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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 3  |  Issue : 4  |  Page : 235-242

Carrier rate analysis of single-gene disorders based on 1000 genome project and exome aggregation consortium data


Laboratory of Medical Foods, NHC Key Laboratory of Reproduction Regulation (Shanghai Institute of Planned Parenthood Research), School of Basic Medical Sciences, Fudan University, Shanghai 200032, China

Date of Submission16-May-2019
Date of Web Publication2-Jan-2020

Correspondence Address:
Hua-Jun Zheng
No. 2140, Xietu Road, Xuhui District, Shanghai 200032
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2096-2924.274546

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  Abstract 


Objective: Screening variants underlying the single-gene disorder in the general population can help reduce the incidences of birth defects. To determine the most prevalent pathogenic variants causing autosomal recessive diseases, we investigated the frequencies of these variants in six major geographic ancestry groups from Exome Aggregation Consortium (ExAC) database and 26 populations from the 1,000 Genome Project, including three Chinese ethnic groups.
Methods: We selected 64 autosomal recessive diseases and collected corresponding causal genes and variants from ClinVar for the analysis. The RS (reference single-nucleotide polymorphism) IDs of these variants were used to search the corresponding VCF file from the 1,000 Genomes Project and ExAC databases. We calculated the frequencies of heterozygotes of each disease variants in the 1,000 Genomes Project and ExAC samples and compared the distribution of disease alleles among different populations.
Results: Our analysis revealed that 1,151/212 variants were carried by 60,706/2,504 individuals sequenced in the ExAC/1,000 Genomes Project. The average number of autosomal recessive disease alleles carried by samples from ExAC and 1,000 Genomes Project were 0.53 and 0.68, respectively. These disease alleles showed differential distribution among populations, and some disease alleles were significantly enriched in certain ethnic groups. In addition, 1–2 main pathogenic variants were identified in each disease. Meanwhile, several ClinVar variants with relatively high frequency (>1%) in the samples were found to be benign instead of “conflicting evaluations of pathogenicity.”
Conclusions: Our observations revealed that main pathogenic variants existed in certain autosomal recessive disease, suggesting that screening of disease hypermutations in different populations is valuable in reducing the occurrence of birth defects.

Keywords: Single-Gene Disorders; Exome Aggregation Consortium; 1,000 Genomes Project; Carrier Rate


How to cite this article:
Duan MM, Zheng HJ. Carrier rate analysis of single-gene disorders based on 1000 genome project and exome aggregation consortium data. Reprod Dev Med 2019;3:235-42

How to cite this URL:
Duan MM, Zheng HJ. Carrier rate analysis of single-gene disorders based on 1000 genome project and exome aggregation consortium data. Reprod Dev Med [serial online] 2019 [cited 2020 Jan 27];3:235-42. Available from: http://www.repdevmed.org/text.asp?2019/3/4/235/274546




  Introduction Top


There are currently more than 7,000 known rare diseases,[1] including albinism, Gaucher's disease, and pulmonary cystic fibrosis (CF). The prevalence of rare diseases is less than 1/2,000,[2] and 80% are genetic diseases.[3] Single-gene disorders are genetic diseases caused by a single or a pair of mutational alleles on homologous chromosomes, also known as Mendelian diseases. If a healthy couple carries the same recessive variant, their offspring have a 25% chance of having the disease. These diseases can be devastating, and there are currently no effective treatments, which exerts considerable pressure on families and society. Therefore, it is necessary to carry out carrier screening for such single-gene disorders. Screening carriers with normal phenotypes in the population by adopting economical, accurate, and reliable methods would help risk assessment and marriage and childbirth guidance and would help prevent birth defects.

Carrier screening is of great significance in preventing birth defects. Tay–Sachs disease (TSD), an autosomal recessive disorder with stunting, blindness, and epilepsy, is a progressive disease with low survival rates after 4 years and which is highly prevalent in the Jewish (especially German Jewish) population.[4] There is currently no effective treatment for this disease. In the 1970s, California launched statewide TSD screening.[5] After 30 years of hard work, the incidence of TSD in the Jewish population in the United States,[6] Canada,[7] and Israel decreased by 90%.[8] In addition, CF carrier screening is performed routinely on the majority of sperm donor applicants in the United States.[9] Screening of pregnant women and newborns at risk for sickle cell disease in Italy also proved necessary for the early diagnosis.[10]

High-throughput sequencing techniques can detect genetic diseases, both in diagnosis and in finding new or rare variants,[11],[12],[13],[14] thus it can be used for carrier screening. The National Health Commission of China announced 121 rare diseases in 2018, including 64 autosomal recessive diseases. In this study, the carrier rates of variants involving these 64 autosomal recessive diseases were analyzed based on the data from the 1000 Genomes Project, which provides a global reference for human genetic variation through reconstructing the genomes of 2,504 individuals from 26 populations.[15] Exome sequence data from 60,706 individuals in the Exome Aggregation Consortium (ExAC) were also used to validate our analysis results.[16]


  Methods Top


Data set

We downloaded the VCF file of the Phase 3 release of the 1,000 Genomes Project from its website (http://www.internationalgenome.org/), which contained 84.7 million variants involving 2,504 individuals from 26 populations worldwide. Among them, 105 individuals were Southern Han Chinese, 93 were Chinese Dai from Xishuangbanna, and 103 were Han Chinese from Beijing. VCF file of 60,706 human exomes (Release 1.0) was downloaded from ExAC Browser Beta (http://exac.broadinstitute.org/), which included high-quality variant sites of individuals corresponding to Finnish, non-Finnish European, African, South Asian, East Asian, and admixed American (Latino) ancestry.

Analysis procedures

We selected 64 autosomal recessive diseases from 121 rare diseases that are regarded as common in China. The genes and variants associated with these 64 diseases were then collected from ClinVar.[17] The RS (reference single-nucleotide polymorphism [SNP]) IDs of the disease-causing variants found in ClinVar were then used to search the corresponding VCF file of the 1,000 Genomes Project and ExAC, respectively. Then, the frequencies of heterozygotes of each disease variants in the 1,000 Genomes Project and ExAC population were calculated [Figure 1]. The 95% confidence intervals of carrier rate of single-gene disorders were estimated based on a normal approximate method using the following formula:
Figure 1: Strategy for carrier rate statistics for autosomal recessive diseases using the data of ExAC and 1,000 Genomes Project.

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P: Probability; Zα/2: Critical value; SP: Sampling error; n: Sample size, where Zα/2 = 1.96 when α = 0.05.

Enrichment of autosomal recessive diseases for each ethnic group was calculated using a classical hypergeometric distribution statistical comparison of the disease carrier rate in each ethnic group against the disease carrier rate in 2,504 individuals or 60,706 individuals. The calculated P values underwent the false discovery rate (FDR) correction, taking a corrected P < 0.05 as a threshold. Diseases fulfilling this condition were defined as significantly enriched in each ethnic group.

Data availability

The variant sites associated with the 64 autosomal recessive diseases can be downloaded from ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/.


  Results Top


Mutated genes and sites associated with autosomal recessive diseases

Sixty-four autosomal recessive diseases regarded as common in China were selected in our analysis. Searching the ClinVar database revealed 319 genes associated with these diseases, including 2,712 variant sites [Supplementary Table 1]. Some diseases, such as biotinidase deficiency (BTD), had 145 known associated variant sites, whereas sickle cell disease had only two.



Percapita carrier rates of variants associated with autosomal recessive diseases

Carrier rates of the 2,712 variants associated with the 64 autosomal recessive diseases were analyzed in the 2,504 individuals, and a total of 276 variants were identified. After excluding benign variants, we found that 212 (7.8%) pathogenic, likely pathogenic or conflicting interpretations of pathogenicity variants associated with 50 (78.1%) of diseases were carried by the 2,504 individuals, with an average of 0.68 variant sites per individual [Supplementary Table 2].



In ExAC, a total of 1,574 variants were identified in 60,706 individuals. After excluding benign variants, we found that 1,132 (41.7%) pathogenic, likely pathogenic or conflicting interpretations of pathogenicity variants associated with 63 (98.4%) of diseases were carried by the 60,706 individuals, with an average of 0.53 variant sites per individual [Supplementary Table 3].



Some conflicting interpretations of pathogenicity in ClinVar may cause erroneous classification and statistics, and hence, we deleted conflicting interpretations of pathogenicity before further statistical analysis.

Of the 64 diseases, 14 had no associated variants in the 26 populations investigated, including arginase deficiency (13 variants), lysinuric protein intolerance (7 variants), tetrahydrobiopterin deficiency (6 variants), congenital adrenal hypoplasia (32 variants), hypophosphatemic rickets (24 variants), idiopathic hypogonadotropic hypogonadism (12 variants), Silver– Russell syndrome More Details (7 variants), and Wiskott-Aldrich syndrome (17 variants). This might result from the low carrier rate of these diseases in the population, with the low prevalence of 1/2,000,000,[18] 1/60,000,[19] 1/500,000–1/1,000,000,[20] 1/140,000–1/1,200,000,[21] 1/20,000,[22] 1–10/100,000,[23] 1/30,000–1/100,000,[24] and 1/100,000–1/1,000,000,[25] respectively. In ExAC, variants associated with Silver–Russell syndrome were not revealed, further indicating its low carrier rate in the population.

Population differences in carrier rates

The number of variants carried by different populations was different. Among them, the per capita number of variants in the three populations of Chinese Dai, Han in Beijing, and Southern China was 0.44, 0.67, and 0.60, respectively.

There were significant population and regional differences in the risk and carrier rates of different hereditary diseases. As we can see, the disease exhibiting the highest carrier rate in six major geographic ancestry groups differed from each other [Table 1]. For example, BTD exhibited the highest carrier rates in South Asian (2.75%). This was supported by the 1000 Genome Project data [Table 2], which showed that BTD exhibited the highest carrier rates in the four populations of South Asian, varying from 1.96% in Indian Telugu in the UK to 5.88% in Sri Lankan Tamils in the UK (FDR <0.05, meaning BTD carriers were significantly enriched in Indian Telugu in the UK). BTD is an inherited metabolic disorder of biotin (Vitamin B) recycling that leads to multiple carboxylase deficiencies. According to OrphaNet, a data source on rare diseases,[26] the prevalence of BTD is estimated to be 1/61,000, and the carrier frequency in the general population is approximately 1/120.
Table 1: Single-gene diseases with the highest carrier rates in six major geographic ancestry groups

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Table 2: Single-gene diseases with the highest carrier rates in 26 ethnic groups

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Other diseases showing higher carrier frequency included progressive familial intrahepatic cholestasis (PFIC), with a carrier frequency of 14.14% in Luhya in Webuye, Kenya, and 4.67% carrier frequency in the Iberian populations in Spain (both populations showed a significant enrichment of PFIC, with FDR < 0.05). PFIC refers to a heterogeneous group of autosomal recessive disorders of childhood that disrupt bile formation and present with cholestasis of hepatocellular origin, with an estimated incidence between 1/50,000 and 1/100,000 births.[27] CF carriers have been reported to be more common in Europe and America,[28] and our analysis revealed that 4.81% of Japanese from Tokyo are at risk for this disease [Table 2].

Collectively, these data showed that genetic diseases with high carrier rates varied across different ethnic groups.

We further analyzed diseases with a high carrier rate in Asia. BTD is the most commonly carried diseases in four populations of South Asia, whereas the five populations of East Asia exhibited high carrier rates of different genetic disorders [Table 3]. Carnitine deficiency is a disease exhibiting the highest carrier rate (2.15%) in Chinese Dai in Xishuangbanna. The disease with the highest carrier rate in Han Chinese in Beijing is Parkinson Disease (Young-onset, Early-onset, 3.88%), and in Southern Han Chinese is nonsyndromic deafness (2.86%). However, no autosomal recessive diseases displayed significant enrichment in the Chinese populations, while both Kallmann syndrome and pulmonary CF showed significant enrichment in Japanese in Tokyo, Japan [Table 3].
Table 3: Carrier rates of different single-gene diseases in the Asian populations

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Single-gene diseases are mainly caused by 1–2 pathogenic variants in populations

Although each autosomal recessive disease has 2–145 different gene variant sites, only one or two hypermutations were observed in the populations studied [Figure 2]. Among the 212 variants carried by the 26 ethnic groups, carrier rates of 55 variants involving 31 genetic diseases were >0.1% in the 2,504 individuals, and ten variants of six genetic diseases were hypermutations with a carrier rate >1% [Figure 3]. Then, in the ExAC data, eight of the 10 variants also showed a carrier rate >1% [Table 4]. It can be seen from [Table 5] that several genetic diseases are mainly caused by 1–2 pathogenic variants. For example, over half of BTD carriers have a variant involving rs397514333, and carriers of PFIC have a variant affecting rs45575636.
Figure 2: Number of pathogenic variants in different single-gene diseases. 212 pathogenic variants were carried by 2,504 individuals in the 1,000 Genomes Project. The blue bars correspond to the ratio of these variants relative to known variants associated with the disease.

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Figure 3: Variant carrier rates. Among the 2,712 variants, the population carried 212 (7.8%) variants, of which 10 variants involving 6 genetic diseases were carried in more than 1% of the population.

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Table 4: Ten hypermutations with carrier rates >1% in populations

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Table 5: Main SNP contributing to single-sgene diseases and carrier rates in different populations

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These genetic diseases are mainly caused by 1–2 SNP variants, and thus, the hypermutation of genetic diseases can be selected as a target in disease carrier screening, which can greatly reduce costs and ensure effective screening.

High carrier rates versus low prevalence validate conflicting evaluations of pathogenicity

In ClinVar, specific standard terminology such as “pathogenic,” “likely pathogenic” and “benign” were used to interpret clinical significance of each variant,[29] with “likely pathogenic” defined as >90% certainty of a variant being disease-causing. However, interpretation of the same variant by multiple clinical laboratories may differ,[30] and result in a clinical significance as “conflicting interpretations of pathogenicity.”

It has been reported that some of the variants in ClinVar have either low penetrance or inaccurate pathogenicity assignment, and a large proportion of the variants with conflicting clinical significance are benign.[31]

Of the 2,712 variants deposited in ClinVar, 1,151 variants involving 63 diseases were revealed in the individuals of the 1,000 Genomes Project and ExAC. By analyzing the carrier rate of these variants, we found that the carrier rate of 10 variants was >1% [Table 4]. rs1126809, a variant causing albinism, has a population carrier rate of 13.62% in the 1,000 Genome Project and 30.99% in ExAc. The prevalence of all forms of albinism has been estimated at approximately 1/17,000,[32] so such a high carrier rate is unreasonable if we compare the calculated disease risk (1/215 if population carrier rate is 13.62%) with the disease prevalence (1/17,000). Although the clinical significance of rs1126809 in ClinVar is “conflicting interpretations of pathogenicity,” we were able to validate that it is a benign variation.

Similarly, the carrier rates of several other variants listed in [Table 4] are strikingly high, indicating possible incorrect classifications in ClinVar. Because most of these variation sites with high carrier rates are deposited in ClinVar with no clinical evidence, our analysis provided a robust validation that these variant sites are indeed benign.


  Discussion Top


The birth defect ratio is consistently increasing due to environmental factors and increased childbearing age. China's population determines that it is a country with a high birth-defect burden. These birth defects include 21-hydroxylase deficiency,[33] albinism,[34] Kallmann syndrome,[35] and mitochondrial encephalomyopathy.[36] These diseases are currently not well treated, and the patient's family and society bear a heavy burden. Through the statistical analysis, we identified that each individual, on an average, carries 0.53–0.68 pathogenic variants involving recessive genetic diseases and that each population has a different risk of developing specific autosomal recessive diseases. If a couple carry the same variant site of a certain recessive genetic disease, the chance that their child will have the disease is 25%. Hence, the higher carrier rate of recessive genetic diseases associated variants in certain ethnic group means higher disease prevalence in this population. Therefore, it is necessary to screen these genetic carriers to provide childbirth guidance and to prevent the birth of defective infants.

The carrier rates among different ethnic groups were different, and the genetic diseases involved were mainly caused by 1–2 variants. Therefore, we selected variants with a high frequency in different genetic diseases to screen according to ethnic differences. Genetic diseases with high carrier rates included BTD, albinism, and nonsyndromic deafness [Table 2]. Because the high carrier rate of each disease is usually caused by one or two pathogenic variants, high-throughput screening for common autosomal recessive diseases is feasible.

An important factor hampering genetic screening is cost. CRISPR Cas-12a technology and mass arrays can be used for the detection of a small number of variants (like the 100 main variant sites associated with 64 autosomal recessive diseases in China).[37] Cas-12a identifies SNPs by forming a Cas12a/crRNA/target DNA ternary complex that cleaves nonspecific fluorescently labeled ssDNA probes.[38],[39] Massarrays, developed by Agena Bioscience,[40],[41] amplify the target sequence through PCR, adds an SNP sequence-specific extension primer and extends 1 base at the SNP site. The precise molecular weight of the sample analyte is obtained by detecting the time of flight of the nucleic acid molecule in a vacuum tube and thereby SNP locus information is obtained. Both platforms have the following advantages: high accuracy, high throughput, easy operation, flexibility, and cost-effectiveness.

The key step to screen genetic variant sites is to select the most important variant site for each disease. The 1,000 Genome Project and ExAC provided us with valuable information. We used these data to obtain the carrier rates of recessive genetic diseases in different populations and determined the hypermutation of each disease in each population. Based on regional and population differences, high-risk genetic diseases in various geographical locations were identified, and targeted screening for carriers could be carried out to reduce the incidence of birth defects.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Rodwell C, Aymé S. Rare disease policies to improve care for patients in Europe. Biochim Biophys Acta 2015;1852:2329-35. doi: 10.1016/j.bbadis.2015.02.008.  Back to cited text no. 1
    
2.
Richter T, Nestler-Parr S, Babela R, Khan ZM, Tesoro T, Molsen E, et al. Rare disease terminology and definitions-A systematic global review: Report of the ISPOR rare disease special interest group. Value Health 2015;18:906-14. doi: 10.1016/j.jval.2015.05.008.  Back to cited text no. 2
    
3.
Chu SY, Weng CY. Introduction to genetic/Rare disease and the application of genetic counseling. Hu Li Za Zhi 2017;64:11-7. doi: 10.6224/JN.000063.  Back to cited text no. 3
    
4.
Lew RM, Burnett L, Proos AL, Barlow-Stewart K, Delatycki MB, Bankier A, et al. Ashkenazi Jewish population screening for Tay-Sachs disease: The international and Australian experience. J Paediatr Child Health 2015;51:271-9. doi: 10.1111/jpc.12632.  Back to cited text no. 4
    
5.
Monaghan KG, Feldman GL, Palomaki GE, Spector EB; Ashkenazi Jewish Reproductive Screening Working Group, Molecular Subcommittee of the ACMG Laboratory Quality Assurance Committee. Technical standards and guidelines for reproductive screening in the Ashkenazi Jewish population. Genet Med 2008;10:57-72. doi: 10.1097/GIM.0b013e31815f6eac.  Back to cited text no. 5
    
6.
Kaback M, Lim-Steele J, Dabholkar D, Brown D, Levy N, Zeiger K. Tay-Sachs disease – Carrier screening, prenatal diagnosis, and the molecular era. An international perspective, 1970 to 1993. The international TSD data collection network. JAMA 1993;270:2307-15. doi: 10.1001/jama.1993.03510190063028.  Back to cited text no. 6
    
7.
Kaplan F. Tay-Sachs disease carrier screening: A model for prevention of genetic disease. Genet Test 1998;2:271-92. doi: 10.1089/gte. 1998.2.271.  Back to cited text no. 7
    
8.
Zlotogora J. Population programs for the detection of couples at risk for severe monogenic genetic diseases. Hum Genet 2009;126:247-53. doi: 10.1007/s00439-009-0669-y.  Back to cited text no. 8
    
9.
Sims CA, Callum P, Ray M, Iger J, Falk RE. Genetic testing of sperm donors: Survey of current practices. Fertil Steril 2010;94:126-9. doi: 10.1016/j.fertnstert.2009.01.139.  Back to cited text no. 9
    
10.
Lodi M, Bigi E, Palazzi G, Vecchi L, Morandi R, Setti M, et al. Universal screening program in pregnant women and newborns at-risk for sickle cell disease:First report from northern Italy. Hemoglobin 2017;41:230-3. doi: 10.1080/03630269.2017.1405820.  Back to cited text no. 10
    
11.
Ku CS, Naidoo N, Pawitan Y. Revisiting mendelian disorders through exome sequencing. Hum Genet 2011;129:351-70. doi: 10.1007/s00439-011-0964-2.  Back to cited text no. 11
    
12.
Ku CS, Cooper DN, Polychronakos C, Naidoo N, Wu M, Soong R. Exome sequencing: Dual role as a discovery and diagnostic tool. Ann Neurol 2012;71:5-14. doi: 10.1002/ana.22647.  Back to cited text no. 12
    
13.
Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ, Nickerson DA, et al. Exome sequencing as a tool for mendelian disease gene discovery. Nat Rev Genet 2011;12:745-55. doi: 10.1038/nrg3031.  Back to cited text no. 13
    
14.
Kiezun A, Garimella K, Do R, Stitziel NO, Neale BM, McLaren PJ, et al. Exome sequencing and the genetic basis of complex traits. Nat Genet 2012;44:623-30. doi: 10.1038/ng.2303.  Back to cited text no. 14
    
15.
1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature 2015;526:68-74. doi: 10.1038/nature15393.  Back to cited text no. 15
    
16.
Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 2016;536:285-91. doi: 10.1038/nature19057.  Back to cited text no. 16
    
17.
Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, et al. ClinVar: Public archive of interpretations of clinically relevant variants. Nucleic Acids Res 2016;44:D862-8. doi: 10.1093/nar/gkv1222.  Back to cited text no. 17
    
18.
Scaglia F, Lee B. Clinical, biochemical, and molecular spectrum of hyperargininemia due to arginase I deficiency. Am J Med Genet C Semin Med Genet 2006;142C: 113-20. doi: 10.1002/ajmg.c.30091.  Back to cited text no. 18
    
19.
Mauhin W, Habarou F, Gobin S, Servais A, Brassier A, Grisel C, et al. Update on lysinuric protein intolerance, a multi-faceted disease retrospective cohort analysis from birth to adulthood. Orphanet J Rare Dis 2017;12:3. doi: 10.1186/s13023-016-0550-8.  Back to cited text no. 19
    
20.
Bókay J. Tetrahydrobiopterin (BH4) deficiency – Diagnosis and treatment. Orv Hetil 2017;158:1897-902. doi: 10.1556/650.2017.30895.  Back to cited text no. 20
    
21.
Nagel SA, Hartmann MF, Riepe FG, Wudy SA, Wabitsch M. Gonadotropin-and adrenocorticotropic hormone-independent precocious puberty of gonadal origin in a patient with adrenal hypoplasia congenita due to DAX1 gene mutation – A case report and review of the literature: Implications for the pathomechanism. Horm Res Paediatr 2019;91:336-45. doi: 10.1159/000495189.  Back to cited text no. 21
    
22.
Pavone V, Testa G, Gioitta Iachino S, Evola FR, Avondo S, Sessa G. Hypophosphatemic rickets: Etiology, clinical features and treatment. Eur J Orthop Surg Traumatol 2015;25:221-6. doi: 10.1007/s00590-014-1496-y.  Back to cited text no. 22
    
23.
Fraietta R, Zylberstejn DS, Esteves SC. Hypogonadotropic hypogonadism revisited. Clinics (Sao Paulo) 2013;68 Suppl 1:81-8. doi: 10.6061/clinics/2013(sup01)09.  Back to cited text no. 23
    
24.
Wakeling EL, Brioude F, Lokulo-Sodipe O, O'Connell SM, Salem J, Bliek J, et al. Diagnosis and management of silver-Russell syndrome:First international consensus statement. Nat Rev Endocrinol 2017;13:105-24. doi: 10.1038/nrendo.2016.138.  Back to cited text no. 24
    
25.
Worth AJ, Thrasher AJ. Current and emerging treatment options for Wiskott-Aldrich syndrome. Expert Rev Clin Immunol 2015;11:1015-32. doi: 10.1586/1744666X.2015.1062366.  Back to cited text no. 25
    
26.
Rath A, Olry A, Dhombres F, Brandt MM, Urbero B, Ayme S. Representation of rare diseases in health information systems: The orphanet approach to serve a wide range of end users. Hum Mutat 2012;33:803-8. doi: 10.1002/humu.22078.  Back to cited text no. 26
    
27.
Davit-Spraul A, Gonzales E, Baussan C, Jacquemin E. Progressive familial intrahepatic cholestasis. Orphanet J Rare Dis 2009;4:1. doi: 10.1186/1750-1172-4-1.  Back to cited text no. 27
    
28.
Elborn JS. Cystic fibrosis. Lancet 2016;388:2519-31. doi: 10.1016/S0140-6736(16)00576-6.  Back to cited text no. 28
    
29.
Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: A joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17:405-24. doi: 10.1038/gim.2015.30.  Back to cited text no. 29
    
30.
Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP, Landrum MJ, et al. ClinGen – The clinical genome resource. N Engl J Med 2015;372:2235-42. doi: 10.1056/NEJMsr1406261.  Back to cited text no. 30
    
31.
Shah N, Hou YC, Yu HC, Sainger R, Caskey CT, Venter JC, et al. Identification of misclassified ClinVar variants via disease population prevalence. Am J Hum Genet 2018;102:609-19. doi: 10.1016/j.ajhg. 2018.02.019.  Back to cited text no. 31
    
32.
Grønskov K, Ek J, Brondum-Nielsen K. Oculocutaneous albinism. Orphanet J Rare Dis 2007;2:43. doi: 10.1186/1750-1172-2-43.  Back to cited text no. 32
    
33.
Su L, Yin X, Cheng J, Cai Y, Wu D, Feng Z, et al. Clinical presentation and mutational spectrum in a series of 166 patients with classical 21-hydroxylase deficiency from South China. Clin Chim Acta 2018;486:142-50. doi: 10.1016/j.cca.2018.07.039.  Back to cited text no. 33
    
34.
Kamaraj B, Purohit R. Mutational analysis of oculocutaneous albinism: A compact review. Biomed Res Int 2014;2014:905472. doi: 10.1155/2014/905472.  Back to cited text no. 34
    
35.
Gu WJ, Zhang Q, Wang YQ, Yang GQ, Hong TP, Zhu DL, et al. Mutation analyses in pedigrees and sporadic cases of ethnic Han Chinese Kallmann syndrome patients. Exp Biol Med (Maywood) 2015;240:1480-9. doi: 10.1177/1535370215587531.  Back to cited text no. 35
    
36.
Guo Y, Zhang Y, Li F, Liu P, Liu Y, Yang C, et al. The biochemical characterization of a missense mutation m. 8914C>T in ATP6 gene associated with mitochondrial encephalomyopathy. Int J Dev Neurosci 2018;71:172-4. doi: 10.1016/j.ijdevneu.2018.09.007.  Back to cited text no. 36
    
37.
Ellis JA, Ong B. The massARRAY® system for targeted SNP genotyping. Methods Mol Biol 2017;1492:77-94. doi: 10.1007/978-1-4939-6442-0_5.  Back to cited text no. 37
    
38.
Li SY, Cheng QX, Wang JM, Li XY, Zhang ZL, Gao S, et al. CRISPR-Cas12a-assisted nucleic acid detection. Cell Discov 2018;4:20. doi: 10.1038/s41421-018-0028-z.  Back to cited text no. 38
    
39.
Chen JS, Ma E, Harrington LB, Da Costa M, Tian X, Palefsky JM, et al. CRISPR-cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science 2018;360:436-9. doi: 10.1126/science.aar6245.  Back to cited text no. 39
    
40.
Oeth P, del Mistro G, Marnellos G, Shi T, van den Boom D. Qualitative and quantitative genotyping using single base primer extension coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MassARRAY). Methods Mol Biol 2009;578:307-43. doi: 10.1007/978-1-60327-411-1_20.  Back to cited text no. 40
    
41.
Min KW, Kim WS, Jang SJ, Choi YD, Chang S, Jung SH, et al. MassARRAY, pyrosequencing, and PNA clamping for EGFR mutation detection in lung cancer tissue and cytological samples: A multicenter study. J Cancer Res Clin Oncol 2016;142:2209-16. doi: 10.1007/s00432-016-2211-7.  Back to cited text no. 41
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

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