Cardiovascular Journal of Africa: Vol 29 No 2 (March/April 2018)

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CARDIOVASCULAR JOURNAL OF AFRICA • Volume 29, No 2, March/April 2018 AFRICA 127
be adjusted to reflect the position within the phylogeny, since
there are large differences in the average number of common
population variants between haplogroups. This approach can
also further be modified to, for example, exclude low-impact
variants, highlighting the role of likely deleterious functional
variants.
Determining the likely impact or pathogenicity of mtDNA
variants can be achieved by using several computational
pathogenicity-predicting methods.128 An example of such a
method is the MutPred system, which assigns a MutPred score
to any protein-coding mtDNA variant, according to 14 gain/
loss properties of protein structure and function.129 The use of
this system has been widely validated in the context of mtDNA
studies,124 and performs better in an accuracy test when compared
with several other methods.128 Therefore the question can be
asked whether individuals in the disease group are impacted on
by a combination of rare (mildly deleterious) variants or simply
whether such variants are more common in the disease cohort
than in the controls.
The mutational load approach moves away from the study
of haplogroups and looks at the collective effect of rare (or
recent) variants, which are more likely to be deleterious. It distils
the likely impact of a person’s mtDNA variation into a single
value on a continuous scale rather than a letter. Consequently it
will have more statistical power than conventional haplogroup-
association studies, as more powerful parametric statistics can
be applied and fewer comparisons are required.130 It offers an
alternative method to explore the involvement of mtDNA
variants in disease phenotypes, including diseases thought to be
related to mitochondrial dysfunction, such as CVDs.
The unique challenges faced by studies in
African populations
While communicable diseases are still the leading causes of
mortality in sub-Saharan Africa (SSA), CVD particularly is a
growing concern here, since the prevalence has risen markedly
in recent times as more populations of developing countries
become urbanised and are exposed to a diet and lifestyle that
increase risk factors for CVD.131 Taking into account the many
differences among ethnic groups in the onset and development
of CVD,132,133 genomic investigations have also been used to
investigate these disparities.131,134-136 However, the number of
well-powered genetic studies on CVDs in African populations
or people of African descent is much lower than in European
populations. As yet, no conclusive nDNA genetic factor/s has
been identified to help understand these disparities.137
Current Eurocentric reference panels used in GWAS studies
to examine the involvement of population variants in disease
have been shown to be of limited use in even common SSA
population groups.138 This is indicative of the lack of African
representation in our current databases. This lack extends to
mtDNA as well. Of the more than 30 000 mtDNA sequences
available on GenBank, only 13% of these are of African lineages
(L0–6). This bias in published data results in the resolution of the
phylogenetic tree being much higher in the European branches
(especially super-haplogroup N descendant) than in the African
roots,139 despite greater diversity within the latter. Comparatively
few studies have been done where the involvement of mtDNA
variation in CVD has been considered.135,136,140-142
Although of small size, one such study helps to highlight the
challenges posed by these gaps in our current data. Ameh et
al .142 could not find the tRNA mutation m.3243A> G in Nigerian
type 2 diabetes patients, despite an association being previously
reported in other European and Asian populations. This and
other studies 143 illustrate the difficulty of extrapolating genetic
risk factors for disease from one population group to the next,
and the need for population-specific studies.
Conclusion
SSA is facing a growing burden of CVD, while the discrepancies
in onset and progression between different ethnicities are still
poorly understood. Additionally, there are large data gaps
when genetic studies on Africans are considered, especially for
complex disease phenotypes. The unique genetic backgrounds
of different populations also make it difficult to apply advances
made in well-studied populations to understudied populations.
While great efforts are being made to address these data gaps
by initiatives such as the Human Heredity and Health in Africa
(H3Africa) initiative,131 the Southern African Human Genome
Programme, and the African Genome Variation Project, 138 there
is an urgent need for even more large-scale African-specific
investigations (which should also consider mtDNA variation)
to be undertaken if we are to provide the necessary care to all
vulnerable groups.144
Realistically, for some time still, it is likely that studies
in African populations will be hampered by financial and
logistic/infrastructural difficulties,144 limiting the sizes thereof.
Fortunately, these studies can benefit from retrospective lessons
we have learned thus far in other populations, highlighted in
the above discussions. New studies could particularly benefit
by asking better-formulated questions, and using alternative
approaches that aim to address the challenges associated with
many of the classic approaches used, when the role of mtDNA
in common disease is investigated.
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