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ISSN 2469-9853
Journal of Next Generation Sequencing & Applications
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Effective Size and Effectiveness: Next Generation Sequencing and the Practice of Genomics in Africa

Muntaser Ibrahim* and Mahmoud Musa
Institute of Endemic Diseases, University of Khartoum, Medical Campus, Khartoum, Sudan
*Corresponding Author : Muntaser Ibrahim
Institute of Endemic Diseases
University of Khartoum, Medical Campus, Khartoum, Sudan
Tel: +249 11 310102
E-mail: [email protected]
Rec date: Oct 29, 2015; Acc date: Feb 26, 2016; Pub date: Feb 29, 2016
Citation: Ibrahim M, Musa M (2016) Effective Size and Effectiveness: Next Generation Sequencing and the Practice of Genomics in Africa. Next Generat Sequenc & Applic S1:008. doi:10.4172/2469-9853.S1-008
Copyright: © 2016 Ibrahim M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

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Abstract

The tremendous and unprecedented insights provided by next generation sequencing into genome functions, variations and interactions promises an enormous shift in our attitude towards individual and population genetics, both in health and disease. Concepts and paradigms could be verified or nullified based on large complete sets of data rather than few genes or inference from large sets of comparison with substantial gaps like genome wide association studies (GWAS). Functional variant(s) may be associated with phenotypes at a “personalized” level. Rare variants underlying common diseases-and even underlying variations in health-are a frequent encounter in the genomic era. Simple inductive generalizations would hastily blur the lines between what is a true abnormality and what is not.

Introduction
The tremendous and unprecedented insights provided by next generation sequencing into genome functions, variations and interactions promises an enormous shift in our practice of genomics and attitude towards individual and population genetics, both in health and disease. Concepts and paradigms could be verified or nullified based on large complete sets of data rather than few genes or inference from large sets of comparison with substantial gaps like Genome Wide Association (GWA) studies. Functional variant(s) may be associated with phenotypes at a “personalized” level. Rare variants underlying common diseases – and even underlying variations in health – are a frequent encounter in the genomic era. Simple inductive generalizations would hastily blur the lines between what is a true abnormality and what is not.
Effective size, imputation and generalizations
Effective population size is a term denoting the amount of genetic variation that may contribute to the founding of a population. The caveat of doing genomics against a background of large effective size with lack of representative sampling, as it is often the case in Africa, is the problem of establishing a reliable reference. The extent of existing variation is unknown – or quite large at best assumptions. Experiences gained especially from GWA studies have shown the pronounced impact of population structure. Imputation will only compensate for a certain frequency threshold of variants within the population [1]. This has its implications on complex disease as well as monogenic disease mapping [2,3]. We have witnessed a huge shift in imputation accuracy between the HapMap and 1000Genome [4]. But can we ever be satisfied? When dealing with exome and genome sequencing in African populations, the deeply rooted evolutionary history renders a degree of uncertainty in the practice of imputation. Imputation accuracy is low in most African populations largely due to low levels of Linkage Disequilibrium (LD) and high levels of genetic diversity [3,5]. Reich et al reported that LD in a United States population of north-European descent typically extends 60 kb from common alleles; by contrast, LD in a Nigerian population extends markedly less far [6]. The high genetic diversity of African population comes on a background of large effective size and deep ancestry. The resulting decreased imputation accuracy in turn merits an enormous sample size to maintain the power in imputation-based GWA studies in African populations [5].
Efficient Annotation of Variant Calls
When making variant calls and subsequent annotations, the “reliable” reference should take into account these issues of population structure and large effective size. The premise of a putative ancestral genome that may be reconstructed from existing genomes may be flawed. Here we mention two arguments that support this: firstly, human populations in and out of Africa were influenced and shaped by drift and serial founder effects, and secondly, functional variants are multiple, formed through an extended evolutionary history in Africa and retained within its extant populations. Both assumptions have multiple backings and could be tested afresh in global genomic data and specially African genomes. Variant calling and annotation is dependent on our use of reference genomes and annotations. Using genetic variations data from the population under study markedly improves variant calling accuracy in sampled individuals (e.g. by providing better likelihood estimates to separate true variations from errors). Eventually and with such scenarios in place, genomics is at a greater challenge. Identifying association and causality for any disease model departing from the simple form of “common variant common phenotype” would be a big hurdle to overcome. Studying an exome for example, we are faced by thousands of variants, many of them are peculiar to the background population which eliminates the luxury of blindly utilizing available allele frequency data as a filter for variants. Looking for changes in “common” disease genes might not hold the answer either considering what we argued on the evolutionary history of these populations. Other genes might take their place, promoting other models, for example the rare variant model. Prioritizing a disease variant because it is 'rare' might be challenged if this variant turns out to be common in a specific population or sub-population. Using familial or population controls while considering stratification is one way around. Africa is bound to face another challenge even greater for personalized medicine specially with the practice of projecting annotation based on published data, for example from the 1000Genome, and other relevant databases without accompanying the structure and genetic history into the analysis.
Effective Genomics
Extensive efforts should be made in order to identify not only the gene functions but the unique epistatic effects and interaction networks. Using systems biology and network analysis in studying phenotypes as a substitute or a complementary approach to single gene prioritization in African populations will yield valuable information. Networks are regarded as conserved evolutionary units that can undergo selection [7,8]. Evolutionary plasticity of genetic interaction networks [9] adds another interesting perspective. Genes that are conserved between species have been functionally maintained since their last shared common ancestor. On the other hand, non-additive genetic interactions are at best poorly conserved, as they are the direct consequence of network rearrangements that are themselves not conserved. The notion that networks should be conserved at a functional level is plausible given the fluidity of cellular processes and gene regulation on a background of differing variations. Hence, loss of the same gene sets can effect differently on certain phenotypes in different populations, the mere link being the orchestrated interaction networks affected by their demise. We take the example of TP53, a frequent finding in cancer in Western populations but not necessarily in African populations. Other genes with interaction centrality, for instance HuR/ELAVL1, might underly oncogenesis instead. We have recently touched upon the centrality of ELAVL1 and it's possible role in conjunction with other viral oncogenesis in African populations [10]. This by no means is a call to abandon all current practices. Existing genotype and phenotype databases are certainly the basis for annotation. Yet, another level of scrutiny is warranted. The formulation of standards for this level should be our next priority.
In summary, effective genomic analysis in founder populations should take into account the complex evolutionary history. Existing annotations should be interpreted with caution. The large effective population size merits the utilization of population specific data and the use of systems biology approaches to prioritize variations.
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