High-throughput sequencing, also known as next-generation sequencing (NGS), reduced the cost and increased the yield of DNA sequencing. As whole-exome sequencing (WES) and whole-genome sequencing (WGS) are increasingly integrated into practical medical care, the importance of studying the genetic structure of ethnically diverse populations using NGS rises. Although most of the variant sites in the human genome are shared among individuals, allele frequencies vary substantially between populations (The International HapMap Consortium, 2005; 1000 Genomes Project Consortium et al., Reference Abecasis, Auton, Brooks, DePristo, Durbin, Handsaker, Kang, Marth and McVean2012; Visscher et al., Reference Visscher, Brown, McCarthy and Yang2012; Carmi et al., Reference Carmi, Hui, Kochav, Liu, Xue, Grady, Guha, Upadhyay, Ben-Avraham, Mukherjee, Bowen, Thomas, Vijai, Cruts, Froyen, Lambrechts, Plaisance, Van Broeckhoven, Van Damme, Van Marck, Barzilai, Darvasi, Offit, Bressman, Ozelius, Peter, Cho, Ostrer, Atzmon, Clark, Lencz and Pe'er2014; The Genome of the Netherlands Consortium, 2014; Gudbjartsson et al., Reference Gudbjartsson, Helgason, Gudjonsson, Zink, Oddson, Gylfason, Besenbacher, Magnusson, Halldorsson, Hjartarson, Sigurdsson, Stacey, Frigge, Holm, Saemundsdottir, Helgadottir, Johannsdottir, Sigfusson, Thorgeirsson, Sverrisson, Gretarsdottir, Walters, Rafnar, Thjodleifsson, Bjornsson, Olafsson, Thorarinsdottir, Steingrimsdottir, Gudmundsdottir, Theodors, Jonasson, Sigurdsson, Bjornsdottir, Jonsson, Thorarensen, Ludvigsson, Gudbjartsson, Eyjolfsson, Sigurdardottir, Olafsson, Arnar, Magnusson, Kong, Masson, Thorsteinsdottir, Helgason, Sulem and Stefansson2015; Nagasaki et al., Reference Nagasaki, Yasuda, Katsuoka, Nariai, Kojima, Kawai, Yamaguchi-Kabata, Yokozawa, Danjoh, Saito, Sato, Mimori, Tsuda, Saito, Pan, Nishikawa, Ito, Kuroki, Tanabe, Fuse, Kuriyama, Kiyomoto, Hozawa, Minegishi, Douglas Engel, Kinoshita, Kure, Yaegashi and Yamamoto2015). The value and advantages of sequencing diverse populations has already been shown in: genome-wide association studies (Visscher et al., Reference Visscher, Brown, McCarthy and Yang2012); discovering rare and de novo variants; improving variant calling sensitivity and specificity; and improving the accuracy of curating pathogenic variants (Carmi et al., Reference Carmi, Hui, Kochav, Liu, Xue, Grady, Guha, Upadhyay, Ben-Avraham, Mukherjee, Bowen, Thomas, Vijai, Cruts, Froyen, Lambrechts, Plaisance, Van Broeckhoven, Van Damme, Van Marck, Barzilai, Darvasi, Offit, Bressman, Ozelius, Peter, Cho, Ostrer, Atzmon, Clark, Lencz and Pe'er2014; The Genome of the Netherlands Consortium, 2014; Gudbjartsson et al., Reference Gudbjartsson, Helgason, Gudjonsson, Zink, Oddson, Gylfason, Besenbacher, Magnusson, Halldorsson, Hjartarson, Sigurdsson, Stacey, Frigge, Holm, Saemundsdottir, Helgadottir, Johannsdottir, Sigfusson, Thorgeirsson, Sverrisson, Gretarsdottir, Walters, Rafnar, Thjodleifsson, Bjornsson, Olafsson, Thorarinsdottir, Steingrimsdottir, Gudmundsdottir, Theodors, Jonasson, Sigurdsson, Bjornsdottir, Jonsson, Thorarensen, Ludvigsson, Gudbjartsson, Eyjolfsson, Sigurdardottir, Olafsson, Arnar, Magnusson, Kong, Masson, Thorsteinsdottir, Helgason, Sulem and Stefansson2015). Substantial efforts have been devoted to sequencing large number of individuals from diverse populations in order to create public databases that can assist human genetic studies such as the 1000 Genomes Project (1KG) (1000 Genomes Project Consortium et al., Reference Abecasis, Auton, Brooks, DePristo, Durbin, Handsaker, Kang, Marth and McVean2012), the Exome Sequencing Project (ESP; http://evs.gs.washington.edu/EVS/) and the Exome Aggregation Consortium (ExAC; http://exac.broadinstitute.org/).
The Ashkenazi Jewish population (AJP) is known to have a high rate of several diseases affecting individuals of that ethnic origin compared with other world ethnicities (Rosner et al., Reference Rosner, Rosner and Orr-Urtreger2009). These include both autosomal recessive disorders due to the founder effect (Slatkin, Reference Slatkin2004; Bray et al., Reference Bray, Mulle, Dodd, Pulver, Wooding and Warren2010; Carmi et al., Reference Carmi, Hui, Kochav, Liu, Xue, Grady, Guha, Upadhyay, Ben-Avraham, Mukherjee, Bowen, Thomas, Vijai, Cruts, Froyen, Lambrechts, Plaisance, Van Broeckhoven, Van Damme, Van Marck, Barzilai, Darvasi, Offit, Bressman, Ozelius, Peter, Cho, Ostrer, Atzmon, Clark, Lencz and Pe'er2014), such as Gaucher disease (Beutler et al., Reference Beutler, Nguyen, Henneberger, Smolec, McPherson, West and Gelbart1993), cystic fibrosis (Abeliovich et al., Reference Abeliovich, Lavon, Lerer, Cohen, Springer, Avital and Cutting1992) and Tay–Sachs disease (Myerowitz & Costigan, Reference Myerowitz and Costigan1988), as well as more common, adult-onset autosomal dominant diseases such as Parkinson's disease (PD) (Ozelius et al., Reference Ozelius, Senthil, Saunders-Pullman, Ohmann, Deligtisch, Tagliati, Hunt, Klein, Henick, Hailpern, Lipton, Soto-Valencia, Risch and Bressman2006) and hereditary BC and ovarian cancer (Struewing et al., Reference Struewing, Hartge, Wacholder, Baker, Berlin, McAdams, Timmerman, Brody and Tucker1997). Notably, the AJP has not been included as part of large-scale international sequencing projects. A recent NGS study of an AJP cohort demonstrated an improvement in imputation accuracy and modelling of Jewish history (Carmi et al., Reference Carmi, Hui, Kochav, Liu, Xue, Grady, Guha, Upadhyay, Ben-Avraham, Mukherjee, Bowen, Thomas, Vijai, Cruts, Froyen, Lambrechts, Plaisance, Van Broeckhoven, Van Damme, Van Marck, Barzilai, Darvasi, Offit, Bressman, Ozelius, Peter, Cho, Ostrer, Atzmon, Clark, Lencz and Pe'er2014). However, further research is warranted in order to elucidate the possible clinical implications of the AJP allelic architecture and to improve the curation and accuracy of pathogenic variant screening in current and future AJP studies.
Recently, new recommendations for the AJP screening panel were published based on the same dataset as ours (Baskovich et al., Reference Baskovich, Hiraki, Upadhyay, Meyer, Carmi, Barzilai, Darvasi, Ozelius, Peter, Cho, Atzmon, Clark, Yu, Lencz, Pe'er, Ostrer and Oddoux2016). However, that study focused only on the identification of pathogenic variants for the purpose of clinical screening in the AJP, whereas the current study takes a more global view by focusing on the genome and gene-level trends, rather than particular genetic variants, examining the utility of using an AJP-specific reference panel in interpreting clinical sequencing projects involving AJP individuals.
In this study, we focused on the clinical utility and practical implications resulting from WES analysis of 128 Ashkenazi Jews, of whom 74 individuals had no discernible disease and 54 were controls in a PD study. We examined the genetic differences between the AJP and other non-Jewish populations (NJPs) and searched for genes that are more likely to carry pathogenic variants among the AJP than in NJPs. Finally, we applied our findings to 49 independent Ashkenazi Jewish BC patients in order to evaluate the value of utilising an Ashkenazi Jew-specific database as a filtering tool.
Ashkenazi Jew variants
We used an unfiltered variant calling file (VCF) of 128 verified Ashkenazi Jewish individuals who underwent WGS as a part of a population genetic study of the AJP (Carmi et al., Reference Carmi, Hui, Kochav, Liu, Xue, Grady, Guha, Upadhyay, Ben-Avraham, Mukherjee, Bowen, Thomas, Vijai, Cruts, Froyen, Lambrechts, Plaisance, Van Broeckhoven, Van Damme, Van Marck, Barzilai, Darvasi, Offit, Bressman, Ozelius, Peter, Cho, Ostrer, Atzmon, Clark, Lencz and Pe'er2014). WGS was conducted by Complete Genomics with a high coverage (average coverage >50×). Seventy-four of the individuals were considered healthy and 54 were controls in a PD study. We extracted variants from the whole-exome region only, based on Ilumina's TruSeq Exome Enrichment Kit targets (https://www.illumina.com/content/dam/illumina-marketing/documents/products/datasheets/truseq-exome-data-sheet-770-2015-007.pdf), and did not include areas outside this region in our bioinformatics analysis. The target region size was 62 Mb, which targets 20,794 genes and 96·4% of RefSeq43-coding exons. We performed quality check (QC) and applied different filtrations (see Supplementary Methods; available online), which resulted in 222,179 high-quality single-nucleotide variants (SNVs).
BC patient variants
The VCF of 49 Ashkenazi Jewish BC patients, suspected to be hereditary, was obtained using the Genome Analysis Toolkit (GATK) best practice pipeline (McKenna et al. Reference McKenna, Hanna, Banks, Sivachenko, Cibulskis, Kernytsky, Garimella, Altshuler, Gabriel, Daly and DePristo2010), followed by QC (see Supplementary Methods), which resulted in 173,300 variants for the same exome region as the 128 Ashkenazi Jews.
1KG control groups
As control groups, and in order to compare the AJP with other populations, we used the European, African, East Asian (EAS) and South Asian (SAS) populations from the 1KG Project version 3 database (1000 Genomes Project Consortium et al., Reference Abecasis, Auton, Brooks, DePristo, Durbin, Handsaker, Kang, Marth and McVean2012). The data for these datasets were generated using the Illumina platform, and the variants were called by combining different variant callers, among them GATK's variant caller (http://www.1000genomes.org/analysis). For each population, 128 individuals were selected randomly, and the same region that was examined for the AJP was extracted.
In this study, we analysed the whole-exome data of 128 Ashkenazi Jewish individuals. We detected 222,179 SNVs, of which 30·6% (68,139) were singletons and 81·7% were shared and were annotated in other European population databases, including the European samples of ESP, ExAC and 1KG. Although this rate of overlap between the AJP and the European population is in line with the known relatedness and genetic similarity between the European population and the AJP (Behar et al., Reference Behar, Thomas, Skorecki, Hammer, Bulygina, Rosengarten, Jones, Held, Moses, Goldstein, Bradman and Weale2003; Costa et al., Reference Costa, Pereira, Pala, Fernandes, Olivieri, Achilli, Perego, Rychkov, Naumova, Hatina, Woodward, Eng, Macaulay, Carr, Soares, Pereira and Richards2013), approximately 20% of the detected variants were unique to the AJP. The overlap rates between AJP variation and genetically more distant populations including African, EAS and SAS populations (inferred from ExAC and 1KG databases) were significantly smaller, as expected (68–49%, Fig. 1(a)), further strengthening the validity of our data. Only 3·2% of the AJP variants were present in one of these distantly related populations but not in the European dataset, resulting in 13·3% (29,221) AJP-unique (i.e. novel) variants not reported in any of the population databases or in dbSNP142 (Fig. 1(b)).
Next, we functionally annotated the coding variants and classified the exonic variants into three categories by severity: (i) ‘high impact’ including stop-gain or stop-loss variants and variants within 2-bp of a splicing junction; (ii) ‘moderate impact’ included exonic missense variants; and (iii) ‘low impact’ included synonymous variants and exonic variants of unknown type due to incomplete gene structure information. Using this classification scheme, 831 variants (19 splice site variants) with high impact were identified, 54,585 were moderate-impact variants and 45,876 were low-impact variants. A similar distribution of variant severity was observed in the 128 European individuals (Supplementary Fig. S1).
Evaluating ACMG and COSMIC set of genes
We evaluated the clinical implications of the high-impact, very rare variants by comparing the existence of these variants in two gene sets: the Catalogue of Somatic Mutations in Cancer (COSMIC; http://cancer.sanger.ac.uk/cosmic) and the American College of Medical Genetics and Genomics (ACMG; https:// www.acmg.net/). COSMIC's Cancer Genes Census catalogues genes that exhibit mutations that are causally implicated in cancer pathogenesis (see Supplementary Material for the complete list). Of all COSMIC genes harbouring germline cancer mutations (n = 87) associated with cancer predisposition, six high-impact variants in five cancer predisposition genes were noted (Supplementary Table S2). Five of the variants were singletons, and one was a doubleton: rs34295337 in ERCC3, a gene associated with xeroderma pigmentosum type B (Ma et al., Reference Ma, Siemssen, Noteborn and van der Eb1994), which is a rare autosomal recessive disease that is associated with skin cancer (Paszkowska-Szczur et al., Reference Paszkowska-Szczur, Scott, Serrano-Fernandez, Mirecka, Gapska, Górski, Cybulski, Maleszka, Sulikowski, Nagay, Lubinski and Dębniak2013). One variant, rs11571833, in the BRCA2 gene, was described previously as being associated with an increased risk of developing a variety of cancer types including lung, breast, prostate, gastric and aerodigestive tract cancer (Wang et al., Reference Wang, McKay, Rafnar, Wang, Timofeeva, Broderick, Zong, Laplana, Wei, Han, Lloyd, Delahaye-Sourdeix, Chubb, Gaborieau, Wheeler, Chatterjee, Thorleifsson, Sulem, Liu, Kaaks, Henrion, Kinnersley, Vallée, LeCalvez-Kelm, Stevens, Gapstur, Chen, Zaridze, Szeszenia-Dabrowska, Lissowska, Rudnai, Fabianova, Mates, Bencko, Foretova, Janout, Krokan, Gabrielsen, Skorpen, Vatten, Njølstad, Chen, Goodman, Benhamou, Vooder, Välk, Nelis, Metspalu, Lener, Lubiński, Johansson, Vineis, Agudo, Clavel-Chapelon, Bueno-de-Mesquita, Trichopoulos, Khaw, Johansson, Weiderpass, Tjønneland, Riboli, Lathrop, Scelo, Albanes, Caporaso, Ye, Gu, Wu, Spitz, Dienemann, Rosenberger, Su, Matakidou, Eisen, Stefansson, Risch, Chanock, Christiani, Hung, Brennan, Landi, Houlston and Amos2014; Delahaye-Sourdeix et al., Reference Delahaye-Sourdeix, Anantharaman, Timofeeva, Gaborieau, Chabrier, Vallée, Lagiou, Holcátová, Richiardi, Kjaerheim, Agudo, Castellsagué, Macfarlane, Barzan, Canova, Thakker, Conway, Znaor, Healy, Ahrens, Zaridze, Szeszenia-Dabrowska, Lissowska, Fabianova, Mates, Bencko, Foretova, Janout, Curado, Koifman, Menezes, Wünsch-Filho, Eluf-Neto, Boffetta, Fernández Garrote, Polesel, Lener, Jaworowska, Lubiński, Boccia, Rajkumar, Samant, Mahimkar, Matsuo, Franceschi, Byrnes, Brennan and McKay2015; Thompson et al., Reference Thompson, Gorringe, Rowley, Li, McInerny, Wong-Brown, Devereux, Li, Trainer, Mitchell, Scott, James and Campbell2015; Meeks et al., Reference Meeks, Song, Michailidou, Bolla, Dennis, Wang, Barrowdale, Frost, McGuffog, Ellis, Feng, Buys, Hopper, Southey, Tesoriero, James, Bruinsma, Campbell, Broeks, Schmidt, Hogervorst, Beckman, Fasching, Fletcher, Johnson, Sawyer, Riboli, Banerjee, Menon, Tomlinson, Burwinkel, Hamann, Marme, Rudolph, Janavicius, Tihomirova, Tung, Garber, Cramer, Terry, Poole, Tworoger, Dorfling, van Rensburg, Godwin, Guénel, Truong, Stoppa-Lyonnet, Damiola, Mazoyer, Sinilnikova, Isaacs, Maugard, Bojesen, Flyger, Gerdes, Hansen, Jensen, Kjaer, Hogdall, Hogdall, Pedersen, Thomassen, Benitez, González-Neira, Osorio, Hoya Mde, Segura, Diez, Lazaro, Brunet, Anton-Culver, Eunjung, John, Neuhausen, Ding, Castillo, Weitzel, Ganz, Nussbaum, Chan, Karlan, Lester, Wu, Gayther, Ramus, Sieh, Whittermore, Monteiro, Phelan, Terry, Piedmonte, Offit, Robson, Levine, Moysich, Cannioto, Olson, Daly, Nathanson, Domchek, Lu, Liang, Hildebrant, Ness, Modugno, Pearce, Goodman, Thompson, Brenner, Butterbach, Meindl, Hahnen, Wappenschmidt, Brauch, Brüning, Blomqvist, Khan, Nevanlinna, Pelttari, Aittomäki, Butzow, Bogdanova, Dörk, Lindblom, Margolin, Rantala, Kosma, Mannermaa, Lambrechts, Neven, Claes, Maerken, Chang-Claude, Flesch-Janys, Heitz, Varon-Mateeva, Peterlongo, Radice, Viel, Barile, Peissel, Manoukian, Montagna, Oliani, Peixoto, Teixeira, Collavoli, Hallberg, Olson, Goode, Hart, Shimelis, Cunningham, Giles, Milne, Healey, Tucker, Haiman, Henderson, Goldberg, Tischkowitz, Simard, Soucy, Eccles, Le, Borresen-Dale, Kristensen, Salvesen, Bjorge, Bandera, Risch, Zheng, Beeghly-Fadiel, Cai, Pylkäs, Tollenaar, Ouweland, Andrulis, Knight, Narod, Devilee, Winqvist, Figueroa, Greene, Mai, Loud, García-Closas, Schoemaker, Czene, Darabi, McNeish, Siddiquil, Glasspool, Kwong, Park, Teo, Yoon, Matsuo, Hosono, Woo, Gao, Foretova, Singer, Rappaport-Feurhauser, Friedman, Laitman, Rennert, Imyanitov, Hulick, Olopade, Senter, Olah, Doherty, Schildkraut, Koppert, Kiemeney, Massuger, Cook, Pejovic, Li, Borg, Öfverholm, Rossing, Wentzensen, Henriksson, Cox, Cross, Pasini, Shah, Kabisch, Torres, Jakubowska, Lubinski, Gronwald, Agnarsson, Kupryjanczyk, Moes-Sosnowska, Fostira, Konstantopoulou, Slager, Jones, Antoniou, Berchuck, Swerdlow, Chenevix-Trench, Dunning, Pharoah, Hall, Easton, Couch, Spurdle and Goldgar2016; Vijai et al., Reference Vijai, Topka, Villano, Ravichandran, Maxwell, Maria, Thomas, Gaddam, Lincoln, Kazzaz, Wenz, Carmi, Schrader, Hart, Lipkin, Neuhausen, Walsh, Zhang, Lejbkowicz, Rennert, Stadler, Robson, Weitzel, Domchek, Daly, Couch, Nathanson, Norton, Rennert and Offit2016). Two variants, one in the DICER1 gene and one in the NF1 gene, were novel. The NF1 gene harboured one additional high-impact variant. Notably, NF1 germline mutations underlie the neurofibromatosis type 1 phenotype, a disease that is reportedly diagnosed at higher rates in the AJP than in the European population (Garty et al., Reference Garty, Laor and Danon1994).
The ACMG recommendation for reporting incidental findings in clinical sequencing includes 56 genes (22 genes intersect with COSMIC genes; see Supplementary Material for the complete list). High-impact variants were noted in two ACMG genes. The first variant (rs11571833) in the BRCA2 gene was already described and discussed above. The second variant, rs200563280, results in a premature stop codon in the RYR1 gene, a gene that is associated with malignant hyperthermia (Robinson et al., Reference Robinson, Carpenter, Shaw, Halsall and Hopkins2006). Thus, the rate of actionable incidental findings in the AJP is 1·56%, similar to the estimate for Europeans at approximately 2% (Amendola et al., Reference Amendola, Dorschner, Robertson, Salama, Hart, Shirts, Murray, Tokita, Gallego, Kim, Bennett, Crosslin, Ranchalis, Jones, Rosenthal, Jarvik, Itsara, Turner, Herman, Schleit, Burt, Jamal, Abrudan, Johnson, Conlin, Dulik, Santani, Metterville, Kelly, Foreman, Lee, Taylor, Guo, Crooks, Kiedrowski, Raffel, Gordon, Machini, Desnick, Biesecker, Lubitz, Mulchandani, Cooper, Joffe, Richards, Yang, Rotter, Rich, O'Donnell, Berg, Spinner, Evans, Fullerton, Leppig, Bennett, Bird, Sybert, Grady, Tabor, Kim, Bamshad, Wilfond, Motulsky, Scott, Pritchard, Walsh, Burke, Raskind, Byers, Hisama, Rehm, Nickerson and Jarvik2015). None of the above variants were mentioned in a recent study, based on the same dataset that expanded the recommendations for an AJP screening panel (Baskovich et al., Reference Baskovich, Hiraki, Upadhyay, Meyer, Carmi, Barzilai, Darvasi, Ozelius, Peter, Cho, Atzmon, Clark, Yu, Lencz, Pe'er, Ostrer and Oddoux2016).
We next examined AJP-specific variants. We defined variants as AJP specific if they were unique (i.e. novel) or very rare (minor allele frequency (MAF) <1%) in the NJPs, but more prevalent in the AJP (MAF >1%). Of the total AJP variants, 17,977 (8%) were AJP specific. To confirm that our dataset is enriched with variants that are unique to the AJP, we performed the same analysis on 128 verified Europeans from the Personal Genome Project (PGP) (Church, Reference Church2005; see Supplementary methods). Only 8748 variants (3·6% of the PGP dataset) were more than 1% in the PGP dataset but not in NJPs (both European and non-European populations).
We then looked at genes that are enriched for moderate- to high-impact variant groups that are AJP specific. This analysis yielded 5142 variants. Most genes harboured up to one such variant, 840 genes exhibited two variants and 196 genes displayed three or more moderate- to high-impact variants (Supplementary Fig. S2). After QC (see Supplementary Methods), three outlier genes were filtered out (Supplementary Fig. S2). In this analysis, virtually no correlation between the number of variants and the genomic length of the gene was observed (Pearson's correlation = 0·1). Next, we examined the residual variation intolerance score (RVIS) (Petrovski et al., Reference Petrovski, Wang, Heinzen, Allen and Goldstein2013) in order to identify genes under purifying selection that harbour unique or prevalent mutations in the AJP. Briefly, RVIS measures the tolerance of a gene to contain damaging variation. Genes with a low RVIS are predicted to be less tolerant to variation, and hence are more likely to exhibit a phenotype due to non-synonymous variants. The APC gene harboured a high number of AJP-specific variants (n = 7) and is in the lowest 0·2 percentile of RVIS (Fig. 2(a)). Mutations in the APC gene are associated with a specific form of inherited predisposition to colorectal cancer. Overall, colorectal cancer is more prevalent in the AJP than in NJPs (Feldman, Reference Feldman2001). Notably, the p.I1307K missense mutation in APC (rs1801155), which has been previously shown to moderately increase colorectal cancer risk in the AJP (Woodage et al., Reference Woodage, King, Wacholder, Hartge, Struewing, McAdams, Laken, Tucker and Brody1998), was among the identified variants (MAF = 0·047), and was recommended for inclusion in AJP screening (Baskovich et al., Reference Baskovich, Hiraki, Upadhyay, Meyer, Carmi, Barzilai, Darvasi, Ozelius, Peter, Cho, Atzmon, Clark, Yu, Lencz, Pe'er, Ostrer and Oddoux2016). However, additional susceptibility variants were detected in the APC gene, suggesting that other variants may contribute to the increased prevalence of colorectal cancer in the AJP. Other genes with low RVIS and harbouring four AJP-specific damaging variants are ABCA12, TULP4, DNMT1, DMXL1 and HECW1. To the best of our knowledge, the prevalence of the phenotypes associated with these genes (Supplementary Table S3) is not significantly higher in the AJP compared with other NJPs. Hence, the clinical implications and significance of this seemingly high rate of damaging variants in these genes warrant further investigation in additional extended Ashkenazi Jewish studies.
To assess the effect of the AJP-specific variants on protein function, we used the MetaLR (Dong et al., Reference Dong, Wei, Jian, Gibbs, Boerwinkle, Wang and Liu2015) ensemble tool, which integrates different prediction tools using logistic regression to predict whether a variant is deleterious (see Supplementary Methods). Overall, we obtained 649 AJP-specific deleterious variants in 580 different genes. Only eight genes had at least three AJP-specific deleterious variants (Fig. 2(b) and Supplementary Table S3): APC, ABCA12, LRP2, EPPK1, HGFAC, ACAD11, HLCS and NOX1. APC and ABCA12 were discussed; the HGFAC (three variants) gene is a member of the peptidase S1 protein family and is associated with pancreatic cancer (Kitajima et al., Reference Kitajima, Ide, Ohtsuka and Miyazaki2008), a cancer type that is known to be more frequent among the AJP (Feldman, Reference Feldman2001). The EPPK1 gene (four variants) encodes a protein that belongs to the plakin family and is related to ‘vacterl association’ disorder (Hilger et al., Reference Hilger, Schramm, Pennimpede, Wittler, Dworschak, Bartels, Engels, Zink, Degenhardt, Müller, Schmiedeke, Grasshoff-Derr, Märzheuser, Hosie, Holland-Cunz, Wijers, Marcelis, van Rooij, Hildebrandt, Herrmann, Nöthen, Ludwig, Reutter and Draaken2013). The phenotype of this disorder encompasses Fanconi anaemia, a phenotype that is diagnosed at a higher frequency in the AJP compared with NJPs (Kutler & Auerbach, Reference Kutler and Auerbach2004), and hence, these variants may contribute to these higher occurrence rates. The other genes are associated with different types of rare diseases, but to the best of our knowledge, these conditions are not diagnosed at an increased rate in the AJP (Supplementary Table S3).
Furthermore, to examine whether the genes harbouring AJP-specific deleterious variants were previously implicated as AJP-prevalent phenotypes, we queried VarElect (http://varelect.genecards.org/) using the term ‘Ashkenazi’. VarElect can prioritise genotype–phenotype associations based on various databases. Of the 580 queried genes, 14 genes harbouring 17 variants (Table 1) were found to be directly related to the ‘Ashkenazi’ term, denoting conditions that are common to the AJP. Five of the 17 variants are considered to be pathogenic by the Clinvar database, four of the variants were also included in the recent recommendation for the AJP screening panel (Baskovich et al., Reference Baskovich, Hiraki, Upadhyay, Meyer, Carmi, Barzilai, Darvasi, Ozelius, Peter, Cho, Atzmon, Clark, Yu, Lencz, Pe'er, Ostrer and Oddoux2016) and four of the genes are included in the AJP screening panel, but for different variants. To verify our results, we did the same for the 128 European individuals looking at European-specific variants, meaning genes with variants that were very rare in the non-European population but not in the European population (423 genes), and tried to find genes that were related to the ‘Ashkenazi’ phenotype. Although 20 genes were found to be related, none of the variants in them was found to be pathogenic by Clinvar, which further supports our results. Taken together, these results suggest that additional variants, among these 17 variants, are plausibly causal and hence should be further investigated.
a A score given by VarElect to show how much the gene was found to be related to the Ashkenazi phenotype.
b The diseases that were related to this gene in the context of the Ashkenazi phenotype according to VarElect.
c Diseases that were related to the variant according to Clinvar.
d Is the gene or the variant also found in a recent recommended AJP screening panel?
AJP = Ashkenazi Jewish population; RVIS = residual variation intolerance score.
Using the Ashkenazi Jewish database in an analysis of Ashkenazi Jewish early BC patients
The major objective of clinical sequencing is to identify the causative mutation from amongst numerous detected variants. To that end, non-synonymous variants with rare allele frequencies are considered initially as plausible causative mutations. Since the AJP is not included in any of the public databases of international sequencing efforts, the MAFs of closely related populations such as Europeans (Haas et al., Reference Haas, Winter, Lim, Kirby, Blumenstiel, DeFelice, Gabriel, Jalas, Branski, Grueter, Toporovski, Walther, Daly and Farese2012; Lee et al., Reference Lee, Durr, Majczenko, Huang, Liu, Lien, Tsai, Ichikawa, Goto, Monin, Li, Chung, Mundwiller, Shakkottai, Liu, Tesson, Lu, Brice, Tsuji, Burmeister, Stevanin and Soong2012; Rees et al., Reference Rees, Ng, Ruppert, Turner, Beer, Swift, Morken, Below, Blech, Mullikin, McCarthy, Biesecker, Gloyn and Collins2012) are often utilised as surrogates. We evaluated the advantages of using AJP-specific MAFs when screening the WES data of Ashkenazi Jewish samples. Of the 55,416 high- and moderate-impact mutations, 57·7% were classified as very rare based on the general European MAF versus 50·6% based on the AJP MAF, leading to out-filtration of approximately 3900 variants (Fig. 3(a)). Likewise, based on the maximum MAF (MMAF) of all NJPs, 50·1% of the variants were classified as very rare, compared to 40·8% when including the AJP. These results are in line with Carmi et al. (Reference Carmi, Hui, Kochav, Liu, Xue, Grady, Guha, Upadhyay, Ben-Avraham, Mukherjee, Bowen, Thomas, Vijai, Cruts, Froyen, Lambrechts, Plaisance, Van Broeckhoven, Van Damme, Van Marck, Barzilai, Darvasi, Offit, Bressman, Ozelius, Peter, Cho, Ostrer, Atzmon, Clark, Lencz and Pe'er2014). For rare variants (MAF <5%), the advantage of using AJP-specific MAFs is somewhat less significant (1·2% difference), in line with the notion that population-specific variants are predominantly very rare (1000 Genomes Project Consortium et al., Reference Auton, Brooks, Durbin, Garrison, Kang, Korbel, Marchini, McCarthy, McVean and Abecasis2015).
Similarly, potentially deleterious variants are prioritised in clinical NGS applications. Based on the AJP MAF, 79·0% of deleterious variants, based on MetaLR, were considered very rare, whereas 89·6% were considered very rare based on the European MAF (Fig. 3(b)). Furthermore, combining the AJP MAF with the NJP MMAF substantially improved filtering from 85·9% of the variants classified as very rare to just 72·9%. Since the MAFs of numerous populations, but not the AJP, are included in the MetaLR model, adding the AJP MAF can significantly improve the filtering of deleterious variants. Taken together, these significant population-specific differences in rare variants indicate that by utilising AJP-specific MAFs, finer filtration and lower false-positive rates can be achieved in Ashkenazi Jewish sequencing studies.
Importantly, we evaluated the utility of the AJP-specific screening approach using the independent WES data of 49 Ashkenazi Jewish samples derived from high-risk BC cases who do not harbour mutations in the predominant underlying genes – BRCA1 and BRCA2. Of the 2638 predicted deleterious variants, 81·3% were very rare according to the European MAF, compared to 77·5% using the AJP MAF. Similarly, combining the AJP with the NJP MMAF improved filtering by approximately 10% from 75·9% to 64·5% (Supplementary Fig. S3).
In our actual disease gene analysis of the Ashkenazi Jewish BC sample, we screened for very rare variants that are potentially deleterious by MetaLR and are present in at least three BC cases, resulting in 450 potentially deleterious variants. Filtering by using the European MAF resulted in 189 variants in 148 genes, while using the MMAF of the Ashkenazi Jewish and Europeans filtered an additional 69 variants, resulting in 120 potential variants (36%). In comparison, using the MMAF of Europeans and 128 individuals from African, EAS or SAS populations resulted in minor additional filtering of only seven, two and 13 variants, respectively (Supplementary Fig. S4). Using all populations' MMAFs (AJP + NJP) versus only the NJP MMAF resulted in 100 variants in 72 genes compared to 157 variants in 126 genes (36%) (Fig. 3(c)). We then used VarElect to search for genes related to the keyword ‘breast’. The MSH6 gene scored highest using VarElect (Supplementary Table S4) and by the MetaLR deleterious score (0·88). The protein coded by this gene is a member of the DNA mismatch repair MutS family, and rare variants in this gene are associated with familial BC (Wasielewski et al., Reference Wasielewski, Riaz, Vermeulen, van den Ouweland, Labrijn-Marks, Olmer, van der Spaa, Klijn, Meijers-Heijboer, Dooijes and Schutte2010). Mutations in MSH6 are traditionally associated with Lynch syndrome (Baglietto et al., Reference Baglietto, Lindor, Dowty, White, Wagner, Garcia, Vriends, Cartwright, Barnetson, Farrington, Tenesa, Hampel, Buchanan, Arnold, Young, Walsh, Jass, Macrae, Antill, Winship, Giles, Goldblatt, Parry, Suthers, Leggett, Butz, Aronson, Poynter, Baron, Le Marchand, Haile, Gallinger, Hopper, Potter, de la Chapelle, Vasen, Dunlop, Thibodeau and Jenkins2010), a syndrome that seems to encompass BC susceptibility according to recent publications (Win et al., Reference Win, Lindor and Jenkins2013). This finding requires further examination of a larger cohort in order to draw better conclusions about the role of these variants in BC predisposition.
In this study, a comprehensive analysis of the whole exome in 128 Ashkenazi Jewish individuals using high-coverage NGS technology was carried out and compared with the same data generated from a closely related European population.
By targeting AJP-specific variants, the clinical utility of using NGS technology to genotype entire populations is clearly demonstrated. Using such an approach, applying a variety of bioinformatics and predictive tools and querying several publicly available databases, we revealed novel variants and genes that may be associated with an increased risk of developing a host of diseases in the AJP. Some of these variants occur within genes related to diseases that are known to be more commonly diagnosed in the AJP than in NJPs: colorectal cancer (APC gene) and pancreatic cancer (HGFAC gene). Although these variants are predicted to be pathogenic and may indeed affect cancer risk, the current evidence is still tentative and cannot be clinically applied until validation and expansion of these results is provided by future studies. The EPPK1 gene harboured a few AJP-specific deleterious variants. Homozygous mutations in this gene are associated with Fanconi anaemia, a disorder that is more commonly encountered in AJP (Kutler & Auerbach, Reference Kutler and Auerbach2004). Moreover, heterozygous mutations in Fanconi anaemia genes are associated with increased cancer risk, primarily BC (Mathew, Reference Mathew2006; Alan & D'Andrea, Reference Alan and D'Andrea2010), and indeed, two of the four AJP-specific deleterious variants in the EPPK1 gene were also detected in the high-risk BC cohort. Among the observed AJP-specific deleterious variants, five were known to be pathogenic variants that increase the risk of five different diseases that are common to the AJP, and three of them were included in a new recommended screening panel for the AJP (Baskovich et al., Reference Baskovich, Hiraki, Upadhyay, Meyer, Carmi, Barzilai, Darvasi, Ozelius, Peter, Cho, Atzmon, Clark, Yu, Lencz, Pe'er, Ostrer and Oddoux2016). These overlaps confirm the effectiveness of the methodology applied in the present study for finding population-based pathogenic variants, as well as supporting the potential of population screening using NGS. Additionally, by examining specific genes with known and valuable clinical implications and consequences (i.e. ACMG incidental findings genes and COSMIC germline mutation-harbouring genes), a number of variants were identified in genes that lead to a phenotype that is seen at a higher occurrence in the AJP than in other populations (e.g. the NF1 gene).
Based on the results of the present study and the current ACMG incidental findings recommendations, in approximately 3/200 (1·56%) members of the AJP who undergo WES, an incidental finding will emerge. As information about the role of each variant in the exome/genome accumulates and the pathogenicity prediction tools and functional analyses continue to evolve, some of the moderate-impact variants of these genes might also be reclassified as pathogenic, so that the rate of incidental findings may still be altered.
The present study also illustrated the importance of using the Ashkenazi Jewish-specific database in the course of analysing the genetic basis of inherited cancer in the AJP. Using the dataset and analysis tools, the number of potential causal sequence variants underlying an inherited predisposition to BC was reduced by 36%. Such a filtering step is critical to defining a bona fide causal mutation. Therefore, this provides further support for the importance of creating and using a population-specific database when investigating the genetic basis of inherited diseases, rather than using genetically related but not identical populations.
While a recent study of 5685 Ashkenazi Jewish exomes has been published (Rivas et al., Reference Rivas, Koskela, Huang, Stevens, Avila, Haritunians, Neale, Kurki, Ganna, Graham, Glaser, Peter, Atzmon, Barzilai, Levine, Schiff, Pontikos, Weisburd, Karczewski, Minikel, Petersen, Beaugerie, Seksik, Cosnes, Schreiber, Bokemeyer, Bethge, Heap, Ahmad, Plagnol, Segal, Targan, Turner, Saavalainen, Farkkila, Kontula, Pirinen, Palotie, Brant, Duerr, Silverberg, Rioux, Weersma, Franke, MacArthur, Jalas, Sokol, Xavier, Pulver, Cho, McGovern and Daly2016), the current study provides evidence that by using whole-exome data from a relatively small number (n = 128) of Ashkenazi Jewish individuals, clinically relevant information and improvements in filter annotation are feasible. Thus, the research potential value and clinical benefits of using NGS technology at a population level are further emphasised.
The Shomron laboratory is supported by the Israel Cancer Research Fund (ICRF), Research Career Development Award (RCDA); Wolfson Family Charitable Fund; Earlier.org – Friends for an Earlier Breast Cancer Test; Claire and Amedee Maratier Institute for the Study of Blindness and Visual Disorders; I-CORE Program of the Planning and Budgeting Committee, The Israel Science Foundation (grant number 41/11); the Israeli Ministry of Defense, Office of Assistant Minister of Defense for Chemical, Biological, Radiological and Nuclear (CBRN) Defense; Foundation Fighting Blindness; Saban Family Foundation, Melanoma Research Alliance; Binational Science Foundation (BSF); Israel Cancer Research Fund (ICRF) Acceleration Grant; Israel Cancer Association (ICA); Donation from the Kateznik K. Association Holocaust; Margot Stoltz Foundation through the Faculty of Medicine grants of Tel Aviv University; The Varda and Boaz Dotan Research Center in Hemato-Oncology, Idea Grant; ‘Lirot’ Association and the Consortium for Mapping Retinal Degeneration Disorders in Israel; Interdisciplinary grant of the Israeli Ministry of Science, Technology and Space on the Science, Technology and Innovation for the Third Age; The Edmond J. Safra Center for Ethics at Tel Aviv University; Check Point Institute for Information Security; Joint Core Program of Research on the Molecular Basis of Human Disease, Shabbetai Donnolo Fellowships supported by the Italian Ministry of Foreign Affairs; Israel Science Foundation (ISF, 1852/16); and the Edmond J. Safra Center for Bioinformatics at Tel Aviv University.
For supplementary material accompanying this paper visit https://doi.org/10.1017/S0016672317000015.