequencies were obtained using Ensembl’s minor allele frequency calculator [38]. Of course, not all of the genetic variants affecting disease risk have been discovered. The PRS continue to improve as more training data become available. However, the SNPs used in the existing PRS tend to bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

be those that account for the most risk variance. Equivalently, the statistical evidence supporting their association with the disease risk is highest. Relevant SNPs that have yet to be discovered are either common SNPs with very small eﬀect size, or very rare SNPs that are not probed using existing gene arrays.

Variance and Eﬀect Sizes

The majority of this work deals with characterizing the relative sizes of the eﬀect of particular SNPs on the performance of a polygenic predictor. A predictor in this case is a set of weights, {βi}, for a set of SNPs, S. An individual’s phenotype, y, can be described by X y =y ˜ + = xiβi + , (1) i∈S

where xi ∈ {0, 1, 2} is the number of minor alleles for SNP i, y˜ is the predicted value of the phenotype, and is an error term. Our primary object of interest is the variance of this prediction. The contribution of a single SNP to this variance is expressed in terms of the βi and the minor allele frequency (MAF), fi, as:

2 2 var(xiβi) = βi var(xi) = βi 2(1 − fi)fi . (2)

2 2 2 In the limit of small MAF, this becomes 2βi fi + o(fi ) ≈ 2βi fi. The overall variance of the individual’s predicted phenotype can thus be described as: !   X X X var(˜y) = var xiβi = var(xiβi) + 2 cov(xiβi, xjβj) i∈S i∈S j

where the ﬁnal approximation holds when the SNPs are largely uncorrelated. This is true for most minor allele frequencies, and has been checked empirically in particular instances (see for example [6]). For the predictors in this work, there are only a handful of SNPs with correlation of about 0.01 lying within 2000 kilo base pairs of each other. In this sense, the variance due to each SNP can be considered as a linear eﬀect. We can then calculate the variance accounted for by a subset, S ⊂ S, of the predictor SNPs, as a percentage of the total variance of the individual’s predicted phenotype: P P 2 var xiβi 2β (1 − fi)fi var(˜y0) i = i∈S ≈ i∈S , (4) var(˜y) ! P 2 P 2βj (1 − fj)fj var xjβj j∈S j∈S

where again, the ﬁnal approximation holds when the SNPs are uncorrelated.

Results and Analysis Predictor SNPs in genic regions We are interested in investigating how predictor SNPs located in genic regions impact our predictors. The most obvious way to identify predictor SNPs located inside genic regions is to deﬁne a SNP as being within a genic region if its genomic coordinates fall between the start-point and end-point coordinates of any protein-coding gene. The GENCODE Release 19 annotation of the human genome [39] (based on the GRCh37.p13 reference human genome assembly [40]) was chosen as the source of our reference set of gene bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

boundary coordinates. Currently, it is still unclear where exactly genic regions end and intergenic regions begin [41–43], and so there is a possibility that this choice of gene boundary coordinates may not be definitive for our purposes. We asked these questions: as these reference gene boundary coordinates are varied, by how much does the separation of predictor SNPs into genic and non-genic categories vary, and how significantly does the influence of the (increasingly large) genic section of the predictor SNPs change? Figure 1 shows for a selection of disease conditions how the number of the predictor SNPs categorized as located in genic regions - expressed as a percentage of the total number of predictor SNPs for that disease condition - changes as all gene boundaries (according to GENCODE Release 19) are expanded by an increasing number k of kilo base pairs at both ends. At the reference gene boundaries (k = 0), the percentage of predictor SNPs which are genic ranges from about 50% (many disease conditions) to about 60% (gallstones, malignant melanoma, atrial fibrillation); while at k = 30, this percentage rises to between 60% (breast cancer, type-1 diabetes, education years) to 75% (gallstones, malignant melanoma). This description excludes the coronary artery disease predictor (42.5% to 55%), which has a distinctly low proportion of genic predictor SNPs. For all disease conditions, the increase in the genic percentage of predictor SNPs with k occurs at roughly the same rate, which appears to be almost linear. This suggests that the predictor SNPs for every disease condition are approximately uniformly distributed by distance outside the reference gene boundary coordinates.

% of total activated SNPs

gallstones 75 malignant melanoma

height atrial 70 fibrillation gout

menopause

hypothyroidism 65 high cholesterol systolic b.p.

pulse rate 60 diabetes t-2

hypertension

diastolic b.p. 55 heart attack

asthma

glaucoma 50 basal cell carcinoma

diabetes t-1

edu. years 45 breast cancer

coronary artery disease 40 k(kilo bp) 5 10 15 20 25 30

Figure 1: Plots of the number of predictor SNPs located within genic regions, expressed as a percentage of the total number of predictor SNPs for that disease condition, against expansion of GENCODE Release 19 gene boundaries by k kilo base pairs.

Figure 2 shows for each disease condition how the variance accounted for by the predictor SNPs located in genic regions - expressed as a percentage of the total variance accounted for by all predictor SNPs for that condition - changes as the boundaries of every gene (according to GENCODE Release 19) are expanded by k kilo base pairs at both ends. At the reference gene boundaries (k = 0), the percentage of predictor variance accounted for by SNPs in genic regions ranges from about 40% (breast cancer, type-1 diabetes) to 90% (gallstones, gout, malignant melanoma), with notable outliers at 25% (atrial ﬁbrillation) and 20% (coronary bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

artery disease). For the majority of disease conditions, the percentage of variance accounted for by the genic predictor SNPs remains approximately flat as k is increased, meaning that practically all the variance accounted for by genic predictor SNPs is due to SNPs contained within the reference gene boundaries. Noticeable exceptions occur for glaucoma at k = 6.5, breast cancer at k = 17.5, and menopause at k = 27.5, where the observed large jumps in variance indicate the presence of some SNP(s) at that genomic location with a significant effect on that specific disease condition.

% of variance accounted for 100

gallstones gout

menopause malignant melanoma high 80 cholesterol basal cell carcinoma height

diabetes t-2

hypothyroidism

glaucoma

60 heart attack

pulse rate asthma diabetes t-1 diastolic b.p. hypertension

systolic b.p. 40

breast cancer

edu. years

atrial fibrillation coronary 20 artery disease k(kilo bp) 5 10 15 20 25 30

Figure 2: Plots of the variance accounted for by predictor SNPs located within genic regions, expressed as a percentage of the total variance accounted for by all predictor SNPs for that disease condition, against expansion of GENCODE Release 19 gene boundaries by k kilo base pairs..

Now that the extent of the variance accounted for by SNPs located within genic regions has been established for each disease condition, it is natural to investigate next how this genic variance is distributed between individual (protein-coding) genes. When considering a specific disease condition, for each gene, the variance accounted for by all predictor SNPs located within the (effective) gene boundary coordinates is summed and expressed as a percentage of the total variance accounted for by all predictor SNPs for that condition. For the purposes of this calculation, the boundaries of the genic regions were chosen to be at k = 30. Figures 8 through 28 in Appendix A show the percentage of predictor variance accounted for by single genes for asthma, atrial fibrillation, basal cell carcinoma, breast cancer, coronary artery disease, type-1 diabetes, type-2 diabetes, diastolic blood pressure, educational years, gallstones, glaucoma, gout, heart attack, height, high cholesterol, hypertension, hypothyroidism, malignant melanoma, menopause, pulse rate, and systolic blood pressure. Only the fifteen largest values of variance accounted for by a single gene are displayed for each condition. As each genic predictor SNP may lie within the boundaries of more than one gene due to the expanded gene boundaries overlapping, multiple genes may share the exact same set of predictor SNPs and therefore the same value of total variance accounted for by single genes. It is evident that certain disease conditions have one (or perhaps two or three) dominating value(s) of variance accounted for by a single gene. The most striking results are found with basal cell carcinoma (one bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

gene, IRF4, supplying 28% of the predictor variance out of the 87% total genic variance at k = 30 and a second, TGM3, supplying 12% of the predictor variance), breast cancer (two genes, FGFR2 and TOX3, each supplying 14%-15% of the predictor variance out of 66% total genic variance), type-2 diabetes (one gene, TCF7L2, supplying 25% of the predictor variance out of 81% total genic variance), gallstones (three genes, ABCG8, ABCG5 and DYNC2LI1, each supplying 80%-83% of the predictor variance out of 98% total genic variance), glaucoma (two genes, ALDH9A1 and TMCO1, each supplying 19% of the predictor variance out of 78% total genic variance), gout (one gene, ABCG2, supplying 57% of the predictor variance out of 96% total genic variance and another, SLC2A9, supplying 12% of the predictor variance), heart attack (one gene, LPA, supplying 21% of the predictor variance out of 78% total genic variance), malignant melanoma (five genes, AC092143.1, TUBB3, TCF25, MC1R and DEF8, each supplying 37%-43% of the predictor variance out of 92% total genic variance), and menopause (one gene, UTY, supplying 24% of the predictor variance out of 94% total genic variance). The more novel among these results are the strong links observed between glaucoma and ALDH9A1, between gallstones and DYNC2LI1, between menopause and UTY, and between malignant melanoma and the four genes AC092143.1, TUBB3, TCF25, and DEF8. These relationships are either not well-established up until now, or have not yet been suggested to be possible. The rest of our findings confirm previous work by other groups. The association of IRF4 [44] and TGM3 [45] with basal cell carcinoma is well-known, as is the relationship of FGFR2 [46] and TOX3 [47] to breast cancer, the link between TCF7L2 [48] and type-2 diabetes, the association of ABCG8 [49] and ABCG5 [50] with gallstones, the role of TMCO1 [51] in glaucoma, the relationship of ABCG2 [52, 53] and SLC2A9 [54] to gout, the connection between LPA [55] and heart attack / coronary artery disease, and the role of MC1R [56, 57] in malignant melanoma. For every disease condition considered here, the full list of genes responsible for the top fifteen values of variance displayed here can be found in Appendix B.

Overlap between predictor SNPs and whole-exome sequencing data Modern whole-exome sequencing techniques are expected to be able to access about 85% of known disease- related variants [58, 59]. Assuming this is correct, we would expect about the same fraction of genic SNPs belonging to our predictors for disease conditions to be identifiable via exome-sequencing data. To verify this, we compared our sets of predictor SNPs against the whole-exome sequencing data released by the UK Biobank in March 2019 [60] (to be specific, the version of the whole-exome sequencing data set generated using a Functionally Equivalent (FE) pipeline [61]). In this section, once again, genic SNPs are defined as those SNPs located within the GENCODE Release 19 protein-coding gene boundaries extended by 30 kilo base pairs at both ends (or k = 30). Figure 3 shows for each disease condition the percentage of predictor SNPs located in genic regions which are also found in the UK Biobank exome data, where the disease conditions are listed from left to right on the horizontal axis in order of decreasing percentage. For about two-thirds of the conditions surveyed, 10% or so of the genic SNPs for each predictor also formed part of the set of SNPs identified via exome-sequencing. The remaining disease conditions have up to about 17% (in terms of numbers) of their genic predictor SNPs detected by the exome-sequencing data set - with one exception, coronary artery disease, which displays an extraordinarily low (2%) value of this overlap. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

% of Predictor SNPs Belonging to UKBB Exome Data % atrial fibrillation

malignant gallstones melanoma basal cell 15 carcinoma

gout menopause

height diastolic high systolic hypo - blood pulse cholesterol diabetes blood thyroidism pressure rate type-2 pressure asthma hyper- glaucoma 10 tension diabetes breast type-1 heart attack education years

coronary artery disease

0 Phenotype

Figure 3: The percentage of predictor SNPs which are found both in genic regions and the UK Biobank exome data, for each disease condition. The disease conditions are listed from left to right on the horizontal axis in order of decreasing percentage. Each vertical bar is colored red with a depth of shade proportional to the height of the bar. Here, "genic" SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Figure 4 shows for each disease condition the variance accounted for by the genic predictor SNPs from figure 3, expressed as a percentage of the total variance accounted for by all the SNPs in the predictor, where the disease conditions are listed from left to right on the horizontal axis in order of decreasing percentage. For the majority of conditions, 20% or less of the total variance accounted for by the predictor SNPs comes from genic SNPs which show up in the UK Biobank exome data. In fact, less than 5% of the variance accounted for by the the breast cancer, atrial fibrillation, and coronary artery disease predictor SNPs is detected by the exome data. Exceptions to this are the predictors for gallstones (80% of predictor variance detected by the exome data), gout (70% of predictor variance detected by the exome data), malignant melanoma (45% of predictor variance identified by the exome data), and menopause (30% of predictor variance identified by the exome data). bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

% of Predictor Variance Due to SNPs Belonging to UKBB Exome Data %

gallstones

gout

malignant melanoma

menopause basal cell hypo- carcinoma high thyroidism pulse cholesterol rate diastolic systolic 20 height blood blood hyper- pressure pressure tension

diabetes asthma education type-1 diabetes years heart glaucoma type-2 attack breast atrial coronary fibrillation artery disease

0 Phenotype

Figure 4: The variance accounted for by predictor SNPs which are both in genic regions and detected by the UK Biobank exome data, as a percentage of the total variance accounted for by all predictor SNPs, for each disease condition. The disease conditions are listed from left to right on the horizontal axis in order of decreasing percentage. Each vertical bar is colored blue with a depth of shade proportional to the height of the bar. Here, "genic" SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

It may be worth noting that the ordering of the predictors by percentage of predictor SNPs which are genic (in Figure 1) and the ordering according to percentage of predictor SNPs which are both genic and accessible via the exome data in (Figure 3) is about the same. A similar observation can be made regarding Figures 2 and 4 (here in terms of variance accounted for). In Figure 5, an overall view of the extent to which predictor SNPs in genic and non-genic regions are identiﬁable based on exome-sequencing data is given. Figure 5 shows for each disease condition the breakdown of the percentage of predictor SNPs according to whether their location is genic and whether the exome data serves to probe them. This breakdown does not vary much between predictors: in general about 10%-17% of predictor SNPs are both in genic regions and found in the exome data (‘Genic/Exonic’, colored blue), 0% (as might be expected) are not in genic regions but are present in the exome data (‘Non-genic/Exonic’, colored yellow), 55%-65% are located in genic regions but not found in the exome data (‘Genic/Non-exonic’, colored green), and 20%-35% are neither in genic regions nor in the exome data (‘Non-genic/Non-exonic’, colored red). The only deviation comes from the coronary artery disease predictor SNPs - less than 5% are ‘Genic/Exonic’, while nearly 45% are ‘Non-genic/Non-exonic’. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Genic/Exonic Non-genic/Exonic Genic/Non-exonic Non-Genic/Non-exonic

% of Predictor SNPs

100

Out[ ]=

0 Phenotype asthma atrial basal breast coronary diabetes diabetes diastolic education gallstones glaucoma gout heart height high hyper- hypo- malignant menopause pulse systolic fibrillation cell artery type-1 type-2 blood years attack cholesterol tension thyroidism melanoma rate blood carcinoma disease pressure pressure

Figure 5: The breakdown of the percentage of predictor SNPs according to whether their location is genic and whether the exome data serves to probe them, for each predictor. The bar sections representing predictor SNPs in genic regions and in the exome data are labelled ‘Genic/Exonic’ and colored blue, those representing predictor SNPs not in genic regions but present in the exome data are ‘Non-genic/Exonic’ and colored yellow, those representing predictor SNPs which are located in genic regions but not found in the exome data are ‘Genic/Non-exonic’ and colored green, and those representing predictor SNPs neither in genic regions nor in the exome data are ‘Non-genic/Non-exonic’ and colored red. As expected, the yellow ‘Non-genic/Exonic’ bar sections are too small to be discernible. Here, "genic" SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Pairwise comparison of predictors We now focus on finding connections between disease conditions based on similarities in their predictors. In this section, the analysis involving the coronary artery disease predictor was restricted to the top twenty thousand predictor SNPs as ranked by value of variance accounted for. Figure 6 shows the pairwise overlap between disease conditions in terms of the number of predictor SNPs that each pair of conditions has in common. Here, pairs of SNPs less than 4000 base pairs apart were considered to be essentially the same SNP (where this separation was chosen so that most or all SNP pairs with high linkage disequilibrium levels are expected to be identified with one another [62]). Each row of the table corresponds to a particular disease condition, and reading the row from left to right (going from one column to the next) gives the number of predictor SNPs (expressed as a percentage of the total number of SNPs in the row-label predictor) that the conditions labelling each column share with the condition that the row corresponds to. A pair of conditions may be considered to have a significant connection if the percentage of SNPs in common is substantial when read off the table both ways. Analyzing the table in this manner produces two notable groupings, with all the conditions in each group having large pairwise overlap. 1. Asthma – diastolic blood pressure – hypertension – systolic blood pressure – education years – height: The first four disease conditions form a combination which is not too surprising, but the same cannot be bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

said about the last two traits. The table shows that all possible pairs taken from these six conditions overlap by about 10% or so, with the following exceptions which exceed this level by some way: 38% of the systolic blood pressure SNPs also belong to the diastolic blood pressure predictor, while 35% of the diastolic blood pressure SNPs also belong to the systolic blood pressure predictor; 17% of the diastolic blood pressure SNPs belong to the hypertension predictor, while 18% of the hypertension SNPs belong to the diastolic blood pressure predictor; and 18% of the hypertension SNPs belong to the systolic blood pressure predictor, while 19% of the systolic blood pressure SNPs belong to the hypertension predictor. 2. Basal cell carcinoma – malignant melanoma: 7.8% of the basal cell carcinoma SNPs belong to the malignant melanoma predictor, while 8.1% of the malignant melanoma SNPs belong to the basal cell carcinoma predictor. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

diastolic atrial basal cell type 1 type 2 education high hypothyroidi malignant systolic blood asthma breast cancer CAD 20k blood gallstones glaucoma gout heart attack height hypertension menopause pulse rate fibrillation carcinoma diabetes diabetes years cholesterol sm melanoma pressure pressure

asthma 100 0.11 0.09 1.1 3.3 2 2.2 10 11 0.19 0.8 0.31 1.1 14 3.9 10 4.3 0.097 3.5 10 9.6

atrial 7.2 100 0 0.97 2.9 0 1.4 14 12 0 0.97 0.97 0.48 14 3.4 13 3.4 0 3.4 14 11 fibrillation

basal cell 10 0 100 2.6 1.7 2.6 2.6 14 13 0 4.3 0 0.86 21 3.4 15 6.9 7.8 3.4 8.6 13 carcinoma

breast cancer 7.6 0.11 0.17 100 3.6 1.9 2.3 9.8 9.8 0.11 0.89 0.39 0.67 13 3.8 9.7 3.6 0.11 3.4 8.7 8.7

CAD 20k 5.9 0.1 0.055 1 100 1.6 2.6 10 6.8 0.25 0.54 0.57 3.6 13 5.2 9.3 2.5 0.065 2.7 8.5 9.7

type 1 8.3 0 0.13 1.1 3.6 100 3.8 10 9.6 0.19 1 0.41 0.82 13 4.1 9.8 4.3 0.16 3.4 9.6 9.6 diabetes

type 2 8.2 0.086 0.086 1.3 4.5 3.4 100 12 13 0.35 0.98 0.69 1.3 16 6.1 15 3.9 0.029 3.6 12 12 diabetes

diastolic blood 7.7 0.18 0.11 1 4 1.9 2.5 100 11 0.14 0.88 0.44 1 15 4.1 17 3.4 0.13 3.7 14 35 pressure

education 7.5 0.12 0.082 0.96 2.8 1.7 2.4 10 100 0.12 0.74 0.42 0.91 13 3.7 10 3.2 0.092 3.5 9.5 9.9 years

gallstones 8.4 0 0 0.73 6.6 2.2 4.4 8 8 100 1.1 1.1 1.5 13 6.6 11 2.9 0 4 11 9.1

glaucoma 7.5 0.14 0.36 1.2 3.2 2.2 2.4 11 10 0.22 100 0.65 0.72 15 3.1 10 3.4 0.072 3.9 10 11

gout 5.4 0.26 0 0.92 3.5 1.7 2.9 10 10 0.39 1.3 100 0.65 16 3.3 12 4.7 0.13 5.8 9.9 11

heart attack 9.5 0.07 0.07 0.77 10 1.9 3.2 13 12 0.28 0.7 0.35 100 15 8.2 14 4 0.07 3.6 10 12

height 8.1 0.14 0.13 1.1 3.9 1.9 2.5 12 11 0.17 0.99 0.57 0.98 100 4.3 12 4.1 0.15 4.2 11 11

high 7.9 0.14 0.063 1.1 5.2 2 3.3 11 11 0.31 0.71 0.42 2 14 100 17 4.1 0.094 3.4 10 11 cholesterol

hypertension 7.7 0.16 0.1 1.1 3.9 1.8 3.1 18 11 0.18 0.84 0.52 1.2 15 6.3 100 3.5 0.079 3.5 11 18

hypothyroidi 9.4 0.12 0.17 1.2 3.7 2.4 2.3 10 9.8 0.13 0.79 0.61 1.1 15 4.4 10 100 0.067 3.7 9.9 9.2 sm

malignant 7.5 0 8.1 1.2 5 3.1 0.62 16 13 0 0.62 0.62 0.62 20 3.7 9.9 3.1 100 6.8 9.9 13 melanoma

menopause 7.3 0.13 0.081 1.1 3.3 1.8 2 10 11 0.18 0.9 0.68 0.86 15 3.5 9.6 3.6 0.13 100 10 9.8

pulse rate 7.9 0.21 0.059 0.98 3.4 1.9 2.6 15 11 0.17 0.88 0.47 0.87 15 4 11 3.6 0.11 3.8 100 12

systolic blood 7.6 0.17 0.11 1 4 1.9 2.6 38 11 0.16 0.89 0.49 1.1 15 4.4 19 3.3 0.13 3.7 12 100 pressure

Figure 6: Table containing the pairwise overlap between predictors in terms of the number of predictor SNPs in common, expressed as a percentage of the total number of SNPs in the predictor for the condition labelling each row. Pairs of SNPs less than 4000 base pairs apart were considered to be identiﬁed with one another. For a particular row, the number in each column represents the percentage of SNPs from the row predictor that can be identiﬁed with the SNPs from the column predictor.

Next, variance accounted for is used to measure the overlap, as seen in figure 7. This results in the discovery of more relations between different predictors. The method used on each pair of predictors was as follows: For each SNP, i, in the first predictor (corresponding to the condition labelling the row), all SNPs, j, from the second predictor (corresponding to the condition labelling the column) located less than 4000 base bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

pairs away were identified. Every such associated SNP from the second predictor was assigned a weight of uniform magnitude, with the sign of the weight based on the sign of the SNP’s effect size relative to the sign of the effect size of the SNP from the first predictor - positive when the signs were the same, and negative when the signs were different. These weights were then multiplied by the variance due to the SNP from the first predictor and summed. If we label each set of SNPs within 4000 base pairs away as Ci, this correlation estimate, r˜ can be expressed as

X X (1) (2) (1) 2 r˜ ≈ λ sgn βi βj 2(βi ) (1 − fi)fi , (5) i j∈Ci

where the normalization is chosen to be the total predictor variance of the row predictor,

!−1 X (1) 2 λ = 2(βi ) (1 − fi)fi . i This produced a weighted overlap in terms of variance, that accounts for whether the associated SNP pairs are positively correlated (effect sizes have equal signs) or negatively correlated (effect sizes have differing signs). Figure 7 displays this overlap as a percentage, and is basically Figure 6 expressed in terms of variance (weighted according to correlation sign) accounted for by the overlapping predictor SNPs. Once again, significant connections are observed in the case of diastolic blood pressure – hypertension – pulse rate – systolic blood pressure and basal cell carcinoma – malignant melanoma. In the case of the first group of conditions, the overlap of weighted variance ranges from 8% (systolic blood pressure – pulse rate) to 51% (systolic blood pressure – diastolic blood pressure). In the second case, 10% of the basal cell carcinoma predictor variance is shared with the malignant melanoma predictor, while 58% of the malignant melanoma predictor variance belongs to the basal cell carcinoma predictor. We now also have groups of conditions which appear to be strongly positively correlated in terms of variance overlap, but were unremarkable in terms of number of SNPs in common. The most obvious is coronary artery disease – heart attack – high cholesterol – hypertension, where the magnitude of the pairwise overlap in terms of weighted variance ranges from 4% (hypertension – coronary artery disease) to 35% (high cholesterol – heart attack). Other single pairs of conditions with large overlapping variances are gout – high cholesterol (where 62% of the gout predictor variance also belongs to the high cholesterol predictor, while 35% of the high cholesterol predictor variance also belongs to the gout predictor), type-1 diabetes – type-2 diabetes (where 23% of the type-1 diabetes predictor variance also belongs to the type-2 diabetes predictor, while 30% of the type-2 diabetes predictor variance also belongs to the type-1 diabetes predictor), coronary artery disease – type-2 diabetes (4% and 25%), and gallstones – high cholesterol (85% and 4%). Also worth mentioning is the overlap of height with respectively: education years, asthma, diastolic blood pressure, hypertension, pulse rate, systolic blood pressure - which all are about 10%. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

asthma 92 0.074 0.52 1.1 3.2 2 2.3 3.9 4.7 0.00024 0.53 0.18 2 11 3.3 5.5 6.7-0.062 2.7 4.8 2.4

atrial 1.7 100 0 0.11 2.4 0-0.14 11 1.6 0 0.21 0.018 0.25 9.3 0.11 0.045 0.27 0 0.41 60 5 fibrillation

basal cell 3.3 0 100 6.6-0.08-0.0053-0.18 0.94 31 0 3.9 0-2 43 1.7 3.1 31 10-0.53 1.9 2.7 carcinoma

breast cancer 1 0.0021 0.048 100 0.48 0.62-1.1 17 2.8 0.0022 0.43-0.054 2.9-3-0.25 2.9 0.31-7.×10 -6 1.5 2.7 16

CAD 20k 2.4 0.0036-0.04-6.4 100-0.063 3.6-8.5-0.096-0.014-0.099 0.16 15-5.9 6.4 8.4 0.033 0.00028 0.15 0.18 6.7

type 1 0.13 0-0.36-0.34 2.4 97 23-0.3-5.8-0.032 0.087 0.12-0.24 4.9 3.9 7.6 5.8 0.063 0.61 3.5 4.2 diabetes

type 2 2.2-0.0046-0.18-0.18 25 30 99 3 1.7 0.091 0.086 1.1 0.59 7.7 32-14 1.4 0.0002 1.9 31 4.4 diabetes

diastolic blood 3.7-0.041-0.27 0.56 2.2 0.95 1.7 93 5.6 0.24 0.42 0.77 3.8 13 5.6 32 2.7 0.076 1.7 11 50 pressure

education 2.6 0.038 0.17 0.42-0.15 0.66 1.1 4.8 94 0.032 0.053 0.14 0.35 8.1 1.6 4.6 1.7 0.028 1.6 5 5 years

gallstones 0.22 0 0 0.0049 78-0.54 3.4 2.2 1.8 100 0.023 1.5 4.5 6.2 85-80-76 0 0.16 80-0.051

glaucoma-1.1 0.013 0.2 0.41-0.64 0.93 0.4 2.1 5.1-0.3 99 0.0014 2.6 13 1.4 20 1.3 0.0019 1.5 3.9 5.4

gout 58-0.089 0-0.29 1.1 5.4 0.72 5.4 3.6 3.2 0.072 100 0.39-0.35 62 5.9 3-0.0035 7.1 7.7 65

heart attack 3.7 0.00019-0.044 0.12 23 3.5-0.23 6.9 2.2-0.1 0.85 0.91 99 13 32 25 2.4-0.0058 1 12 12

height 4.1 0.026 0.022 0.85 1.5 0.35 2 7.8 6.7 0.3 0.65 0.92 0.45 83 2.2 7.8 2.5-0.026 2.1 6.6 11

high 3-0.22-0.012-0.088 10 12-0.32-3.1 1.6 4.2-0.2 7.4 35 15 89 14 2-0.0021 3.5 20 5.6 cholesterol

hypertension 4.5-0.041-0.65 1.4 4 1.7 3.4 39 4.7 0.067 0.67 1.6 4.7 12 13 93 3 0.051 1.3 7.6 40

hypothyroidi 9.8 0.00089 1.3-0.86 8.5 9.9 8.4 16 4.1 0.058 0.21 6.7 6.5 8.5 1.6 9.6 96 0.016 0.1 11 9.8 sm

malignant 5.3 0 58 0.43 0.35 0.14 0.00059-29 37 0 0.56-0.017-0.039-1.1-0.26-34 0.83 100 34 0.65 1.9 melanoma

menopause 3 0.014-0.092 2.8-0.09 0.4 0.66 2.8 3 0.014 0.1-0.43 3 7 0.45 0.089 0.69-0.01 94 2.6 14

pulse rate 3.1 0.6 0.064 0.4 0.24 1.2 1.9 29 4.7 0.34-0.23 0.64 0.72 12 2.5 5.8 1.2 0.18 2.3 88 18

systolic blood 3.5 0.0031-0.35 0.7 2.9 1.3 0.26 51 5.3 0.31 0.77 0.77 2.9 11 8 37 2.4 0.014 1.8 7.7 91 pressure

Figure 7: As described by Eq.(5), this table contains the pairwise overlap between predictors in terms of the variance accounted for by predictor SNPs in common, expressed as a percentage of the total variance accounted for by the SNPs belonging to the predictor for the condition labelling each row. The overlapping variance is weighted according to the sign of the correlation between each pair of associated SNPs 4000 kilo base pairs or less apart. This can cause diagonal elements to be less than one hundred percent, as anti-correlated SNPs may be included in the overlap calculation. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Conclusions

This paper explores the genetic architectures of a number of common disease conditions and complex traits, as revealed by the most important SNPs used in genomic predictors. The results are complex – primarily summarized in the many figures in the paper and Appendix. However, we can make some general statements: I. The fraction of SNPs in or near genic regions varies widely by phenotype. For example, in the case of Coronary Artery Disease and Atrial Fibrillation, less than 20-30 percent of the total risk variance is due to SNPs near genic regions. II. For the majority of disease conditions studied, most of the variance is accounted for by SNPs whose state cannot be determined from exome-sequencing data. This suggests that exome data alone will miss much of the heritability for these traits. Stated somewhat differently: exome sequencing data for a specific individual misses much of the information necessary to compute their PRS score! III. The DNA regions used in disease risk predictors so far constructed seem to be largely disjoint (with a few interesting exceptions), suggesting that individual genetic disease risks are largely uncorrelated.

Observation III has interesting implications for pleiotropy [63–65]. We found that genetic risks are largely uncorrelated for different conditions. This suggests that there can exist individuals with, e.g., low risk simultaneously in each of multiple conditions, for any essentially any combination of conditions. There is no trade-off required between different disease risks (at least, not among the ones studied here). One could speculate that a lucky individual with exceptionally low risk across multiple conditions might have an unusually long life expectancy. Of course, the same applies for high risk: some unlucky individuals have high risk for multiple conditions simultaneously. In fact, there appear to be combinations of SNPs that could make a specific individual an outlier in each of the conditions studied, simultaneously.

Note

Recently, it was pointed out [66] that the processing of the whole-exome sequencing data via the FE pipeline had been carried out in a manner that failed to take into account the presence of alternative contigs in the GRCh38 reference genome. This is expected to have led to fewer variants being called than there should be in the resultant data set. Out of the total of 204,829 genomic regions comprising 39.20 MBp of the human genome targeted by the whole-exome sequencing process, data from 7554 regions extending across 1.53 MBp were potentially affected by this error. In an analysis of this issue [67], Jia et al. compared the number of exome variants per gene identified when using whole-exome sequencing data from the UK Biobank versus using data from gnomAD. They found 641 genes for which the UK Biobank exome data contains no variants whatsoever. In contrast, they calculated that it is highly probable for the UK Biobank exome data to identify at least one variant per gene in the case of the vast majority (93%) of these 641 genes. With the aim of gauging the extent to which our results may have been impacted by this discrepancy, we examined the overlap between our lists of top genes ranked by variance accounted for by predictor SNPs (Figures 29 - 49) and the 641 potentially problematic genes. We found that for most (17 out of 21) conditions, the genes responsible for the top fifteen values of variance accounted for do not include any of the 641 potentially problematic genes. The exceptions to this are asthma, basal cell carcinoma, type-1 diabetes, and hypothyroidism. To be specific: Asthma has 2 genes (HLA-DQB1 and HLA-DQA1 with variance 2%-3% each) out of its top 18 genes included among the 641 potentially problematic genes. For comparison, asthma has 3% as the highest percentage of predictor variance accounted for by a single gene. Basal cell carcinoma has 1 gene (HLA- DQA1 with variance 2%) out of its top 20 genes included among the 641 potentially problematic genes, bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

while 28% is the highest fraction of predictor variance accounted for by a single gene. Type-1 diabetes has 12 genes (HSPA1L, HLA-DRB1, BTNL2, HLA-DQB1, HLA-DQB2, NEU1, HLA-DOA, HLA-DQA1, HLA-DRA, HSPA1B, C6orf48, LSM2) out of its top 25 genes among the 641 potentially problematic genes. These 12 genes include the top four genes ranked by variance accounted for, with 14%, 9%, 7%, and 4% of predictor variance respectively. Hypothyroidism has 4 genes (HLA-DPA1, HLA-DQB1, HLA-DQA1, and HLA-DPB1, where two have 5% of predictor variance each and two have 1% each) out of its top 17 genes included among the 641 potentially problematic genes, while 8% is the highest value of predictor variance accounted for by a single gene. Clearly, for all conditions except type-1 diabetes, the 641 potentially problematic genes play very little part in determining the variance accounted for by the predictor SNPs. We feel that this justifies our opinion that the upcoming corrections to the UK Biobank exome data will not qualitatively change our findings as regards these conditions. There is a possibility, of course, that there could be a significant shift in our results for the type-1 diabetes predictor.

Acknowledgements

SY, TR, LL, and SH acknowledge support from the Oﬃce of the Vice-President for Research at MSU. LL was also supported by Genomic Prediction, Inc. during part of this project. This work was supported in part by Michigan State University through computational resources provided by the Institute for Cyber- Enabled Research. This research was conducted using the UK Biobank Resource under UK Biobank Main Application 15326.

Competing Interests

Stephen Hsu is a shareholder and serves on the board of directors of Genomic Prediction, Inc. Louis Lello joined the company, becoming an employee and shareholder, during the writing and submission of this paper. The other authors have no commercial interests relevant to the research.

References

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Appendix A

Asthma At k= 30 kbp, genic variance= 75.9% % of Total Predictor Variance Accounted For by SNPs located on Gene

2.5

2.0

1.5

1.0

0.5

0.0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 8: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the asthma predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Atrial Fibrillation At k= 30 kbp, genic variance= 32.7% % of Total Predictor Variance Accounted For by SNPs located on Gene

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 9: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the atrial ﬁbrillation predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Basal Cell Carcinoma At k= 30 kbp, genic variance= 87.4% % of Total Predictor Variance Accounted For by SNPs located on Gene 30

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 10: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the basal cell carcinoma predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Breast Cancer At k= 30 kbp, genic variance= 66.3% % of Total Predictor Variance Accounted For by SNPs located on Gene

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Figure 11: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the breast cancer predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Coronary Artery Disease At k= 30 kbp, genic variance= 27.1% % of Total Predictor Variance Accounted For by SNPs located on Gene

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Figure 12: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the coronary artery disease predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Diabetes- Type-1 At k= 30 kbp, genic variance= 75.4% % of Total Predictor Variance Accounted For by SNPs located on Gene

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 13: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the type-1 diabetes predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Diabetes- Type-2 At k= 30 kbp, genic variance= 80.9% % of Total Predictor Variance Accounted For by SNPs located on Gene

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Figure 14: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the type-2 diabetes predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Diastolic Blood Pressure At k= 30 kbp, genic variance= 75.3% % of Total Predictor Variance Accounted For by SNPs located on Gene

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Figure 15: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the diastolic blood pressure predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Education Years At k= 30 kbp, genic variance= 65.3% % of Total Predictor Variance Accounted For by SNPs located on Gene

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Figure 16: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the education years predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Gallstones At k= 30 kbp, genic variance= 98.4% % of Total Predictor Variance Accounted For by SNPs located on Gene

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 17: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the gallstones predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Glaucoma At k= 30 kbp, genic variance= 78.4% % of Total Predictor Variance Accounted For by SNPs located on Gene 20

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 18: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the glaucoma predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Gout At k= 30 kbp, genic variance= 96.0% % of Total Predictor Variance Accounted For by SNPs located on Gene 60

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 19: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the gout predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Heart Attack At k= 30 kbp, genic variance= 78.1% % of Total Predictor Variance Accounted For by SNPs located on Gene

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 20: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the heart attack predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Height At k= 30 kbp, genic variance= 81.2% % of Total Predictor Variance Accounted For by SNPs located on Gene

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Figure 21: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the height predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

High Cholesterol At k= 30 kbp, genic variance= 88.2% % of Total Predictor Variance Accounted For by SNPs located on Gene

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 22: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the high cholesterol predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Hypertension At k= 30 kbp, genic variance= 75.2% % of Total Predictor Variance Accounted For by SNPs located on Gene

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Figure 23: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the hypertension predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Hypothyroidism At k= 30 kbp, genic variance= 80.1% % of Total Predictor Variance Accounted For by SNPs located on Gene

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 24: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the hypothyroidism predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Malignant Melanoma At k= 30 kbp, genic variance= 92.5% % of Total Predictor Variance Accounted For by SNPs located on Gene

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 25: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the malignant melanoma predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Menopause At k= 30 kbp, genic variance= 93.8% % of Total Predictor Variance Accounted For by SNPs located on Gene

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 26: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the menopause predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Pulse Rate At k= 30 kbp, genic variance= 76.0% % of Total Predictor Variance Accounted For by SNPs located on Gene

0 Gene Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Figure 27: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the pulse rate predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends.

Systolic Blood Pressure At k= 30 kbp, genic variance= 74.7% % of Total Predictor Variance Accounted For by SNPs located on Gene

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Figure 28: The ﬁfteen largest total values of variance accounted for by predictor SNPs located on a single gene (in terms of the percentage of total variance accounted for by all predictor SNPs) for the systolic blood pressure predictor. Each vertical bar is colored violet with a depth of shade proportional to the height of the bar. Here, ’genic’ SNPs are contained within the GENCODE Release 19 gene boundaries plus 30 kilo base pairs at both ends. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Appendix B

Tables that list the top genes, as ordered by variance Basal Cell Carcinoma % accounted for, for various phenotypes. Ensembl ID Variance Explained by Gene of Total Predictor Variance ENSG00000137265 9.11032× 10 -7 28.4835 ENSG00000125780 3.86761× 10 -7 12.0921 ENSG00000140995 2.19694× 10 -7 6.86874 Asthma ENSG00000177946 2.19118× 10 -7 6.85076 Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000003249 2.13993× 10 -7 6.69052 ENSG00000196735 0.0000461821 2.82293 -7 ENSG00000166949 0.0000440566 2.69301 ENSG00000179051 2.12828× 10 6.65409 ENSG00000115604 0.000039764 2.43062 ENSG00000064012 2.10488× 10 -7 6.58092 ENSG00000115602 0.0000395971 2.42042 ENSG00000155749 2.10488× 10 -7 6.58092 ENSG00000145777 0.0000382693 2.33925 ENSG00000003400 2.0926× 10 -7 6.54255 ENSG00000134987 0.0000382693 2.33925 ENSG00000114861 1.11256× 10 -7 3.47842 ENSG00000179344 0.0000381038 2.32914 ENSG00000077498 1.04526× 10 -7 3.26803 ENSG00000137033 0.0000217498 1.32948 -7 ENSG00000038532 0.0000207941 1.27106 ENSG00000205420 1.01298× 10 3.16709 - ENSG00000113522 0.0000160743 0.982561 ENSG00000186081 1.01298× 10 7 3.16709 ENSG00000073605 0.0000150246 0.918398 ENSG00000268716 1.01298× 10 -7 3.16709 ENSG00000172057 0.0000150246 0.918398 ENSG00000139648 9.16756× 10 -8 2.86625 ENSG00000166888 0.0000149384 0.913124 ENSG00000158805 8.50283× 10 -8 2.65842 ENSG00000166881 0.0000146788 0.897259 ENSG00000187741 8.50283× 10 -8 2.65842 ENSG00000166886 0.0000146788 0.897259 × -8 ENSG00000186075 0.0000144062 0.880593 ENSG00000101460 7.16043 10 2.23872 -8 ENSG00000145012 0.0000137849 0.84262 ENSG00000101464 7.16043× 10 2.23872 ENSG00000158636 0.0000137277 0.839121 ENSG00000196735 7.01145× 10 -8 2.19214

Figure 29: For the asthma predictor: list of genes Figure 31: For the basal cell carcinoma predictor: responsible for the top ﬁfteen values of variance ac- list of genes responsible for the top ﬁfteen values of counted for by single genes, and the corresponding variance accounted for by single genes, and the corre- variance values, both explicit and expressed as a per- sponding variance values, both explicit and expressed centage of the total predictor variance. as a percentage of the total predictor variance.

Atrial Fibrillation Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000140836 4.35491× 10 -7 7.45884 Breast Cancer ENSG00000164093 2.86504× 10 -7 4.90708 Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000116132 1.42571× 10 -7 2.44188 ENSG00000066468 0.0000212362 14.7834 ENSG00000143603 1.34099× 10 -7 2.29676 ENSG00000103460 0.0000200173 13.9349 -6 ENSG00000107954 1.16675× 10 -7 1.99835 ENSG00000196588 2.17644× 10 1.51512 -6 ENSG00000148120 1.09383× 10 -7 1.87345 ENSG00000120262 2.06537× 10 1.43779 -6 ENSG00000089225 7.82504× 10 -8 1.34023 ENSG00000163491 1.96784× 10 1.3699 -6 ENSG00000115935 7.60088× 10 -8 1.30184 ENSG00000074527 1.9048× 10 1.32601 - ENSG00000107957 6.85145× 10 -8 1.17348 ENSG00000164362 1.66297× 10 6 1.15766 - ENSG00000105974 6.17914× 10 -8 1.05833 ENSG00000140718 1.49019× 10 6 1.03739 - ENSG00000171385 5.7165× 10 -8 0.97909 ENSG00000108175 1.45587× 10 6 1.01349 ENSG00000173406 4.58225× 10 -8 0.784823 ENSG00000166446 1.29111× 10 -6 0.898797 ENSG00000110318 1.74445× 10 -8 0.298779 ENSG00000049656 1.26148× 10 -6 0.878169 ENSG00000120457 1.71183× 10 -8 0.293193 ENSG00000138311 7.52238× 10 -7 0.523666 ENSG00000174370 1.71183× 10 -8 0.293193 ENSG00000071967 6.70661× 10 -7 0.466876 ENSG00000173572 1.55386× 10 -8 0.266137 ENSG00000167081 6.5404× 10 -7 0.455306 ENSG00000179709 1.55386× 10 -8 0.266137 ENSG00000136267 6.13165× 10 -7 0.426851

Figure 30: For the atrial fibrillation predictor: list of Figure 32: For the breast cancer predictor: list of genes responsible for the top fifteen values of variance genes responsible for the top fifteen values of variance accounted for by single genes, and the corresponding accounted for by single genes, and the corresponding variance values, both explicit and expressed as a per- variance values, both explicit and expressed as a percentage of the total predictor variance. centage of the total predictor variance. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Coronary Artery Disease Diabetes- Type 2 Ensembl ID Variance Explained by Gene% of Total Predictor Variance Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000198670 0.000204929 8.14914 ENSG00000148737 0.0000449243 24.795 ENSG00000112137 0.000154524 6.14475 ENSG00000073792 6.41139× 10 -6 3.53863 ENSG00000122194 0.000115362 4.58747 ENSG00000053918 3.32273× 10 -6 1.83391 ENSG00000146477 0.0000584146 2.3229 ENSG00000145996 2.75949× 10 -6 1.52304 ENSG00000143126 0.000056525 2.24775 ENSG00000145730 1.68939× 10 -6 0.932423 ENSG00000134222 0.0000565249 2.24775 ENSG00000173175 1.50627× 10 -6 0.831356 ENSG00000221986 0.0000565246 2.24774 ENSG00000115970 1.40593× 10 -6 0.775975 ENSG00000091732 0.000022669 0.901448 ENSG00000164756 1.38849× 10 -6 0.766351 ENSG00000130203 0.0000203651 0.809834 ENSG00000029534 1.36035× 10 -6 0.750819 ENSG00000130204 0.0000203646 0.809811 ENSG00000165066 1.36035× 10 -6 0.750815 ENSG00000181652 0.0000170687 0.678747 ENSG00000196781 1.29152× 10 -6 0.712827 ENSG00000164867 0.0000170686 0.678746 × -6 ENSG00000055118 0.0000170678 0.678713 ENSG00000138002 1.27887 10 0.705844 -6 ENSG00000134871 0.0000158485 0.630227 ENSG00000115226 1.27887× 10 0.705844 -6 ENSG00000130208 0.0000151627 0.602955 ENSG00000084734 1.27887× 10 0.705844 ENSG00000233438 1.27887× 10 -6 0.705844 ENSG00000152804 1.27043× 10 -6 0.70119 Figure 33: For the coronary artery disease predictor: ENSG00000108175 1.24929× 10 -6 0.689518 list of genes responsible for the top ﬁfteen values of ENSG00000149948 1.20786× 10 -6 0.666652 variance accounted for by single genes, and the corresponding variance values, both explicit and expressed Figure 35: For the type-2 diabetes predictor: list of as a percentage of the total predictor variance. genes responsible for the top ﬁfteen values of variance accounted for by single genes, and the corresponding variance values, both explicit and expressed as a percentage of the total predictor variance.

Diabetes- Type 1 Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000196735 1.71762× 10 -6 14.2359 ENSG00000196126 1.10313× 10 -6 9.14287 ENSG00000179344 8.43201× 10 -7 6.98856 ENSG00000204290 5.28293× 10 -7 4.37856 ENSG00000116793 4.17049× 10 -7 3.45655 ENSG00000081019 4.17049× 10 -7 3.45655 ENSG00000129965 3.03197× 10 -7 2.51294 ENSG00000254647 3.03197× 10 -7 2.51294 ENSG00000180176 3.03197× 10 -7 2.51294 ENSG00000167244 2.96451× 10 -7 2.45702 Diastolic Blood Pressure ENSG00000237541 2.85331× 10 -7 2.36486 % ENSG00000204287 2.25475× 10 -7 1.86877 Ensembl ID Variance Explained by Gene of Total Predictor Variance ENSG00000205517 0.00026354 0.802109 ENSG00000148737 1.82298× 10 -7 1.51091 ENSG00000111252 0.00026058 0.793101 × -7 ENSG00000232629 1.80438 10 1.49549 ENSG00000204842 0.000260509 0.792884 -7 ENSG00000204536 1.11524× 10 0.924323 ENSG00000198003 0.000259275 0.789127 - ENSG00000137310 1.11524× 10 7 0.924323 ENSG00000130175 0.000246709 0.750883 ENSG00000204531 1.11524× 10 -7 0.924323 ENSG00000168038 0.000239419 0.728695 ENSG00000206344 1.11524× 10 -7 0.924323 ENSG00000123454 0.00021092 0.641956 ENSG00000204392 1.04034× 10 -7 0.862246 ENSG00000123453 0.000209687 0.638203 ENSG00000204390 9.68184× 10 -8 0.802444 ENSG00000138821 0.000181787 0.553286 ENSG00000138675 0.000177214 0.539366 ENSG00000204389 9.68184× 10 -8 0.802444 ENSG00000177000 0.000170691 0.519515 ENSG00000204388 9.68184× 10 -8 0.802444 ENSG00000011021 0.000170045 0.517549 -8 ENSG00000204387 9.68184× 10 0.802444 ENSG00000165995 0.000169235 0.515084 -8 ENSG00000204386 9.68184× 10 0.802444 ENSG00000215910 0.000166846 0.507812 ENSG00000204252 8.09372× 10 -8 0.670819 ENSG00000182511 0.000162822 0.495565

Figure 34: For the type-1 diabetes predictor: list of Figure 36: For the diastolic blood pressure predictor: genes responsible for the top ﬁfteen values of variance list of genes responsible for the top ﬁfteen values of accounted for by single genes, and the corresponding variance accounted for by single genes, and the corre- variance values, both explicit and expressed as a per- sponding variance values, both explicit and expressed centage of the total predictor variance. as a percentage of the total predictor variance. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Education Years Glaucoma Ensembl ID Variance Explained by Gene% of Total Predictor Variance Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000140945 0.000117975 0.234252 ENSG00000143149 1.91058× 10 -6 19.1727 ENSG00000101489 0.000103631 0.20577 ENSG00000143183 1.91058× 10 -6 19.1727 ENSG00000153310 0.0000979671 0.194524 ENSG00000007237 4.46775× 10 -7 4.4834 ENSG00000179915 0.000093737 0.186125 ENSG00000264194 4.38653× 10 -7 4.4019 ENSG00000145934 0.0000880685 0.174869 ENSG00000196526 3.18499× 10 -7 3.19614 ENSG00000108684 0.000079856 0.158563 ENSG00000264545 2.11973× 10 -7 2.12716 ENSG00000078328 0.0000798313 0.158514 ENSG00000136944 1.45785× 10 -7 1.46296 ENSG00000119866 0.0000769443 0.152781 ENSG00000077044 1.33192× 10 -7 1.33658 ENSG00000164061 0.0000741843 0.147301 ENSG00000179008 1.19163× 10 -7 1.1958 ENSG00000188580 0.0000732577 0.145461 ENSG00000184302 1.19163× 10 -7 1.1958 ENSG00000101109 0.000071637 0.142243 ENSG00000171435 1.09544× 10 -7 1.09927 ENSG00000168702 0.0000694311 0.137863 ENSG00000147883 1.03397× 10 -7 1.0376 ENSG00000075884 0.000068552 0.136117 × -8 ENSG00000183715 0.0000669065 0.13285 ENSG00000157110 9.30046 10 0.933304 -8 ENSG00000176171 0.0000657761 0.130606 ENSG00000183044 7.686× 10 0.771292 ENSG00000115970 7.37166× 10 -8 0.739748 ENSG00000138193 7.18263× 10 -8 0.720779 Figure 37: For the education years predictor: list of ENSG00000112685 6.58665× 10 -8 0.660972 genes responsible for the top ﬁfteen values of variance accounted for by single genes, and the corresponding Figure 39: For the glaucoma predictor: list of genes variance values, both explicit and expressed as a per- responsible for the top ﬁfteen values of variance ac- centage of the total predictor variance. counted for by single genes, and the corresponding variance values, both explicit and expressed as a percentage of the total predictor variance.

Gout Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000118777 0.0000202008 56.9739 ENSG00000109667 4.39332× 10 -6 12.3908 Gallstones ENSG00000138002 1.25013× 10 -6 3.52582 Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000115226 1.25013× 10 -6 3.52582 ENSG00000143921 0.0000278316 83.2315 ENSG00000084734 1.25013× 10 -6 3.52582 ENSG00000138075 0.0000276955 82.8245 ENSG00000196616 1.23783× 10 -6 3.49115 ENSG00000138036 0.0000268878 80.4092 ENSG00000248144 1.23783× 10 -6 3.49115 ENSG00000152527 6.84056× 10 -7 2.0457 ENSG00000187758 1.23775× 10 -6 3.49093 ENSG00000197249 5.88871× 10 -7 1.76105 × -6 ENSG00000105398 5.81249× 10 -7 1.73825 ENSG00000233438 1.2312 10 3.47244 × -6 ENSG00000198589 5.33802× 10 -7 1.59636 ENSG00000124568 1.04791 10 2.9555 - - × 6 ENSG00000170390 5.3318× 10 7 1.5945 ENSG00000124564 1.04791 10 2.9555 -6 ENSG00000101076 4.42043× 10 -7 1.32195 ENSG00000168065 1.02306× 10 2.88542 -7 ENSG00000005471 3.63638× 10 -7 1.08748 ENSG00000197891 9.27473× 10 2.61582 -7 ENSG00000169903 3.58726× 10 -7 1.07279 ENSG00000265491 4.92238× 10 1.3883 -7 ENSG00000085563 3.49533× 10 -7 1.0453 ENSG00000174827 4.72186× 10 1.33174 -7 ENSG00000176920 2.11116× 10 -7 0.631352 ENSG00000117281 4.72186× 10 1.33174 - ENSG00000176909 2.11116× 10 -7 0.631352 ENSG00000165449 3.68976× 10 7 1.04065 - ENSG00000105538 2.11116× 10 -7 0.631352 ENSG00000110076 3.15776× 10 7 0.890607 ENSG00000182264 2.11116× 10 -7 0.631352 ENSG00000179912 2.86978× 10 -7 0.809385 ENSG00000215114 1.42874× 10 -7 0.427272 ENSG00000163935 2.22875× 10 -7 0.62859 ENSG00000167910 1.37481× 10 -7 0.411142 ENSG00000272305 2.22875× 10 -7 0.62859

Figure 38: For the gallstones predictor: list of genes Figure 40: For the gout predictor: list of genes re- responsible for the top ﬁfteen values of variance ac- sponsible for the top ﬁfteen values of variance accounted for by single genes, and the corresponding counted for by single genes, and the corresponding variance values, both explicit and expressed as a per- variance values, both explicit and expressed as a percentage of the total predictor variance. centage of the total predictor variance. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Heart Attack High Cholesterol Ensembl ID Variance Explained by Gene% of Total Predictor Variance Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000198670 6.52514× 10 -6 21.325 ENSG00000130164 0.000159927 11.0888 ENSG00000154305 1.30816× 10 -6 4.27522 ENSG00000130208 0.00014979 10.3859 ENSG00000186063 1.30816× 10 -6 4.27522 ENSG00000130204 0.000142489 9.87962 ENSG00000112137 9.77669× 10 -7 3.19514 ENSG00000130203 0.000141593 9.81752 ENSG00000127616 0.00013607 9.43459 ENSG00000143126 8.24884× 10 -7 2.69582 ENSG00000221986 0.000104152 7.22154 ENSG00000134222 8.24884× 10 -7 2.69582 ENSG00000143126 0.000104132 7.22014 × -7 ENSG00000221986 8.24884 10 2.69582 ENSG00000134222 0.000104132 7.22014 -7 ENSG00000130204 6.85983× 10 2.24188 ENSG00000267467 0.000095527 6.62348 - ENSG00000130203 6.85983× 10 7 2.24188 ENSG00000224916 0.000095527 6.62348 ENSG00000130208 6.85983× 10 -7 2.24188 ENSG00000130202 0.0000908673 6.3004 ENSG00000267467 6.69128× 10 -7 2.18679 ENSG00000084674 0.0000722735 5.01117 ENSG00000224916 6.69128× 10 -7 2.18679 ENSG00000137656 0.0000663571 4.60095 ENSG00000118526 6.56704× 10 -7 2.14619 ENSG00000109917 0.0000660421 4.57911 ENSG00000110243 0.0000660421 4.57911 ENSG00000130164 6.16851× 10 -7 2.01594 ENSG00000169174 0.0000637275 4.41863 ENSG00000127616 5.64743× 10 -7 1.84565 ENSG00000162402 0.0000609838 4.22839 × -7 ENSG00000234906 5.05309 10 1.65141 ENSG00000162399 0.000060702 4.20885 ENSG00000187498 3.40483× 10 -7 1.11274 ENSG00000091732 3.29055× 10 -7 1.07539 ENSG00000111252 2.99308× 10 -7 0.978174 Figure 43: For the high cholesterol predictor: list of ENSG00000204842 2.99308× 10 -7 0.978174 genes responsible for the top ﬁfteen values of variance ENSG00000115970 2.8221× 10 -7 0.922297 accounted for by single genes, and the corresponding -7 ENSG00000112531 1.96104× 10 0.640892 variance values, both explicit and expressed as a percentage of the total predictor variance. Figure 41: For the heart attack predictor: list of genes responsible for the top ﬁfteen values of variance accounted for by single genes, and the corresponding variance values, both explicit and expressed as a percentage of the total predictor variance.

Hypertension Height Ensembl ID Variance Explained by Gene% of Total Predictor Variance Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000138675 0.0000739221 1.42576 ENSG00000140470 0.00202515 0.885937 ENSG00000205517 0.0000609586 1.17573 ENSG00000157766 0.00178485 0.780812 ENSG00000198003 0.0000608256 1.17317 ENSG00000116183 0.00169709 0.742421 ENSG00000130175 0.0000590379 1.13869 ENSG00000126001 0.0016775 0.733849 ENSG00000171105 0.000048139 0.928475 ENSG00000101019 0.00165261 0.722961 ENSG00000111252 0.000042734 0.824227 ENSG00000204183 0.00163949 0.717221 ENSG00000204842 0.0000427337 0.824221 ENSG00000125965 0.00163949 0.717221 ENSG00000171303 0.000040641 0.783858 ENSG00000156218 0.00135713 0.593697 ENSG00000183346 0.0000364154 0.702357 ENSG00000164161 0.0012893 0.564027 ENSG00000140564 0.0000340002 0.655775 ENSG00000140443 0.00127966 0.559809 ENSG00000182511 0.0000340002 0.655775 ENSG00000140511 0.00124035 0.54261 ENSG00000172201 0.00120895 0.528875 ENSG00000196547 0.0000338974 0.653792 ENSG00000144535 0.00120723 0.52812 ENSG00000130592 0.000030334 0.585063 ENSG00000066827 0.00116436 0.509369 ENSG00000130598 0.0000302174 0.582815 ENSG00000115380 0.00115624 0.505814 ENSG00000165995 0.0000293892 0.56684 ENSG00000176124 0.00115192 0.503927 ENSG00000138193 0.0000276911 0.534089

Figure 42: For the height predictor: list of genes re- Figure 44: For the hypertension predictor: list of sponsible for the top ﬁfteen values of variance ac- genes responsible for the top ﬁfteen values of variance counted for by single genes, and the corresponding accounted for by single genes, and the corresponding variance values, both explicit and expressed as a per- variance values, both explicit and expressed as a percentage of the total predictor variance. centage of the total predictor variance. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Hypothyroidism Menopause Ensembl ID Variance Explained by Gene% of Total Predictor Variance Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000081019 0.0000352729 7.77261 ENSG00000183878 0.339579 24.2854 ENSG00000134242 0.0000342818 7.55422 ENSG00000125885 0.117767 8.42228 ENSG00000111252 0.0000295005 6.50062 ENSG00000089195 0.116968 8.3651 ENSG00000204842 0.0000295005 6.50062 ENSG00000127311 0.0924778 6.61366 ENSG00000196735 0.0000240816 5.30654 ENSG00000160469 0.0629791 4.50402 ENSG00000179344 0.0000222207 4.89647 ENSG00000180061 0.0629791 4.50402 ENSG00000163599 0.0000145232 3.20029 ENSG00000133247 0.0629791 4.50402 ENSG00000273167 8.09495× 10 -6 1.78378 ENSG00000160471 0.0629791 4.50402 ENSG00000182957 8.09495× 10 -6 1.78378 ENSG00000267531 0.0629791 4.50402 ENSG00000187840 0.0441477 3.15727 ENSG00000134215 7.84936× 10 -6 1.72966 ENSG00000163312 0.0322135 2.30379 -6 ENSG00000112182 6.94025× 10 1.52933 ENSG00000163319 0.0322135 2.30379 ENSG00000204520 6.40646× 10 -6 1.41171 ENSG00000163322 0.0322135 2.30379 ENSG00000145012 5.63275× 10 -6 1.24122 ENSG00000087206 0.0251368 1.79769 ENSG00000100385 5.00989× 10 -6 1.10396 ENSG00000113761 0.023128 1.65403 ENSG00000196531 0.017758 1.26998 ENSG00000138378 4.95025× 10 -6 1.09082 ENSG00000198056 0.017758 1.26998 × -6 ENSG00000223865 4.75552 10 1.04791 ENSG00000025423 0.017758 1.26998 -6 ENSG00000231389 4.74254× 10 1.04505 ENSG00000151726 0.0165154 1.18112 ENSG00000158315 0.0160452 1.14749 ENSG00000116954 0.0160272 1.14621 Figure 45: For the hypothyroidism predictor: list of ENSG00000214114 0.0160272 1.14621 genes responsible for the top ﬁfteen values of variance ENSG00000274944 0.0160272 1.14621 accounted for by single genes, and the corresponding ENSG00000131233 0.0160272 1.14621 ENSG00000155974 0.0154474 1.10474 variance values, both explicit and expressed as a per- ENSG00000234719 0.0121138 0.866332 centage of the total predictor variance. Figure 47: For the menopause predictor: list of genes responsible for the top ﬁfteen values of variance accounted for by single genes, and the corresponding variance values, both explicit and expressed as a per- Malignant Melanoma centage of the total predictor variance. Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000198211 5.68246× 10 -7 42.8898 ENSG00000258947 5.68246× 10 -7 42.8898 ENSG00000141002 5.51776× 10 -7 41.6467 ENSG00000258839 5.51776× 10 -7 41.6467 ENSG00000140995 4.88084× 10 -7 36.8394 ENSG00000077498 1.79479× 10 -7 13.5467 Pulse Rate ENSG00000158805 1.08341× 10 -7 8.17731 % ENSG00000187741 1.08341× 10 -7 8.17731 Ensembl ID Variance Explained by Gene of Total Predictor Variance ENSG00000092054 0.0016225 3.9998 ENSG00000164362 7.85712× 10 -8 5.93036 ENSG00000197616 0.00157533 3.8835 × -8 ENSG00000049656 7.85712 10 5.93036 ENSG00000163492 0.00150449 3.70887 -8 ENSG00000151789 2.6662× 10 2.01238 ENSG00000166091 0.00120182 2.96274 - ENSG00000005884 2.06455× 10 8 1.55827 ENSG00000166090 0.00114524 2.82326 ENSG00000005882 2.06455× 10 -8 1.55827 ENSG00000155657 0.000932079 2.29777 ENSG00000172292 1.84488× 10 -8 1.39247 ENSG00000149633 0.000932021 2.29762 ENSG00000177946 1.64698× 10 -8 1.2431 ENSG00000100842 0.000742851 1.83128 ENSG00000101460 1.60221× 10 -8 1.20931 ENSG00000196376 0.000581659 1.43391 ENSG00000101464 1.60221× 10 -8 1.20931 ENSG00000182732 0.00052007 1.28208 ENSG00000165801 0.000430485 1.06123 ENSG00000159110 1.16468× 10 -8 0.879072 ENSG00000165804 0.000430485 1.06123 ENSG00000249624 1.16468× 10 -8 0.879072 ENSG00000232070 0.000430485 1.06123 -8 ENSG00000243646 1.16468× 10 0.879072 ENSG00000165795 0.000428352 1.05598 -8 ENSG00000139644 1.11346× 10 0.840415 ENSG00000173431 0.000428338 1.05594 ENSG00000145996 9.83099× 10 -9 0.742018 ENSG00000174059 0.000394401 0.972278 ENSG00000150995 9.41989× 10 -9 0.71099 ENSG00000089225 0.000315557 0.777911

Figure 46: For the malignant melanoma predictor: Figure 48: For the pulse rate predictor: list of genes list of genes responsible for the top ﬁfteen values of responsible for the top ﬁfteen values of variance ac- variance accounted for by single genes, and the corre- counted for by single genes, and the corresponding sponding variance values, both explicit and expressed variance values, both explicit and expressed as a per- as a percentage of the total predictor variance. centage of the total predictor variance. bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Systolic Blood Pressure Ensembl ID Variance Explained by Gene% of Total Predictor Variance ENSG00000205517 0.000244798 0.849217 ENSG00000165995 0.000244118 0.846856 ENSG00000198003 0.000241788 0.838773 ENSG00000130175 0.00023594 0.818486 ENSG00000011021 0.00020999 0.728465 ENSG00000177000 0.000203954 0.707526 ENSG00000215910 0.000203195 0.704894 ENSG00000070961 0.000200379 0.695125 ENSG00000171105 0.000196777 0.682628 ENSG00000138675 0.00019149 0.664289 ENSG00000198373 0.000156504 0.54292 ENSG00000157322 0.000156504 0.54292 ENSG00000138193 0.00014907 0.517132 ENSG00000196547 0.000140354 0.486896 ENSG00000165895 0.000137298 0.476292 ENSG00000065054 0.000124649 0.432413

Figure 49: For the systolic blood pressure predictor: list of genes responsible for the top ﬁfteen values of variance accounted for by single genes, and the corresponding variance values, both explicit and expressed as a percentage of the total predictor variance.

Appendix C: UK Biobank Data

C.1 Array Sequencing Data Predictors (with the exception of the CAD predictor from [7]) were originally trained using the 2018 release of the UK Biobank. Predictor training was restricted to genetically British individuals (as de- ﬁned using ancestry principal component analysis performed by UK Biobank) [35, 36]. In 2018, the UK Biobank re-released the dataset representing approximately 500,000 individuals genotyped on two Aﬀymetrix platforms - approximately 50,000 samples on the UKB BiLEVE Axiom array and the re- mainder on the UKB Biobank Axiom array. The genotype information was collected for 488,377 individuals, and 805,426 SNPs, which were then subsequently imputed to a much larger number of SNPs. More details about the design of the array can be found on these documents from the UK Biobank website: an Axiom array content summary: http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/ UK-Biobank-Axiom-Array-Content-Summary-2014-1.pdf, and the document detailing the Axiom array: http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UK-Biobank-Axiom-Array-Datasheet-2014-1. pdf. Further information about the genotyping and phenotyping used to build the original predictors can be found in [5, 7].

C.2 Exome Sequencing Data In March 2019, the UK Biobank released whole-exome sequencing (WES) data for 49,960 participants [60]. Selection of participants for the study prioritized individuals with whole-body MRI imaging data from the UK Biobank Imaging Study, enhanced baseline measurements, hospital episode statistics (HES), linked primary care records, and admission to hospital with a primary diagnosis of asthma. In regards to age, sex and ancestry, the sequenced individuals are representative of the overall UK Biobank cohort. The sample set has 194 parent-offspring pairs, including 26 mother-father-child trios, 613 full-sibling pairs, 1 monozygotic twin pair and 195 second-degree genetically determined relationships. Exomes were captured using a version of the IDT xGen Exome Research Panel v1.0. Multiplexed samples were sequenced with dual-indexed 75 x 75 bp paired-end reads on the Illumina NovaSeq 6000 platform using S2 flow cells. The specific genomic regions targeted for sequencing covered 39 megabases of the human bioRxiv preprint doi: https://doi.org/10.1101/2020.02.12.946608; this version posted February 13, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

genome, corresponding to 19,396 genes. In addition, the regions measuring 100 bp and located directly upstream and downstream of each target region were also sequenced. A total of 4,735,722 variants located in targeted regions were identiﬁed. With adjacent (non-targeted) 100 bp regions included in the tally, a total of 9,693,526 indel and single nucleotide variants (SNVs) were observed. While only the target regions are required to meet all sequencing quality standards such as unique read coverage, variants in both target and adjacent regions were subjected to the same variant quality control metrics. Approximately 14% of coding variants identiﬁed via whole-exome sequencing were observed in the imputed sequence of 49,797 participants with both whole-exome sequencing and imputed data. 22.6% of the coding variants in the imputed data were not observed in the whole-exome sequencing data.