Page 187 - Contributed Paper Session (CPS) - Volume 7

CPS2057 Ana C. M. Ciconelle et al.
insertions/deletions (indels) and copy-number variations (CNVs), are also
available.
Several studies are being performed to catalogue the human genetic
variants to facilitate GWAS, such as the HapMap Project and 1000 Genomes
Project. In both projects, samples are majorly from African, Asian and
European populations and they aim to identify genetic variants with
frequencies of at least 1% in the studied populations, including not only SNPs,
but also structural variants and small insertions/deletions (The International
HapMap Project, 2003; 1000 Genomes Project Consortium, 2016). Even though
there is a major success in gene discovery, the percentage of variance
explained by GWAS loci for many traits is relatively low. Thus, a substantial
part of the traits variation is still unexplained. This phenomenon is called
missing heritability. One example of trait with a high missing heritability is the
height. In Manolio et al. (2010), two of the solutions cited to revealing the
missing heritability is to use different types of genetic variants including
common and rare variants.
Based on these scenarios, in this work, our focus is on CNV detection since
this kind of variant is not as well characterized as SNPs, but it is expected to
have an important role on the association with several traits and diseases.
Copy number variation occurs when the number of copies of a particular
region (one or more loci) of the DNA differs from two in autosomes or one/two
in allosomes and can to explain phenotypic variability in humans. The effects
of CNVs to human diseases are not yet well known although several diseases
have been associated to this kind of polimorphism, such as uric acid (Scharpf
et al., 2014).
GWAS are usually based on reference maps which do not take into account
the population-specific and rare variants. In addition, Sanna et al. (2011) shows
that adding rare variants in association studies doubled the explained
heritability of traits. Therefore, identifying different types of variants and
including data from specific populations can explain the missing heritability of
traits and diseases. This motivates to build genomic reference maps for
specific populations, as proposed by the project Genome of Netherlands
(Boomsma et al., 2014), which aims to characterize genetic variants from Dutch
population, including rare variants.
Considering the unknown influence of CNVs on anthropometric
measurements and the lack of studies based on Brazilian population, this work
was developed in collaboration with the Laboratory of Genetics and Molecular
Cardiology (Heart Institute-USP, Brazil). Using the database from the Bapendi
Heart Study described by Egan et al. (2016), we analysed the genotype (SNP
data) and phenotype data from 80 families to characterize the CNVs in the
Brazilian population and to understand their association with phenotypes,
such as height. The main purpose of this project is to present methodologies

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