Genetic diversity of SARS-CoV-2 infections in Ghana from 2020-2021

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Demographic and clinical characteristics of study participants

A total of 2213 samples were availed for WGS, 1987 samples were processed for WGS, and 1573 samples made it to lineage assignment (Supplementary Table 1). From the 1573 samples, 31% (496/1573) had frameshift mutations on several viral proteins such as the ORF7a/b, ORF8, ORF6, ORF1a/b, E, S, N, and ORF3a. Sixty-nine percent (1077/1573) of these sequences have been submitted to the GISAID and European Nucleotide Archive (ENA) (Supplementary Data 1), and are analysed herein alongside 46 genomes from Ngoi et al.; the rest (496/1573) are under investigation before submission to GenBank/GISAID. Thus, a total of 1123 COVID-19 PCR positive samples from ten regions in Ghana between March 2020 and September 2021 were successfully sequenced. Of these, 45% (500/1123) were near full-length genomes (missingness (N) < 500), 48% (539/1123) had 500–4000 unresolved nucleotide assignments (N), and only 7% (84/1123) had 4000–8913 missingness (N). Only 19% (215/1123) of all the samples were collected in 2020, while the remaining were collected in 2021 (n = 908/1123). Of the 1123 samples, 89% (n = 1002) were community samples while 11% (n = 121) were from travellers arriving in the country through the KIA. Sampling contribution from the different regions in Ghana, was as follows: Ashanti (4%, n = 47), Bono East (1%, n = 13), Central (12%, n = 133), Eastern (5%, n = 52), Greater Accra (44%, n = 490), Northern (5%, n = 55), Upper East (<1%, n = 4), Upper West (1%, n = 12), Volta (9%, n = 103) and Western (8%, n = 93) (Fig. 1a). With the highest number of active COVID-19 cases (GHS, 2021), the Greater Accra region had the highest number of sequenced samples (Supplementary Table 2).

Fig. 1: Overall genomic epidemiology of the circulating variants across various regions in Ghana (N = 1123).
figure 1

a Map showing the ten sampled regions (n = 1002) and travellers arriving in the country (121) at the Kotoka International Airport in the Greater Accra Region. The colour scheme shows the population density per hectare in each region, while the numbers are samples sequenced per region. b Overall SARS-CoV-2 lineages in Ghana for both local participants and travellers (n = 1123); the lineages are represented by different colours, while the percentages represent the proportion of cases with that particular lineage out of 1123 participants. Lineages with <6 participants are represented as “Other Variants”. c Out of the 1002 samples sequenced from community samples, 200 samples were sequenced in 2020 and 802 samples sequenced in 2021. The stacked plot shows the percentage distribution of lineages in six regions in 2020. Lineages are represented in unique colours as indicated on the plots. d The stacked plot shows the percentage distribution of lineages in 2021 from ten regions across Ghana (n = 802).

There were more males (56%, 561/1002) compared to females (42%, 418/1002), with only a few missing entries (2%, 23/1002). Most of the study participants were between 21–40 years (54%, n = 507) as compared to the other categories; ≤20 (11%, n = 104), 41–60 (24%, n = 230), and 61+ (11%, n = 106) years (Supplementary Table 3). Although most of the samples were from asymptomatic individuals who reported for COVID-19 testing, a good number of genomes (121/1002) were from individuals who presented at treatment centres with mild/moderate or severe/critical symptoms. Majority of the individuals classified as mild/moderate (69.4%, 84/121) presented with fever/chills, cough, pains, sore throat, diarrhoea, runny nose, nausea/vomiting, loss of smell, loss of taste or headache. Our data show that less than a third of individuals presenting at the hospital were classified as severe/critical (30.5%, 37/121) with difficulty breathing (shortness of breath), hypoxia or multiorgan system dysfunction.

Genomic epidemiology of variants circulating in Ghana

Overall, the Delta lineages (32%, 360/1123), Alpha (20%, 224/1123), B.1.1.318 (16%, 180/1123), B.1.1 (9%, 106/1123), and Eta (4%, 47/1123) made up the top viral lineages within the sequenced SARS-CoV-2 genomes in Ghana over the period (Fig. 1b). Since the B.1.1.318 had the third-highest frequency, it is considered a variant under monitoring in Ghana. In 2020, the Ashanti, Central, Eastern, Greater Accra and Western regions had the highest numbers of confirmed cases of COVID-19 in Ghana. Different regions showed variations in relative frequencies of the variants. In 2020, genomes which clustered within the B.1.1 lineage dominated samples from Greater Accra (37%, 15/41), Volta region (56%, 10/18), Western region (51%, 23/45) and Central region (45%, 22/49). B.1.623 (28%, 5/18) co-dominated with B.1.1 (28%, 5/18) in the Ashanti region whereas B.1.1.359 (48%, 14/29) dominated alongside B.1.1 (35%, 10/29) in the Eastern region (Fig. 1c).

In 2021, there was a marked shift in the circulating variants and occurrence of regional specific outbreaks, with Eta dominating in Northern and middle belt regions, while B.1.1.318 dominated the major cities. The highest frequencies of Eta variants were observed in the Northern (24%, 13/55), Bono East (31%, 4/13) and Eastern (13%, 3/23) regions (Fig. 1d). The city of Tamale in the Northern region is the gateway, and central trading hub with Ghana’s northern neighbours, whilst the Bono East region harbours major interaction routes with Ivory Coast in the western corridor of Ghana. Meanwhile, nearly a third of all the variants detected in 2021 were B.1.1.318 (22%, 176/802), and Greater Accra, where the capital city and the major international airport are located, had 80% (140/176) of all the B.1.1.318 genomes (Fig. 1d). These data suggest that Eta and B.1.1.318 variants, which dominated transmission in these areas in April–May 2021, could have been introduced through these major land borders. The B.1, B.1.1.359, B.1.1, and B.1.623 that dominated Ghana in 2020 became supplanted by Alpha and Delta VOCs in most of the regions. It is worth noting that in regions where more than 50 samples were sequenced in 2021, there was penetration or transmission of various VOCs, including the Central region, Bono East, Greater Accra, and Volta Region (Fig. 1d).

Importation of SARS-CoV-2 variants into Ghana by travellers

One hundred and twenty-one of the sequenced samples (11%, 121/1123) were obtained from travellers identified as COVID-19 positive at the KIA. Of this number, Alpha accounted for 39% (n = 47) of the genomes while the other VOCs accounted for lower proportions; Beta (6%, n = 7), and Delta lineages (7%, n = 8) (Supplementary Table 4). The VOIs such as Eta (4%, n = 5), and the local variant under monitoring, B.1.1.318, 3% (n = 4) were detected at low proportions (Supplementary Table 4). Importantly, the VOC Alpha was identified in travellers entering Ghana from all over the World, including other African countries, in January and March 2021 (Table 1). Furthermore, VOCs were detected in travellers from several of Ghana’s neighbouring countries including, Nigeria, Ivory Coast, and Burkina Faso, demonstrating that these variants were already in those countries even though not reported or detected (Table 1). In most cases, VOCs and VOIs were identified amongst quarantined travellers before their detection within local samples. Travellers from Nigeria, Dubai, and the UK accounted for most detections of Alpha, Beta, Eta, and Delta variants (Table 1). Interestingly, the Beta and Kappa variants did not become dominant in Ghana; instead, B.1.1.318, which was detected in travellers from Nigeria, Gabon, and Dubai, became dominant in Ghana between April and June 2021.

Table 1 Transmission of SARS-CoV-2 variants into Ghana from other countries (n = 121).

Temporal trends of SARS-CoV-2 variant detection and frequency

Ghana was one of the last African countries to detect COVID-19 cases in March 2020, and the waves of COVID-19 in Ghana have lagged slightly behind other African countries and significantly behind the rest of the World (Fig. 2a). Previous work from our group described the viral genome dynamics between March and May 2020, when Ghana was largely closed to international travel (Ngoi et al.9). Different variants rose to dominance at different times and during different infection waves across the country (Fig. 2a, b). Variants that cluster closely to B.1.1 were first detected in March 2020 (50%, 2/4) and peaked in July 2020 (59%, 27/46). The B.1.1 lineage remained dominant until November 2020 (55%, n = 16/29). In September 2020, the B.1.1.359 (56%, n = 10/18) became dominant while in December 2020, the B.1.623 (33%, n = 9/27) was dominant alongside B.1.1 (30%, n = 8/27) (Fig. 2b). The Alpha VOC, first detected in local samples from 1st January 2021, quickly supplanted the B.1.1 lineage as the most dominant circulating lineage (72%, n = 102/141) in January 2021. Alpha maintained high frequency (60%, n = 42/70) in February 2021, followed by the VOI, Eta (20%, n = 14/70) (Fig. 2b, c). Alpha was still dominant in March 2021 (71%, 22/31) but was almost completely supplanted in April 2021, as B.1.1.318 rose to prominence. Sequences clustering to B.1.1.318 had been detected as early as February 2021 (4%, n = 3/70) but became dominant in April 2021 (63%, n = 38/60) alongside Eta (23%, n = 14/60). May 2021 represented the peak of B.1.1.318 (82%, n = 79/96) and the emergence of the Delta lineages (5%, n = 5/96) in local circulation. Delta lineages increased in proportion over time, overtaking the B.1.1.318 in June 2021. Delta lineages surged in July and dominated with a significant presence in Ghana as at September 2021 (Fig. 2c).

Fig. 2: Trends in the variant epidemiology in Ghana.
figure 2

a Trends of COVD-19 in the World, Africa and Ghana, which indicates the date reported against the number of new cases. This shows the number of confirmed COVID-19 cases recorded daily in the World, Africa, and Ghana from January 2020 to October 2021. The data were obtained from World Health Organisation (https://covid19.who.int/WHO-COVID-19-global-data.csv). b Trends in prevalence of major variants circulating in Ghana from March 2020 to September 2021. The Y-axis shows the percentage distribution (n = 1002/1123) of various variants including VOCs across the various months (X-axis) while the lineages are represented by different colours. c Impact of emergence of variants of concern (VOCs) on dynamics of viral lineages dominating community transmission in Ghana in 2021 (802/1002).

Genetic diversity and evolutionary relationships of the SARS-CoV-2 variants

Amongst the many individual lineages represented in the data presented here, Delta lineages, Alpha, B.1.1.318, B.1.1.359, B.1.1, and Eta were the most evolved, with the highest genetic diversity (Fig. 3a). These variants exhibited a variation in the number of mutations from sample to sample, with Delta, Alpha and B.1.1.318 presenting a mean ~30 (spread/range of 20–45) mutations in the majority of the genomes (Fig. 3a). The Delta VOCs had the highest mean (~35) and presented an interquartile range of mutations from 25 to 45 mutations across all the samples (Fig. 3a). It is worth noting that this level of genetic diversity in Delta lineages was mainly attributed to the sublineages (n = 200/360); AY.39 (174/200) and AY.37 (15/200) lineages (Supplementary Table 5). Most of the other lineages with a small range of mutations were reported in 2020 and occurred spontaneously in very few samples hence the relatively low genetic diversity (Fig. 3a). The high level of genetic diversity in most VOCs, including the B.1.1318, is probably indicative of Ghana’s local evolution and consequential adaptation compared to the other variants that did not gain prominence in the Ghanaian population (Fig. 3a).

Fig. 3: Genetic diversity and molecular evolutionary relationships of variants identified in Ghana.
figure 3

a The spread/range and magnitude of mutation per lineage (n = 1002). Each filled circle represents a sample, and the circle’s width is proportional to the number of mutations present in a particular sample. b Maximum likelihood phylogenetic tree with ancestral state reconstruction in a backdrop of reference sequences from Wuhan and evolutionary relationship of the Ghanaian variants over time (n = 1002). Colours show the VOC; Delta lineages, Alpha, B.1.1.318, and Beta, based on Nextstrain’s emerging lineages designations. c Root-to-tip divergence as a function of sampling time. The Y-axis denoted divergence (the number of mutations in the genome relative to the root), and the X-axis shows the sampling date of each genome. d Annotated mutational fitness of all the B.1.1.318 lineage in Ghana; the samples with the highest fitness are coloured red.

A snapshot of the evolutionary relationship of these VOCs in Ghana shows a relationship of variants through space and time throughout the epidemic (Fig. 3b). Using a phylogenetic tree, we outline the phylogenetic relationships of VOCs and how they gained prominence coinciding with the COVID-19 waves in Ghana. The outbreak of the COVID-19 pandemic started in mid-November 2019, but then the tree shows that the earliest lineages in Ghana are dated March 2020, although most VOCs were introduced in 2021 (Fig. 3b). The phylogenetic analysis of the genomes from Ghana shows similarities to VOCs around the World, with all the VOCs having the same common ancestor (Wuhan). Still, as they diverge, they share uniquely more recent ancestors; for example, we show that the B.1.1.318 and Alpha variants share several recent ancestors (Fig. 3b). The root-to-tip divergence of the VOCs as a function of sampling time show a molecular clock of the various VOCs, and with strong evidence, the variants are evolving in a clocklike manner (R2 = 0.71) (Fig. 3c). The variants in Ghana are gaining ~26 mutations per year, and of particular interest is the B.1.1.318 that did not gain prominence worldwide, but its molecular clock is similar to most of the VOCs in Ghana (Fig. 3c). Mutational fitness of the B.1.1.318 lineage showed that ten samples had spike mutations that were likely to confer viral fitness (mutational fitness > 1) (Fig. 3d).

Mutational analysis of the amino acid substitutions

The most abundant substitution in all the samples was the spike D614G (97%, 972/1002), followed by ORF1b: P314L (91%, 915/1002) (Fig. 4a). For most of the genes, one or more amino acid substitutions occurred in more than 100 samples, although spike protein dominated the profile (Fig. 4a). Interestingly, some variants with different evolutionary lineages had similar amino acid substitutions, mainly spike glycoprotein. The Eta variant had the highest (three) individual amino acid substitutions (Q52R, Q677H and F888L) in the spike protein compared to other VOCs, probably contributing to its adaptability in Africa. Compared to other VOCs, the substitutions unique to the Alpha variant were S13I, R567K, A570D, and T716I. The only substitution unique to the B.1.1.318 on the spike protein was the D1127G compared to other VOCs and VOIs (Fig. 4b). Within these samples, the Delta lineages shared 14 substitutions (T19R, G142D, R158G, A222V, L452R, T478K, E484Q, D614G, S680F, P681R, D950N, K1191N, G1219V and C1253F) (Fig. 4b). The unique substitutions were fewer than shared amino acid substitutions among lineages (Fig. 4b), thus explaining the increased abundance of some substitutions among the VOCs. Those with the highest frequency in the spike protein among Delta lineages, B.1, B.1.1, B.1.1.318, Alpha, Beta and Eta were fitness substitutions D614G and P681R/H (Fig. 4b). Alpha and B.1.1.318 had the P681H substitution while P681R was present in Delta lineages. Immune escape substitution E484K was present in B.1.1.318, Beta, and Eta, while Delta variants frequently presented with E484Q substitution.

Fig. 4: Analysis of amino acid substitutions in variants circulating in Ghana.
figure 4

a Frequencies of amino acid substitutions across all the SARS-CoV-2 proteins in all genomes that were sequenced. The mutations are sorted and coloured per gene. b Spike glycoprotein amino acid substitutions present/absent in major SARS-CoV-2 variants circulating in Ghana. Purple shading indicates substitutions within the lineages, whereas white indicates the absence of the substitution in a particular variant in all the samples. The bottom panel plot shows the frequencies of individual substitutions across all the samples.

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