Category Archives: methods

Genetic Genealogical Methods Used to Identify African American Relatives of Members of the Ghanaian Kassena Ethnic Group

By: LaKisha David and Leia Jones

Part 1: Introduction

Between 1501 and 1866, 10.5 million enslaved Africans were taken from families and communities and disembarked in various diasporic locations in the world as part of the Transatlantic Slave Trade (Slave Voyages v2.2.3, 2019). With very few exceptions, genetic genealogy is the only way for people of African descent in diasporic locations to identify their extra-extended relatives among the descendants of those who remained in Africa. African Americans are increasingly engaging in historically significant processes of reunification with African relatives by using genetic genealogy to identify members of their ancestral families. By inference, families that were separated during the Transatlantic Slave Trade are reuniting. Researchers studying family formation or family reunification need to be cognizant of the methods of using genetic genealogy to identify biological extra-extended relatives.

In this methodological blog series, we demonstrate the use of publicly accessible genetic genealogy methods to identity genetic extra-extended relatives. There is an archive within the human genome which can provide information about an aspect of family history such as maternal or paternal ethnic lineages and biogeographical ancestry (BGA) estimates (Wagner, Cooper, Sterling, & Royal, 2012). One of our objectives was to identify genetic relatives among populations that experienced significant historical mass trauma, specifically family disruptions by the Transatlantic Slave Trade, by using criteria from established findings within population genetics that provides strong evidence of relatedness between our Ghanaian project participants and diaspora African Americans within the GEDmatch database. Specifically, we identified Ghanaian and African American dyads who shared a common ancestor within 10 generations (i.e., sharing ancestral great grandparents) in contrast to studying genetic matching within a biologically determined African ethnic group which has been the focus of previous genetic genealogy studies involving Africans and African Americans.

The aim of this methodological blog series is to inform family and identity researchers about the use of genetic genealogy as a method of inquiry and intervention in identifying close and extra-extended relatives. This series is based on our study that explored family identity among Ghanaians who interacted with their diaspora African American relatives we discovered using autosomal genetic genealogy. In this series, we provide our rationale for selecting publicly available autosomal genetic genealogy services for genetic matching and the specific criteria drawn from population genetics to identify genetic matches. We present the results of our genetic matching and discuss our finding diaspora African American relatives of our participants from Ghana and the inference that families that were separated during the Transatlantic Slave Trade are reuniting. We then provide details of our materials and methods, including our rationale for selecting our participants and steps researchers unfamiliar with population genetics could duplicate in their research involving a reunification or biological relatedness element. We conclude with ethical considerations in the use of genetic genealogy as a tool in international research.

Part 2: Genetic Genealogy Type for Genetic Matching

Social science research on genetic genealogy typically covers associations between the results of a genetic genealogy test and racial-ethnic identity. This blog series is about the use of genetic genealogy to discover genetic matches (e.g., relatives), individuals sharing a minimum threshold of DNA with the participant tester indicating that the newly discovered individual is a potential relative sharing a common ancestral great grandparent within 10 generations (e.g., the eighth great-grandparent). The participant and the discovered genetic relative would be members of an ancestral family group such as a clan. In the context of our study of reunification after the Transatlantic Slave Trade, the names of the ancestral great grandparents are typically unknown with genetic genealogy being a tool that enables African and African diaspora ancestral family members (i.e., extra-extended relatives) to identify each other as genetic matches, relatives, or family. The difference between using genetic genealogy to identify individuals who are genetic matches for genealogical and kinship purposes rather than for the purposes of claiming an ancestral ethnic identity or membership is a key distinction in this study when selecting the type of genetic genealogy test and interpreting the social meanings associated with the results.

Advancements made in molecular biology and supercomputing propelled genealogy into the realm of genetics (Nelson & Robinson, 2014). The first genetic testing for genealogical purposes was made available in 2000 by Family Tree DNA. By 2010, there were 38 companies offering such services (Nelson & Robinson, 2014; Wagner, Cooper, Sterling, & Royal, 2012). AncestryDNA (of Ancestry.com) launched in 2012 and by July 2015, they tested 1 million people (Swayne, 2015). By April 2017, they tested 4 million people (Ancestry Team, 2017). It is no question that the use of direct-to-consumer (DTC) genetic genealogy testing is rapidly increasing. The type of tests that consumers purchase is associated with the type of information the tests provide and consumers’ motivation for taking the test (Nelson, 2016). The motivation to engage in genetic genealogy and the meanings associated with the results can be better understood by understanding the types of information that the tests provide.

Although companies vary in their methods and product offerings, genetic genealogy tests provide three types of information: maternal or paternal ethnic lineages, biogeographical ancestry (BGA) estimates, and genetic matches (Wagner et al., 2012).

The first type of information is maternal or paternal ethnic lineages (i.e., haplotypes). Lineage information enables geneticists to provide ancestral spatiotemporal information (i.e., geographical location and time) using mitochondrial DNA (mtDNA) and Y-chromosomal DNA (Y-DNA). These tests are used to provide African American consumers with African origin ethnic groups.

A limitation with offering to identify origin countries or ethnic groups is that common haplotypes are present in multiple ethnic groups due to migration within Africa (Ely, Wilson, Jackson, & Jackson, 2006). Results consisting of geographical information based on common haplotypes or poorly sampled populations are problematic (Shriver & Kittles, 2008). According to a study on the ability to identify the African origin country or ethnic group of African Americans’ ancestors based on their mtDNA haplotypes, only 5% of the African American sample’s maternal lineages exactly matched a single African ethnic group using a database representing West, West Central, South, Southeast, and East Africa (Ely et al., 2006; Ely, Wilson, Jackson, & Jackson, 2007). Additionally, 21% exactly matched 2 – 9 ethnic groups, 31% exactly matched over 9 different ethnic groups, and 43% exactly matched no ethnic group within the database (Ely et al., 2007). In other words, using research databases, a single African origin ethnic group often cannot be identified for African American maternal lineages; their maternal lineage more often matches multiple ethnic groups whose mtDNA were not distinctive from each other. Additionally, lineage information based on DNA represents only 1% of the genome (Shriver & Kittles, 2008) and so is not representative of the consumer’s ancestral relatedness.

Another limitation is that mtDNA and Y-chromosome testing is only somewhat useful in learning about even recent shared ancestors such as paternity (Shriver & Kittles, 2008). A practical example is the case of using Y-chromosome DNA tests to settle the dispute over President Thomas Jefferson’s paternity of enslaved Sally Hemings’s youngest son, Eston Hemings Jefferson, thought by some to be the biological child of Thomas Jefferson (Foster et al., 1998). Eston has a perfect match on the Y-chromosome with four of five male descendants of Thomas Jefferson’s paternal uncle (i.e., four of Thomas Jefferson’s male cousins), meaning that Eston’s paternal lineage is biologically related to the family of Thomas Jefferson (Foster et al., 1998; King et al., 2007) and share ancestral origins with the Jefferson paternal lineage among peoples of indigenous Europe, East Africa, or the Middle East (King et al., 2007). Eston’s Y-chromosome matching other Jefferson males means that based solely on the results of the Y-chromosome test and not using other information, Eston could have been fathered by Thomas Jefferson or Thomas Jefferson’s paternal male relatives. For the same reason, King and colleagues (2007) said that “[i]f we did not have prior knowledge about the ancestry of the Jefferson haplotype, we might assign it to an Egyptian origin” (King et al., 2007, p. 588). The point here is that mtDNA and Y-chromosome tests are very informative in certain aspects but are limited in determining genealogical relatedness for the purposes of our study. They should also be used with caution when determining ancient ancestry on small-scale geographical regions.

The second type of information provided by genetic genealogy is biogeographical ancestry (BGA) estimates which are typically referred to as ethnicity estimates by companies and the general population (Pfaff, Parra, & Shriver, 2000). In contrast to the very small percentage of ancestors represented in the lineage-based tests using mtDNA and Y-chromosome, BGA is representative of the tester’s ancestors from whom the tester has inherited DNA at specific locations along the 22 pairs of chromosomes (i.e., autosomes). Developed by biological anthropologist Mark Shriver and molecular biologist Tony Frudakis (Gannett, 2014), BGA “refers to the component of ethnicity that is biologically determined and can be estimated using genetic markers that have distinctive allele frequencies for the populations in question” (Pfaff et al., 2000). This refers to the use of alleles, variants in genes at specific locations along the 22 pairs of chromosomes, to estimate a tester’s ancestral continental population(s) (e.g., African, European, Native American) (Pfaff et al., 2000) or regional populations (e.g., Southern European, Northern European) (Shriver & Kittles, 2008) in some cases. Then the tester’s total proportional ancestry is estimated as admixture proportions (Pfaff et al., 2000). For example, one African American tester’s results could indicate that their total ancestry is 80% African, 15% European, and 5% Native American. However, there is variability in the admixture of even full siblings based on which parents’ gene variants just happened to have been inherited by the siblings. For example, one sibling may appear to inherit 80% African variants while another sibling may inherit 73% African variants. The percentages provided by companies should be understood to include a confidence interval and not be interpreted as an exact number (Shriver & Kittles, 2008).

Until 2009, studies that included enough positions along the genome to produce relatively high specificity in continental populations “included few African populations” (Bryc et al., 2009, p. 1). This limited the ability to provide more regional level population analysis for African populations. Bryc and colleagues (2009) conducted a study that consisted of African Americans, people of European descent, and several populations from West and South Africa. Using principal component analysis (PCA) and a clustering algorithm and assuming two source populations (i.e., African, non-African), they find that 77% of African Americans’ ancestors were from Africa (Bryc et al., 2009). This is similar to findings from other researchers who found that 73.2 % – 84.9 % of African American ancestry comes from sub-Saharan Africa. African American remaining admixture is 21.3 % – 24.0% European and 0.8 % – 2.8 % Native American (Baharian et al., 2016; Bryc, Durand, Macpherson, Reich, & Mountain, 2015). Bryc and colleagues (2009) also find that the African component of African American ancestry “is most similar to the profile from non-Bantu Niger-Kordofanian-speaking populations, which include the Igbo, Brong, and Yoruba, with FST values to African segments of the African Americans ranging from 0.074 to 0.089%” (Bryc et al., 2009, p. 5). There are very small differences between the African portion of African Americans’ ancestors and non-Bantu Niger-Kordofanian-speaking populations.

One limitation is that BGA estimates tell African American consumers generally what they already know, that most of their ancestors come from Africa. Additionally, companies offering African BGA estimates at regional levels offer country names and geopolitical borders for current day countries that were not used by people living in Africa 500 years ago. There is more genetic diversity in Africa than in the rest of the world. Evidence based on language, DNA, and geographical distributions indicate that people have dispersed across Africa tens of thousands of years before the Transatlantic Slave Trade. Because of this, the African diversity found in the African American genome today is not solely the result of African ethnic mixing in the U.S. during slavery (Ely, Wilson, Jackson, & Jackson, 2006; Jackson & Borgelin, 2010). Companies continuing to use geopolitical borders in their results will continue to provide information that is not as informative as they could be for people of African descent. Like lineage information, BGA is dependent on the quality of a reference database consisting of population-specific gene variants. Although some commercial companies, such as Ancestry.com, promote regional level specificity for Africa, BGA information based on a reference database does not enable researchers to identify relatives among a general population of testers.

This study makes use of the third type of information provided by genetic genealogy testing: genetic matches. By comparing the DNA profiles of each customer with every other customer within its own database, companies provide each customer with a list of persons with whom a certain amount of DNA is shared. These are genetic matches or potential relatives. Companies offering genetic matches use the amount of shared DNA measured in centiMorgans (cMs) to provide an estimate of the class of relatedness. For example, Ancestry.com uses the following class of relatedness and approximate amount of shared DNA: Parent/Child (3,475 cMs), Close Family (2,800 – 680 cMs), 2nd Cousin (620 – 200 cMs), 3rd Cousin (180 – 90 cMs), 4th Cousin (85 – 20 cMs), and Distant Cousin (20 – 6 cMs) (Ancestry, 2018). The most distant class of relatedness provided by Ancestry.com is in a section consisting of 5th – 8th cousins. The most common recent ancestors among matches within this group shares 4th – 7th great grandparents or have shared ancestors approximately 6 to 9 generations ago.

Companies provide matches from among their own customer database. However, having millions of customers, genetic matching results are not as limiting as using haplotype reference databases in other types of tests because consumers are bound to have at least one relative in the database (Henn et al., 2012; Ramstetter et al., 2017). The primary consideration for genetic matching is the accuracy of the genetic matching results, particularly when consumers go on to initiate contact with the genetically matching person identified in the results to learn about shared family history.

Commercial companies use modified versions of algorithms developed by academic population geneticists to provide relatedness information. For example, Ancestry.com base their genetic matching on BEAGLE (B. L. Browning, Zhou, & Browning, 2018; S. R. Browning & Browning, 2007) and GERMLINE (Gusev et al., 2009) within its procedures for providing genetic matches to consumers (Ball et al., 2016). Ramstetter and colleagues (2017) conducted a study based on a Mexican American sample consisting of 2,485 Mexican Americans with known pedigree information to evaluate the accuracy of 12 pairwise relatedness inference methods. They find that GERMLINE is one of the most accurate methods available (Ramstetter et al., 2017).

Ramstetter and colleagues’ (2017) study measured the ability of various methods accurately specifying the degree of relatedness of a selected pair from 1 to 7 degrees or being unrelated. A degree refers to the number of birthing events separating two people. For example, a parent and child have 1 degree of relatedness, full siblings have 2 degrees of relatedness, and third cousins sharing great-great grandparents have 8 degrees. Although their study focused on accuracy of various methods predicting degree of relatedness compared to reported degree of relatedness, we are reporting accuracy of the fact of relatedness from their study. For our study, we are concerned with the methods’ ability to determine if members of a dyad are related rather than how the members of the dyad are related. Based on Ramstetter and colleagues’ (2017) findings, GERMLINE determined fact of relatedness at accuracy levels of 100% (for known 1st degree relatives), 99.89 – 99.19% (2nd – 5th degree relatives), 98.53% (6th degree relatives), and 83.57% (7th degree relatives). For those reporting being unrelated, GERMLINE predicted that 80.58% were unrelated and 19.42% were 4th – 8th degree relatives. Ramstetter and colleagues (2017) states that this could attributed to false positives predicted by GERMLINE, but it could also be due to cryptic relatedness (i.e., relatives not knowing they are related). In other words, algorithms can accurately or fairly accurately determine if two people are related up to 6th and 7th degrees of relatedness.

This is significant because some genetic genealogy services used by the general population provide results on relatedness based on these scientific methods. For example, Ancestry.com uses an adapted version of GERMINE called J-GERMLINE that is designed to work with growing databases (Ball et al., 2016). The usefulness of results produced by algorithms that are based on detecting IBD segment sharing (e.g., GERMLINE, J-GERMLINE) relies on the use of DNA profiles that are already phased. Genotype phasing is the process of ordering allele assignments by parent across the SNP locations. Whereas statistical methods such as those used by Ancestry.com are available to infer phasing, comparing the child’s DNA profile to the parents’ DNA profiles is the best way to ensure phasing accuracy (Ball et al., 2016; S. R. Browning & Browning, 2007; Roach et al., 2010; Tewhey, Bansal, Torkamani, Topol, & Schork, 2011). “Leveraging parental information to phase genomes provides excellent accuracy” (Tewhey et al., 2011, p. 220). By first using family-based phasing to order the child’s allele assignments and to identify segments shared by both the child and a parent, this ensures that the DNA segment being compared to other profiles of unknown relatedness to the child is truly a segment that was inherited by the child from a parent (i.e., IBD). Algorithms provided by GEDmatch enables the general public to create phased genetic profiles and then to conduct IBD segment sharing matching with other users in their database. IBD segment sharing among two target child profiles and one of each of their parents (i.e., among two parent-child dyads) infers that the segment was inherited from a shared ancestor and that the two target persons (and the matching parents) are related.

Part 3: Criteria of Relatedness for this Study

For the first time in history, technology is available to identify living members of African ancestral family groups. For African Americans, this means that they can identify the specific African ancestral family groups from whom their African enslaved ancestors were taken during the Transatlantic Slave Trade. For our Kassena participants from northern Ghana, this is a new social context in which to make kinship meanings. The emphasis we are making here is not on ancestral geography or ethnic group identification but on the ability to identify and communicate with the direct living African and African diaspora relatives within 10 generations of shared ancestral great grandparents. This challenges the common narrative that African American separation from Africa was too long ago for family history or relatives to be discovered, but it also presents new questions about the meaning of family.

For our criteria, we drew from work on cryptic distant relatives (Henn et al., 2012). We searched for 4th to 9th cousin genetic relatedness between Ghanaians and people of African descent, meaning that the Ghanaian and person of African descent share a common ancestor within the last 5 to 10 generations and within 300 years ago (Henn et al., 2012). Genetic relatedness is measured using DNA segment sharing algorithms on GEDmatch. We used “the length of DNA segments that are consistent with identity by descent (IBD) from a common ancestor” (S. R. Browning & Browning, 2007; Gusev et al., 2009; Henn et al., 2012, p. 1) measured in centiMorgans (cMs) as the genetic similarity metric. Matching is based on similarity of autosomal single nucleotide polymorphisms (SNPs, pronounced “snips”). The amount of DNA shared in a cousin dyad depends on the number of shared ancestors and the number of generations between the cousins and the shared ancestors. The greater the number of shared ancestors or the shorter the generational distance, the greater the amount of shared DNA in cMs between the cousins. The amount shared between cousins vary greatly such that at certain generational distances, cousins will not show matching DNA at the SNP locations even though they are biologically related (Henn et al., 2012).   

To ensure IBD segments, we used family-based phased matching. This means that our final results consist of segments that match the parent and progeny of both our Ghanaian participants and the unknown relatives in the database such that all four matched and indicated that they shared common ancestors within 10 generations. We show that there is genetic evidence that Ghanaians and people of African descent show relatedness within 10 generations, supporting the claim that families that were separated during the Transatlantic Slave Trade are reuniting.

Drawing from Henn et al.’s (2012) work, 4th to 8th cousins share 14 to 0.055 cMs of DNA (Henn et al., 2012). Their threshold was set to a minimum of 7 cM based on their ability to find progeny-other and parent-other segments where the progeny also matched the parent (i.e., IBD for the parent and progeny) for 90% of the segments. They used unphased data in their work (Henn et al., 2012). Our minimum segment threshold was set to 7 cMs. However, for the phased data that we used, we could have lowered the threshold to 2 cMs (S. R. Browning & Browning, 2007; Henn et al., 2012). Although there are benefits to being able to identify genetic matches using computationally phased data between 2 persons, our use of family-based phasing between 2 parent-progeny dyads ensures greater accuracy (Roach et al., 2010). Every segment recognized as a genetic match will be IBD based on matching both parent and progeny inherent in family-based phased data. Additionally, our method also enables the person of African descent to learn more about their genetic family history than with the use of data between 2 persons. For example, with our phased data, a person of African descent could learn that they, through their mother, are related to a person born in Ghana through the Ghanaian person’s father. This additional information discovered using family-based phased data is of value to people of African descent testing to learn about their relatedness with Africans.

Part 4: Results

Confirming Parent-Progeny Dyads

            We did a one-to-once comparison (GEDmatch) between parent and progeny before creating the phased datafiles to ensure that each dyad consisted of biological parent and progeny. Parent-progeny dyads will have at least 3,400 cM of shared DNA 100% of the time (Bettinger & Perl, 2017). We expected the members of each dyad to share at least 3,400 cMs of DNA. Members of dyads shared 3,538.7 to 3,568.7 cMs indicating that each dyad consisted of a biological parent-progeny pair (see Table 1). We then used the GEDmatch Phasing tool to create one phased profile for each dyad.     

Table 1: Shared DNA between parent and progeny

ParentOffspringTotal DNA
Shared
(cMs)
SNPsLargest
Segment
(cMs)
Parent 1Offspring 1.13,568.7511,616151.8
Parent 2Offspring 2.13,554.8515,810151.8
Parent 3Offspring 3.13,547.4514,166151.8
Parent 4Offspring 4.13,560.4513,923151.8
Parent 5Offspring 5.13,553.0515,325151.8
Parent 6Offspring 6.13,551.6515,063151.8
Parent 7Offspring 7.13,559.2514,105151.8
Parent 8Offspring 8.13,543.5458,832151.8
Parent 8Offspring 8.23,538.7459,252131.7

Note: As of 7 July 2019

Identifying Matching Parent- Progeny Dyads within GEDmatch

            We used the phased profile for the rest of the matching. We used GEDmatch’s one-to-many tool to find all profiles within the database that matched at least one of our phased profiles at a minimum of 7 cMs on a single segment. The number of matching profiles for each participant dyad ranged from 7 to 50. These resultant matching profiles were unphased and so we were uncertain if the segments in the matching database profiles were actually IBD. To resolve this, we searched for parent-progeny dyads within the results for each participant phased profile.

            We used GEDmatch 3-D Chromosome Browser to identify matching profiles that also matched each other sharing at least 3,400 cMs, indicating parent-progeny relatedness (Bettinger & Perl, 2017). Each parent-progeny dyad had from 2 to 6 matches with the exception being for the siblings (i.e., Progeny 8.1 and Progeny 8.2) who had 20 and 28 matches respectively (see Table 2). Every 2 matches consisted of 1 identified dyad. For example, the phased profile for Parent 1 and Progeny 1 matched 2 identified parent-progeny dyads in the database. Parent 5 and Progeny 5 matched 3 identified dyads in the database.

            We then did a one-to-one comparison between the phased profile and each member of the dyad to ensure that the matching segment matched. This is useful in ensuring that the segment found in the GEDmatch database is also identical-by-descent for the discovered dyad. The Total DNA Shared (cMs, single segment) in Table 2 is the amount that all four match each other (i.e., Ghanaaian parent-progeny dyad and parent-progeny found in database). In each matching, the dyad set matched on a single segment such that the shared cM is the amount shared on a single segment matching all four in the dyad set. For example, Parent 1 and Progeny 1 shared 9.7 cMs with a parent-progeny dyad found in the database. We regarded each dyad match as having an IBD segment. This means that the DNA segment was inherited by the Ghanaian parent and progeny and the parent and progeny found in the database from a common ancestor within 10 generations, making them biological relatives.

Table 2: Number of Genetic Matches in GEDmatch Database

ParentOffspringNumber of Genetically Matching
Profiles
in
GEDmatch
Total DNA
Shared
(cMs,
single
segment)
SNPs
Parent 1Offspring 1.149.7819
Parent 3Offspring 3.128.3751
Parent 4Offspring 4.127.0883
Parent 5Offspring 5.167.1 – 9.8522 – 1,236
Parent 6Offspring 6.1210.4950
Parent 8Offspring 8.1208.1 – 8.3614 – 893
Parent 8Offspring 8.2288.1 – 11.1617 – 938

Note: Note: As of 7 July 2019

Part 5: Discussion

The study of family identity among the Kassena people of Ghana toward their diaspora relatives is linked to the phenomenon of ancestral families separated during the Transatlantic Slave Trade reuniting using genetic genealogy. With such a reunification claim, we seek to illuminate the methods used in our study to identify extended relatives. For research, policy, and programs involving family reunification as an intervention, there is a need to develop methods vetted through the disciplines of population genetics and genetic anthropology for determining and interpreting relatedness with tools that are readily available for use in the general population.

Using the tools provided by GEDmatch, we were able to first confirm that our participants consisted of parent-progeny dyads and then to identify parent-progeny dyads within the GEDmatch database who were related to our participants. For discovered relatives in the database who are a part of the African diaspora, this provides evidence that African American and Ghanaian members of ancestral families that were separated during the Transatlantic Slave Trade can be identified and reunited.

After identifying matching parent-progeny dyads within the GEDmatch database, additional steps must be taken to learn more about the dyad’s ancestral history including using GEDmatch’s admixture tools and contacting the match’s representative using the email information that the representative provided. The match’s representative need to also confirm that the discovered parent-progeny dyad is in fact a parent and progeny and not two DNA profiles of one potential genetic match (e.g., duplicate upload or profiles from two different companies) or twins.

Part 6: Materials and Methods

Participants are 9 parent-offspring dyads (n = 18) and 2 parent-offspring dyads consisting of the same parent for 2 siblings (n = 3) for a total of 11 parent-offspring dyads consisting of 21 individual participants. All participants are at least 18 years of age and are self-identified as members of the Kasena ethnic group residing in Paga, Ghana. Because 2 DNA kits for offspring failed to process, they and their parents were removed from the sample leaving us with a subsample of 9 parent-offspring dyads consisting of 17 individuals. Parents consisted of 4 men and 4 women with an age range of 47 to 80 (M = 64.88, SD = 12.30). Their offspring consisted of 5 men and 4 women with an age range of 19 to 39 (M = 29.44, SD = 8.49). The mean age of the subsample is 46.12 (SD = 20.84).

The (former) Pikworo Slave Camp is located in the Nania village of Paga, Ghana, about 10 km north of Bolgatanga. The Pikworo Slave Camp, primarily used 1500s to 1800s, is associated with both the Transatlantic Slave Trade and the African Slave Trade as a site of bondage before captured people entered slave markets. It is also used for local memorial practices. Although elders in nearby villages still hold memories of the local slave trade, the emphasis of the tour guide is on the site’s connection to people taken to the dungeons along the southern coast and eventually to diaspora locations such as North and South America and the Caribbean Islands. African Americans familiar with the site regard it as having a great historical significance to their own ancestry and family narratives. Genetic genealogy could be used to support claims of relatedness and contemporary biological connections to the African diaspora.

Paga is a town that borders the country of Burkina Faso. It is the capital of the Kassena-Nankana West district. According to the 2010 Housing & Population Census, the Paramount Chief is Paga Pio. The main ethnic groups of the region are Mole-Dagbon, Grusi (of which the Kassena ethnic group is a part of), Mande-Busanga, and Gruma. It has a patrilineal system of inheritance. As of 2010, the population of the Kassena-Nankana West district was 70,667 individuals. In the Kassena-Nankana West district, 14.0% of the population lives in urban areas. The median age is 20, the average age is 26 with 96.7 males per 100 females. The average household size is 5.0 for urban areas and 5.6 for rural areas. Among those 11 years and older, 47.8% are literate in English (some of whom are also literate in a Ghanaian language and/or French) and 49.8% are not literate for any language. Among those 15 years and older, 72.2% are employed.

Saliva Sample Collection

Saliva samples were collected in June – July 2018 and 2016 using collection tubes containing a DNA stabilizing solution. Participants who tested in 2016 (n = 3) were randomly selected by residents of the neighborhood. Offspring (n = 4) of the participants who tested in 2016 were purposively selected in 2018 based on our need to have parent-offspring dyads in the study. The remaining participants (n = 14) who tested in 2018 were randomly selected from a list of potential participants created by one resident of the neighborhood who was not a participant of the study. Potential participants were listed based on their willingness to have both a parent and offspring participate in the study. Saliva samples were collected in one public group gathering in 2016 and one in 2018 at the project site located at the former Pikworo Slave Camp in Paga, Ghana.

Overview of Project Procedures

In June 2018 we organized a community event to continue rapport building and to explain the project to the community. Because the project was developed in consultation with a community member, the emphasis was on being transparent and continuing to build rapport. The next day, selected participants gathered at the project site. Members of the research team explained the project, provided time to answer questions participants may have had, and gathered informed consent. We then gathered the saliva samples, conducted round 1 (July 2018) of focus group discussion data about family meanings and the diaspora, provided a communal project mobile phone for use by participants to communicate with genetic matches, returned to the U.S. with the saliva samples, and then sent the saliva samples to a commercial lab for processing. After the DNA was genotyped, meaning the specific variations of gene markers (alleles) are found, we identified relatives within the GEDmatch database, provided the diaspora genetic matches with the contact information of their genetic matches in Paga, and provided the project coordinator selected from the community in Paga with the email address of the newly discovered diaspora relative. In March 2019, the community project coordinator collected a round 2 of focus group discussion data about family meanings and the diaspora and analyzed it using inductive and deductive thematic analyses.

Procedures for Identifying Genetic Matches in GEDmatch

The essential task was to determine which persons of African descent within a database is related to the participants residing in Ghana, supporting the claim that families that were separated during the Transatlantic Slave Trade are reuniting using autosomal genetic genealogy. We used several tools provided by the web platform GEDmatch (Software Version May 19 2019 00:02:33, Build 37) to identify, and then contact, genetic matches within their database.

Step one was to obtain genetic information in a text datafile for each participant. To obtain genetic information, we sent our saliva samples to Ancestry to process the DNA and create a DNA text datafile. Beyond genotyping accuracy, we selected AncestryDNA services from Ancestry.com for two main reasons: (1) the level of accuracy of GERMLINE based matching and (2) access to millions of consumers to potentially match and connect with after the study ends.

Rather than reading the entire genome, AncestryDNA reads the DNA sequence at approximately 700,000 locations, called single nucleotide polymorphisms (SNPs), along the genome (Ball et al., 2016). Along with the company specific products such as the ethnicity estimate and DNA Matches, AncestryDNA provides the raw DNA text datafile that contains the Reference SNP cluster ID (rsID), chromosome and position of allele, and the unordered values of the alleles for up to approximately 700,000 SNPs. This raw DNA text datafile can be downloaded and used in other applications.

Step two was to create phased profiles for each of the 9 participant parent-offspring dyads. Although computational methods are improving, family-based phasing is the only certain way to align the allele datafile by biological parent (Roach et al., 2010; Tewhey et al., 2011), which enabled us to have greater confidence that the DNA segments were identical-by-descent (IBD). IBD segments are segments that were truly inherited from one parent to the offspring. IBD segments are needed to identify genetic matches among unknown testers. To create the phased datafiles, we used the GEDmatch’s Phasing tool which compared the DNA datafiles of the offspring participant with their parents’ DNA datafiles. This created two phased datafiles. One phased datafile consists of the DNA segments shared between the offspring and the biological mother (denoted with “M1”). The other phased datafile consists of the DNA segments shared between the offspring and the biological father (denoted with “P1”). We expected each parent-offspring dyad to share at least 3,400 cMs of DNA. We used the phased datafile that was created between offspring and participating parent who contributed saliva for the study and deleted the phased datafile that was created for the non-participating parent.

Step three was to identify potential genetic matches within the GEDmatch database. To do this, we used their one-to-many comparison tool individually for each of our parent-offspring phased profiles. This provided us with a list of unphased profiles that shared at least 7 cMs in common with our parent-offspring phased participant profiles. When using the one-to-many comparison tool, we had the option to adjust the threshold for the minimum amount of DNA that unphased profiles in the database must share with the phased profile of the participating parent-offspring dyads. As the length of an IBD segment decreases, the tools become increasingly less accurate in identifying genetic matches. While several sources claimed that a minimum of 4 or 5 cM is the appropriate cutoff, we conservatively set the cutoff to a minimum of 7 cM for a single segment. 

Step four was to identify parent-offspring dyads among the list of potential genetic matches for each of our parent-offspring phased datafiles. Selection of parent-offspring dyads among the unphased DNA matching datafiles is necessary to ensure that the matching segment that was IBD for our participant dyad was also IBD for the discovered dyad, thereby further reducing the chances of false-positive matches. To identify parent-offspring dyads among the potential genetic matches, we used a GEDmatch’s 3D chromosome browser. For each phased profile, we viewed the resultant matrix that compared the potential genetic matches with each other and displayed how much DNA each potential genetic match shared with each other. We selected potential genetic matches that shared at least 3,400 cMs with another potential genetic match, indicating a parent-offspring dyad, identical twins, or a second profile for the potential genetic match. If criteria are met, this produced at least one set of matching parent-offspring dyads consisting of four individuals: the parent and their offspring from Ghana who were participants in this study and the parent and their offspring newly discovered within the GEDmatch database.

Step five was to provide additional evidence that each of the four individuals of within a matching set were related to each other by having overlapping segments, meaning that their DNA matched at the same locations along the genome. Observing the same IBD segment in all four samples indicates that the four individuals share a recent common ancestor within 10 generations based on the use of autosomal DNA, SNPs, and current technology. To provide supporting evidence of relatedness, we used GEDmatch’s one-to-one comparison tool which provides the chromosome, start and end location on the genome, amount of shared DNA in cMs, and number of shared SNPs for each segment shared by the two profiles in the comparison. We compared each of the phased participant DNA profiles to both individual profile in the discovered parent-offspring dyad and confirmed that they had overlapping segments. Those meeting this criterion were listed as genetic matches to our participants.

Step six was to contact the administrator (sometimes referred to as the manager) of the parent and offspring profiles by the email provided in the GEDmatch database. For each initial contact, we provided information about the Ghanaian parent and offspring genetic matches, the project, and the contact information for the project phone with the research team member in Ghana. That team member was copied on each email. For newly identified genetic matches who replied, we confirmed that the non-participant members of the genetic match were African American parent and offspring. These newly discovered persons were recorded as being genetic matches with the Ghanaian participant parent and offspring, sharing a common ancestor within 10 generations.

Part 7: Conclusion

This methodological blog series demonstrated the use of publicly accessible genetic genealogy tools to identify African American extended relatives of Ghanaians of the Kassena ethnic group. By inference, families that were separated during the Transatlantic Slave Trade are reuniting. The results of this study were used in a subsequent study about family identity among members of the Kassena ethnic group who engage in social interactions with their African American biological extended relatives.

Part 8: Reflections and Thoughts

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