A systematic survey of loss-of-function variants in human protein-coding genes
Daniel G. MacArthur1,2,*, Suganthi Balasubramanian3,4, Adam Frankish1, Ni Huang1, James Morris1,
Klaudia Walter1, Luke Jostins1, Lukas Habegger3,4, Joseph K. Pickrell5, Stephen B. Montgomery6,7, Cornelis
A. Albers1,8, Zhengdong Zhang9, Donald F. Conrad10, Gerton Lunter11, Hancheng Zheng12, Qasim Ayub1,
Mark A. DePristo13, Eric Banks13, Min Hu1, Robert E. Handsaker13,14, Jeffrey Rosenfeld15, Menachem
Fromer13, Mike Jin3, Xinmeng Jasmine Mu3,4, Ekta Khurana3,4, Kai Ye16, Mike Kay1, Gary Ian Saunders1,
Marie-Marthe Suner1, Toby Hunt1, If H.A. Barnes1, Clara Amid1,17, Denise R. Carvalho-Silva1, Alexandra H
Bignell1, Catherine Snow1, Bryndis Yngvadottir1, Suzannah Bumpstead1, David N. Cooper18, Yali Xue1,
Irene Gallego Romero1,5, 1000 Genomes Project Consortium, Jun Wang12, Yingrui Li12, Richard A. Gibbs19,
Steven A. McCarroll13,14, Emmanouil T. Dermitzakis7, Jonathan K. Pritchard5,20, Jeffrey C. Barrett1, Jennifer
Harrow1, Matthew E. Hurles1, Mark B. Gerstein3,4,21,†, Chris Tyler-Smith1,†
1 Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK
2 Discipline of Paediatrics and Child Health, University of Sydney, Sydney, 2006, Australia
3 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
4 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
5 Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
6 Departments of Pathology and Genetics, Stanford University, Stanford, CA 94305-5324, USA
7 Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva,
8 Department of Haematology, University of Cambridge & NHS Blood and Transplant, Cambridge, CB2
9 Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
10 Department of Genetics, Washington University School of Medicine, Saint Louis, MO 63110, USA
11 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
12 BGI-Shenzhen, Shenzhen 518083, China
13 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
14 Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
15 IST/High Performance and Research Computing, University of Medicine and Dentistry of New Jersey,
Newark, NJ 07103, USA
16 Molecular Epidemiology Section, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
17 The European Nucleotide Archive, EMBL-EBI, Hinxton CB10 1SD, UK
18 Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN,
19 Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
20 Howard Hughes Medical Institute, University of Chicago, Chicago, IL 60637, USA
21 Department of Computer Science, Yale University, New Haven, CT, USA
* to whom correspondence should be addressed. E-mail: email@example.com
† These authors contributed equally to this work as senior authors.
Genome sequencing studies indicate that all humans carry many genetic variants predicted to cause loss
of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome.
Here we apply stringent filters to 2,951 putative LoF variants obtained from 185 human genomes to
determine their true prevalence and properties. We estimate that human genomes typically contain
~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious
LoF alleles, including 26 known and 21 predicted severe disease-causing variants, as well as common LoF
variants in non-essential genes. We describe functional and evolutionary differences between LoF-
tolerant and recessive disease genes, and a method for using these differences to prioritize candidate
genes found in clinical sequencing studies.
Genetic variants predicted to severely disrupt protein-coding genes, collectively known as loss-
of-function (LoF) variants, are of considerable scientific and clinical interest. Traditionally such variants
have been regarded as rare and having a high probability of being deleterious, on the basis of their well-
established causal roles in severe Mendelian diseases such as cystic fibrosis and Duchenne muscular
dystrophy. However, recent studies examining the complete genomes of apparently healthy subjects
have suggested that such individuals carry at least 200 (1, 2) and perhaps as many as 800 (3) predicted
LoF variants. These numbers imply a previously unappreciated robustness of the human genome to
gene-disrupting mutations, and have important implications for the clinical interpretation of human
genome sequencing data.
Comparison of reported LoF variants between published genomes is complicated by differences
in sequencing technology, variant-calling algorithms and gene annotation sets between studies (4, 5),
and by the expectation that LoF variants will be highly enriched for false positives. The basis for this
predicted enrichment is that strong negative natural selection is expected to act against the majority of
variants inactivating protein-coding genes, thereby reducing the amount of true variation at these sites
relative to the genome average, while sequencing error is expected to be approximately uniformly
distributed; as a result, highly functionally constrained sites should show lower levels of observed
polymorphism and substantially higher false positive rates (4). To date, no large-scale attempt has been
made to validate the LoF variants reported in published human genome sequences.
LoF variants found in healthy individuals will fall into several overlapping categories: severe
recessive disease alleles in the heterozygous state; alleles that are less deleterious but nonetheless have
an impact on phenotype and disease risk; benign LoF variation in redundant genes; genuine variants that
do not seriously disrupt gene function; and, finally, a wide variety of sequencing and annotation
artifacts. Distinguishing between these categories will be crucial for the complete functional
interpretation of human genome sequences.
Obtaining and filtering candidate LoF variants
We identified 2,951 candidate LoF variants using whole-genome sequencing data from 185
individuals analyzed as part of the pilot phase of the 1000 Genomes Project (2), as well as detailed
analysis of high-coverage whole-genome sequencing data from a single anonymous European individual
(6). The individuals represented 3 population groups: Yoruba individuals from Ibadan, Nigeria (YRI), 60
individuals of Northern and Western European origin from Utah (CEU) and 30 Chinese individuals from
Beijing and 30 Japanese individuals from Tokyo that were analyzed jointly (CHB+JPT).
We adopted a definition for LoF variants expected to correlate with complete loss of function of
the affected transcripts: stop codon-introducing (nonsense) or splice site-disrupting single nucleotide
variants (SNVs), insertion/deletion (indel) variants predicted to disrupt a transcript’s reading frame, or
larger deletions removing either the first exon or more than 50% of the protein-coding sequence of the
affected transcript. We further sub-divided these variants into “full” LoF variants predicted to affect all
known protein-coding transcripts of the affected gene, and “partial” variants affecting only a fraction of
known coding transcripts. All annotation was performed against the Gencode v3b annotation (7) using
the algorithm VAT (8).
We then subjected our candidate list to a series of stringent informatic and experimental
validation steps (9). Informatic filtering was based on local sequence context (such as the presence of
highly repetitive sequence), gene annotation (such as variants affecting non-canonical splice sites, or
located close to the end of the affected open reading frame), analysis of the effects of nearby variants
(such as neighboring SNVs altering the predicted functional effect of the candidate LoF variant), and
measures of sequence read mapping and quality (Fig. S1). Where possible, thresholds for filtering were
derived from the experimental validation experiments below.
We validated all candidate LoF SNVs and indels that were not excluded by other filters and for
which we could design assays (n = 1,877) with experimental genotyping using three Illumina genotyping
arrays and 819 custom Sequenom assays run, where possible, on all 185 samples from the low- and
high-coverage 1000 Genomes pilot projects. Large deletions had previously been subjected to extensive
validation (10). All candidate LoF variants identified in NA12878 were assessed by comparison with
independent 454 sequencing and array-based data from the same individual, as well as targeted
capillary sequencing of variants in highly repetitive regions. Finally, 786 variants were re-examined by
complete manual reannotation of the 689 affected gene models by experienced curators, using the
HAVANA annotation pipeline (7), to identify annotation errors and flag variants unlikely to profoundly
affect gene function. All 589 candidate LoF variants identified in NA12878 were subjected to
independent genotype validation and complete gene model reannotation.
As expected, the proportion of likely sequencing and annotation errors in the initial candidate
set was high, with overlapping sets of 25.0%, 26.8% and 11.1% examined LoF variants being excluded as
representing likely sequencing/mapping errors, annotation/reference sequence errors, and variants
unlikely to cause genuine LoF, respectively. Candidate LoF variants removed by filtering tended to be
more common than high-confidence variants (Fig. 1A). False positive rates due to sequencing errors (Fig.
1B) were higher for LoF variants than for missense and synonymous variants in the CHB+JPT and YRI
populations (P < 10-8 for all comparisons) and significantly higher than for missense variants in CEU (P <
0.05). Because most variants in a given genome are common, the comparatively high rate of annotation
errors among high-frequency LoF variants meant that filtering resulted a large reduction in LoF variants
per individual (Table 1).
We identified several sources of false positive LoF annotation that will require careful
consideration in clinical sequencing projects. For instance, the predicted functional effect of a nonsense
or frameshift variant can be altered by other nearby variants on the same chromosome (Table S1; Fig.
S2), and predicted splice-disrupting SNVs and indels can be rescued by nearby alternative splice sites
(Fig. S3). Both nonsense SNVs and frameshift indels are enriched towards the 3’ end of the affected
gene, consistent with a greater tolerance to truncation close to the end of the coding sequence (Fig. 1C);
putative LoF variants identified in the last 5% of the coding region were thus systematically removed
from our high-confidence set, with the single exception of a known LoF indel in the NOD2 gene. There is
also a discernible peak close to the 5’ end of genes, suggesting that some disrupted transcripts are
rescued by transcriptional reinitiation at an alternative start codon (Fig. 1C).
Notably, 415 (32.3%) of our high-confidence LoF variants are partial LoF variants, affecting only a
subset of the known transcripts from the affected gene, meaning that functional protein may still be
produced. We chose not to discard such cases, as it is currently impossible to assess the relative
functional importance of different transcripts for most genes, and partial LoF mutations have previously
been shown to be causal in Mendelian diseases (11).
In total, 43.5% (1,285/2,951) of our candidate LoF variants survived filtering. The resulting
catalogue of high-confidence LoF variants is not complete: the 1000 Genomes pilot projects had low
power to detect extremely rare variants (2), and we will not have detected certain classes of LoF
variants, such as large gene-disrupting duplications, non-coding variants that disrupt gene expression or
splicing regulation, or coding variants that destroy protein function without overtly disrupting an open
reading frame (such as missense SNVs or in-frame indels). Several known LoF variant-containing genes
such as ACTN3 (12) and CASP12 (13) were labeled as “polymorphic pseudogenes”, meaning that the
reference genome contains non-functional allele of the gene, whereas in other haplotypes the gene is
functional (14); it is likely that we missed LoF variants in other uncharacterized genes from this class.
Nonetheless, this catalogue represents the largest available set of high-confidence human
variants predicted to disrupt protein-coding genes. We note that the majority of the LoF variants
identified here are novel: 70% of the high-confidence LoF SNVs and indels were not present in dbSNP
prior to the 1000 Genomes pilot project.
The true number of LoF variants in an individual genome
Using the systematically curated list of variants from NA12878, we estimate that this
anonymous individual with European ancestry carries 97 LoF variants, with 18 present in a homozygous
state (Tables 1, S2). These numbers, while still indicating an unexpected tolerance for gene inactivation
in humans and being considerably higher than those based on genotyping known nonsense SNVs alone
(15), are substantially lower than most previously published estimates based on whole-genome
sequencing (e.g. (2, 3, 16), and provide a benchmark for further studies of individual variation in
functional gene content. This analysis also provides a robust estimate of different variant classes on
gene inactivation: for instance, we find that 39% of genes inactivated in the NA12878 genome are the
result of frame-shifting indels, a potentially serious concern given that indels are typically under-called
using short-read sequencing approaches (2). Over a quarter (28.7%) of the LoF SNVs and indels in
NA12878 affect only a subset of the known transcripts from the affected genes, emphasizing the need to
consider alternative splicing in the annotation of functional effects.
Properties of LoF variants and affected genes
LoF SNVs are strikingly enriched for low-frequency alleles compared to synonymous and
missense SNVs (Fig. 1A), suggesting that many LoF variants are deleterious to human health and hence
are prevented from increasing in frequency by purifying natural selection. Interestingly, the number of
high-confidence LoF variants per individual is 25% higher in the YRI (Nigerian) sample than in the three
non-African populations (P = 5.0 x 10-21; Table 1), suggesting a higher level of variation in functional gene
content in African individuals consistent with their greater overall genetic diversity. However, we
caution that larger samples with more homogeneous sequencing quality across populations will be
required to confirm this finding and assess its likely functional impact.
We compared the properties of genes carrying at least one high-confidence LoF variant with
those of other protein-coding genes. Genes containing high-confidence LoF alleles are relatively less
evolutionarily conserved, showing a higher ratio of protein-altering to silent substitutions in coding
regions between human and macaque (P = 2.8 x 10-52) and less evolutionary conservation in their
promoter regions (GERP score; P = 3.7 x 10-16). On average, they have more closely related gene family
members (paralogs) than other genes (P = 0.0058) and show greater sequence identity to paralogs (P =
0.0068), suggesting that in many cases their function may be partially redundant, and also increasing the
possibility that LoF variants may be gained or lost through the process of gene conversion (17) as has
recently been reported for disease mutations (18). They also have lower connectivity in both protein-
protein interaction (P = 6.8 x 10-6) and gene interaction (P = 4.2 x 10-19) networks, suggesting that LoF-
containing genes are generally less central to key cellular pathways, although there are caveats to this
interpretation (9). LoF-containing genes are strongly enriched for functional categories related to
olfactory reception, and depleted for genes implicated in protein-binding, transcriptional regulation and
anatomical development (Table S8).
We estimated the probability that heterozygous inactivation of a given gene will be deleterious
(a state known as haploinsufficiency) using a combination of functional and evolutionary parameters (9,
19). Our filtering process disproportionately removed candidate LoF variants with a higher predicted
probability of haploinsufficiency, P(HI), consistent with the majority of putative LoF variants in highly
functionally constrained genes being artifactual (Fig. 2A). High-confidence LoF variants remaining after
filtering have significantly lower P(HI) than variants discarded by our filters (P = 2.1 x 10-16) or known
haploinsufficient genes (P = 1.8 x 10-73).
We identified 365 genes with multiple candidate LoF variants. The majority of the genes with
three or more independent LoF variants were found to represent systematic sequencing errors: for
instance, the CDC27 gene contained 10 separate candidate splice-disrupting variants, all of which were
found to represent mapping errors due to an inactive gene copy absent from the human reference
sequence. Most of these variants were removed by filtering (Table S3). Of the remaining genes, some
likely represent genes drifting towards inactivation in the population: for instance, the VWDE gene
contains four separate high-confidence LoF variants, with 42.7% of the sequenced 1000G samples
carrying at least one non-functional copy of this gene.
Effects of LoF variants on human phenotypes and disease risk
The high-confidence LoF set includes many known LoF variants reported to have effects on
human traits (Table S4). We also found a number of previously uncharacterized LoF variants likely to
have phenotypic effects. For instance, we identified three separate LoF variants in PKD1L3 and one in
PKD2L1; the protein products of these two genes form a putative sour taste receptor complex (20, 21),
so these variants may underlie variation in sour taste sensitivity between humans.
Our high-confidence LoF set includes many variants relevant to severe human disease. We
identified 26 known recessive disease-causing mutations in our high-confidence LoF set, including
mutations associated with the severe early-onset conditions Leber congenital amaurosis, harlequin
ichthyosis, osteogenesis imperfecta and Tay-Sachs disease (Table S5). We also identified 21 strong
candidates for novel disease-causing mutations: high-confidence LoF variants affecting all known
transcripts of genes in which other null mutations have been convincingly associated with Mendelian
disease, including adult-onset muscular dystrophy, Charcot-Marie-Tooth disease and mucolipidosis
(Table S6). With one exception (a variant associated with transplant graft-versus-host disease) no
individuals were homozygous for the putative disease-causing alleles.
Given the evidence for the presence of known deleterious variants, we hypothesized that LoF
variants may also be enriched for association with risk of common, complex diseases. We investigated
this hypothesis by imputing genotypes for 417 LoF SNVs and indels into a total of 13,241 patients
representing seven complex diseases such as Crohn’s disease and rheumatoid arthritis, along with 2,938
shared controls, who had previously been subjected to genome-wide SNP genotyping (22). We
confirmed a previously known frameshift indel in the NOD2 gene associated with Crohn’s disease, with a
genome-wide significant imputed P value of 1.78 x 10-14 (two orders of magnitude more significant than
the best tag SNP). However, no other LoF variants achieved genome-wide significance, and there was no
overall excess of association signals in LoF variants compared to other coding variants (Fig. 2B). Since our
catalogue is expected to contain most genuine LoF variants at greater than 5% frequency this result
suggests that common gene-disrupting variants play a minor role in complex disease predisposition.
One explanation for the paucity of common LoF variants associated with complex disease risk is
purifying selection, which is expected to prevent most severely deleterious alleles from reaching high
population frequencies; this is consistent with the skew towards low frequencies amongst high-
confidence LoF variants (Fig. 1A). In addition, genes containing homozygous LoF variants have more
gene family members (median 5 vs 3; P = 3.76 x 10-3) and are less conserved between macaque and
human (P = 1.87 x 10-4) than genes containing only heterozygous LoF variants, suggesting greater
redundancy in genes affected by high-frequency loss of function. Similarly small effects on complex
disease risk have previously been noted for large, common copy-number variations, another class of
variant with a high prior probability of functional impact (23).
Genotype imputation and case-control association studies have low power to detect
associations for low-frequency variants, so further experiments involving direct genotyping of LoF
variants in large disease cohorts will be required to characterize the impact of rare LoF variation on
human complex disorders.
Effects of nonsense SNVs on gene expression
We examined the impact of validated nonsense SNVs on gene expression using RNA sequencing
data generated from lymphoblastoid cell lines of 119 samples from two populations (24, 25).
Comparison of the relative expression of the LoF and functional alleles within experimentally genotyped
heterozygous individuals (Fig. 2C; Table S7) revealed a statistically significant reduction in expression
from the LoF allele in 8/49 (16.3%) of variants with sufficient sequencing depth to be assayed. As
expected, this reduction in expression is most common for variants predicted to trigger nonsense-
mediated mRNA decay (NMD), a cellular process that degrades premature stop codon-containing
transcripts: 7/28 (25.0%) of predicted NMD-triggering variants show significant evidence of decay,
compared to 1/21 (4.8%) of predicted NMD-evading variants, and the proportion of reads mapping to
the alternate allele was significantly lower for predicted NMD-triggering variants (median 0.352 vs
0.481; P = 0.0023). However, most predicted NMD-triggering variants have no detectable effect on gene
These results provide functional confirmation of true loss of gene function for a minority of LoF
variants. In addition, they demonstrate that the most widely-used algorithm for NMD prediction (26) is
an imperfect indicator of the effects of nonsense SNVs on RNA expression.
Natural selection on LoF variants
We explored whether LoF variants as a class showed evidence of recent positive selection, as
expected under the “less is more” hypothesis of adaptive gene loss proposed by Olson (27). We
examined the overlap between high-confidence LoF variants and regions showing potential signatures of
positive selection using frequency spectrum and haplotype length-based tests on 1000 Genomes pilot
data (2). In contrast to the “less is more” hypothesis, LoF variants overlapped with positively selected
regions no more often than frequency-matched synonymous SNVs. However, we have identified 20
high-confidence LoF variants in candidate regions for positive selection that warrant further analysis
In some cases, selection for gene inactivation may act through the accumulation of multiple rare
LoF variants rather than increased frequency of a specific LoF allele. We identified one potential
example of this: in addition to a relatively common nonsense SNV in the CD36 gene reported to be the
target of positive selection in African populations (28) we identified two rare, novel splice-disrupting
SNVs in the same gene. All three of these variants were specific to the Yoruban (YRI) population,
suggesting that multiple null alleles for CD36 may be accumulating in African populations under the
influence of selection.
Using LoF-tolerant genes to predict the probability of disease causation for novel variants
Homozygous inactivation of a gene can have a range of phenotypic effects: at one end of the
spectrum are severe recessive disease genes, while at the other end are genes that can be inactivated
without overt clinical impact, referred to here as LoF-tolerant genes. Clinical sequencing projects seeking
to identify disease-causing mutations would benefit from improved methods to distinguish where along
this spectrum each affected gene lies.
Genes homozygously inactivated in 1000 Genomes Project samples are likely to fall close to the
LoF-tolerant end of the spectrum. These genes therefore represent a comparison group that can be
used to define the functional and evolutionary characteristics that distinguish these genes from severe
recessive disease genes.
We examined the 253 genes containing validated LoF variants that were found to be
homozygous in at least one individual. These LoF-tolerant genes are significantly less conserved and
have fewer protein-protein interactions than the genome average (Fig. 3A). They are also enriched for
functional categories related to chemosensation, largely explained by the enrichment of olfactory
receptor genes in this class (13.0% vs 1.4% genome-wide), and depleted for genes involved in embryonic
development and cellular metabolism (Table S8).
We then identified parameters that could be used to classify candidate genes along the
disease/LoF-tolerant spectrum. We first removed olfactory receptors from the LoF-tolerant set, as these
genes could be easily excluded as candidates for most severe Mendelian diseases, leaving 213 LoF-
tolerant genes to compare with 858 known recessive disease genes. These two gene categories were
found to display marked differences in a wide range of properties (Fig. 3A).
We developed a linear discriminant model based on human-macaque conservation and
proximity to recessive disease genes in a protein-protein interaction network to classify genes into LoF-
tolerant and recessive disease classes (Fig. 3B, 3C). Although insufficient to definitively discriminate
between the two classes, this algorithm could be used to prioritize candidates identified by sequencing
recessive disease patients for replication and functional follow-up. We have calculated a recessive
disease probability score for each protein-coding gene in the genome for use in such analyses (9).
Here we describe a stringently filtered catalogue of variants disrupting the reading frame of
human protein-coding genes, including the majority of such variants present at a population frequency
of 5% or greater. Because large numbers of candidate LoF variants are present in the genomes of all
individuals, but are highly enriched for a variety of sequencing and annotation errors, there is a need for
caution in assigning disease-causing status to novel gene-disrupting variants found in patients. More
reliable reference gene sets will help: reference sequence and automated gene annotation errors
accounted for 44.9% of candidate LoF variants in our deeply characterized individual genome, but most
of these have now been corrected as a result of this project and other manual annotation efforts.
Our stringent filtering of the LoF variants found in a single high-quality human genome suggests
that a typical “healthy” genome contains ~100 genuine LoF variants, with most of them carried in the
heterozygous state. Given that humans (29) and other species (30) have been estimated to carry fewer
than 5 recessive lethal alleles per genome, it seems likely that the majority of LoF variants found in an
individual genome are common variants in non-essential genes, although these may still have an effect
on human phenotypic variation. Nonetheless, the signature of strong purifying selection against high-
confidence LoF variants as a class, and the discovery of numerous known and predicted severe recessive
disease alleles, indicates that many LoF alleles with large effects on human fitness exist at low frequency
in the human population. Large sequencing and genotyping projects will be required to uncover the full
spectrum of these variants and their effects on human disease risk.
We have found that LoF-tolerant and recessive disease genes have differing functional and
evolutionary properties, allowing us to develop a potential approach for prioritizing novel candidate
recessive disease variants identified in patient samples for functional follow-up. As further examples of
LoF-tolerant genes are obtained from high-throughput sequencing studies the power of this type of
classification approach is likely to grow considerably.
Finally, we note that our catalogue of validated LoF variants comprises a list of naturally
occurring “knock-out” alleles for over 1,000 human protein-coding genes, many of which currently have
little or no functional annotation attached to them. Identification and systematic phenotyping of
individuals homozygous for these variants could provide valuable insight into the function of many
poorly characterized human genes.
References and notes
1. P. C. Ng et al., PLoS Genet. 4, e1000160 (2008).
2. 1000 Genomes Project Consortium, Nature 467, 1061 (2010).
3. K. Pelak et al., PLoS Genet. 6, e1001111 (2010).
4. D. G. MacArthur, C. Tyler-Smith, Hum. Mol. Genet. 19, R125 (2010).
5. S. Balasubramanian et al., Genes Dev. 25, 1 (2011).
6. M. A. DePristo et al., Nat. Genet. 43, 491 (2011).
7. J. Harrow et al., Genome Biol. 7 Suppl 1, 1 (2006).
9. See supporting material on Science online.
10. R. E. Mills et al., Nature 470, 59 (2011).
11. A. Uzumcu et al., J. Med. Genet. 43, e5 (2006).
12. D. G. MacArthur et al., Nat. Genet. 39, 1261 (2007).
13. Y. Xue et al., Am. J. Hum. Genet. 78, 659 (2006).
14. Z. D. Zhang, A. Frankish, T. Hunt, J. Harrow, M. Gerstein, Genome Biol. 11, R26 (2010).
15. B. Yngvadottir et al., Am. J. Hum. Genet. 84, 224 (2009).
16. J. R. Lupski et al., N. Engl. J. Med. 362, 1181 (2010).
17. J. M. Chen, D. N. Cooper, N. Chuzhanova, C. Ferec, G. P. Patrinos, Nature Reviews Genetics 8,
18. C. Casola, U. Zekonyte, A. D. Phillips, D. N. Cooper, M. W. Hahn, Genome Res. 22, (2011),
19. N. Huang, I. Lee, E. M. Marcotte, M. E. Hurles, PLoS Genet. 6, e1001154 (2010).
20. Y. Ishimaru et al., Proc. Natl. Acad. Sci. U. S. A. 103, 12569 (2006).
21. A. L. Huang et al., Nature 442, 934 (2006).
22. Wellcome Trust Case Control Consortium, Nature 447, 661 (2007).
23. D. F. Conrad et al., Nature 464, 704 (2010).
24. S. B. Montgomery et al., Nature 464, 773 (2010).
25. J. K. Pickrell et al., Nature 464, 768 (2010).
26. E. Nagy, L. E. Maquat, Trends Biochem. Sci. 23, 198 (1998).
27. M. V. Olson, Am. J. Hum. Genet. 64, 18 (1999).
28. A. E. Fry et al., Hum. Mol. Genet. 18, 2683 (2009).
29. A. H. Bittles, J. V. Neel, Nat. Genet. 8, 117 (1994).
30. A. R. McCune et al., Science 296, 2398 (2002).
T. Shah provided the Pyvoker software used for manual assignment of genotypes based on intensity
clusters, S. Edkins was involved in the Sequenom validation, and the genotyping groups at Illumina, the
Wellcome Trust Sanger Institute and The Broad Institute of Harvard and MIT provided raw intensity data
for the three Illumina arrays used for genotyping validation. The work performed at the Wellcome Trust
Sanger Institute was supported by Wellcome Trust grant 098051; DM was supported by a fellowship
from the Australian National Health and Medical Research Council; GL by the Wellcome Trust
(090532/Z/09/Z); ETD and SBM by the Swiss National Science Foundation, the Louis Jeantet Foundation
and the NIH-NIMH GTEx fund; KY by NWO VENI grant 639.021.125; and HZ, YL and JW by a National
Basic Research Program of China (973 program no. 2011CB809200), the National Natural Science
Foundation of China (30725008; 30890032; 30811130531), the Chinese 863 program
(2006AA02A302;2009AA022707), the Shenzhen Municipal Government of China (grants
JC200903190767A; JC200903190772A; ZYC200903240076A; CXB200903110066A; ZYC200903240077A;
and ZYC200903240080A) and the Ole Rømer grant from the Danish Natural Science Research Council, as
well as funding from the Shenzhen Municipal Government and the Local Government of Yantian District
of Shenzhen. JKP is on the scientific advisory board of 23andMe and RAG has a shared investment in Life
Technologies. Raw sequence data for the 1000 Genomes pilot projects are available from
www.1000genomes.org, and a curated list of the loss-of-function variants described in this manuscript is
provided in the Supplementary Online Material.
Table 1. Numbers of LoF variants before and after filtering. Total numbers of candidate LoF variants
and average number of LoF sites per individual (homozygous sites in brackets) are shown for each LoF
class. For large deletions, numbers represent total number of genes predicted to be inactivated.
average per individual
CEU CHB+JPT YRI
(21.8) (26.7) (23.7)
(29.5) (35.6) (30.4)
(112.1) (121.7) (118.7)
(12.2) (11.8) (9.7)
(175.6) (195.8) (316.0)
average per individual
115 (25) 565
95 (32) 267
217.8 225.5 247.2
348 (159) 337
32.4 31.2 31.4
31 (5) 116
654 (286) 1285
Figure 1. A. Derived allele frequency distribution in the CEU population for raw and high-confidence LoF
variants, compared to missense and synonymous coding variants. Inset, distribution of the proportion of
SNVs in each class at low allele counts (1-5). B. False positive rates (based on independent array
genotyping) for LoF variants filtered for annotation artifacts and frequency-matched missense and
synonymous SNVs. C. Distribution of frameshift indels along the coding region of affected genes, before
and after filtering (a similar pattern is also seen for nonsense SNVs; data not shown).
Figure 2. A. Estimated probability of
haploinsufficiency (presence of disease due
to heterozygous loss of function), using a
model trained using an independent set of
LoF deletions as well as a set of known
haploinsufficient genes. B. Association of
coding variants with complex disease risk.
Observed -log10(P) values for disease
association in 17,000 individuals from 7
complex disease cohorts and a shared
control group, following imputation of
variants identified by the 1000 Genomes
low-coverage pilot, are plotted against the
expected null distribution for all LoF
variants and frequency-matched missense
and synonymous SNPs. C. Allele-specific
expression analysis of nonsense variants,
using RNA sequencing data from 119
lymphocyte cell lines. Circles show the
proportion of LoF-carrying reads spanning
each site across all heterozygous
individuals. Variants predicted to cause
nonsense-mediated decay (NMD, red) and
those predicted to escape NMD (blue) are
arbitrarily ordered by genome position
within each class. Blue and red dashed
horizontal lines indicate mean values in
each class. Error bars, 95% CI.
ERROR: undefined Download full-text
OFFENDING COMMAND: ‘~