Clusters of rare disorders and congenital anomalies in South America

ABSTRACT

Objective.

To map geographic clusters of rare disorders and congenital anomalies reported in South America.

Methods.

Qualitative systematic review conducted in Medline/PubMed, Lilacs, and Scielo electronic databases to identify studies meeting eligibility criteria. The strategy resulted in 1 672 unique articles, from which 164 were selected for full reading by a pair of reviewers.

Results.

Fifty-five articles reported at least one cluster of genetic disorders or congenital anomalies in South American territory. From these papers, 122 clusters were identified, of which half (61) were related to autosomal recessive disorders. Sixty-five (53.3%) of the clusters were located in Brazil.

Conclusions.

The results of the review reinforce that rare diseases and congenital anomalies can occur in a non-random way in space, which is discussed in the perspective of the complex history of formation, social organization, and genetic structure of the South American population. Mapping clusters in population medical genetics can be an important public health tool, given that such places concentrate cases of rare diseases that frequently require multiprofessional, specialized care. Therefore, these results can support important agendas in public health related to rare diseases and congenital anomalies, such as health promotion and surveillance.

Clusters of genetic disorders are defined as geographical areas that present a high frequency of genetic diseases (1, 2). This concept is close to “genetic isolates,” which are cultural and/or geographically isolated subpopulations, some of which may have high frequencies of genetic diseases as a consequence of processes related to their foundation (such as founder effect) and social organization (such as reproductive or cultural isolation and endogamy) (1, 3).

However, according to our experience in the National Census of Isolates (Censo Nacional de Isolados, CENISO), a nationwide, systematic register of human population clusters in Brazil, clusters of disorders related to medical genetics also may present environmental (such as thalidomide embryopathy and congenital Zika syndrome) and multifactorial (such as certain types of congenital anomalies) origins, and they do not occur only in isolates but also in large urban centers. Therefore, the definition of geographical clusters (from now on, only “clusters”) considered in this work is a place with an unexpectedly high frequency of rare diseases and congenital anomalies (4, 5).

Clusters are the main object of population medical genetics, an area of medical genetics that interacts with public health as it involves diagnosis, care, and surveillance of rare (and genetic, in most cases) disorders and congenital anomalies at the community level. Appropriate care of these communities can be a challenging task, especially when cases are concentrated in places far from reference centers and with poor socioeconomic indices (6). In addition, working with clusters has allowed advancing our knowledge about diseases and health care, including identification of genetic causes and risk factors for some disorders, improvement of diagnostic and therapeutic methods, studying of complex traits, among others (3, 7).

Clusters can be understood as biosocial phenomena, as their origin is related to a combination of biological, social, and historical factors of a given human populational group. In this sense, South America represents a unique opportunity to deepen our knowledge about the origin and biosocial dynamics of the clusters, considering its wide diversity of natural and geographical environments, with different ancestral origins of territorial occupation and socio-cultural organization (8). Its population presents a complex multiethnic admixture from the 15th century, with strong contributions of native South American populations (Amerindians), European settlers, and enslaved people from Africa brought with them (9).

Part of the South American population is organized in small rural semi-isolated centers, with little immigration (8). These features are commonly found in clusters, such as Maracaibo Lake, in Venezuela, where the world’s largest and best characterized population with Huntington’s disease (Mendelian inheritance in man [MIM] #143100) is found. Working with this community since the 1950s has contributed to mapping the HD gene (HTT, 4p16.3) and other molecular insights, searching for modifier factors, and characterizing the natural history of the disease (10). However, with a few other better-known examples, the literature on clusters in South America is diffuse and multilingual. In this work, our main goal was to describe clusters of rare disorders and congenital anomalies in South America.

MATERIALS AND METHODS

We carried out a qualitative systematic literature review through an extensive search by keywords in English and Spanish and countries (including “South America”) in three major scientific literature search engines, including two Latin American-specific search engines: Medline/PubMed (https://www.ncbi.nlm.nih.gov/pubmed), Scielo (https://www.scielo.br/), and Lilacs (http://lilacs.bvsalud.org/), covering all the period previous to 31 December 2021. We used Biopython v1.79 Entrez and Medline modules in Python v3.9.7 for automated PubMed searches (11).

The keywords in English were: founder effect OR consanguineous marriages OR isolated population genetic diseases OR consanguinity marriage OR geographical cluster genetic disease OR geographic cluster genetic disease OR cluster genetic disease OR rumor AND [country], where country stands for “South America,” “Argentina,” “Brazil,” “Bolivia,” “Chile,” “Colombia,” “Ecuador,” “Guyana,” “Paraguay,” “Peru,” “Suriname,” “Uruguay,” and “Venezuela.” We also used the same terms in Spanish: efecto fundador OR matrimonios consanguineos OR poblaciones aisladas OR población aislada OR matrimonio consanguineo OR enfermedad genética cluster geográfico OR cluster enfermedad genética OR rumor AND [country].

For Scielo and Lilacs, we manually entered the keywords in their respective websites and downloaded the summary output. For Brazil, the search was performed from 2017 to 2021, in order to update the previous systematic review carried out by Cardoso et al. (5).

In the first phase, two authors (AS and GR) read all titles and abstracts independently, considering articles in English, Portuguese, and Spanish, without time restriction, and made a selection based on the following inclusion criteria: articles must (1) describe a human population living in one of the selected countries, and (2) describe a cluster of a rare disorder or congenital anomaly. We excluded articles without a title or abstract.

We then compared shortlisted articles and discussed discrepancies. Only articles deemed relevant by both researchers passed to the next phase, in which we read the articles in full to retrieve more details about suspected clusters. We read selected articles in detail and collected key information about the population’s location and characteristics.

Clusters were grouped by country and described according to the associated inheritance pattern. Detailed geographical location and molecular information were obtained from the articles. We created a map of the distribution of the populations identified in South America using rnaturalearth package v0.1.0 (South 2017) in R version v4.1.0 software.

RESULTS

The systematic review resulted in 1 672 unique articles, of which 164 were selected for full reading and 55 reported at least one cluster of genetic disorders or congenital anomalies in South American countries (Figure 1). From these papers, we identified 122 different clusters in 10 of the South American countries (no clusters identified for Guyana and Paraguay).

FIGURE 1. Flowchart of the selection of articles in the systematic review.

As shown in Table 1, more than half (n = 65) of these clusters were reported in Brazil, with another six clusters added to the previous review (5). Outside Brazil, Colombia and Venezuela showed the highest number of clusters (13; 10.7% each), followed by Argentina (12; 9.8%) and Ecuador (8; 6.6%). The majority were autosomal recessive genetic disorders (61; 50.0%), followed by autosomal dominant (27; 22.1%) and multifactorial (12; 9.8%). Two clusters of environmental disorders in Brazil (thalidomide embryopathy and microcephaly by Zika virus) and two of X-linked (fragile X syndrome, in Colombia, and progressive muscular dystrophy, in Brazil) were also found. In 18 (14.8%) cases, the inheritance pattern was not identified (individually, the highest proportion (5/8 or 62.5%) was found in Ecuador).

TABLE 1. Number of disease clusters according to the country and the inheritance pattern.

Country	AD (%)	AR (%)	E (%)	M (%)	X (%)	NI (%)	Total (%)
Argentina	0	8 (66.7)	0	2 (16.7)	0	2 (16.7)	12 (9.8)
Bolivia	0	0	0	0	0	1 (100)	1 (0.8)
Brazil	13 (20.0)	36 (55.4)	2 (3.1)	10 (15.4)	1 (1.5)	3 (4.6)	65 (53.3)
Chile	2 (40.0)	2 (40.0)	0	0	0	1 (20.0)	5 (4.1)
Colombia	6 (46.1)	4 (30.8)	0	0	1 (7.7)	2 (15.4)	13 (10.7)
Ecuador	0	3 (37.5)	0	0	0	5 (62.5)	8 (6.6)
French Guianaa/Suriname	0	0	0	0	0	1 (100)	1 (0.8)
Peru	0	2 (66.7)	0	0	0	1 (33.3)	3 (2.5)
Uruguay	1 (100)	0	0	0	0	0	1 (0.8)
Venezuela	5 (38.5)	6 (46.1)	0	0	0	2 (15.4)	13 (10.7)
Total	27 (22.1)	61 (50.0)	2 (1.6)	12 (9.8)	2 (1.6)	18 (14.8)	122 (100)

AD, autosomal dominant; AR, autosomal recessive; E, environmental; M, multifactorial; X, X-linked; NI, not identified.

Note:

^aProperly, a French single territorial collectivity.

Source: Prepared by the authors based on the review data.

Individual details of each cluster, such as location aspects, phenotype, inheritance pattern, and molecular alteration, are shown in Table 2. Spatial distribution of clusters in South America are shown in Figure 2. In complement to Cardoso et al. (5), clusters from Brazil are shown separately in Table 3.

TABLE 2. Disease clusters in South America identified from the literature review (clusters in Brazil are shown in Table 3).

Country	Location details	Lat	Long	Phenotype	MIM	Etiology	Reference
ARGENTINA
	San Luis	-33.41	-66.22	Citrullinemia type I	#215700	AR	(49)
	Aicuña	-29.50	-67.76	Oculocutaneous albinism	#203200	AR	(50)
	Aicuña	-29.50	-67.76	Ataxia–telangiectasia	#208900	AR	(50)
	La Caldera (Salta)	-24.60	-65.38	Werner syndrome	#277700	AR	(50)
	Puna Jujeña (Jujuy)	-22.75	-65.90	HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP)	NI	M	(51)
	San Luis del Palmar (Corrientes)	-27.51	-58.56	Ellis van Creveld syndrome	#225500	AR	(50)
	San Luis del Palmar (Corrientes)	-27.51	-58.56	Bloom syndrome	#210900	AR	(50)
	Patagoniaa	-42.92	-71.33	Cleft lip with or without cleft palate	NI	NI	(36)
	Catamarca, La Rioja, and Tucuman	-28.47	-65.78	Cleft lip with or without cleft palate	NI	NI	(36)
	West region of Córdoba	NI	NI	Pediatric renal tumors, especially Wilms tumor	NI	M	(52)
	Córdoba	-32.04	-65.15	Sandhof disease	#268800	AR	(53)
	Córdoba	-32.04	-65.15	Argininosuccinate synthetase deficiency	#215700	AR	(54)
BOLIVIA
	La Paz, Cochabamba, Tarija	-16.50	-68.15	Cleft lip with or without cleft palate	NI	NI	(36)
CHILE
	Robinson Crusoe Island	-33.64	-78.83	Specific language impairment	#602081	AD	(29, 30)
	Chiloe Islands	-42.63	-73.65	Chondrocalcinosis	#600668	AR	(28)
	Cachapoal	-36.45	-71.73	Achromatopsia	#262300	AR	(55)
	Chillán	-36.60	-72.10	Creutzfeldt–Jakob disease	#123400	AD	(55, 56)
	Maule	-34.98	-71.23	Cleft lip with or without cleft palate	NI	NI	(36)
COLOMBIA
	Providencia Island	13.35	-81.37	Non-syndromic deafness	#220290	AR	(19)
	Providencia Island	13.35	-81.37	Waardenburg syndrome	NI	AD	(19)
	Ricuarte (Bolívar Department)	4.31	-76.21	Fragile X syndrome	#300624	X	(57)
	Cali	3.43	-76.53	Sirenomelia	NI	NI	(45)
	Antioquia	6.27	-75.56	Lynch syndrome	#609310	AD	(25)
	Antioquia	6.27	-75.56	Alzheimer’s disease	#607822	AD	(24)
	Antioquia	6.27	-75.56	Renal tubular acidosis with deafness	#267300	AR	(23)
	Antioquia	6.27	-75.56	Juvenile parkinsonism	#600116	AR	(21)
	Antioquia	6.27	-75.56	Blepharophimosis–ptosis–epicanthus syndrome	#110100	AD	(22)
	Antioquia	6.27	-75.56	Jarcho–Levin syndrome	#277300	AR	(26)
	Bogotá, Manizales, La Mesa	5.05	-75.52	Preaxial polydactyly	NI	NI	(46, 47)
	Cauca Department	3.36	-76.63	Calpainopathy	#253600	AR	(35)
	Andean region	3.36	-76.63	Mucopolysaccharidosis type IVA	#253000	AR	(58)
ECUADOR
	Cañar	-2.55	-78.93	Microtia	NI	NI	(46, 47)
	Cañar and Azogues	-2.55	-78.93	Oral clefts	NI	NI	(46, 47)
	Manabi	-0.99	-80.70	Lamellar ichthyosis	#242300	AR	(59)
	East Ecuador	-1.27	-77.46	Hyperimmunoglobulinemia-E	NI	NI	(32)
	Loja	-4.00	-79.20	Laron syndrome	#262500	AR	(60)
	Quito	-0.18	-78.47	Microtia	NI	NI	(38)
	Multiple placesb	0.62	80.43	Cleft lip with or without cleft palate	NI	NI	(36)
	Pacific coast	NI	NI	Mucopolysaccharidosis type IIIB	#252920	AR	(61)
FRENCH GUIANA/SURINAME
	Maroni River (Bushinengue Maroons)	4.43	-54.41	β-thalassemia	NI	NI	(39)
PERU
	Widespread Peru (native populations)	NI	NI	Chitotriosidase deficiency	#614122	AR	(34)
	Trujillo	-8.12	-79.03	Aplasia cutis congenita	NI	NI	(62)
	Loma Negra (La Arena District of Piura Province)	-5.40	-80.73	Berardinelli–Seip syndrome	#269700	AR	(63)
URUGUAY
	Canelones County	-34.53	-56.29	Oculopharyngeal muscular dystrophy	#164300	AD	(64)
VENEZUELA
	Coro, Bolivar	8.13	-63.55	Postaxial polydactyly	NI	NI	(46, 47)
	Pregonero	8.02	-71.76	Chediak–Higashi syndrome	#214500	AR	(65)
	Margarita Island (Macanao Peninsula)	10.99	-63.84	Usher syndrome	#276900	AR	(18)
	Margarita Island (Macanao Peninsula)	10.99	-63.84	Cleft lip/palate-ectodermal dysplasia syndrome (CLPED1)	#225060	AR	(16, 17)
	Margarita Island (Macanao Peninsula)	10.99	-63.84	L-2-hydroxyglutaric aciduria	#236792	AR	(20)
	Western Venezuela (Barí indians)	8.82	-72.69	Oral clefts	NI	AR	(33)
	Colonia Tovar	10.41	-67.29	Inherited deafness	NI	NI	(66)
	Santa Lucia (Miranda State)	10.33	-66.64	Acute intermittent porphyria	#176000	AD	(40)
	Nirgua (Yaracuy State)	10.16	-68.56	Spinocerebellar ataxia 7	#164500	AD	(67)
	El Tocuyo (Lara State)	9.79	-69.79	Spinocerebellar ataxia 7	#164500	AD	(67)
	Monagas, Anzoátegui, and Bolívar	NI	NI	Spinocerebellar ataxia 1	#164400	AD	(67)
	Pueblo Nuevo del Sur, Merida State	-8.59	-71.15	5α-reductase type 2 deficiency	#264600	AR	(68)
	Lake Maracaibo (Zulia State)	9.01	-71.93	Huntington's disease	#143100	AD	(10)

MIM, Mendelian inheritance in man; AD, autosomal dominant; AR, autosomal recessive; M, multifactorial; X, X-linked; NI, not identified.

Notes:

^aThis cluster involves other two places (Puerto Montt and Valdivia) in southern Chile.

^bThis cluster involves other three places (Manizales, Cali, and Neiva) in Colombia.

Source: Prepared by the authors based on the review data.

FIGURE 2. Clusters of rare diseases and congenital anomalies in South America according to the inheritance pattern.

Disclaimer: Country borders or names do not necessarily reflect the PAJPH or PAHO’s official position. This map is for illustrative purposes only and does not imply the expression of any opinion concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries.

Source: Prepared by the authors based on the review data.

TABLE 3. Disease clusters from Brazil.

ID	UF	Phase	Location details	Lat	Long	Phenotype	MIM	Etiology
1	AL	4	Agua Branca	-5.89	-42.64	Aniridia	106210	AD
2	AL	4	Mata Grande	-9.12	-37.74	Chondrodysplasia, Blomstrand type	215045	AR
3	AL	3	Craibas/ Marruas village	-9.62	-36.77	Consanguinity and skeletal disorder	NI	NI
4	AL	3	Feira Grande	-9.90	-36.68	Huntington disease	143100	AD
5	AL	3	Maravilha	-9.24	-37.35	Kindler syndrome	173650	AR
6	BA	3	South of Bahia State			Chondrodysplasia, Grebe type	200700	AR
7	BA	4	Monte Santo	-10.44	-39.33	Deafness autosomal recessive 1A (DFNB1A)	220290	AR
8	BA	3	Vitória da Conquista/ Barra da Estiva/ Livramento de Nossa senhora	-14.86	-40.84	Epidermolysis bullosa	NI	AR
9	BA	4	Monte Santo	-10.44	-39.33	Mucopolysaccharidosis type VI (MPS6)	253200	AR
10	CE	4	Tabuleiro do Norte	-5.25	-38.12	Gaucher disease, type I	230800	AR
11	CE	4	Jericoacara and North region	NI	NI	Pycnodysostosis	265800	AR
12	CE	3	Crateús	-5.25	-40.74	Spinocerebellar ataxia 7 (SCA7)	164500	AD
13	CE	4	Aracati	-4.56	-37.77	Cutaneous CYLD syndrome	NI	AD
14	GO	4	Araras/ Faina village	-22.36	-47.38	Xeroderma pigmentosum, complementation group D (XPD)	278730	AR
15	MA	4	Cururupu/ Ilha dos Lençóis	-1.83	-44.86	Albinism, oculocutaneous	203200	AR
16	MA	4	Cajari/ Regada district	-3.30	-44.88	Thalidomide embryopathy	NI	E
17	MG	4	Minas Gerais	NI	NI	Acheiropodia	200500	AR
18	MG	4	Pouso Alegre/ São José do Pântano	-22.23	-45.94	Neu–Laxova syndrome (NLS)	256520	AR
19	MG	4	Alfenas	-21.42	-45.95	Oral clefts	119530	M
20	MG	3	Bueno Brandão	-22.44	-46.35	Osteogenesis imperfecta, type VI	613982	AR
21	MG	3	Diamantina	NI	NI	Enamel renal syndrome	204690	AR
22	MG	4	Ervália	-20.84	-42.65	Huntington’s disease	143100	AD
23	PB	4	Lagoa	-6.67	-35.36	Consanguinity with increased prevalence of disabilities (mental or physical)	NI	M
24	PB	3	Gado Bravo	-6.73	-37.67	Usher syndrome	NI	AR
25	PB	4	Alagoa Nova, Cabeceiras, and Taperoa	-7.07	-35.76	Mucopolysaccharidosis type IIIC	252930	AR
26	PB	4	Campina Grande	NI	NI	Mucopolysaccharidosis type IVA	253000	AR
27	PE	4	Fernando de Noronha	-3.84	-32.41	Alzheimer’s disease	NI	AR
28	PE	4	Orobó	-7.74	-35.60	Laron syndrome	262500	AR
29	PE	4	Brazil/ Recife	-8.05	-34.90	Microcephaly by Zika virus	NI	E
30	PE	3	Gameleira	-8.58	-35.39	Verma–Namouff Syndrome	613091	AR
31	PR	4	Paraná	NI	NI	Adrenocorticalcarcinoma, hereditary (ADCC)	202300	AD
32	PR	4	Mangueirinha/ Reserva Kaingang	-25.95	-52.19	Rheumatoid arthritis (RA)	180300	M
33	PR	4	Curitiba and South Brazil	NI	NI	p.R337H mutation in TP53 locus	NI	M
34	RS	4	Colônia Witmarsum, Palmeira (PR)	NI	NI	Skin cancer in Mennonite communities	NI	M
35	RJ	4	Rio de Janeiro	-22.91	-43.20	Breast cancer	NI	M
36	RJ	4	Duque de Caxias	-22.78	-43.31	Periodontitis, aggressive 1	170650	AR
37	RN	4	São Miguel	-6.22	-38.50	Lipodystrophy, congenital generalized, type 2 (CGL2)	269700	AR
38	RN	4	Riacho de Santana	-6.26	-38.32	Santos syndrome	613005	AR
39	RN	4	Serrinha dos Pintos	-6.20	-37.99	Spastic paraplegia, optic atrophy, and neuropathy (SPOAN)	609541	AR
40	RN	4	Seridó territory (Carnaúba dos Dantas and Timbaúba dos Batistas)	-6.55	-36.59	Berardinelli–Seip congenital lipodystrophy	NI	AR
41	RS	4	Geographically dispersed	NI	NI	Breast and ovarian cancer, familial	604370	AD
42	RS	4	Grande Porto Alegre	-30.03	-51.23	GM1-gangliosidosis, type I	230500	AR
43	RS	3	Geographically dispersed	NI	NI	Machado Joseph disease (MJD)	109150	AD
44	RS	4	Cândido Godói	-27.95	-54.77	Twinning	NI	M
45	RS	4	Colônia Nova, Aceguá	NI	NI	Skin cancer in Mennonite communities	NI	M
46	SC	4	Criciúma	-28.68	-49.37	Growth hormone insensitivity with immunodeficiency	245590	AR
47	SC	4	Coastal region (Itajaí)	-26.90	-48.66	Spinocerebellar ataxia 10 (SCA10)	603516	AD
48	SE	4	Itabaianinha	-11.27	-37.79	Isolated growth hormone deficiency, type IA (IGHD1A)	262400	AR
49	SE	4	Itabaiana	-10.69	-37.42	Spectrum of pubertal delay	NI	AR
50	SP	4	São Paulo	-23.53	-46.62	Breast and ovarian cancer	NI	M
51	SP	4	Indaiatuba	-23.09	-47.21	Dandy–Walker syndrome (DWS)	220200	AR
52	SP	4	Vinhedo	-23.03	-46.98	Fraser syndrome 1	219000	AR
53	SP	4	Ribeirão Preto	-21.18	-47.82	Gomez–Lopez–Hernandez syndrome (GLHS)	601853	AR
54	SP	4	Campinas	-22.91	-47.06	GAPO syndrome	NI	NI
55	SP	4	São Paulo	-23.53	-46.62	Amyotrophic lateral sclerosis 8 (ALS8)	NI	AD
56	SP	3	Jacupiranga/ Vale do Ribeira	-24.70	-48.01	Hypertension and consanguinity	145500	AR
57	SP	4	São Paulo	-23.53	-46.62	Isolated growth hormone deficiency	NI	AR
58	SP	3	Vale do Ribeira	NI	NI	Obesity and consanguinity	601665	M
59	SP	4	São Paulo	-23.53	-46.62	Progressive muscular dystrophy	NI	X
60	SP	4	São Paulo	-23.53	-46.62	R337H Mutation in TP53 gene in adrenocortical tumors	NI	M
61	SP	4	São Paulo	-23.53	-46.62	Richieri–Costa–Pereira syndrome	268305	AR
62	SP	4	Ribeirão Preto	-21.18	-47.82	Spinocerebellar ataxia 1 (SCA1)	164400	AD
63	SP/MG	4	Mococa e Guaxupe	-21.47	-47.00	Multiple endocrine neoplasia type 1 (MEN1	131100	AD
64	-	4	South and southeast of Brazil	NI	NI	Li–Fraumeni syndrome type 1 (LFS1)	151623	AD
65	-	4	Geographically dispersed (Northeast of Brazil)	NI	NI	Familial chylomicronemia syndrome	612757	NI

AD, autosomal dominant; AR, autosomal recessive; M, multifactorial; E, environmental; X, X-linked; NI, not identified; MIM, Mendelian inheritance in man; UF, Federative Units; AL, Alagoas; BA, Bahia; CE, Ceara; GO, Goiás; MA, Maranhão; MG, Minas Gerais; PB, Paraíba; PE, Pernambuco; PR, Paraná; RS, Rio Grande do Sul; RJ, Rio de Janeiro; RN, Rio Grande do Norte; SC, Santa Catarina; SE, Sergipe; SP; São Paulo.

Source: Table prepared by the authors. Data from Cardoso et al. (5) and from ^a(69); ^b(70); ^c(71); ^d(72); ^e(73); ^f(74).

DISCUSSION

The 122 clusters of rare diseases or congenital anomalies were reported in almost all South American countries. The multiplicity of peoples with diverse ancestry and cultural patterns, in combination with the wide range of natural environments, has allowed the creation of a complex scenario of clusters in South America, similar to what we have described for Brazil (4, 5). The population history and diversity have important medical genetic implications (12).

Most of the South American clusters were located in Brazil, consistent with it being the largest and most populous country, and its remarkable tradition in the study of communities with a high concentration of genetic diseases or their risk factors. In fact, some of the oldest works found by this review, published from the 1950s onwards, date back to the pioneering work of the Brazilian researcher Newton Freire-Maia, who greatly contributed to the studies of inbreeding, genetic diseases, and genetic isolates (13, 14).

In addition, Brazil has a national census (the CENISO) to map clusters by the National Institute of Population Medical Genetics (or INAGEMP). INAGEMP was created in 2008 supported by the Federal Government, with its headquarters located at the Hospital de Clínicas de Porto Alegre (HCPA) in Southern Brazil, with several associated institutions across the country (1). The cluster scenario in Brazil has been specifically discussed in previous works (4, 5) and from now on we will focus on the other South American countries.

Half of the South American clusters corresponded to autosomal recessive diseases, strongly associated with endogamy and consanguinity. Other works describing cluster sets or similar worldwide have obtained the same results (3, 5, 7, 15). For example, Charoute et al. (15) set a database of 219 Mendelian diseases caused by founder mutations across the Mediterranean basin (in which many clusters of different genetic diseases have been reported), of which 61.7% were autosomal recessive (15).

In this work, we have described some communities with more than one genetic disease; in other words, regions equivalent to “multi-clusters” (16–20). For instance, Antioquia, in northwestern Colombia, represented the most extreme example of a multi-cluster. With six identified clusters of Mendelian disorders (three autosomal recessive and three autosomal dominant), the population from Antioquia was established in the 16th–17th century through the admixture of Native Americans, Europeans (mainly Spanish), and Africans and grew in relative isolation until the late 19th century (21–26).

The Antioquian population has an Amerindian–Caucasian admixture with heterogeneous and specific patterns of sex-biased gene flow, experiencing cultural and geographical isolation from the total Colombian population (3, 27). With large and multigenerational genealogies, the Antioquian population can be compared to Finland, another classic example of a genetic isolate with multi-founder effects, in terms of potential contribution to studies regarding mapping genetic diseases and complex traits (3).

In another example, in the native people from Providencia Island (about 3 400 individuals), Colombia, at least 17 individuals were diagnosed with congenital deafness with two distinct genetic etiologies. A non-syndromic genetic deafness (MIM #220290; 35delG genetic variant in the GJB2 gene), found among individuals with Caucasian origin; and Waardenburg syndrome, found in families with African ancestry. Therefore, the authors argued that this finding was a “direct consequence of the multi-ethnic history of the island” (19).

Islands constitute a model of geographic isolation and, sometimes, with a small population showing high levels of inbreeding. Other studies in South America reported clusters in island communities, such as in the Macanao Peninsula of Margarita Island, Venezuela (Usher syndrome, #276900; cleft lip/palate-ectodermal dysplasia syndrome, #225060; and L-2-hydroxyglutaric aciduria, #236792) (16–18, 20); and Robinson Crusoe (language impairment, MIM #602081) and Chiloe Islands (chondrocalcinosis, #600668), in Chile (28–30). As they are generally derived from a few founding families and exposed to similar environmental factors, these populations also constitute a valuable source of information for the study of complex characteristics (3, 30).

Another important model of spatial and cultural isolation in South America is constituted by the native communities, as the Amazon Rainforest is home to some of the most isolated human groups in the world, many of which have remained relatively unknown until very recent times (31). The forest basin encompasses 7 000 000 km² (2 700 000 square miles), with a territory belonging to nine nations and 3 344 formally acknowledged Indigenous territories. Some of these territories concentrate many small ancestral communities that are reciprocally isolated by both cultural (linguistic) and geographical barriers (32–34).

For instance, Manno et al. (34) have described a high prevalence of chitotriosidase deficiency (MIM #614122) among small, isolated Amerindian populations from Peru, in association with a very high frequency of 24-base pair duplication in CHIT1 gene (34). In other work, Landires et al. (35) reported the first Amerindian family with calpain 3-related, limb-girdle muscular dystrophy type r1 (MIM #253600) from an isolated, consanguineous, Indigenous community in Colombia (35). Affected people presented a novel deletion of four base pairs in CAPN3. Amerindian ethnic background was associated with high birth prevalence rates of cleft lip with or without cleft palate in clusters from Argentina, Bolivia, and Ecuador (36).

However, important bioethical issues restrict the development of studies (and, sometimes, any other type of contact) with native communities (33, 37). This helps to explain the scarcity of studies reporting clusters in communities from the North region in Brazil, which is sparsely populated by people with a strong Native American component, many of them in isolated or semi-isolated communities (5). Besides the Amazon Rainforest, other South American landscapes associated with clusters were related to high altitudes areas, which were hypothesized to be associated with the concentration of microtia in Quito, Ecuador, (38) and oral clefts in different places on the continent (36).

In addition, some clusters in South America occurred in communities whose origin is related to the escape from the slavery regime, which was established throughout Latin America based on the trafficking of Africans from the 16th century, such as concentration of spinocerebellar ataxias type 7 (MIM #164500) in Yaracuy state and β-thalassemia among Bushinengue Maroon people on the French Guiana–Suriname border (19, 39, 40). In Brazil, some studies have shown a high frequency of hemoglobinopathies and/or genetic variants related to them among these communities, commonly known as quilombos (41, 42). In recent work, we have found high rates of isonymy, congenital anomalies at birth, and clusters of genetic diseases in some places within the historic limits of Quilombo dos Palmares in the Brazilian Northeast, the largest conglomerate of escaped slaves in Latin America (43).

It is known that many of these clusters reported here (and mainly those not reported in the scientific literature) occur in regions with multiple social and health vulnerabilities. The concentration of many cases of uncommon, complex diseases, which are sometimes related to prejudice and social exclusion, can affect patients, their families, and community in multiple ways, sometimes requiring the reorganization of health care adjusted to the reality of each location. Therefore, mapping of clusters can contribute to the design of health policies, focusing on health promotion and equity (6, 44).

In terms of cluster detection and public health in South America, work using data from hospitals registered by the ECLAMC (Latin-American Collaborative Study of Congenital Malformations) network deserves to be highlighted, as many clusters of congenital anomalies (mainly, oral clefts) have been described there (36, 45–47). For instance, Gili et al. (47) applied spatial scan analysis in order to identify clusters from clinical epidemiological data by ECLAMC. With this approach, they have described five high birth prevalence rate regions associated with five congenital anomalies in South America. An additional study has investigated risk factors related to these (47).

The timely detection of these clusters of congenital anomalies can allow the identification of risk or etiological factors, mitigation of damages, and prevention of new potential cases. For this, different surveillance programs for congenital anomalies, such as the ECLAMC network, have alarms to systematically observe the fluctuations in the frequencies of different birth defects from birth registries (36, 48).

In fact, there is a growing interest in the subject of congenital anomalies and rare diseases across the South American continent. In South America, many countries promote the surveillance of congenital anomalies at the local and national levels, in addition to collaborating with international networking initiatives, such as the Latin American Network on Congenital Malformations (RELAMC) and the International Clearinghouse for Birth Defects Surveillance and Research (ICBDSR) (48).

Therefore, this work reinforces the importance of population medical genetics in the public health debate, as mapping clusters may support agendas such as health promotion and surveillance. It is important to consider that clusters published in the scientific literature may not have been captured by our search strategy, and this is a potential limitation of our work. However, in addition to constantly reviewing the scientific literature, we have mapped clusters in Brazil based on rumors; that is, by the report (based or not on evidence) of anyone about the possible presence of clusters, by filling out an online form: https://www.inagemp.bio.br/ceniso/. In the same link, it is possible to report populations in South America in four different languages. This can be an initial step toward creating a continental census of clusters of rare disorders and congenital anomalies in Latin America.