Propósitos para la deliberación de políticas en salud

In the 1977, Sanger et al. (1977) and Maxam & Gilbert (1977) developed methods to sequence DNA by chain termination and fragmentation techniques, respectively. This transformed biology by providing the tools to decipher complete genes and, later, entire genomes. The technique developed by Sanger et al. (1977), commonly referred to as ‘Sanger sequencing’, required less handling of toxic chemicals and radioisotopes than Maxam and Gilbert’s method, and as a result it became the prevailing DNA sequencing method for the next 30 years. A growing demand for increased throughput led to laboratory automation and process parallelization, which eventually resulted in the establishment of factory-like outfits with hundreds of sequencing instruments. Thanks to these advances, the Sanger sequencing ultimately enabled the completion of the first human genome sequence in 2004 (International Human Genome Sequencing Consortium, 2004).

Nevertheless, the Human Genome Project required vast amounts of time and resources; it was clear that faster, higher throughput, and cheaper technologies were required. For this reason, research institutes and companies developed and commercialized the Next-Generation Sequencing technologies (or NGS), as opposed to the previous methods. These new sequencing methods share three major improvements. First, instead of requiring bacterial cloning of DNA fragments they rely on the preparation of NGS libraries in a cell free system. Second, instead of hundreds, from thousands to many millions of sequencing reactions are produced in parallel.

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Third, the sequencing output is directly detected without the need for electrophoresis; base interrogation is performed cyclically and in parallel (van Dijk et al., 2014). The enormous numbers of reads generated by NGS enabled the sequencing of entire genomes at an unprecedented speed.

The first NGS technology to be released in 2005 was the pyrosequencing method by 454 Life Sciences (now Roche 454) (Margulies et al., 2005). The 454 Genome Sequencer generated about 200,000 reads (~20 Mb) of 110 base-pairs (bp). During the past decade, tremendous progress has been made in terms of speed, read length, and throughput, along with a sharp reduction in per-base cost. Today, NGS platforms such as Roche 454, SOLiD, and Illumina, provide cheaper and larger genome-wide SNP data, which has new extraordinary applications in research areas such as clinical diagnostics, agro-genomics, and forensic science (van Dijk et al., 2014). For instance, Illumina human whole-genome genotyping microarrays provide large datasets with a genomic coverage from around 250,000 SNP markers to the whole genome (Figure 30). Interestingly, after the whole genome sequencing, Illumina HumanOmni5 microarray delivers the most comprehensive up-to-date coverage of the genome.

Figure 30: Illumina Omni SNP microarrays can perform from thousands to millions of markers.

Illumina HumanOmni5 SNP microarray (red square) delivers the most comprehensive coverage of the genome, with the exception of whole genome sequencing. © 2015 Illumina, Inc.

Initially, with only a few hundred autosomal microsatellites, genome-wide polymorphism data have established differences in allele frequency among continental regions (Jakobsson et al., 2008; Rosenberg et al., 2002). More recently, genome-wide SNP data derived from SNP arrays found evidence of appreciable fine-scale structure within continents, and even within countries. For instance, genome-wide SNP data have revealed strong statistical evidence for genetic

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substructure within Europe (Lao et al., 2008; Novembre et al., 2008), the Near East (Li et al., 2008), India (Reich et al., 2009), East Asia (Tian et al., 2008), the Americas (Risch et al., 2009), and Africa (Henn et al., 2011; Henn et al., 2012). Currently, these studies are based on Ancestry Informative Markers (or AIMs), which are genetic markers that show substantial differences in allele frequency across population groups.

Generally, patterns of genetic substructure suggest that geographic barriers to gene flow play a key role. By using nearly 200,000 common SNPs, Novembre et al. (2008) reconstructed a ‘genetic map’ of Europe by projecting the first two principal axes of genetic variation onto geographic coordinates. This phenomenon can clearly extend to variation within other continents or countries. For instance, it has been reported genetic evidence for fine-scale substructure within United Kingdom (Leslie et al., 2015), Finland (Jakkula et al., 2008), Mexico (Silva-Zolezzi et al., 2009), Puerto Rico (Tang et al., 2007), Ethiopia (Pagani et al., 2012), Madagascar (Pierron et al., 2014), and South Africa (de Wit et al., 2010; Schlebusch et al., 2012). However, not all regions appear to have clearly differentiated populations. For instance, West Africa is striking for having very little fine-scale structure, at least at the level of resolution captured by common SNP data (Bryc et al., 2010a; Zakharia et al., 2009), even though these data included populations of Bantu and Non-Bantu Niger-Kordofanian, Afro-Asiatic and Nilo- Saharan speakers spread out over broad geographic regions (Henn et al., 2010).

Although many initial large-scale genetic association studies have focused primarily on homogeneous populations, increasingly studies are addressing samples in which individuals have more complex backgrounds, including admixed ancestry of African American (Bryc et al., 2010a; Hinch et al., 2011; Perera et al., 2013; Wegmann et al., 2011). Such studies depend crucially on accurate and unbiased ancestry inference both at a genome-wide level as well as at each locus in the genome (Pasaniuc et al., 2013). Hence, inference of ancestry from genetic data is a critical aspect of genetic studies, with applications ranging from the inference of population history to the estimation of population structure (Novembre et al., 2008; Rosenberg et al., 2003). Recent methodological and technical advances in genomic technologies and computing resources have made possible the emergence of genome-wide studies, whose main advantage for demographic inference is allowing to identify and quantify admixture event among populations with different ancestries (Novembre & Ramachandran, 2011). Hence, we can apply genome-wide studies to accurately infer overall ancestry, as well as ancestry at a fine-scale across an individual’s genome. Ancestry estimation is a frequently encountered problem and has been used in a variety of applications such as tracing someone’s geographic origin in forensic investigations (Kayser & de Knijff, 2011), correcting for population stratification in genome-wide association studies (Bush & Moore, 2012), and developing personalized

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approaches to treatment (Huser et al., 2014). Importantly, in genetic association studies, ancestry inference can be used to account for the effects of population stratification which is a serious confounding factor and can lead to elevated rates of false positives (Price et al., 2010).

There are currently two different paradigms underlying ancestry inference: global ancestry estimation and local ancestry estimation. Global ancestry inference involves estimating the proportion of ancestry contributed by different populations averaged across the entire genome. Such methods have been applied to study population structure in humans (Lao et al., 2014; Pritchard et al., 2000; Rosenberg et al., 2002; Wollstein & Lao, 2015), and also other mammals such as chimpanzee populations (Becquet et al., 2007). In contrast, in local ancestry inference, we interpret each chromosome in an individual’s genome as a mosaic of segments that originate from different ancestral populations and the goal is to find the ancestral population of origin at each position (Tang et al., 2006).

Local ancestry-based methods, such as LAMP (Sankararaman et al., 2008), HAPMIX (Price et al., 2009), RFMix (Maples et al., 2013), and PCAdmix (Brisbin et al., 2012), devolve ancestry at each locus in the genome and provide individual-level information about ancestry and admixture mapping. While these methods provide valuable insights into the recent history of populations, they have reduced power to detect older events. Local ancestry inference methods have been used mainly to study recently admixed populations such as African Americans (Bryc et al., 2010a; Kidd et al., 2012) and Hispanic populations (Bryc et al., 2010b; Johnson et al., 2011; Moreno-Estrada et al., 2013).

The most commonly used methods for studying global ancestry are the Principal Component Analysis (or PCA) (Patterson et al., 2006; Price et al., 2006), and model-based clustering methods such as STRUCTURE (Pritchard et al., 2000), FRAPPE (Tang et al., 2005), and ADMIXTURE (Alexander et al., 2009). They are also the most powerful tools for detecting population substructure. ADMIXTURE employs the same model as STRUCTURE but uses a maximum likelihood estimation procedure involving high-dimensional optimization algorithms. ADMIXTURE is over an order of magnitude faster than STRUCTURE and produces estimates of similar accuracy (Alexander et al., 2009).

The PCA was firstly introduced to the study of genetic data almost thirty years ago by Menozzi et al. (1978), since then the PCA has become as a standard tool in genetics, especially to study scenarios of genetic geographic variation and population structure (Cavalli-Sforza et al., 1994; Cavalli-Sforza & Feldman, 2003). EIGENSTRAT (Patterson et al., 2006; Price et al., 2006) is a well-known program that implements PCA, which seeks to construct projections in lower dimensional space that capture a large fraction of the variation in the marker genotypes. The projections inferred by such approach tend to be highly correlated with the geographic locations

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from where individuals were sampled (Novembre et al., 2008; Wang et al., 2012). For dense polymorphism datasets such as those obtained from sequencing, haplotype based analysis has the potential to leverage this information and provide improved ability to detect population substructure. ChromoPainter and fineSTRUCTURE are recently developed programs that aim to make use of haplotype structure for high quality PCA and population structure inference respectively (Lawson et al., 2012).