<p>Obesity is a medical condition
whose rates have seen a rise in both the United States and worldwide in recent
decades. Numerous studies have been done to understand obesity, and through the
use of GWAS researchers have been able to find multiple genetic factors that
can contribute to obesity in mammals. One proposed cause of obesity are genetic
impacts on cilia formation in the CNS, which causes downstream effects on food
intake and energy expenditure, causing obesity via overeating and decreased
activity.</p>
<p>In the first half of this thesis, I
describe a study, in collaboration with the Berbari Lab at IUPUI, that explored
the human chromosome 16:53801550-53808600 (GRCh37/hg19), an intron of the FTO
alpha-ketoglutarate Dependent Dioxygenase (<i>FTO</i>) gene for transcriptional
regulators that impact BMI and obesity. First, using control DNA, PCR, and
gel-electrophoresis, we created an assay for 44 primer sets (forward and
reverse) covering the genomic region. After optimizing the assay, we then
selected 111 human DNA samples across three weight groups (underweight, normal
weight, and obese) to sequence using the assay. The samples were selected from
subjects enrolled in the Walsh Lab FDP study. Sequencing was completed using
the Illumina MiSeq System, and sequenced results were viewed using the
Integrative Genomics Viewer (IGV) program. Variants that showed in the results
were analyzed across and within the weight groups, and their locations were
researched for previously known BMI or enhancer activity using online genome
browsers Ensembl and UCSC Genome Browser.<sup></sup></p>
<p>The results of this study revealed
two SNPs, rs8055197 and rs11642015, that provided the best correlation with the
weight categories among the samples. These results were consistent with
literature that previously linked these single-nucleotide polymorphisms (SNPs)
to obesity, particularly in relation to genes that are regulated by <i>FTO</i>
(<i>CUX1</i>, <i>POMC</i>, and <i>IRX3/5</i>). Both SNPs lie within areas that
show high enhancer activity in neural crest cells, important cells for cilia
formation. Although there were SNPs in high LD within both regions, these two SNPs
were chosen due to their homologous variant locations within the mouse genome (rs8055197 - GRCm38/mm10 8:91376305; rs11642015 - GRCm38/mm10
8:91375651), which provides a means of testing this obesity correlation, with a
proposed enhancer relationship through <i>FTO</i>, in mouse models. </p>
<p>In the second half of this thesis, I explored new methods
for quantitatively defining natural hair color categories, and attempted to
find novel SNPs impacting hair color in a GWAS using the quantitative values as
phenotypes. In previous publications, the development and validation of the
HIrisPlex-S Prediction Tool for hair prediction was made using categorical hair
colors, which were defined and classified by individual researchers or lab
personnel. Using spectrophotometer measurements and HSV color values, we used a
machine-learning tool to objectively classify sample hair photos into natural
hair color classes. We then used this quantitative data as the input phenotype
for a GWAS, using both linear regression and linear mixed model regression, to
search for new genetic associations with these objectively defined hair color classes.
Lastly, we also measured correlations between these hair color phenotypes and a
SNP array consisting of all currently known pigment SNPs cited in recent
literature.</p>
<p>The results of this study showed that quantitative values
can be used as a means of classifying human hair colors. Both models used in
the GWAS highlighted previously known SNPs that contributed to quantitative hair
color. By utilizing the linear mixed model approach which has the ability to
generate more power due to the normalization of hidden population structure,
there was one near genome-wide significant SNP found that is currently not
linked with hair color, rs2037697 (<i>IQUB</i>), which showed strong associations
with light brown hair (p-value = 1.83192E-07), however this would need to be
confirmed with increased numbers to validate its association. </p>
<p>The results of the correlation analysis showed that SNPs
cited as having impacts on pigmentation (eye, skin, and hair) also show strong
associations with these objectively defined quantitative hair color classes and
these rankings may prove very useful as the field moves towards quantitative
hair color prediction.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12659294 |
| Date | 12 October 2021 |
| Creators | Racquel LeShawn Hopkins (9128195) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Quantitatively_Assessing_the_Genetics_of_Hair_Color_in_Addition_to_Identifying_Regulatory_Elements_Impacting_Body-Mass_Index_in_the_FTO_Gene/12659294 |
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