Human Knockout Carriers: Dead, Diseased, Healthy, or Improved?
Authors: Vagheesh M. Narasimhan, Yali Xue, Chris Tyler-Smith
Journal: Trends in Molecular Medicine (2016)

Genome and exome sequencing are revealing many candidate loss-of-function (knockout) variants in every human genome. Sequencing consanguineous populations is the most efficient way of discovering additional knockouts. The phenotypic consequences of apparent knockouts are difficult to predict accurately because of (i) imperfect variant calling, gene annotation, and prediction of the molecular consequences at the RNA and protein levels, and (ii) variation in the biological consequences of knocking out different genes. Human knockouts provide opportunities to investigate gene function and essentiality, as well as to suggest and validate potential drug targets. The clinical interpretation of knockouts is complicated by all the above factors, in addition to variable penetrance and a lack of suitable databases. Consistent calling, annotation, and database standards for variants are presently needed. Whole-genome and whole-exome sequence data from large numbers of individuals reveal that we all carry many variants predicted to inactivate genes (knockouts). This discovery raises questions about the phenotypic consequences of these knockouts and potentially allows us to study human gene function through the investigation of homozygous loss-of-function carriers. Here, we discuss strategies, recent results, and future prospects for large-scale human knockout studies. We examine their relevance to studying gene function, population genetics, and importantly, the implications for accurate clinical interpretations. Whole-genome and whole-exome sequence data from large numbers of individuals reveal that we all carry many variants predicted to inactivate genes (knockouts). This discovery raises questions about the phenotypic consequences of these knockouts and potentially allows us to study human gene function through the investigation of homozygous loss-of-function carriers. Here, we discuss strategies, recent results, and future prospects for large-scale human knockout studies. We examine their relevance to studying gene function, population genetics, and importantly, the implications for accurate clinical interpretations. Early in 2017, parents bring their newborn baby into Dr Wuhabi's clinic. There is nothing obviously wrong, but the parents are worried. Dr Wuhabi has the baby's genome sequenced: there are no variants from the actionable list, but a homozygous knockout of little-studied gene is called. How should she advise the parents? This scenario is imaginary, but may soon be reality. Knockouts of some genes in humans certainly cause genetic diseases, but for other genes the consequences depend on the genetic background or environment; yet other knockouts may have no detectable effect, or may even be beneficial. A flurry of recent papers has begun to reveal not only the prevalence of knockouts in the population, and their scientific interest, but also the complexity of understanding their medical implications. We review here these new developments, the steps necessary for their clinical interpretation (Figure 1, Key Figure), and consider possible future steps to resolve some of these complexities. While sequencing technology is becoming a ubiquitous part of genetic diagnosis, understanding the impact of the variation discovered on the human phenotype remains a challenge, as illustrated above. Naturally-occurring knockout or loss-of-function (LoF, see Glossary) variants (the terms are interchangeable), in other words genetic variants that are predicted to severely disrupt the function of human protein-coding genes [1MacArthur D.G. Tyler-Smith C. Loss-of-function variants in the genomes of healthy humans.Hum. Mol. Genet. 2010; 19: R125-R130Crossref PubMed Scopus (133) Google Scholar], are often prime candidates for follow-up. However, significant difficulties remain: first, with the identification and calling of DNA variants and, second, with the annotation of whether they truly disrupt protein function or not (Figure 1). LoF variants as a class are rare (Figure 2) [21000 Genomes Project Consortium et al.A map of human genome variation from population-scale sequencing.Nature. 2010; 467: 1061-1073Crossref PubMed Scopus (5937) Google Scholar] and are poorly called by current methods. While exome sequencing and whole-genome sequencing technologies allow reliable calling of SNPs, calling small insertions and deletions remains a developing area. Moreover, differences in coverage as well as in an inability to span breakpoints decrease sensitivity for calling large structural variants [3Linderman M.D. et al.Analytical validation of whole exome and whole genome sequencing for clinical applications.BMC Med. Genomics. 2014; 7: 20Crossref PubMed Scopus (73) Google Scholar]; these non-SNP variants make up a large fraction of naturally-occurring knockout variation and may still have high error rates. From a clinical perspective, validation of variants of interest (using an independent technology such as Sanger sequencing or Sequenom genotyping) is always needed, and must be part of standard practice [4Biesecker L.G. et al.Diagnostic clinical genome and exome sequencing.N. Engl. J. Med. 2014; 371: 1169-1170Crossref Scopus (77) Google Scholar]. Furthermore, when studying homozygous variants, the possibility of mosaic homozygous/heterozygous status due to somatic crossover needs to be considered. Similarly, compound heterozygous LoF variants in the same gene on different chromosomes knock out the gene, while equivalent variants on the same chromosome only knock out a single copy. Further, inconsistencies in gene reference sets and the annotation of protein-coding genes add an additional layer of complexity. There can be considerable differences [5McCarthy D.J. et al.Choice of transcripts and software has a large effect on variant annotation.Genome Med. 2014; 6: 26Crossref PubMed Scopus (121) Google Scholar] between knockouts that are called using different widely used gene models for human protein coding genes such as RefSeq [6Pruitt K.D. et al.NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins.Nucleic Acids Res. 2005; 33: D501-D504Crossref PubMed Scopus (1304) Google Scholar] and GENCODE [7Harrow J. et al.GENCODE: the reference human genome annotation for The ENCODE Project.Genome Res. 2012; 22: 1760-1774Crossref PubMed Scopus (3023) Google Scholar]. In addition, software packages used to derive the consequences of sequence variation on proteins, such as Annovar [8Yang H. Wang K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR.Nat. Protoc. 2015; 10: 1556-1566Crossref PubMed Scopus (505) Google Scholar] or Variant effect predictor (VEP) [9McLaren W. et al.Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor.Bioinformatics. 2010; 26: 2069-2070Crossref PubMed Scopus (1204) Google Scholar], can produce further differences even when using the same gene models [10Balasubramanian S. et al.Gene inactivation and its implications for annotation in the era of personal genomics.Genes Dev. 2011; 25: 1-10Crossref PubMed Scopus (26) Google Scholar]. In addition to the subtleties in drawing up an initial list of knockout variants, predicting the effect of a specific variant on protein production and on the phenotypic consequences of an observed transcript reduction remain even more challenging. Transcript levels can readily be measured, and are relevant because large deletions may remove a transcript entirely, while smaller LoF variants may lead to nonsense-mediated decay (NMD) (Box 1) which reduces the transcript level. Surprisingly, even if genetic variation triggers NMD and there is degradation of the RNA, the reduction in RNA levels may not reduce the protein level [11Battle A. et al.Impact of regulatory variation from RNA to protein.Science. 2015; 347: 664-667Crossref PubMed Scopus (267) Google Scholar]. Finally, the effect of alternative splicing may lead to partial LoF variants, which affect only a subset of the transcripts of a gene, meaning that a functional protein may still be produced from other transcripts. It is currently effectively impossible to assess the relative functional importance of different transcripts for most genes, and partial LoF variants can cause Mendelian disease [12Uzumcu A. et al.Loss of desmoplakin isoform I causes early onset cardiomyopathy and heart failure in a Naxos-like syndrome.J. Med. Genet. 2006; 43: e5Crossref PubMed Google Scholar]. To sidestep these limitations, strategies which filter variants based on deterministic rules that best predict true LoF behavior have been developed (LOFTEE: loss-of-function transcript effect estimator; https://github.com/konradjk/loftee) but their systematic evaluation using large-scale RNA and protein data is still incomplete. In settings where annotation is important for diagnosis, further confirmation of loss needs be obtained by direct observation of an absence of the protein product or activity from a suitable sample. Only then can we be fully confident of a knockout.Box 1Understanding Nonsense-Mediated Decay (NMD)The NMD pathway is found in all eukaryotes; its main function is to degrade and eliminate mRNA molecules that contain aberrant stop codons. This protects against the production of aberrant proteins which may be harmful. Despite years of effort and the formulation of rules to predict when NMD will be triggered, predictions remain unreliable [59Rivas M.A. et al.Effect of predicted protein-truncating genetic variants on the human transcriptome.Science. 2015; 348: 666-669Crossref PubMed Scopus (156) Google Scholar]. The recent discovery of a protein that prevents mRNA degradation, PTBP1, is therefore of considerable interest. It has been reported that, when bound near a stop codon, PTBP1 blocks the NMD protein UPF1 from binding to 3′-untranslated regions (UTRs). PTBP1 can thus mark natural stop codons and prevent their degradation, allowing NMD to act on transcripts with premature stop codons and thus degrade aberrant mRNAs [60Ge Z. et al.Polypyrimidine tract binding protein 1 protects mRNAs from recognition by the nonsense-mediated mRNA decay pathway.Elife. 2016; 5: e11155Crossref PubMed Google Scholar]. The NMD pathway is found in all eukaryotes; its main function is to degrade and eliminate mRNA molecules that contain aberrant stop codons. This protects against the production of aberrant proteins which may be harmful. Despite years of effort and the formulation of rules to predict when NMD will be triggered, predictions remain unreliable [59Rivas M.A. et al.Effect of predicted protein-truncating genetic variants on the human transcriptome.Science. 2015; 348: 666-669Crossref PubMed Scopus (156) Google Scholar]. The recent discovery of a protein that prevents mRNA degradation, PTBP1, is therefore of considerable interest. It has been reported that, when bound near a stop codon, PTBP1 blocks the NMD protein UPF1 from binding to 3′-untranslated regions (UTRs). PTBP1 can thus mark natural stop codons and prevent their degradation, allowing NMD to act on transcripts with premature stop codons and thus degrade aberrant mRNAs [60Ge Z. et al.Polypyrimidine tract binding protein 1 protects mRNAs from recognition by the nonsense-mediated mRNA decay pathway.Elife. 2016; 5: e11155Crossref PubMed Google Scholar]. Many proteins are unnecessary for general life and good health: olfactory receptors, our largest gene family, provide a prime example [13Gilad Y. et al.Human specific loss of olfactory receptor genes.Proc. Natl. Acad. Sci. U.S.A. 2003; 100: 3324-3327Crossref PubMed Scopus (228) Google Scholar]. Thus, even for a confirmed knockout, we still need to determine whether it has a relevant phenotypic effect. The traditional way to do this is to look in a list or database of known disease-causing variants. Decades of work by clinical geneticists and physicians have led to the compilation of such databases. The predominant approach has been to discover candidate causal genes/variants segregating in families and follow them by analyzing additional patients with similar phenotypes. After assessing the mode of inheritance (dominant, recessive, etc.), the presence of the same or equivalent variant (often LoF or a damaging amino acid substitution) in the same gene, and its absence from a sample of unaffected individuals, has been considered to establish causality. More recently, tools have been designed to enable computational prediction of mutations (Box 2).Box 2Computational Prediction of Phenotypic Consequence of VariationRecently, several tools have been designed to predict phenotypic consequence for knockout variation, for example the genome-wide annotation of variants (GWAVA) score, and the Combined Annotation-Dependent Depletion (CADD) score [61Kircher M. et al.A general framework for estimating the relative pathogenicity of human genetic variants.Nat. Genet. 2014; 46: 310-315Crossref PubMed Scopus (3675) Google Scholar, 62Ritchie G.R.S. et al.Functional annotation of noncoding sequence variants.Nat. Methods. 2014; 11: 294-296Crossref PubMed Scopus (363) Google Scholar], allowing novel variants to be assessed. These tools are primarily variant-driven and determine pathogenicity by looking at sequence context, evolutionary constraint, and their impact on proteins. However, in vivo studies in model organisms have shown that these methods have high false-positive rates [63Miosge L.A. et al.Comparison of predicted and actual consequences of missense mutations.Proc. Natl. Acad. Sci. U.S.A. 2015; 112: E5189-E5198Crossref PubMed Scopus (144) Google Scholar]. Complementary methods that utilize gene-level methods such as residual variation intolerance score (RVIS [64Petrovski S. et al.Genic intolerance to functional variation and the interpretation of personal genomes.PLoS Genet. 2013; 9: e1003709Crossref PubMed Scopus (640) Google Scholar] and gene damage index (GDI) [65Itan Y. et al.The human gene damage index as a gene-level approach to prioritizing exome variants.Proc. Natl. Acad. Sci. U.S.A. 2015; 112: 13615-13620Crossref PubMed Scopus (152) Google Scholar] have also been used for this purpose, and recent work has investigated the utility of gene-level thresholds in improving predictivity [66Itan Y. et al.The mutation significance cutoff: gene-level thresholds for variant predictions.Nat. Methods. 2016; 13: 109-110Crossref PubMed Scopus (184) Google Scholar]. Furthermore, individual genes can be assessed together with others in close biological proximity to refine their phenotypic effect, as well as their susceptibility to disease, with network-based approaches such as the Human Gene Connectome Map [67Itan Y. et al.The human gene connectome as a map of short cuts for morbid allele discovery.Proc. Natl. Acad. Sci. U.S.A. 2013; 110: 5558-5563Crossref PubMed Scopus (60) Google Scholar]. Recently, several tools have been designed to predict phenotypic consequence for knockout variation, for example the genome-wide annotation of variants (GWAVA) score, and the Combined Annotation-Dependent Depletion (CADD) score [61Kircher M. et al.A general framework for estimating the relative pathogenicity of human genetic variants.Nat. Genet. 2014; 46: 310-315Crossref PubMed Scopus (3675) Google Scholar, 62Ritchie G.R.S. et al.Functional annotation of noncoding sequence variants.Nat. Methods. 2014; 11: 294-296Crossref PubMed Scopus (363) Google Scholar], allowing novel variants to be assessed. These tools are primarily variant-driven and determine pathogenicity by looking at sequence context, evolutionary constraint, and their impact on proteins. However, in vivo studies in model organisms have shown that these methods have high false-positive rates [63Miosge L.A. et al.Comparison of predicted and actual consequences of missense mutations.Proc. Natl. Acad. Sci. U.S.A. 2015; 112: E5189-E5198Crossref PubMed Scopus (144) Google Scholar]. Complementary methods that utilize gene-level methods such as residual variation intolerance score (RVIS [64Petrovski S. et al.Genic intolerance to functional variation and the interpretation of personal genomes.PLoS Genet. 2013; 9: e1003709Crossref PubMed Scopus (640) Google Scholar] and gene damage index (GDI) [65Itan Y. et al.The human gene damage index as a gene-level approach to prioritizing exome variants.Proc. Natl. Acad. Sci. U.S.A. 2015; 112: 13615-13620Crossref PubMed Scopus (152) Google Scholar] have also been used for this purpose, and recent work has investigated the utility of gene-level thresholds in improving predictivity [66Itan Y. et al.The mutation significance cutoff: gene-level thresholds for variant predictions.Nat. Methods. 2016; 13: 109-110Crossref PubMed Scopus (184) Google Scholar]. Furthermore, individual genes can be assessed together with others in close biological proximity to refine their phenotypic effect, as well as their susceptibility to disease, with network-based approaches such as the Human Gene Connectome Map [67Itan Y. et al.The human gene connectome as a map of short cuts for morbid allele discovery.Proc. Natl. Acad. Sci. U.S.A. 2013; 110: 5558-5563Crossref PubMed Scopus (60) Google Scholar]. Beyond simple Mendelian conditions, this approach has also been successful in identifying causal genes for more complex disorders by focusing on extreme and rare phenotypes. The first large-scale sequencing study performed in consanguineous families led to the identification of 50 novel candidate genes for developmental disorders [14Najmabadi H. et al.Deep sequencing reveals 50 novel genes for recessive cognitive disorders.Nature. 2011; 478: 57-63Crossref PubMed Scopus (690) Google Scholar]. This success was soon followed by the sequencing of an even larger cohort of 1113 trios and the implementation of a robust translational genomics workflow to allow feedback of potentially diagnostic findings to clinicians and research participants [15Wright C.F. et al.Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data.Lancet. 2015; 385: 1305-1314Abstract Full Text Full Text PDF PubMed Scopus (475) Google Scholar]. Importantly, by utilizing a genotype-driven approach to identify subsets of patients with similar disorders, the newly implicated genes increased by 10% the proportion of subjects who received a diagnosis [16Deciphering Developmental Disorders Study Large-scale discovery of novel genetic causes of developmental disorders.Nature. 2015; 519: 223-228Crossref PubMed Scopus (713) Google Scholar]. As such, exome sequencing of single patients with extreme phenotypes has been applied more widely. For example, a knockout of the immune gene IRF7 was shown to confer susceptibility to flu viruses, leading to life-threatening influenza in an otherwise healthy child [17Ciancanelli M.J. et al.Life-threatening influenza and impaired interferon amplification in human IRF7 deficiency.Science. 2015; 348: 448-453Crossref PubMed Scopus (301) Google Scholar]. In a similar vein, sequencing of fetuses lost preterm has identified novel knockout variants in CHRNA1, a muscle acetylcholine receptor, as a cause of lethal fetal akinesia [18Shamseldin H.E. et al.Lifting the lid on unborn lethal Mendelian phenotypes through exome sequencing.Genet. Med. 2013; 15: 307-309Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar]. More generally, family-based designs to uncover recessive forms of embryonic lethality by examining significant depletion of transmitted homozygote genotypes have implicated THSD1, a thrombospondin type 1 domain-containing protein of poorly understood function, as a candidate for a monogenic cause of embryonic lethality [19Shamseldin H.E. et al.Identification of embryonic lethal genes in humans by autozygosity mapping and exome sequencing in consanguineous families.Genome Biol. 2015; 16: 116Crossref PubMed Scopus (68) Google Scholar]. Taken together, Mendelian disease genes and embryonically lethal genes provide a spectrum of knockout variants ascertained as disease-causing by analyzing carriers of clinically diagnosed phenotypes. Further sequencing in this domain with larger sample sizes, better curation, and deeper phenotyping will steadily increase this catalog. Moreover, a complementary approach is to sequence healthy people: the knockouts they carry are unlikely to be disease-causing. However, that interpretation of such lists is not as simple as it seems. The logical end to the approach described above is to discover knockouts in all of the 20 000 or so human protein-coding genes and classify them as either being lethal before birth, compatible with life but disease-causing, or as having no disease consequences. However, LoF variants typically have very low frequencies, meaning that very large sample sizes are required to systematically discover LoFs in every gene. With the cost of sequencing decreasing, there have been several approaches to uncover novel knockout variants on a large scale, using different strategies. A simple approach is to collect a large number of individuals from multiple cohorts that have already been sequenced for diverse studies. The Exome Aggregation Consortium (ExAC) has put together such a collection of >60 000 exomes from a wide range of phenotypes and ages. This non-trivial exercise required performing reproducible variant calling and quality control across the entire set of exomes that have been sequenced on different platforms and time-periods [20Exome Aggregation Consortium et al.Analysis of protein-coding genetic variation in 60,706 humans.bioRxiv. 2015; (Published online October 30, 2015)https://doi.org/10.1101/030338Crossref Google Scholar]. At this scale, sequencing has been able to identify a variant in at least one individual at one in every eight bases of coding sequence, as well as many sites with recurrent mutations. This work has enabled us to understand the extent of haploinsufficiency in the genome with the observation that 3230 genes exist with a severe depletion of heterozygous knockout variants, most of which do not have an established human disease phenotype [21Fujikura K. Multiple loss-of-function variants of taste receptors in modern humans.Sci. Rep. 2015; 5: 12349Crossref PubMed Scopus (7) Google Scholar]. Given the large sample size of the data, it is also possible to investigate the tolerance to dominant consequences of knockouts of individual genes by employing a model that compares the synonymous mutational load with that of LoF mutations, taking into account gene length and base composition [22Samocha K.E. et al.A framework for the interpretation of de novo mutation in human disease.Nat. Genet. 2014; 46: 944-950Crossref PubMed Scopus (611) Google Scholar]. For example, an excess of LoF mutations in a particular novel gene for a disease cohort can indicate that certain mutations are disease-causing [23Akawi N. et al.Discovery of four recessive developmental disorders using probabilistic genotype and phenotype matching among 4,125 families.Nat. Genet. 2015; 47: 1363-1369Crossref PubMed Scopus (102) Google Scholar]. While sequencing individuals without selecting for particular population-genetic properties is an effective approach, such studies are in practice currently limited to the study of heterozygous LoF variants [24Fu W. et al.Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants.Nature. 2013; 493: 216-220Crossref PubMed Scopus (679) Google Scholar]. In randomly-mating populations, a variant present in 1 in 1000 individuals in a heterozygous state will only be present in 1 in 1 000 000 in a homozygous state, and discovering homozygous mutations by sequencing outbred individuals will therefore require very large sample sizes. Nevertheless, two complementary approaches have been used to discover rare homozygous knockouts. Bottlenecked populations with extensive identity-by-descent (identical-by-descent genomic portions) present the most direct approach, and recently, ∼100 000 individuals from Iceland [25Sulem P. et al.Identification of a large set of rare complete human knockouts.Nat. Genet. 2015; 47: 448-452Crossref PubMed Scopus (154) Google Scholar] and ∼30 000 individuals from Finland [26Lim E.T. et al.Distribution and medical impact of loss-of-function variants in the Finnish founder population.PLoS Genet. 2014; 10: e1004494Crossref PubMed Scopus (249) Google Scholar] (two such bottlenecked populations) have now been sequenced. Mildly pathogenic variants in small populations such as these are also more likely to drift to higher frequencies than in large populations, and association studies aiming to find pathogenic variation have also discovered knockout variants that lead to chronic disease. A striking example involved the identification of a LoF variant leading to insulin resistance, with an allele frequency of 17% in Greenland [27Moltke I. et al.A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes.Nature. 2014; 512: 190-193Crossref PubMed Scopus (260) Google Scholar]. However, the potential of this strategy for discovering homozygous knockouts is limited by two factors. First, the portion of the genome that is identical-by-descent in these individuals, while higher than in outbred populations, is still small, especially when education programs reduce marriage between close relatives [28Jorde L.B. Pitkänen K.J. Inbreeding in Finland.Am. J. Phys. Anthropol. 1991; 84: 127-139Crossref PubMed Scopus (39) Google Scholar]. Therefore, the number of rare homozygous knockouts discovered per person is low. Second, the number of knockouts present in the entire population is limited to those present in founders (plus new mutations), and thus existing studies may already have discovered most of the LoF variation [26Lim E.T. et al.Distribution and medical impact of loss-of-function variants in the Finnish founder population.PLoS Genet. 2014; 10: e1004494Crossref PubMed Scopus (249) Google Scholar]. This would mean that future sequencing of individuals from these cohorts is less likely to yield novel mutations. An alternative approach is to investigate consanguineous populations, which have high degrees of parental relatedness, and large portions of their genome that are identical-by-descent because of family structure in the immediate preceding generations. Two recent studies have sequenced individuals of Pakistani descent and shown that one in every two individuals who are the offspring of first cousins has a rare knockout variant [29Saleheen D. et al.Human knockouts in a cohort with a high rate of consanguinity.bioRxiv. 2015; : 031518Google Scholar, 30Narasimhan V.M. et al.Health and population effects of rare gene knockouts in adult humans with related parents.bioRxiv. 2016; (Science Published online March 3, 2016)https://doi.org/10.1126/science.aac8624Crossref Scopus (186) Google Scholar]. This rate is almost 50-fold higher than that discovered in bottlenecked populations. Reassuringly, overlap of genes from the datasets that have been produced using this approach suggests that rare LoF variants are often not shared between populations and that the rate of discovery of knockouts from consanguineous cohorts is sufficiently high to increase our understanding of homozygous knockouts substantially (Figure 3). Although we have catalogs of knockout variants, and strategies for large-scale discovery of more such variants, understanding the impact of gene knockouts, and thereby gene function, is considerably more difficult. Large cohorts with linked health records evaluating gross patient phenotypic status have been examined in recent studies [24Fu W. et al.Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants.Nature. 2013; 493: 216-220Crossref PubMed Scopus (679) Google Scholar, 29Saleheen D. et al.Human knockouts in a cohort with a high rate of consanguinity.bioRxiv. 2015; : 031518Google Scholar]. However, information on particular knockouts or genes remains difficult to extract because these knockouts are generally extremely rare and may be seen only in a single individual. Because the ascertainment is based on the genotype, recall and deep phenotyping are often required. Once a particular knockout is identified, family-based designs can potentially be used to ascertain more individuals sharing the same (heterozygous or homozygous) variant. An example of this strategy was demonstrated in the discovery of a rare complete knockout in APOC3, which encodes an LDL protein, where a single individual with extremely low fasting triglyceride levels from a remote village in Pakistan was initially identified [29Saleheen D. et al.Human knockouts in a cohort with a high rate of consanguinity.bioRxiv. 2015; : 031518Google Scholar]. His extended family was later contacted and four more homozygous knockout individuals from the large pedigree were found. This greatly improved the association signal and provided evidence implicating APOC3 in the control of triglyceride levels in the blood [29Saleheen D. et al.Human knockouts in a cohort with a high rate of consanguinity.bioRxiv. 2015; : 031518Google Scholar]. Similarly, a homozygous knockout variant of PRDM9 (PRDM9 directs and initiates recombination in mammalian cells) was found in one woman from a cohort of 3222 individuals [30Narasimhan V.M. et al.Health and population effects of rare gene knockouts in adult humans with related parents.bioRxiv. 2016; (Science Published online March 3, 2016)https://doi.org/10.1126/science.aac8624Crossref Scopus (186) Google Scholar]. Follow-up by single-molecule phasing of her genome, together with that of her child, validated the predicted altered recombination pattern, and thus revealed PRDM9 redundancy in humans [30Narasimhan V.M. et al.Health and population effects of rare gene knockouts in adult humans with related parents.bio