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Published in final edited form as: Science. 2021 Sep 23;373(6562):1499–1505. doi: 10.1126/science.abg8289

Protein-coding repeat polymorphisms strongly shape diverse human phenotypes†

Ronen E Mukamel 1,2,*, Robert E Handsaker 2,3,4,*, Maxwell A Sherman 1,2,5, Alison R Barton 1,2,6, Yiming Zheng 2,3, Steven A McCarroll 2,3,4,**, Po-Ru Loh 1,2,**
PMCID: PMC8549062  NIHMSID: NIHMS1744021  PMID: 34554798

Abstract

Many human proteins contain domains that vary in size or copy number due to variable numbers of tandem repeats (VNTRs). However, the relationships of VNTRs to most phenotypes are unknown due to difficulties measuring such repetitive elements. We developed methods to estimate VNTR lengths from whole-exome sequencing data and impute VNTR alleles into SNP-haplotypes. Analyzing 118 protein-altering VNTRs in 415,280 UK Biobank participants for association with 786 phenotypes identified some of the strongest associations of common variants with human phenotypes including height, hair morphology, and biomarkers of health. Accounting for large-effect VNTRs further enabled fine-mapping of associations to many more protein-coding mutations in the same genes. These results point to cryptic effects of highly polymorphic common structural variants that have eluded molecular analyses to date.

The human genome contains thousands of variable-number-of-tandem-repeat (VNTR) polymorphisms (1, 2), but the effects of these polymorphisms on human phenotypes are largely unknown. VNTRs are multi-allelic variants at which a nucleotide sequence – from seven to thousands of bp long – is repeated several to hundreds of times, with the number of repeats varying among individuals (Fig. S1). Extreme alleles of VNTRs have been implicated in diseases including progressive myoclonus epilepsy (3) and facioscapulohumeral muscular dystrophy (4). However, since most VNTRs are invisible to SNP arrays and difficult to measure with short-read sequencing, VNTRs have not been considered in genotype-phenotype association studies that have been central to recent work in human genetics.

We hypothesized that exome-sequence data might contain unappreciated information about VNTR lengths, and that VNTR alleles might segregate on specific SNP haplotypes, enabling statistical imputation (5) in SNP-phenotype data sets from hundreds of thousands of people, such as UK Biobank (UKB) (6).

Exploring the phenotypic effects of coding VNTRs

We identified candidate VNTRs by scanning the human reference genome for tandemly repeated sequences (7). For each repeat, we estimated “diploid VNTR content”—the sum of maternally- and paternally-derived allele lengths—in N=49,959 exome-sequenced UKB participants (8) by measuring numbers of reads that aligned to the repeated sequence (7). We then used surrounding SNPs to identify haplotypes likely to have been co-inherited from a recent common ancestor, enabling resolution of diploid measurements into allele-specific contributions and imputation of VNTR lengths into SNP-haplotypes of N=437,612 additional UKB participants. We developed statistical algorithms to perform such analysis on extended SNP haplotypes for hundreds of thousands of individuals, using sibling IBD information to benchmark accuracy and optimize analysis parameters (7). We focused subsequent analysis on autosomal exon-overlapping repeats in 118 genes for which these measurements exhibited cis-heritability in sibling pairs (Table S1).

We applied this approach to identify relationships between coding VNTR alleles and 786 phenotypes (Table S2) in up to 415,280 unrelated UKB participants (depending on phenotype) of European ancestry. This analysis found 185 statistically significant associations (Table S3). To determine whether such associations were driven by VNTR length variation, rather than by other variants with which the VNTRs were in linkage disequilibrium (LD), we performed fine-mapping analyses (9) considering nearby genotyped and imputed variants (6, 10). Because variation at most VNTRs arises from three or more alleles, VNTR variation was only partially correlated with individual SNPs, enabling this analysis to distinguish VNTR from SNP effects.

Nineteen phenotype associations involving five distinct VNTRs (Tables 1 and S3; Fig. S1) exhibited evidence (FINEMAP (9) posterior probability >0.95) that VNTR length variation, rather than nearby SNPs, drove genotype-phenotype associations. For these five VNTRs, we improved genotyping accuracy by incorporating additional information from within-repeat variation or spanning reads to confirm the associations (Figs. S2-S3; (7)).

Table 1. VNTRs within protein-coding sequences affect diverse human phenotypes.

For each of five protein-altering VNTRs involved in phenotype associations that passed stringent fine-mapping criteria, P-values (in linear mixed model analyses of N=415,280 unrelated UKB participants of European ancestry) and estimated effect-size ranges (across the longest and shortest alleles sufficiently common to be amenable to our computational analysis) are listed for the most-strongly associated phenotype. aa, amino acids.

Gene Cytoband Repeat unit
size		 Repeat
count
(EUR)		 Protein
domain
(effect)		 Phenotype Effect range (± s.e.) P-value
LPA 6q25.3-q26 ~5.6kb (114aa, 2 exons) 2-40 Kringle-IV (number) Lipoprotein(a) concentration 5.1 (± 0.5) s.d.(= 233 ± 23 nmol/L) 4.4 x 10−(25,121)
ACAN 15q26.1 57bp (19aa) 13-44 Chondroitin sulfate (size) Height 0.49 (± 0.04) s.d.(= 3.2 ± 0.3 cm) 1.7 x 10−234
TENT5A 6q14.1 15bp (5aa) 2-7 Unknown (size) Height 0.09 (± 0.01) s.d.(= 0.6 ± 0.1 cm) 2.5 x 10−53
MUC1 1q22 60bp (20aa) 20-125 Extracellular (size) Serum urea 0.16 (± 0.01) s.d.(= 0.22 ± 0.01 mmol/L) 2.7 x 10−163
TCHH 1q21.3 18bp (6aa) 5-15 α-helix rod (size) Male pattern baldness score −0.063 (± 0.006) s.d. 1.6 x 10−55
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These associations appeared to explain some of the largest known GWAS signals for human phenotypes, including height, serum urea, and hair phenotypes, with some associations exhibiting strength comparable to or exceeding that of any single SNP in the genome.

Three VNTRs—within exons of TENT5A, MUC1, and TCHH—had not previously been implicated at these loci; a fourth (in ACAN) was recently reported in parallel work (11). Analysis also replicated an association between the length of the KIV-2 repeat in LPA and lipoprotein(a) concentration (12) (P = 4.4 x 10−(25,121), BOLT-LMM (13)). All five VNTRs were genotyped and imputed accurately (RMSE ~ 1 repeat unit and/or R2 ≥ 0.7) according to benchmarks using cross-validation (Fig. S4 and Table S1) and the HGSVC2 long-read sequencing data set (Figs. S5-9) (7, 14).

Fine-mapping of LPA variants influencing lipoprotein(a) concentration

Complex genetics involving VNTRs and SNPs at the same locus was revealed in analysis of lipoprotein(a) concentration (Lp(a)), for which elevated levels are a major risk factor for coronary artery disease (15). Lp(a) is almost completely heritable, with roughly half of its population variance explained by a VNTR-generated size polymorphism in the kringle-IV (KIV) domain of apo(a) (12). Each KIV-2 repeat unit (~5.6 kb) spans two exons of LPA, which together encode a 114-amino-acid copy of this domain. Longer alleles—with more copies of the encoded kringle repeat—are known to associate with lower Lp(a) levels (12, 16), reflecting retention of longer apo(a) isoforms in the endoplasmic reticulum (17). In UKB, inheritance at the LPA locus explained most of the variance in Lp(a) measurements (R=0.93 in sib-pairs sharing both LPA alleles, consistent with previous work (18)), with KIV-2 length explaining ~61% of this variance in a nonparametric model.

To identify additional LPA variants that might more completely explain Lp(a) variation, and to explore their interactions with KIV-2 length, we utilized individuals heterozygous for either of two coding variants (combined MAF=0.05) that create null alleles that produce undetectable serum Lp(a) (7). This approach created an effective haploid model for Lp(a) and made it possible to systematically identify and measure the effects of Lp(a)-altering alleles (Fig. S10). We performed stepwise conditional analysis to identify LPA sequence variants that associated with low Lp(a) despite occurring on short or medium-length KIV-2 alleles that typically associate with higher Lp(a) levels (7).

These analyses identified associations to 17 protein-altering variants, each of which appeared to greatly reduce Lp(a) (P<1 x 10−17 for each variant, Fisher’s exact test or linear regression, Table S4); 43% of European haplotypes were affected by at least one of these variants. Six variants predicted to partially or fully abolish constitutive splice sites and six missense variants achieved the strongest associations in 12 consecutive stages of stepwise analysis; five additional rare (MAF<1%) coding variants exhibited top or near-top associations in further conditional analyses (Figs. 1A, S11, and Table S4). The two variants with the largest impacts on Lp(a) variation in the European population (owing to their high allele frequencies; MAF=13% and 21%) were variants within the KIV-2 region that are computationally predicted to impair splicing (19) of KIV-2 exon 2. One of these splice variants has been experimentally validated (20). These variants reduced Lp(a) by 85% and 89%, respectively, when present within a single KIV-2 repeat unit; alleles carrying either variant on multiple repeat units within the VNTR produced nearly undetectable Lp(a) (Fig. S12). Fine-mapping analyses identified three other common variants (MAF=14-28%)—two in the 5’ untranslated region of LPA (observed to regulate translational activity (21, 22)) and one missense variant—associated with more modest effects on Lp(a) levels across a broad range of KIV-2 alleles (Fig. 1A and Table S4).

Figure 1. Kringle IV-2 repeat length variation and 23 LPA SNPs together explain ~90% of lipoprotein(a) heritability.

Figure 1.

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A, Serum lipoprotein(a) concentration vs. KIV-2 VNTR length in an effective-haploid model of Lp(a), involving N=24,969 LPA alleles (in exome-sequenced UKB participants of European ancestry) for which the allele on the homologous chromosome was predicted to produce negligible Lp(a) (<4 nmol/L). Colors indicate the 15 most common Lp(a)-modifying SNPs identified by fine-mapping analysis. Curves indicate parametric fits of Lp(a) to KIV-2 length (gray: alleles not carrying any Lp(a)-modifying SNP; red, blue, green: carriers of a single common Lp(a)-modifying SNP); large points, mean Lp(a) among such alleles in KIV-2 length bins (error bars, 95% CIs). Histograms (top/bottom), counts of Lp(a) measurements outside the reportable range (<3.8 nmol/L or >189 nmol/L), colored by Lp(a)-modifying SNPs (7). B, Observed and predicted median Lp(a) among individuals of African (AFR; N=893), European (EUR; N=42,162), South Asian (SA; N=954), and East Asian (EAS; N=156) ancestry. C, LPA allele frequencies by ancestry. VNTR alleles in cis with a large-effect Lp(a)-reducing variant (respectively, the Lp(a)-increasing 5’ UTR variant rs1800769) are indicated in gray (respectively, red). D,E, Myocardial infarction risk (respectively, type 2 diabetes prevalence) vs. measured or genetically predicted Lp(a). Error bars, 95% CIs.

The strong effects of the VNTR and SNPs at LPA, the large sample size of UK Biobank, and the ability to chromosomally phase all these variants accurately, made it possible to identify nonlinear and cis-epistatic effects at LPA. Accounting for the effects of the 17 implicated coding variants at LPA showed that the inverse relationship between KIV-2 length and Lp(a) (12, 17) breaks down for very short (high-protein-level) alleles (Fig. 1A). Throughout most of the KIV-2 length range (12-24 repeats), each one-repeat-unit decrease in KIV-2 length resulted in a 37% increase in Lp(a) (Fig. 1A). However, this effect was attenuated for alleles with fewer than 12 repeats and appeared to invert around 8 repeats (P=9.4 x 10−31, linear regression; Figs. 1A and S13). Accounting for the nonlinear effect of KIV-2 length and for phase-resolved LPA sequence variants explained 90% of the heritable variance (83% of total variance) in Lp(a) (vs. ~60% of total variance in earlier work (12, 23)).

Serum Lp(a) levels vary across populations (12), with median measurements 4-fold higher among Africans than among Europeans, but the reason for this cross-population variation has been unclear. We found that this variation was largely explained by population differences in the allele frequencies of LPA sequence variants (Fig. 1B). Elevated Lp(a) in UKB participants of African ancestry (median 80.1 nmol/L vs.18.5 nmol/L in Europeans) was primarily explained by the paucity of alleles carrying variants that greatly reduced Lp(a) (~13% of African alleles vs. ~43% of European alleles, despite sufficient discovery power in both populations) and the higher frequency of the Lp(a)-increasing 5’ UTR variant among African alleles (MAF=46% vs. 17% in European alleles for rs1800769; Fig. 1C). These allele frequency differences also explained the apparent difference in shape of the Lp(a)-KIV-2 curve in different populations (Fig. S14).

The accuracy of genetically predicted Lp(a) (R2=0.83 in Europeans) enabled insights into epidemiological associations involving Lp(a). We observed that the myocardial infarction risk-increasing effect of higher Lp(a) (15, 24) extends to extreme Lp(a) levels (OR=3.1, 95% CI=1.9-5.2 for individuals with genetically predicted Lp(a)>400 nmol/L; Fig. 1D). In contrast, lower genetically predicted Lp(a) did not associate with increased type-2 diabetes (T2D) risk, suggesting that the 17% (s.e. 1%) lower levels of Lp(a) observed in T2D patients represents reverse causation resulting from T2D itself, T2D-related liver comorbidities, or T2D medication (Figs. 1E, S15, and Table S5).

Human height is strongly affected by VNTRs in ACAN and TENT5A

Human height associates with hundreds of common alleles (25), generally with small effect sizes (<0.05 standard deviations). In contrast, size variation of a 57bp (19 amino acid) repeat in the ACAN gene strongly associated with height (P=1.7 x 10−234, BOLT-LMM), with an effect size differential of 0.49 standard deviations (s.e. 0.04)—i.e., 3.2 centimeters—between the longest and shortest European alleles (Fig. 2). This association, which appears to underlie one of the first reported genetic associations with height (26), was also observed in a parallel study using long-read sequencing in the deCODE cohort (11). Here, analysis in the larger, more diverse UKB cohort—which contains double the range of allelic variation, including a very short 6-repeat African allele and European alleles with up to ~44 repeats (Fig. 2B,D)—uncovered several additional insights.

Figure 2. Lengths of protein-coding repeat polymorphisms in ACAN and TENT5A associate with human height.

Figure 2.

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A, Genetic associations with height in UKB participants of European (top; EUR N=415,280) and African (bottom; AFR N=7,543) ancestry. B, ACAN VNTR allele length distributions. C, Height association statistics at ACAN in three consecutive steps of stepwise conditional analysis (EUR N=415,280). Large diamond/squares, likely-causal coding mutations; colored dots, variants in partial LD (R2>0.1) with labeled variants. Height phenotypes were adjusted for genetic predictions computed using the rest of the genome (7). D, Mean height of carriers (lines, left axis) and EUR allele frequencies (histograms, right axis) of ACAN alleles defined by VNTR length and missense SNP haplotype; error bars, 95% CIs. Rare long alleles (40-42 repeats) were grouped into one bin. E, Height associations at TENT5A. F, Mean height and EUR allele frequencies for TENT5A VNTR alleles; error bars, 95% CIs.

Height exhibited an approximately linear relationship with length of the ACAN VNTR. Consistent increasing effects were observed across a series of at least nine distinct VNTR allele lengths, resulting in an association signal (P=1.7 x 10−234, BOLT-LMM) stronger than that of any nearby variant, explaining 0.19% of height variance among European-ancestry UKB participants (Fig. 2C,D). Moreover, among 7,543 UKB participants of African ancestry, the ACAN VNTR association was nearly 50% stronger than the association of any other variant in the genome (P=5.2 x 10−12 for the VNTR vs. P=1.4 x 10−8 for the strongest SNP association) and explained 0.60% of height variance, primarily owing to greater VNTR length variation (s.d.=3.7 repeats vs. 1.5 repeats in Europeans; Fig. 2B). Imputation of the VNTR association into height association statistics from the African-ancestry AAAGC cohort (27) replicated these results (with the VNTR explaining an estimated 0.42% of height variance; imputed P=5.8 x 10−40 vs. linear regression P=3.4 x 10−20 for the strongest SNP association genome-wide; Fig. S16; (7)).

Aggrecan, the protein encoded by ACAN, is a component of the extracellular matrix in growth plate cartilage and is required for normal growth plate cytoarchitecture (28). The VNTR generates 2.4-fold size variation in aggrecan’s first chondroitin sulfate domain (CS1), a domain whose amino-acid residues are modified by long, charged polysaccharide chains that endow this extracellular matrix with key properties including the ability to hold large amounts of water (29).

As at LPA, incorporation of the ACAN VNTR into genetic association analysis (by stepwise conditional analysis) made it possible to identify additional genetic effects, driven at ACAN by two common missense SNPs (Fig. 2C and Table S6). These two missense SNPs, which affect ACAN globular domains, had two of the top three predicted deleteriousness scores (30) (CADD = 23.1 for rs3817428 and 27.6 for rs34949187) among common missense SNPs in ACAN and were corroborated by Bayesian fine-mapping (9) analysis (FINEMAP posterior probability of causality >0.99). A combined model including the VNTR and these SNPs explained 0.33% of height variance in Europeans.

Despite the strong effects of ACAN VNTR alleles on height, neither end of the allelic spectrum appeared to compromise ACAN function in any way detrimental to health. Whereas loss-of-function mutations in ACAN cause autosomal dominant skeletal disorders (31), VNTR length variation did not associate at Bonferroni significance with any disease in UK Biobank (P>3 x 10−4, logistic regression). A participant homozygous for the short 6-repeat allele (AF=1.2% among participants with African ancestry) had no reported musculoskeletal disease phenotypes.

A distinct coding VNTR in the TENT5A gene (previously named FAM46A) consisting of 2-7 repeats of 15bp also associated with height (P=2.5 x 10−53, BOLT-LMM), with six VNTR alleles exhibiting monotonically increasing effects (Fig. 2E,F). TENT5A—a poly(A) polymerase in which multiple coding variants have been linked to autosomal recessive osteogenesis imperfecta (OI) (32)—polyadenylates and increases expression in osteoblasts of the collagen genes COL1A1 and COL1A2 and other genes mutated in OI (33).

Kidney-function phenotypes shaped by a VNTR in MUC1

The MUC1 gene encodes a secreted (cell-surface-associated) protein (mucin 1) with cell-adhesive and anti-adhesive properties. MUC1 harbors a VNTR that contains 20-125 repeats (34) of a 60bp (20 amino acid) coding sequence that determines the length of a heavily glycosylated extracellular domain. Ultra-rare frameshift mutations within the MUC1 VNTR cause autosomal dominant tubulointerstitial kidney disease (35). In our analyses, length of the MUC1 VNTR associated with several renal phenotypes (Fig. 3), including serum urea (P=2.7 x 10−163, BOLT-LMM) and serum urate (P=4.7 x 10−99, BOLT-LMM). Longer VNTR alleles also associated with gout (P=3.6 x 10−17, logistic regression), a disease caused by excessive uric acid crystallization in the joints.

Figure 3. MUC1 VNTR length associates with multiple renal phenotypes.

Figure 3.

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A,C, Genetic associations with serum urea (A) and serum urate (C) at MUC1 (top; orange dots indicate variants in LD with MUC1 VNTR length (R2>0.1)) and genome-wide (bottom); N=415,280 UKB EUR participants. B,D, Mean phenotypes in carriers (B) or disease odds ratios (D) (lines, left axis) and allele frequencies (histograms, right axis) of MUC1 VNTR alleles. VNTR alleles were stratified into three groups for phenotype analyses: short (<55 repeat units), long (55-95 repeat units), and very long (>95 repeat units). Error bars, 95% CIs; eGFR, estimated glomerular filtration rate.

The MUC1 VNTR length polymorphism appeared to underlie some of the strongest, earliest reported SNP associations with serum urea and serum urate, two biomarkers of renal function that otherwise have somewhat independent heritability (genetic correlation = 0.25 (s.e. 0.01); Fig. 3A,C). For urea, the VNTR exhibited the strongest association genome-wide (matching that of a SNP on chromosome 5), explaining ~1% of heritable variance (~0.2% of total variance) in Europeans and accounting for nearly all of the association signal at the MUC1 locus (previously reported as MTX1-GBA (36); Fig. 3A). For urate, the VNTR also appeared to be the primary causal variant at a locus previously reported as TRIM46 (37) (Fig. 3C). Longer MUC1 alleles associated with increasing levels of both serum urea and urate across the VNTR length spectrum, with an incompletely dominant effect on urea (P=2.3 x 10−20 for interaction, linear regression; Fig. S17) but an additive effect on urate (P=0.56 for interaction).

Associations with additional renal phenotypes indicated a complex relationship between MUC1 VNTR length and kidney function (Fig. 3B,D). Long MUC1 alleles (>55 repeat units) increased the risk of gout (OR=1.10; 95% CI, [1.08-1.13], P=1.2 x 10−16, logistic regression) and chronic tubulointerstitial nephritis (OR=1.31 [1.09-1.57], P=3.4 x 10−3, logistic regression, which remained significant after correcting for 13 kidney diseases tested). However, MUC1 VNTR allele length did not associate with chronic kidney disease (OR=1.01 [0.99-1.04], P=0.33, logistic regression) reported in 14,573 cases and only weakly influenced glomerular filtration rate as estimated from serum creatinine (beta=−0.19% [0.11-0.28%] for long vs. short alleles). Long MUC1 alleles associated with modest reductions in red blood cell counts (beta=−0.029 s.d., s.e.=0.002, P=1.5 x 10−39, linear regression) and hemoglobin levels (beta=−0.031 s.d., s.e.=0.002, P=9.9 x 10−44, linear regression), possibly reflecting an impact of reduced kidney function on erythropoietin production.

TCHH VNTR strongly associates with hair phenotypes

Repeat length variation in a coding VNTR in TCHH associated strongly with male pattern baldness (P=1.6 x 10−55, BOLT-LMM). TCHH encodes trichohyalin, a protein that associates in regular arrays with keratin intermediate filaments and confers mechanical strength to the inner root sheath (38). The 18bp VNTR encodes part of a highly stabilized alpha-helix that forms an elongated rod structure (39). A rare nonsense mutation in TCHH has been implicated in uncombable hair syndrome (40), and a common haplotype containing the TCHH missense SNP rs11803731 (encoding a leucine to methionine substitution in TCHH) is by far the strongest genetic determinant of hair curl in individuals of European ancestry (41, 42). In UKB, the TCHH VNTR and rs11803731 exhibited independent associations with male pattern baldness (Fig. 4A,B).

Figure 4. TCHH VNTR length and missense SNP rs11803731 associate independently with hair phenotypes.

Figure 4.

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A, Genetic associations with male pattern baldness at TCHH (N=189,537 male UKB EUR participants). Colors indicate partial LD (R > 0.1) with missense SNP rs11803731 (blue), the TCHH VNTR (red), or both rs11803731 and VNTR length (purple). B, Mean baldness score in carriers (lines, left axis) and allele frequencies (histograms, right axis) of TCHH alleles. TCHH alleles were binned by VNTR length quintile and missense SNP rs11803731 status. C,D, Genetic associations with hair curl at TCHH in N=3,334 TwinsUK participants (conditioned on rs11803731 in D). E, Genome-wide associations with hair curl in TwinsUK. F, Relationship between TCHH allele length and hair curl (analogous to B).

Intriguingly, the TCHH VNTR appeared to be hypermutable and was poorly tagged by all nearby individual SNPs (R2<0.1), leading us to wonder whether it might also contribute to hair curl in a way invisible to genome-wide association studies of this phenotype. Imputing TCHH VNTR alleles into the TwinsUK cohort (43) (N=3,334 genotyped individuals with hair curl phenotypes) revealed that the TCHH VNTR appeared to be the human genome’s second-largest contributor to hair curl variation genome-wide (explaining ~1% of variance; P=3.6 x 10−8, BOLT-LMM) after the missense SNP rs11803731 in TCHH (which explained ~4% of variance; Fig. 4C-F). Linkage disequilibrium between the VNTR and rs11803731 further explained an association reported near LCE3E (450kb upstream of TCHH) previously thought to be independent of TCHH (42) (Fig. 4C,D).

Discussion

These results identify many strong effects of protein-coding VNTRs on human phenotypes. Most were among the strongest effects of all common variants identified for these phenotypes to date and resolved previously mysterious genetic associations for multiple traits. Incorporation of multi-allelic VNTRs into fine-mapping analyses also helped identify many more functional variants at the same loci, revealing the importance of incorporating allelic series of SNP and VNTR alleles into functional studies and epidemiological research.

These results are likely just the leading edge of a far-larger set of VNTR-phenotype associations that future studies will reveal. In this work with exome sequence data, we were unable to analyze VNTRs that exist in noncoding sequences, are too short for depth-of-coverage to accurately measure length variation, or are too mutable to segregate well with SNP haplotypes. We anticipate that newer sequencing technologies applied to large, diverse cohorts will yield further insights into the mutational and evolutionary processes of VNTRs and their contribution to the “missing heritability” of human phenotypes.

A frustration in human genetics has been that the majority of reported genetic associations involve haplotypes of noncoding and missense SNPs whose potential phenotypic contributions are challenging to dis-entangle from one another, and whose first-order molecular effects are opaque. VNTRs have several attributes that help overcome these challenges. First, multi-allelic VNTRs usually share only partial LD with nearby di-allelic SNP and indel variants. Second, associations to protein-coding VNTRs implicate the size and copy number of specific protein domains, leading to specific, testable hypotheses about the effects of protein domains in biological systems. Third, the directions of coding VNTR associations have clear meaning, revealing whether risk is generated by having more or less of a domain. Finally, VNTRs generate natural allelic series of functionally distinct alleles that can be used for dose-response studies in human tissues and cellular models. We anticipate that these attributes will lead to new insights about the mechanisms by which gene and protein variation affect human biology.

Supplementary Material

Supplementary Materials
Supplementary Tables

Acknowledgments

The authors are grateful to R. Gupta, J. Hirschhorn, M. Hujoel, S. Raychaudhuri, and M. Warman for helpful discussions. This research was conducted using the UK Biobank Resource under application #40709. Computational analyses were performed on the O2 High Performance Compute Cluster, supported by the Research Computing Group, at Harvard Medical School (http://rc.hms.harvard.edu).

Funding:

R.E.M. was supported by NSF grant DMS-1939015 and US National Institutes of Health (NIH) grant K25 HL150334. R.E.H. and S.A.M. were supported by NIH grant R01 HG006855. M.A.S. was supported by the MIT John W. Jarve (1978) Seed Fund for Science Innovation and NIH fellowship F31 MH124393. A.R.B. was supported by NIH fellowship F31 HL154537 and training grant T32 HG 2295-16. P.-R.L. was supported by NIH grant DP2 ES030554, a Burroughs Wellcome Fund Career Award at the Scientific Interfaces, the Next Generation Fund at the Broad Institute of MIT and Harvard, and a Sloan Research Fellowship. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, Chronic Disease Research Foundation (CDRF), Zoe Global Ltd and the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.

Footnotes

Competing interests: The authors declare no competing interests.

Code availability: The following publicly available software resources were used to perform analyses in this work: Eagle2 (v2.3.5), https://data.broadinstitute.org/alkesgroup/Eagle/; Minimac4 (v1.0.1), https://genome.sph.umich.edu/wiki/Minimac4; BOLT-LMM (v2.3.5), https://data.broadinstitute.org/alkesgroup/BOLT-LMM/; FINEMAP (v1.3.1), http://www.christianbenner.com/; plink (v1.9 and v2.0), https://www.cog-genomics.org/plink2/; Tandem Repeats Finder (v4.09.1), tandem.bu.edu/trf/trf.html; the TOPMed Imputation Server, https://imputation.biodatacatalyst.nhlbi.nih.gov/; BLAT (v35), http://hgdownload.soe.ucsc.edu/admin/exe/; susieR (v0.10.1), https://stephenslab.github.io/susieR/; LDstore (v2.0), http://www.finemap.me; ImpG (v1.0.1), https://github.com/huwenboshi/ImpG. Code and scripts used to perform analyses are available at http://doi.org/10.5281/zenodo.4776804 (44).

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Data availability:

Access to the following data resources is available to all bona fide researchers by application: UK Biobank (http://www.ukbiobank.ac.uk/); Twins UK (https://twinsuk.ac.uk/); the Haplotype Reference Consortium imputation panel (http://www.haplotype-reference-consortium.org/); AAAGC height summary statistics (https://www.ebi.ac.uk/gwas/). Individual-level VNTR allele length estimates (resolved to phased SNP-haplotypes) and genetically predicted Lp(a) values are available from UK Biobank as a Return from application #40709.

References and Notes

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Materials
NIHMS1744021-supplement-Supplementary_Materials.pdf (4.7MB, pdf)
Supplementary Tables
NIHMS1744021-supplement-Supplementary_Tables.xlsx (132.9KB, xlsx)

Data Availability Statement

Access to the following data resources is available to all bona fide researchers by application: UK Biobank (http://www.ukbiobank.ac.uk/); Twins UK (https://twinsuk.ac.uk/); the Haplotype Reference Consortium imputation panel (http://www.haplotype-reference-consortium.org/); AAAGC height summary statistics (https://www.ebi.ac.uk/gwas/). Individual-level VNTR allele length estimates (resolved to phased SNP-haplotypes) and genetically predicted Lp(a) values are available from UK Biobank as a Return from application #40709.

RESOURCES