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Review

A Systematic Review of Extreme Phenotype Strategies to Search for Rare Variants in Genetic Studies of Complex Disorders

1
Otology & Neurotology Group CTS495, Department of Genomic Medicine, GENYO—Centre for Genomics and Oncological Research—Pfizer/University of Granada/Junta de Andalucía, PTS, 18016 Granada, Spain
2
Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh EH8 9JZ, UK
3
Department of Otolaryngology, Instituto de Investigación Biosanitaria ibs.GRANADA, Hospital Universitario Virgen de las Nieves, Universidad de Granada, 18016 Granada, Spain
4
Department of Surgery, Division of Otolaryngology, Universidad de Granada, 18016 Granada, Spain
*
Author to whom correspondence should be addressed.
Genes 2020, 11(9), 987; https://doi.org/10.3390/genes11090987
Submission received: 17 July 2020 / Revised: 14 August 2020 / Accepted: 19 August 2020 / Published: 25 August 2020
(This article belongs to the Special Issue Complex Genetic Loci, 2nd Edition)

Abstract

:
Exome sequencing has been commonly used to characterize rare diseases by selecting multiplex families or singletons with an extreme phenotype (EP) and searching for rare variants in coding regions. The EP strategy covers both extreme ends of a disease spectrum and it has been also used to investigate the contribution of rare variants to the heritability of complex clinical traits. We conducted a systematic review to find evidence supporting the use of EP strategies in the search for rare variants in genetic studies of complex diseases and highlight the contribution of rare variations to the genetic structure of polygenic conditions. After assessing the quality of the retrieved records, we selected 19 genetic studies considering EPs to demonstrate genetic association. All studies successfully identified several rare or de novo variants, and many novel candidate genes were also identified by selecting an EP. There is enough evidence to support that the EP approach for patients with an early onset of a disease can contribute to the identification of rare variants in candidate genes or pathways involved in complex diseases. EP patients may contribute to a better understanding of the underlying genetic architecture of common heterogeneous disorders such as tinnitus or age-related hearing loss.

1. Introduction

A clinical phenotype is a set of observable signs, symptoms, and behavioral features associated with a human disorder. The phenotype includes multiple features or traits and it may be categorical (male or female sex) or quantitative (glucose levels or hearing thresholds). These observable variations in the phenotype of a disorder is known in Mendelian genetics as expressivity and it may range from mild to severe [1,2] Phenotypic variation in quantitative traits can be represented by a bell-shaped graph where mild and severe phenotypes are located at the tails of the distribution. However, the majority of the subjects show an intermediate phenotype (Figure 1).
The genetic architecture of human diseases allows a better understanding of the genetic variants that can influence the phenotype in complex diseases [3]. Next-Generation Sequencing (NGS) technology has been used to uncover missing heritability and elucidate the genetic contribution to common and rare diseases with underlying heterogeneity. In particular, Whole-Exome Sequencing (WES) provides an opportunity to capture rare and ultra-rare alleles of protein-coding genes, which highly influence disease risk. In the last few years, several novel genes have been identified by utilizing WES for various neurological diseases, such as epileptic encephalopathies (KCNQ2, STXBP1, and KCNB1) and Parkinson’s disease (VPS13C, ARSB, PTPRH, GPATCH2L, and UHRF1BP1L) [4,5,6].
A significant increase in the prevalence of complex diseases such as bipolar disorder, coronary artery disease [7], type 2 diabetes, hypertension, obesity, and cancer has been reported the last decades [8]. This increase could be related to environmental factors such as diet or lifestyle changes. However, the genetic contribution to complex conditions is still largely unknown, since the contribution of rare variations to heritability is still undetermined. There are several factors that limit the power of gene discovery approaches, such as phenotypic variance [9], the overlap of clinical features observed for similar conditions, minor allelic frequency (MAF), the heterogeneous nature of loci, and the low effect size of potential risk alleles [10].
There is a well-established inverse relationship between the allelic frequency of a given variant and its effect size on the phenotype (Figure 2). The underlying hypothesis is that extreme phenotypes (EP) will occur in extreme cases with an excess of rare variants, with a moderate effect size on the phenotype in addition to the effect of the common variants for the trait of interest. The EP strategy aims to identify rare genetic variants causing a large effect on disease risk [11,12]. The EP study design includes the selection of individuals whose phenotypes are at the extreme ends of a disease phenotype distribution. These extreme subjects may be characterized by early or late age of onset, benign or severe forms of disease, family history, fast progression of symptoms, very high or very low scores in psychometric tests, or extreme levels of a biomarker [13,14,15]. This strategy may identify rare genetic variants by sequencing a relatively small sample size and it can target novel candidate genes, since rare variants that contribute to a particular trait are enriched at the two extremes of a disease distribution [10]. The combination of EP with WES has successfully identified several rare variants and candidate genes for diabetic retinopathy [16], bipolar disorder [17], and cystic fibrosis [18] across diverse ethnic groups.
The aim of this systematic review is to critically analyze the contribution of strategies based on EPs to uncover rare or novel variants or candidate genes in genetic studies of complex disorders.

2. Materials and Methods

2.1. Study Design

This is a systematic review of genetic studies in complex diseases and it follows Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines (Table S1) [20] and recommendations from the Human Genome Epidemiology Network (HuGENet) review handbook (https://www.cdc.gov/genomics/hugenet/).

2.2. Search Strategies

Literature search for EP strategies was performed on 12 December 2019 using two bibliographic databases (PubMed and Embase). For EP strategies the keywords “phenotypic extreme”, “extreme phenotype”, “rare variant” and “genetics” were used to formulate the search string. The selected keywords could appear in the title, abstract, text word, author keywords, or MeSH Terms of the articles. The keyword string used for the literature search in PubMed was: ((((“phenotypic extreme”[Title/Abstract] OR “extreme phenotype”)[Title/Abstract] AND (“rare variant”[Title/Abstract] OR “genetics”)[Title/Abstract])) OR ((“phenotypic extreme”[Text Word] OR “extreme phenotype”)[Text Word] AND (“rare variant”[Text Word] OR “genetics”)[Text Word])) OR ((“phenotypic extreme” OR “extreme phenotype”) AND (“rare variant” OR “genetics”) [MeSH Terms]); that for Embase was: (‘phenotypic extreme’: ti, ab, kw OR ‘extreme phenotype’: ti, ab, kw) AND (‘rare variant’: ti, ab, kw OR ‘genetics’: ti, ab, kw) AND [2009–2019]/py AND [english]/lim. Records published in the last 10 years, studies in English language, and only human studies were included in the literature search by configuring filters if available, e.g., on PubMed.

2.3. Research Question and Selection Criteria

The objective of this systematic review is to assess the evidence supporting the design of genetic studies using extreme phenotype strategies to find rare or novel variants or genes involved in complex disorders. According to this hypothesis, we formulated the following research question: “Are EP strategies useful to establish the genetic contribution in complex diseases?”. To answer this question, we followed the “Population, Intervention, Comparison, Outcome, Study design” (PICOS) strategy:
  • Population: Patients with a complex disease or condition.
  • Intervention: Selection of individuals according to extreme phenotype criteria (i.e., early onset, fast progression of disease, very high or very low scores in psychometric tests, or extreme levels of a biomarker).
  • Comparison: Genetic association studies (genome-wide association studies (GWAS), WES, genotyping, Sanger sequencing, or targeted sequencing).
  • Outcome: genetic findings reported (rare variants, candidate genes, or pathways associated with the condition of interest).
  • Study design: case–control, case report, case–cohort, or trios.

2.4. Exclusion Criteria

  • Studies in non-human models.
  • Studies not published in English.
  • Studies with a publication date 10 years.

2.5. Quality Assessment of Selected Studies

The extracted records were screened to remove duplicate entries. The title and abstract of all articles were reviewed to exclude reviews, meta-analysis, and irrelevant records (non-genetic studies, pharmacogenomics or clinical studies). The search was conducted primarily for rare variants, but any type of variants were retained and included in this systematic review. After screening, the obtained records were considered for full-text assessment in the next step. To assess the quality of these articles, we formulated 8 questions for EP studies (Table 1). For each question, a positive answer was scored as 1 and a negative answer as 0. Each author classified and rated each record independently of each other. Differences in the scores were discussed to get a final consensus score. If a record achieved ≥60% of the total score, the response to Q8 was “yes”, and the reported rare variants have a MAF < 0.05, then the record was selected for synthesis. So, only studies with significant results were included. Two of the authors carried out the synthesis (SA, JALE). The outcome for each selected study was assessed according to Q8 and the following criteria: if a given study had found any rare or de novo variant, common variant, copy number variants, candidate genes, or pathways for EP subjects, then the major outcome was considered as positive.

2.6. Data Extraction and Synthesis

The following information was extracted from each article selected for synthesis: first author’s last name, publication year, disease/disorder name, population ancestry, study design, sequencing method, EP/disease phenotype criteria, sample size for cases, age of disease onset, sex of individuals, MAF, and main genetic findings. Moreover, the phenotype criteria and the main genetic findings for EP were of great interest for synthesis.

2.7. Risk of Bias

The Cochrane collaboration tool [21] was used to assess the risk of bias for each selected study (Table S2).

3. Results

3.1. Selection and Characteristics of EP Studies

For the EP strategy, we retrieved 106 records in total, 66 records from PubMed and 40 from Embase, by using the search strings reported in the search strategy section. After duplicates’ removal, we retained 89/106 records aggregated from the two databases. Next, after screening by title and abstract of the articles, we retrieved 30/89 records that were included for full-text assessment. The discarded records were reviews, meta-analyses, non-genetic studies, pharmacogenomics studies, posters, or abstracts presented at scientific meetings. All studies including variants with MAF > 0.05, single cases, or <5 patients with EP were excluded. We performed quality assessment for 30 articles, and 19/30 records surpassed the minimum quality assessment score and were considered for synthesis. (Figure 3, Table S3).
Among the 19 studies selected for synthesis, 16 records were related to physical conditions, 1 was on bipolar disorder, and 2 were related to neurological disorders including epilepsy and Alzheimer’s disease. All of these studies reported rare variants, candidate genes, or potential pathways associated with a particular trait using an EP approach. These 19 EP studies covered 18 complex diseases.
Information about population ancestry and sample size of cases was available for all 19 studies. Only 11/19 studies reported the age of disease onset, and 18/19 records reported the sex of the individuals. The most common criteria to define EP included early onset, late onset, family history, acute form, and/or fast progression of a disease. In addition, disease-specific features were also considered to define an EP, such as the worst score in biomarkers levels including Bone Mass Density (BMD) and spirometry-based severity according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade. The reported sample size was between 12 and 32,965 individuals. A summary of the characteristics of these 19 EP studies is shown in Table 2.

3.2. Synthesized Findings of EP Studies

In the 19 EP studies, the combination of general and disease-specific EP criteria was used to select individuals. Information on the study design, sequencing technique, and ancestry population was available for all 19 studies. The reported sample size varied according to the design and sequencing method: 1711 ± 2513 (mean ± SD) for GWAS, 929 ± 2389 for genotyping, 1274 ± 9380 for WES, 29 ± 9 for targeted sequencing, and 949 ± 8742 for Sanger sequencing. All 19 examined studies using EP to select individuals reported significant findings including several rare variants, copy number variants, potential candidate genes or pathways associated with the condition of interest. WES was able to find rare variants in 13/19 studies (MAF = 0.00–0.05) in identified variants. It also helped in the identification of several novel candidate genes including TACC2 [22], PRKCD, C1QTNF4, DNMT3A [23], LOC728699, and FASTK [16]. GWAS identified a rare variant in 1/19 study (MAF = 0.04). In addition, genotyping and targeted and Sanger sequencing contributed in the identification of many candidate genes and micro-deletions.

4. Discussion

4.1. Summary of the Main Findings

Our systematic review shows that individuals with an EP may reveal rare variants that can influence genetic susceptibility in most complex disorders. Complex disorders have a heterogeneous spectrum of symptoms, with variable expressivity observed in each patient. By cluster analysis, it is possible to identify subgroups of patients, and by selecting patients with EP (high expressivity), we would expect to find an enrichment of rare variations associated with the EP [37]. However, we cannot recommend a particular EP strategy to select patients, although the selection of individuals with an early-onset disease and/or a severe phenotype (genetic anticipation) will probably help in the search of rare variations. In contrast, elderly patients can show mutations associated with exposure to environmental factors along life (ultraviolet radiation, chemical agents, pollutants) [38]. In general, the criteria to define EP combine common and disease-specific features such as the chronic state of a disease, very high or low biomarker levels such as BMD, spirometry-based severity level according to GOLD, family history, and early/late age of disease onset.
Of note, a large sample size was not required in WES studies for the discovery cohort, and 10/19 records had a number of cases <100. Therefore, a moderate sample size of individuals with EP was sufficient to identify candidate rare variants or genes. These individuals with EP were carriers of rare variants with a high effect size to target new candidate genes. The EP approach was reproducible across different populations, since the selected studies recruited cases with different ethnic backgrounds including Asian, African, and European ancestry and with monogenic diseases such as cystic fibrosis [13] with an extreme phenotype (persistent tracheobronchial infection with early onset) [39]. Therefore, the information about age of disease onset and sex of the selected individuals is essential to define an EP [40].

4.2. Selection of EP in Quantitative Traits

Individuals with EP are characterized by extreme clinically relevant attributes, toxic effects, or extreme responses to a treatment [1]. From a theoretical perspective, a very EP is more informative than an almost EP, but in practice there are several limitations associated with the very EP, such as vulnerability to phenotype heterogeneity and measurement errors. If a significant proportion from both sides of an extreme is discarded, the almost EP can still be more powerful than random sampling of the same size. The benefits of EP sampling were demonstrated by proposing power calculation methods with the help of the maximum likelihood approach [11,41]. It was also indicated that EP sampling to detect rare variants is more cost-efficient as compared to traditional study designs with a large cohort [42]. Replication in a second independent EP cohort to enhance the power of a study is highly recommended, but it is unlikely to obtain a large sample size of EP subjects from a single region [43]. However, the EP approach is considered more efficient than random sampling for the detection of rare variants associated with a trait [11]

4.3. Familial Disorders and EP Strategy

Some common disorders show rare familial phenotypes with Mendelian inheritance associated with rare variants with large effect size. There are many studies using the EP strategy for familial cases of complex disorders, such as Alzheimer’s disease (AD) [25], polyautoimmunity disorder [24], and congenital hypothyroidism [44]. For example, a recent study using linkage analysis demonstrated that by selecting individuals with familial autoimmunity and polyautoimmunity as EP, it was possible to identify the SRA1 gene (LOD score = 5.48) [24]. Furthermore, a WES study on AD analyzed non-Hispanic White patients and Caribbean Hispanic families to find genes associated with early-onset AD. Heterozygous non-synonymous variants with global MAF < 0.001 were selected for variant prioritization and showed autosomal-dominant segregation in these families. Several genes such as RUFY, TCIRG1, PSD2, and RIN3 were identified that could be involved in endolysosomal transport in both early- and late-onset AD [25]. In some complex diseases such as Meniere disease (MD), a syndrome characterized by hearing loss, episodic vertigo, and tinnitus, there is also a strong evidence of genetic predisposition in most affected families, showing an autosomal-dominant inheritance with almost 60% penetrance. By using WES in familial MD analysis, a burden of multiplex rare missense variants in the OTOG gene was reported in 30% of familial cases [45], which illustrates the success of considering familial cases as EP. Furthermore, a study on genetic epilepsy with hay febrile seizures plus (Dravet syndrome) has reported a SCN1A missense variant in a large Jewish family (14/17 cases) with epilepsy syndrome at both extremes (low and high) [15], and a study on thyroid dysgenesis with congenital hypothyroidism found a familial PAX8 variant associated with EP [44].

4.4. An EP Strategy to Investigate the Genetic Contribution to Tinnitus

Tinnitus is the perception of noise in the absence of an external acoustic stimulation, affecting more than 15% of the population and causing a decrease in health-related quality of life [46]. Several specific instruments have been defined to characterize chronic or severe tinnitus, and these instruments have been proposed to measure tinnitus annoyance to define EP for genetic studies [47]. Epidemiological evidence to support a genetic contribution to tinnitus is still weak because of the heterogeneous nature of this condition. In fact, tinnitus can occur together with multiple comorbidities including hearing loss, migraine, sleep disorders, anxiety, other psychological conditions, and some rare monogenic disorders [48]. The careful selection of phenotypes for genetic studies is crucial. The inclusion criteria should consider young individuals with severe forms of bilateral tinnitus to investigate the genetic contribution of rare variations to tinnitus. These individuals may carry a greater susceptibility and lower environmental load; however, severe forms of tinnitus in young individuals are rare [49] and multicenter studies are needed to reach a minimum sample size [50]

4.5. Limitations

Some weaknesses were found in the design of EP strategies; therefore, further research is required. The replication of the genetic studies across different populations with different ethnic backgrounds has enough potential to validate genetic associations [13,36]; however, the frequency of allelic variants is different across different populations, and specific reference data for allelic frequencies are needed for each population. The rare variants reported in simplex families with EPs should be validated in more patients with a severe phenotype [24]. Most of the studies used WES rather whole-genome sequencing (WGS) and this can cause the loss of useful genetic information and erroneous results in calculating the effect size of rare variants at the individual level across a particular phenotype [17].

5. Conclusions

Genetic studies have confirmed the effectiveness of the EP strategies to establish the genetic contribution of rare variations to complex diseases.

Supplementary Materials

The following is available online at https://www.mdpi.com/2073-4425/11/9/987/s1, Table S1: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), Table S2: Risk of bias of 19 EP studies; Table S3: Quality assessment of EP studies.

Author Contributions

J.A.L.-E. conceived the study design and develop the scientific arguments. J.A.L.-E. and S.A. performed literature search, quality assessment of the studies, interpretation of the data, drafting of the manuscript, and revision of the final version. T.R. also helped in the interpretation of the data, developing the scientific arguments, and revision of the final draft. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by H2020 MSCA-ITN-2016–722046 Grant (JALE). The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/DE18/52010002 (JALE). This project is part of the European School of Interdisciplinary Tinnitus (ESIT) research; Sana Amanat is a student in the Biomedicine Program at the University of Granada.

Acknowledgments

We would like to thank Patricia Perez-Carpena, for the critical review and the useful comments to improve the manuscript and Marisa Flook for English language editing.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Phenotypic variation in quantitative traits. Individuals’ phenotypes can be classified as benign, intermediate, or severe according to general and disease-specific criteria. Extreme phenotypes are identified at the ends of the normal distribution (green, orange, and red areas).
Figure 1. Phenotypic variation in quantitative traits. Individuals’ phenotypes can be classified as benign, intermediate, or severe according to general and disease-specific criteria. Extreme phenotypes are identified at the ends of the normal distribution (green, orange, and red areas).
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Figure 2. Distribution of genetic variants according to allelic frequency and effect size on the phenotype in quantitative traits. Individuals with extreme phenotypes will show a burden of rare variations with a moderate to large effect size (modified from Manolio et al., 2008 [19]).
Figure 2. Distribution of genetic variants according to allelic frequency and effect size on the phenotype in quantitative traits. Individuals with extreme phenotypes will show a burden of rare variations with a moderate to large effect size (modified from Manolio et al., 2008 [19]).
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Figure 3. Flowchart to select extreme phenotype records for synthesis.
Figure 3. Flowchart to select extreme phenotype records for synthesis.
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Table 1. Criteria used to assess the quality of the selected genetic studies using an extreme phenotype approach.
Table 1. Criteria used to assess the quality of the selected genetic studies using an extreme phenotype approach.
No.QuestionAnswer
Q1Is there a thorough description of the study design?Yes/No
Q2Has the study described the method of sequencing/genotyping?Yes/No
Q3Has the study provided information about population ancestry?Yes/No
Q4Is there any information on the sex of the selected individuals?Yes/No
Q5Is there any information on the age of disease onset?Yes/No
Q6Has the study used extreme phenotype criteria for sample recruitment?Yes/No
Q7Has the study performed sex-specific analysis for genetic associations?Yes/No
Q8Has the study reported significant genetic findings?Yes/No
Table 2. Summary of the 19 genetic studies using an extreme phenotype approach selected for synthesis.
Table 2. Summary of the 19 genetic studies using an extreme phenotype approach selected for synthesis.
ReferenceDiseaseEP CriteriaStudy DesignSequencing MethodAncestryNumber of PatientsOnsetSexGenetic findingsAF (Ancestry-Dependent)
Gene/
Pathway
SNP/Indel
Pullabhatla et al. (2017) [23]Systemic lupus erythematosusProband with early onset and clinical features with poor outcomeFamily trios,
Replication cohort
WESEU30 trios,
10995
<25 yNot reportedPRKCD 3: 53223122 G>A De novo variants and novel genes
C1QTNF4 11: 47611769 G> C
DNMT3A 2: 25457236 G> A
Johar et al. (2016) [24]PolyautoimmunityPolyautoimmunity and familial autoimmunityCase–control,
Cross-sectional
WESColombian47Not reportedM,FPLAUR rs4760 0.1
DHX34 rs151213663 0.004
SRA1rs5871740,
rs202193903
Not found
ABCB87:150744528:G>T, 7:150744370: CGT/-Not found
MLL4rs1862687020.0007
Kunkle et al. (2017) [25]Alzheimer’s diseaseEarly-onset Alzheimer’s disease, familial or sporadicCase–control,
Replication cohort
WESNHW and Caribbean Hispanic93,
8570
<65 y M,FRUFY1 5:179036506:T>G 0.001
RIN3 14:93022240:G>T 0.0005
TCIRG 11:67810477:C>T 0.0007
PSD2 5:139216541:G>A, 5:139216759:G>A 0.0006,
0.00005
Emond et al. (2012) [13]Cystic fibrosis (CF)CF with early onset of persistent Pseudomonas aeruginosa infection Case–control,
Replication cohort
WESEU America, African American, White Hispanic, NHW, Asian, Aleut43,
696
≤2.5 yM,FDCTN4 rs11954652,
rs35772018
0.048,
0.017
Shtir et al. (2016) [16]DiabetesDiabetes for at least 10 years without diabetic retinopathyCase–control,
Cross-sectional
WESSaudi43Not reportedM,FFASTK 7:150774771:C>T, 7:150777859:A>T 0,
0
LOC728699 rs149540491,
rs117616768, 12:20704520:C>A
0.05,
0.01,
0.02
Liu et al. (2016) [26]Lung cancerFamilial or sporadic lung cancer cases, ever smokers or severe chronic obstructive pulmonary disease (COPD) Case–control,
Cross-sectional
WESNHW48 sporadic 54 familial56 y familial
61 y sporadic
M,FDBH rs76856960 0.0034
CCDC147 rs41291850 0.0026
Husson et al. (2018) [17]Bipolar I disorderFamily history of mood disorder and early onsetCase–control,
Cross-sectional
WESEU92mean: 24 yM,F>13 genes>100 SNPs 0.000015-0.009
Johar et al. (2015) [27]Multiple autoimmune syndromeMultiple autoimmune syndrome with Sjögren’s syndromeCase–control,
Cross-sectional
WESColombian1228–67 yFLRP1/STAT6 12:57522754:A>C Novel mutation
Hiekkala et al. (2018) [28]Hemiplegic migraine≥2 migraine attacks, completely reversible motor weaknessCase report,
Cross-sectional
WESFinnish293median: 12 yM,FATP1A2 rs765909830,
1:160100376:G>A
0,
0
CACNA1A rs121908212 0
Qiao et al. (2018) [29]COPDCOPD cases with GOLD grade 3 or 4Case–control,
Cross-sectional
WESEU, NHW, African American≈1769>45 y,
≤65 y
M/Fjak-stat signaling pathway - Not reported
TBC1D10A, RFPL1Not reported
Bruse et al. (2016) [22]COPDCOPD cases with GOLD grade 3 or 4Case–control,
Cross-sectional
WESNHW62Not reportedM/FTACC2chr10:123842508,
10:123844900,
10:123903149,
10:123970638,
10:123987443,
10:123996970,
10:124009124
0.000008901, 0.000008796, 0.001851, 0.000008999,
Not found
0.03476,
0.07
Nuytemans et al. (2018) [30]Thrombotic storm (TS)Severe onset of ≥2 arterial, unusual clot location, refractory, reoccurrenceCase report,
Cross-sectional
WES, Targeted sequencingWhite and Indian26 (13 trios)Not reportedM,FSTAB2 rs779748342,
rs758868186,
rs201799617,
rs17034336,
rs149382223
Not found,
Not found,
0.0002,
0.0441,
0.0008
CHPF 2:220405189:C>T Not found
CHST3 rs145384892 Not found
SLC26A2 rs104893919,
rs78676079
Not found,
0.0076
CHST12 rs17132399 Not found
CHPF2 rs776052782,
rs117332591,
rs377232422
Not found,
0.0028,
Not found
CHST15 rs34639461 0.011
PAPSS2 rs45467596 0.0219
Aubart et al. (2018) [31]Marfan syndromeSevere aortic features (dissection or preventive thoracic aortic aneurysm rupture surgery at a young age) or sib pairsCase–control,
Cross-sectional
WESEU51 EP and 8 sib-pairs≈10–30 y M,FCOL4A1 c.4615C>T,
c.1630G>C,
c.4453T>C,
0.02,
0.04,
0.003
FBN1 c.1585C>T 0
SMAD3 c.6424T>C 0
Gregson et al. (2018) [32]Bone mass density Extremely high or moderately high bone mass densityCase–control,
Replication cohort
GWASEU1258,
32965
Not reportedM,FWNT4/ZBTB40 rs113784679 0.04
Lee et al. (2018) [33]Ulcerative colitisUlcerative colitis patients with good or poor prognosisCase–control,
Replication cohort
GenotypingKorean881,
274
35.6 ± 13.9 yM,FHLA-DRA and HLA-DRB rs9268877 0.000
Tomaiuolo et al. (2012) [34]Acute myocardial infarction (AMI)AMI patients with first episode before or after 45 years of ageCase–control,
Replication cohort
GenotypingEU1653,
909
Not reportedM,FMTHFR C677T, FII G20210A, Factor V Leiden -455G>A -
Goldberg-Stern et al. (2013) [15]Epilepsy with febrile seizures plusGeneralized epilepsy with febrile seizures plus, a proband with Dravet syndromeCase-control,
Cross-sectional
Sanger sequencingAshkenazi Jewish14 familial casesinfancy to 7 yM,FSCN1Ac.4114A>G: p.K1372E; exon 21 -
Shen et al. (2017) [35]Spermatogenic failure Spermatogenic failure with azoospermia, mild oligozoospermia or severe oligozoospermiaCase–control,
Cross sectional
Sanger sequencingChinese Han884Not reportedMMAGEA9 Deletion (chrX:149580739-149580850) -
Uzun et al. (2016) [36]Preterm birthPatients delivering <34 weeksCase report,
Cross-sectional
Targeted Sequencing of 329 genesAfrican-American; Asian; Hispanic; White; Native American32Not reportedFWASF3 rs17084492 0.01357(NFE), 0.07(African)
AZU1 rs28626600 0.1(NFE),
0.01662(African)
Legend: NHW, Non-Hispanic White; EU, European; WES, Whole-Exome Sequencing; GWAS, genome-wide association studies; EP, extreme phenotype; SNP, Single Nucleotide Polymorphism; AF, allelic frequency.

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Amanat, S.; Requena, T.; Lopez-Escamez, J.A. A Systematic Review of Extreme Phenotype Strategies to Search for Rare Variants in Genetic Studies of Complex Disorders. Genes 2020, 11, 987. https://doi.org/10.3390/genes11090987

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Amanat S, Requena T, Lopez-Escamez JA. A Systematic Review of Extreme Phenotype Strategies to Search for Rare Variants in Genetic Studies of Complex Disorders. Genes. 2020; 11(9):987. https://doi.org/10.3390/genes11090987

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Amanat, Sana, Teresa Requena, and Jose Antonio Lopez-Escamez. 2020. "A Systematic Review of Extreme Phenotype Strategies to Search for Rare Variants in Genetic Studies of Complex Disorders" Genes 11, no. 9: 987. https://doi.org/10.3390/genes11090987

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