Indiana University-Purdue University Indianapolis (IUPUI) / Externally visible characteristics (EVCs) predicted from an unknown sample of DNA are particularly useful in forensics as they can provide information beyond that of an STR profile. Current EVCs which are highly studied and well-predicted include iris, hair, and skin color. Notably, models predicting iris color, such as IrisPlex, are the most accurate with up to ~95% accuracy; however, some inaccurate predictions occur, as is evidenced by the ~5%. Often, these are due to green or hazel eyes, which are frequently viewed as intermediate. Though, some of the inaccurate predictions are due to true-blue being predicted as brown and vice versa. Previous research has theorized the possibility of two SNPs, rs12913832 and rs1800407, acting as a functional haplotype affecting iris color. rs12913832 is recognized as the most predictive SNP for iris color and highly significant in other pigmentation phenotypes; presently, rs1800407 is the second-ranked SNP in the IrisPlex 6-SNP system. Both SNPs are highly variable in Europe, where the majority of variation in iris color originates. In the present study, we explore the SNP variation present in the genetic regions of OCA2-HERC2 as well as possible haplotypes. Our research centers around the functional haplotype and the addition of SNPs to the functional haplotype. In addition, three different ways of classifying the phenotype are assessed simultaneously. First, using a 4-point categorical phenotype—blue/blue grey, blue/green yellow, hazel/light brown, and dark brown. Second, calculating a continuous scale from a quantitative phenotype in which the percentage of each categorical color has been measured. Third, using the IrisPlex 6-SNP system to predict eye color and identify individuals which have been inaccurately predicted. Exploration of the SNP and haplotype variation resulted in two SNPs for both the categorical and quantitative phenotypes which were significantly correlated with hazel/light brown—rs1448484 and rs61335644, both as independent SNPs and when assessed in a haplotype with rs1800407-rs12913832. SNP rs1448484 has been associated with skin pigmentation previously and is located in a possible transcription factor binding site. SNP rs61335644 is not presently associated with pigmentation but is in complete LD with two SNPs in and around regulatory regions present in HERC2. Finally, the addition of rs1448484 and rs61335644 into the current IrisPlex 6-SNP system slightly improved each of the tested performance metrics for hazel/light brown and dark brown. Within the inaccurately predicted phenotypes, rs1800407 is confirmed to affect both inaccurately predicted groups and is the most significant SNP. Additionally, rs121918166, a missense variant in OCA2, is the second most significant SNP in true blue predicted as brown. Both SNPs were also the two most significant haplotypes with at least one allele being derived. Therefore, the next steps should include the addition of the functional haplotype and rs121918166 into the current IrisPlex model, and further testing of rs1448484 and rs61335644 on a molecular level. Consequently, the current IrisPlex model should also be reassessed on an independent test set using the 4-point categorical scale rather than the present 3-point scale.

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A comprehensive population study in the Middle East was conducted using different genetic markers in order to establish a wider genetic profile of the Middle Eastern populations. The main goal of this study was to analyze DNA from samples collected from different locations, and produce genetic motifs and patterns that could be used to identify and distinguish the target populations. This information will allow us to analyze the ancestry of these populations, their interactions through time and space, and the effects these interactions have on the populations' structure. We have collected around 1300 individual samples from different populations in the Middle East ranging from Oman in southern Arabia, to the West Bank and Gaza in Palestine. Our samples can be divided into two primary groups: 320 samples come from Oman; this region is important because of its geographical proximity to Yemen which is perceived as the historical area where the Arabs originated and 800 samples came from Palestine, a central region in the Middle East that connects Asia and Africa and was a passageway between the two continents through history. The samples collected from Oman have genealogy charts that were provided by the participants, while the samples from Gaza lack the genealogy charts. DNA was extracted from the samples, amplified using PCR technology, sequenced and genotyped using non-recombining genetic markers. Short Tandem Repeats(STR) were screened in the samples.A specific STR marker, DYS458, exhibited an alternative allele at repeat number 18 which was a 2 base pair shift identified as microvariant 18.2. The samples showed an unusually high frequency of microvariant 18.2.When microvariant 18.2 haplotype was combined with Modal Cohen Haplotype (MCH) motifs, we were able to infer some genetic characteristics about our samples. The Cohen Modal haplotype was used in combination with the results of the 18.2 DYS458 analyses to construct a snapshot of the Middle East, using NETWORK software to analyze the relationship within and between the samples from Oman, which is located in Southern Arabia, and the areas North of Arabia (Jordan, Syria, and Palestine), which is also known as the Levant.

Complement proteins play an important role in the body's immune processes and involve in the pathology of many diseases in human, such as major depressive disorder and schizophrenia. Understanding the genetics of those proteins is an important step toward unraveling their effects on psychiatric disorders.  The complement protein levels differ between neonates and adults. There are many studies investigating the genetic architecture of the complement proteins, but evidence about the genetics of neonatal complement proteins is scarce. In this study, I investigated the SNPs and haplotypes that are associated with the five complement proteins, C1q, C3, C4, CFB, and CFH among newborns. I also compared the effects of the identified SNPs among neonates and adults. This study uses 75 samples from Swedish newborns whose blood were primarily collected for Phenylketonuria screening. Genotype data and protein levels were measured from dried blood spots. To investigate the genetics of the complement proteins, I first conducted single SNPs association analyses. The single SNPs used in this study was derived from previous research of European adult samples and has been shown to be significantly associated with the target protein. Then, after imputing the SNPs for the MHC region, I conducted haplotype analysis in the MHC region for the five complement proteins. Finally, I compared the effects of the variants identified in the single SNPs association analysis with the effects that were reported for adult protein levels in previous studies. The results of single SNP association analysis showed that among the 14 SNPs that were associated with adult protein levels, SNP rs4151669 that associated with complement factor B (CFB) was significantly associated with the target protein in the neonatal population. Some SNPs (rs8283, rs4151669 and rs10737680) may have opposite effects in the two populations. This study found 86 haplotypes potentially associated with the complement proteins. Among them, the haplotype “H840” which located at chr6:32594217-32597172 was significantly associated with five complement proteins. This study provides evidence for the genetic component of the 5 complement protein levels among neonates. The results also suggested that the genetic influence of the complement protein among adult and neonatal population are different.

Alleles, genotypes and haplotypes (combinations of alleles) have been widely used in gene-disease association studies. More recently, association studies using diplotypes (haplotype pairs on homologous chromosomes) have become increasingly common. This article reviews the rationale of the four types of association analyses and discusses the situations in which diplotype-based analyses are more powerful than the other types of association analyses. Haplotype-based association analyses are more powerful than allele-based association analyses, and diplotype-based association analyses are more powerful than genotype-based analyses. In circumstances where there are no interaction effects between markers and where the criteria for Hardy-Weinberg Equilibrium (HWE) are met, the larger sample size and smaller degrees of freedom of allele-based and haplotype-based association analyses make them more powerful than genotype-based and diplotype-based association analyses, respectively. However, under certain circumstances diplotype-based analyses are more powerful than haplotype-based analysis.

The completion of the human genome project in 2003 paved the way for studies to better understand and catalog variation in the human genome. The International HapMap Project was started in 2002 with the aim of identifying genetic variation in the human genome and studying the distribution of genetic variation across populations of individuals. The information collected by the HapMap project will enable researchers in associating genetic variations with phenotypic variations. Single Nucleotide Polymorphisms (SNPs) are loci in the genome where two individuals differ in a single base. It is estimated that there are approximately ten million SNPs in the human genome. These ten million SNPS are not completely independent of each other - blocks (contiguous regions) of neighboring SNPs on the same chromosome are inherited together. The pattern of SNPs on a block of the chromosome is called a haplotype. Each block might contain a large number of SNPs, but a small subset of these SNPs are sufficient to uniquely dentify each haplotype in the block. The haplotype map or HapMap is a map of these haplotype blocks. Haplotypes, rather than individual SNP alleles are expected to effect a disease phenotype. The human genome is diploid, meaning that in each cell there are two copies of each chromosome - i.e., each individual has two haplotypes in any region of the chromosome. With the current technology, the cost associated with empirically collecting haplotype data is prohibitively expensive. Therefore, the un-ordered bi-allelic genotype data is collected experimentally. The genotype data gives the two alleles in each SNP locus in an individual, but does not give information about which allele is on which copy of the chromosome. This necessitates computational techniques for inferring haplotypes from genotype data. This computational problem is called the haplotype inference problem. Many statistical approaches have been developed for the haplotype inference problem. Some of these statistical methods have been shown to be reasonably accurate on real genotype data. However, these techniques are very computation-intensive. With the international HapMap project collecting information from nearly 10 million SNPs, and with association studies involving thousands of individuals being undertaken, there is a need for more efficient methods for haplotype inference. This dissertation is an effort to develop efficient perfect phylogeny based combinatorial algorithms for haplotype inference. The perfect phylogeny haplotyping (PPH) problem is to derive a set of haplotypes for a given set of genotypes with the condition that the haplotypes describe a perfect phylogeny. The perfect phylogeny approach to haplotype inference is applicable to the human genome due to the block structure of the human genome. An important contribution of this dissertation is an optimal O(nm) time algorithm for the PPH problem, where n is the number of genotypes and m is the number of SNPs involved. The complexity of the earlier algorithms for this problem was O(nm^2). The O(nm) complexity was achieved by applying some transformations on the input data and by making use of the FlexTree data structure that has been developed as part of this dissertation work, which represents all the possible PPH solution for a given set of genotypes. Real genotype data does not always admit a perfect phylogeny, even within a block of the human genome. Therefore, it is necessary to extend the perfect phylogeny approach to accommodate deviations from perfect phylogeny. Deviations from perfect phylogeny might occur because of recombination events and repeated or back mutations (also referred to as homoplasy events). Another contribution of this dissertation is a set of fixed-parameter tractable algorithms for constructing near-perfect phylogenies with homoplasy events. For the problem of constructing a near perfect phylogeny with q homoplasy events, the algorithm presented here takes O(nm^2+m^(n+m)) time. Empirical analysis on simulated data shows that this algorithm produces more accurate results than PHASE (a popular haplotype inference program), while being approximately 1000 times faster than phase. Another important problem while dealing real genotype or haplotype data is the presence of missing entries. The Incomplete Perfect Phylogeny (IPP) problem is to construct a perfect phylogeny on a set of haplotypes with missing entries. The Incomplete Perfect Phylogeny Haplotyping (IPPH) problem is to construct a perfect phylogeny on a set of genotypes with missing entries. Both the IPP and IPPH problems have been shown to be NP-hard. The earlier approaches for both of these problems dealt with restricted versions of the problem, where the root is either available or can be trivially re-constructed from the data, or certain assumptions were made about the data. We make some novel observations about these problems, and present efficient algorithms for unrestricted versions of these problems. The algorithms have worst-case exponential time complexity, but have been shown to be very fast on practical instances of the problem.

The genetic structure of juvenile green turtles (Chelonia mydas) foraging on the east coast of central Florida is not well understood, nor has it been examined over time. In the last three decades, the dramatic increase in the number of green sea turtle nests in Florida, in association with other population parameters, has led to this species being down-listed under the Endangered Species Act from “endangered” to “threatened” in the northwest Atlantic. However, it was unclear if the exponential growth in Florida nest numbers had any influence on the genetic structure of juveniles in nearby foraging aggregations. To understand this potential impact mixed-stock analysis was conducted using mitochondrial DNA fragments that were over 800 base pairs long on samples taken from juveniles captured from 2002-2005 and 2016-2018 in the central Indian River Lagoon and Trident Submarine Basin in Port Canaveral. Results indicate the sampled foraging sites are genetically distinct habitats. In both sites, recruitment from Florida nesting beaches remained low despite increases in nesting while contributions from rookeries in Costa Rica and Mexico dominated both foraging aggregations across time. Haplotype diversity and nucleotide diversity decreased at both foraging sites over time. The foraging sites shared the two most frequently occurring haplotypes, but also had haplotypes that were unique to the site or sample period. Our results highlight the need for broader sampling of rookeries and foraging aggregations to understand the impacts of nesting increases in one rookery on juvenile diversity. Future studies should include all life stages of green turtles to enhance understanding of both the census population and effective population to better inform conservation policies necessary for a continued recovery.

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