Nucleomorphs are characterized by one of the smallest genomes known (551 and 380 kb) and as noticed for microsporidia, some genomes are noticeable reduced in length compared to other eukaryotes due to a virtual lack of non-coding DNA. [ 32 ]  The most interesting factor is represented by the coexistence of those small nuclei inside of a cell that contains another nucleus that never experienced such genome reduction. Moreover, even if the host cells have different volumes from species to species and a consequent variability in genome size, the nucleomorph remain invariant denoting a double effect of selection within the same cell.


Genome skimming  is a sequencing approach that uses low-pass, shallow  sequencing  of a  genome  (up to 5%), to generate fragments of DNA, known as  genome skims . [ 1 ] [ 2 ]  These genome skims contain information about the high-copy fraction of the genome. [ 2 ]  The high-copy fraction of the genome consists of the  ribosomal DNA , plastid genome ( plastome ), mitochondrial genome ( mitogenome ), and nuclear repeats such as  microsatellites  and  transposable elements . [ 3 ]   It employs high-throughput,  next generation sequencing  technology to generate these skims. [ 1 ]  Although these skims are merely 'the tip of the genomic iceberg',  phylogenomic analysis  of them can still provide insights on  evolutionary history  and  biodiversity  at a lower cost and larger scale than traditional methods. [ 2 ] [ 3 ] [ 4 ]  Due to the small amount of DNA required for genome skimming, its methodology can be applied in other fields other than genomics. Tasks like this include determining the traceability of products in the food industry, enforcing international regulations regarding biodiversity and biological resources, and  forensics . [ 5 ]


In addition to the assembly of the smaller organellar genomes, genome skimming can also be used to uncover conserved  ortholog  sequences for  phylogenomic studies . In phylogenomic studies of multicellular  pathogens , genome skimming can be used to find  effector genes , discover  endosymbionts  and characterize  genomic variation . [ 6 ]


The  Internal transcribed spacers (ITS)  are non-coding regions within the 18-5.8-28S rDNA in eukaryotes and are one feature of rDNA that has been used in genome skimming studies. [ 7 ]  ITS are used to detect different species within a  genus , due to their high inter-species variability. [ 7 ]  These have low individual variability, preventing the identification of distinct strains or individuals. [ 7 ]  They are also present in all  eukaryotes , have a high evolution rate and has been used in  phylogenetic analysis  between and across species. [ 7 ]


When targeting nuclear rDNA, it is suggested that a minimum final  sequencing depth  of 100X is achieved, and sequences with less than 5X depth are masked. [ 1 ]


The  plastid genome , or plastome, has been used extensively in identification and evolutionary studies using genome skimming due to its high abundance within plants (~3-5% of cell DNA), small size, simple structure, greater conservation of gene structure than nuclear or mitochondrial genes. [ 8 ] [ 9 ]  Plastids studies have previously been limited by the number of regions that could be assessed in traditional approaches. [ 9 ]  Using genome skimming, the sequencing of the entire plastid genome, or plastome, can be done at a fraction of the cost and time required for typical sequencing approaches like  Sanger sequencing . [ 3 ]  Plastomes have been suggested as a method to replace traditional  DNA barcodes  in plants, [ 3 ]  such as the  rbcL  and  matK  barcode genes. Compared to the typical DNA barcode, genome skimming produces plastomes at a tenth of the cost per base. [ 5 ]  Recent uses of genome skims of plastomes have allowed greater resolution of phylogenies, higher differentiation of specific groups within taxa, and more accurate estimates of biodiversity. [ 9 ]  Additionally, the plastome has been used to compare species within a genus to look at evolutionary changes and diversity within a group. [ 9 ]


When targeting plastomes, it is suggested that a minimum final sequencing depth of 30X is achieved for single-copy regions to ensure high-quality assemblies.  Single nucleotide polymorphisms (SNPs)  with less than 20X depth should be masked. [ 1 ]


The  mitochondrial genome , or mitogenome, is used as a  molecular marker  in a great variety of studies because of its  maternal inheritance , high copy-number in the cell, lack of  recombination , and high mutation rate. It is often used for phylogenetic studies as it is very uniform across metazoan groups, with a circular, double-stranded DNA molecule structure, about 15 to 20 kilobases, with 37 ribosomal RNA genes, 13 protein-coding genes, and 22 transfer RNA genes.  Mitochondrial barcode sequences, such as COI,  NADH2 ,  16S rRNA , and  12S rRNA , can also be used for taxonomic identification. [ 10 ]  The increased publishing of complete   mitogenomes  allows for inference of robust phylogenies across many taxonomic groups, and it can capture events such as gene rearrangements and positioning of mobile genetic elements. Using genome skimming to assemble complete mitogenomes, the phylogenetic history and biodiversity of many organisms can be resolved. [ 4 ]


When targeting mitogenomes, there are no specific suggestions for minimum final sequencing depth, as mitogenomes are more variable in size and more variable in complexity in plant species, increasing the difficulty of assembling repeated sequences. However, highly conserved coding sequences and nonrepetitive flanking regions can be assembled using  reference-guided assembly . Sequences should be masked similarly to targeting plastomes and nuclear ribosomal DNA. [ 1 ]


Nuclear repeats in the genome are an underused source of phylogenetic data. When the nuclear genome is sequenced at 5% of the genome, thousands of copies of the nuclear repeats will be present. Although the repeats sequenced will only be representative of those in the entire genome, it has been shown that these sequenced fractions accurately reflect genomic abundance. These repeats can be clustered  de novo  and their abundance is estimated. The distribution and occurrence of these repeat types can be phylogenetically informative and provide information about the evolutionary history of various species. [ 1 ]


Low-copy DNA can prove useful for evolution developmental and phylogenetic studies. [ 11 ]  It can be mined from high-copy fractions in a number of ways such as developing  primers  from databases that contain conserved  orthologous genes , single‐copy conserved orthologous gene, and shared copy genes. [ 11 ]  Another method is looking for novel probes that target low-copy genes using transcriptomics via Hyb-Seq. [ 11 ]  While nuclear genomes assembled using genome skims are extremely fragmented, some low-copy single-copy nuclear genes can be successfully assembled. [ 12 ]


Previous methods of trying to recover degraded DNA were based on  Sanger sequencing  and relied on large intact DNA templates and were affected by contamination and method of preservation. Genome skimming, on the other hand, can be used to extract genetic information from preserved species in  herbariums  and museums, where the DNA is often very degraded, and very little remains. [ 4 ] [ 13 ]  Studies in plants show that DNA as old as 80 years and with as little as 500 pg of degraded DNA, can be used with genome skimming to infer genomic information. [ 13 ]  In  herbaria , even with low yield and low-quality DNA, one study was still able to produce "high-quality complete chloroplast and ribosomal DNA sequences" at a large scale for downstream analyses. [ 14 ]


In field studies, invertebrates are stored in ethanol which is usually discarded during DNA-based studies. [ 15 ]  Genome skimming has been shown to detect the low quantity of DNA from this ethanol-fraction and provide information about the biomass of the specimens in a fraction, the microbiota of outer tissue layers and the gut contents (like prey) released by the vomit reflex. [ 15 ]  Thus, genome skimming can provide an additional method of understanding  ecology  via low copy DNA. [ 15 ]


DNA extraction  protocols will vary depending on the source of the sample (i.e. plants, animals, etc.). The following DNA extraction protocols have been used in genome skimming:


Library preparation  protocols will depend on a variety of factors: organism, tissue type, etc. In the cases of preserved specimens, specific library preparation protocols modifications may have to be made. [ 1 ]  The following library preparation protocols have been used in genome skimming:


Sequencing  with short reads or long reads will depend on the target genome or genes.  Microsatellites  in nuclear repeats require longer reads. [ 23 ]  The following sequencing platforms have been used in genome skimming:


The Illumina MiSeq platform has been chosen by certain researchers for its long read length for short reads. [ 6 ]


After genome skimming, high-copy organellar DNA can be  assembled  with a reference guide or assembled  de novo . High-copy nuclear repeats can be clustered  de novo . [ 1 ]  Assemblers chosen will depend on the target genome and whether short or long reads are used. The following tools have been used to assemble genomes from genome skims:


Annotation  is used to identify genes in the genome assemblies. The annotation tool chosen will depend on the target genome and the target features of that genome. The following annotation tools have been used in genome skimming to annotate organellar genomes:


The assembled sequences are  globally aligned , and then  phylogenetic trees  are inferred using phylogenetic reconstruction software. The software chosen for phylogeny reconstruction will depend on whether a  Maximum Likelihood (ML) ,  Maximum Parsimony (MP) , or  Bayesian Inference (BI)  method is appropriate. The following phylogenetic reconstruction programs have been used in genome skimming:


Various protocols, pipelines, and bioinformatic tools have been developed to help automate the downstream processes of genome skimming.


Hyb-Seq is a new protocol for capturing low-copy nuclear genes that combines target enrichment and genome skimming. [ 29 ]  Target enrichment of the low-copy loci is achieved through designed enrichment probes for specific single-copy exons, but requires a nuclear draft genome and transcriptome of the targeted organism. The target-enriched libraries are then sequenced, and the resulting reads processed, assembled, and identified. Using off-target reads,  rDNA cistrons  and complete plastomes can also be assembled. Through this process, Hyb-Seq is able to produce genome-scale datasets for  phylogenomics .


GetOrganelle is a toolkit that assembles organellar genomes uses genome skimming reads. [ 30 ]  Organelle-associated reads are recruited using a modified “baiting and iterative mapping” approach. The reads  aligning  to the target genome, using Bowtie2, [ 31 ]  are referred to as “seed reads”. The seed reads are used as “baits” to recruit more organelle-associated reads via multiple iterations of extension. The read extension algorithm uses a  hashing approach , where the reads are cut into substrings of certain lengths, referred to as “words”. At each extension iteration, these “words” are added to a  hash table , referred to as a “baits pool”, which dynamically increases in size with each iteration. Due to the low sequencing coverage of genome skims, non-target reads, even those with high sequence similarity to target reads, are largely not recruited. Using the final recruited organellar-associated reads, GetOrganelle conducts a  de novo   assembly , using  SPAdes . [ 32 ]  The  assembly graph  is filtered and untangled, producing all possible paths of the graph, and therefore all configurations of the circular organellar genomes.


Skmer is an assembly-free and alignment-free tool to compute genomic distances between the query and reference genome skims. [ 33 ]  Skmer uses a 2 stage approach to compute these distances. First, it generates k-mer frequency profiling using a tool called JellyFish [ 34 ]  and then these k-mers are converted into hashes. [ 33 ]  A random subset of these hashes are selected to form a so-called "sketch". [ 33 ]  For its second stage, Skmer uses Mash [ 35 ]  to estimate the  Jaccard index  of two of these sketches. [ 33 ]  The combination of these 2 stages is used to estimate the evolutionary distance. [ 33 ]


Geneious  is an integrative software platform that allows users to perform various steps in bioinformatic analysis such as  assembly ,  alignment , and  phylogenetics  by incorporating other tools within a GUI based platform. [ 18 ] [ 28 ]


PhyloHerb  is a bioinformatic pipeline write in   python . It uses built-in database or user specified reference to extract orthologous sequences from  plastid ,  mitochondrial  and  nuclear ribosomal regions  using a BLAST search. [ 36 ]


Although genome skimming is usually chosen as a cost-effective method to sequence organellar genomes, genome skimming can be done  in silico  if (deep) whole-genome sequencing data has already been obtained. Genome skimming has been demonstrated to simplify organellar genome assembly by subsampling the reads of the nuclear genome via  in silico  genome skimming. [ 37 ] [ 38 ]  Since the organellar genomes will be high-copy in the cell,  in silico  genome skimming essentially filters out nuclear sequences, leaving a higher organellar to nuclear sequence ratio for assembly, reducing the complexity of the assembly paradigm.  In silico  genome skimming was first done as a proof-of-concept, optimizing the parameters for read type, read length, and sequencing coverage. [ 1 ]


Other than the current uses listed above, genome skimming has also been applied to other tasks, such as quantifying pollen mixtures, [ 19 ]  monitoring and conservation of certain populations. [ 39 ]  Genome skimming can also be used for variant calling, to examine  single nucleotide polymorphisms  across a species. [ 22 ]


Genome skimming is a cost-effective, rapid and reliable method to generate large shallow datasets, [ 5 ]  since several datasets (plastid, mitochondrial, nuclear) are generated per run. [ 3 ]  It is very simple to implement, requires less lab work and optimization, and does not require  a priori  knowledge of the organism nor its genome size. [ 3 ]  This provides a low-risk avenue for biological inquiry and hypothesis generation without a huge commitment of resources. [ 6 ]


Genome skimming is an especially advantageous approach regarding cases where the genomic DNA may be old and degraded from chemical treatments, such as specimens from herbarium and museum collections, [ 4 ]  a largely untapped genomic resource. Genome skimming allows for the molecular characterization of rare or extinct species. [ 5 ]  The preservation processes in ethanol often damage the genomic DNA, which hinders the success of standard PCR protocols [ 3 ]  and other amplicon-based approaches. [ 5 ]  This presents an opportunity to sequence samples with very low DNA concentrations, without the need for DNA enrichment or amplification. Library preparation for specific to genome skimming has been shown to work with as low as 37 ng of DNA (0.2 ng/ul), 135-fold less than recommended by Illumina. [ 1 ]


Although genome skimming is mostly used to extract high-copy plastomes and mitogenomes, it can also provide partial sequences of low-copy nuclear sequences. These sequences may not be sufficiently complete for phylogenomic analysis, but can be sufficient for designing PCR primers and probes for hybridization-based approaches. [ 1 ]


Genome skimming is not dependent on any specific primers and remains unaffected by gene rearrangements. [ 4 ]


Genome skimming scratches the surface of the genome, so it will not suffice for biological questions that require gene prediction and annotation. [ 6 ]  These downstream steps are required for deep and more meaningful analyses.


Although plastid genomic sequences are abundant in genome skims, the presence of mitochondrial and nuclear pseudogenes of plastid origin can potentially pose issues for plastome assemblies. [ 1 ]


A combination of sequencing depth and read type, as well as genomic target (plastome, mitogenome, etc.), will influence the success of single-end and paired-end assemblies, so these parameters must be carefully chosen. [ 1 ]


Both the wet-lab and the bioinformatics parts of genome skimming have certain challenges with scalability. Although the cost of sequencing in genome skimming is affordable at $80 for 1 Gb in 2016, the library preparation for sequencing is still very expensive, at least ~$200 per sample (as of 2016). Additionally, most library preparation protocols have not been fully automated with robotics yet. On the bioinformatics side, large complex databases and automated workflows need to be designed to handle the large amounts of data resulting from genome skimming. The automation of the following processes need to be implemented: [ 40 ]


Some of these scalability challenges have already been implemented, as shown above in the "Tools and Pipelines" section.


In the fields of  bioinformatics  and  computational biology ,  Genome survey sequences (GSS)  are  nucleotide sequences  similar to  expressed sequence tags  (ESTs) that the only difference is that most of them are  genomic  in origin, rather than  mRNA . [ 1 ]


Genome survey sequences are typically generated and submitted to  NCBI  by labs performing  genome sequencing  and are used, amongst other things, as a framework for the mapping and sequencing of genome size pieces included in the standard  GenBank  divisions. [ 1 ]


Genome survey sequencing is a new way to map the genome sequences since it is not dependent on  mRNA . Current genome sequencing approaches are mostly high-throughput shotgun methods, and GSS is often used on the first step of sequencing. GSSs can provide an initial global view of a genome, which includes both coding and  non-coding DNA  and contain repetitive section of the genome unlike  ESTs . For the estimation of repetitive sequences, GSS plays an important role in the early assessment of a sequencing project since these data can affect the assessment of sequences coverage, library quality and the construction process. [ 2 ]  For example, in the estimation of dog genome, it can estimate the global parameters, such as neutral mutation rate and repeat content. [ 3 ]


GSS is also an effective way to large-scale and rapidly characterizing genomes of related species where there is only little gene sequences or maps. [ 4 ]  GSS with low coverage can generate abundant information of gene content and putative regulatory elements of comparative species. [ 5 ]  It can compare these genes of related species to find out relatively expanded or contracted families. And combined with physical clone coverage, researchers can navigate the genome easily and characterize the specific genomic section by more extensive sequencing. [ 3 ]


The limitation of genomic survey sequence is that it lacks long-range continuity because of its fragmentary nature, which makes it harder to forecast gene and marker order. For example, to detect repetitive sequences in GSS data, it may not be possible to find out all the repeats since the repetitive genome may be longer than the reads, which is difficult to recognize. [ 2 ]


The GSS division contains (but is not limited to) the following types of data:


Random “single pass read” genome survey sequences is GSSs that generated along single pass read by random selection. Single-pass sequencing with lower fidelity can be used on the rapid accumulation of genomic data but with a lower accuracy. [ 6 ]  It includes  RAPD ,  RFLP ,  AFLP  and so on. [ 7 ]


Cosmid/BAC/YAC end sequences use  Cosmid / Bacterial artificial chromosome / Yeast artificial chromosome  to sequence the genome from the end side. These sequences act like very low copy plasmids that there is only one copy per cell sometimes. To get enough chromosome, they need a large number of E. coli culture that 2.5 - 5 litres may be a reasonable amount. [ 8 ]


Cosmid/BAC/YAC can also be used to get bigger clone of DNA fragment than vectors like plasmid and phagemid. A larger insert is often helpful for the sequence project in organizing clones.
 [ 9 ]


Eukaryotic proteins can be expressed by using YAC with posttranslational modification. [ 10 ]
BAC can’t do that, but BACs can reliably represent human DNA much better than YAC or cosmid. [ 11 ]


Exon trapped sequence is used to identify genes in cloned DNA, and this is achieved by recognizing and trapping carrier containing exon sequence of DNA. Exon trapping has two main features: First, it is independent of availability of the RNA expressing target DNA. Second, isolated sequences can be derived directly from clone without knowing tissues expressing the gene which needs to be identified. [ 12 ]  During slicing, exon can be remained in mRNA and information carried by exon can be contained in the protein. Since fragment of DNA can be inserted into sequences, if an exon is inserted into intron, the transcript will be longer than usual and this transcript can be trapped by analysis.


Alu repetitive element is member of Short Interspersed Elements (SINE) in mammalian genome. There are about 300 to 500 thousand copies of Alu repetitive element in human genome, which means one Alu element exists in 4 to 6 kb averagely. Alu elements are distributed widely in mammalian genome, and repeatability is one of the characteristics, that is why it is called Alu repetitive element. By using special Alu sequence as target locus, specific human DNA can be obtained from clone of TAC, BAC, PAC or human-mouse cell hybrid.


PCR is an approach used to clone a small piece of fragment of DNA. The fragment could be one gene or just a part of gene. PCR can only clone very small fragment of DNA, which generally does not exceed 10kbp.


Alu PCR is a "DNA fingerprinting" technique. This approach is rapid and easy to use. It is obtained from analysis of many genomic loci flanked by Alu repetitive elements, which are non-autonomous retrotransposons present in high number of copies in primate genomes. [ 13 ]  Alu element can be used for genome fingerprinting based on PCR, which is also called Alu PCR.


There are several ways to analyze the function of a particular gene sequence, the most direct method is to replace it or cause a  mutation  and then to analyze the results and effects. There are three method are developed for this purpose: gene replacement, sense and anti-sense suppression, and  insertional mutagenesis . Among these methods, insertional mutagenesis was proved to be very good and successful approach.


At first,  T-DNA  was applied for insertional mutagenesis. However, using  transposable element  can bring more advantages. Transposable elements were first discovered by  Barbara McClintock  in  maize  plants. She identified the first transposable genetic element, which she called the Dissociation (Ds) locus. [ 14 ]  The size of transposable element is between 750 and 40000bp. Transposable element can be mainly classified as two classes: One class is very simple, called insertion sequence (IS), the other class is complicated, called transposon. Transposon has one or several characterized genes, which can be easily identified. IS has the gene of transposase.


Transposon can be used as tag for a DNA with a know sequence. Transposon can appear at other locus through transcription or reverse transcription by the effect of nuclease. This appearance of transposon proved that genome is not statistical, but always changing the structure of itself.


There are two advantages by using transposon tagging. First, if a transposon is inserted into a gene sequence, this insertion is single and intact. The intactness can make tagged sequence easily to molecular analysis. The other advantage is that, many transposons can be found eliminated from tagged gene sequence when  transposase  is analyzed. This provides confirmation that the inserted gene sequence was really tagged by transposon. [ 15 ]


The following is an example of GSS file that can be submitted to GenBank: [ 16 ]


Genomic and medical data   refers to an area within  genetics  that concerns the recording,  sequencing  and analysis of an organism's  genome .


Genomics  is a concept that was first developed by  Fred Sanger  who first sequenced the complete  genome  of a  virus  and of a  mitochondrion . The term genomics was first coined in 1986 by Tom Roderick, a geneticist at the  Jackson Laboratory  in Maine, during a meeting about the mapping of the human genome.


The genome is the entire  DNA  content that is present within one cell of an organism. Experts in genomics strive to determine consummate  DNA sequences  and perform  genetic mapping  to help understand disease.  Genomics  involves the study of all genes at the DNA (deoxyribonucleic Acid),  mRNA  (messenger ribonucleic acid), and  proteome level  as well as the cellular or tissue level. [ 1 ]


OMOP : It is an acronym for Observational Medical Outcomes Partnership. The OMOP research program was initially established under Foundation for  NIH ) and created first version of OMOP  common data model . The common data model was able to accommodate observational data of different types (both claims and  electronic health records ). It has a single common infrastructure that can accommodate both of the types from different sources around the world. It has successfully developed and executed large-scale statistical analyses capable of enabling active drug safety surveillance across prescription medications. [ 2 ]


OHDSI : It stands for  Observational Health Data Sciences and Informatics  and was initiated in 2013. It is a multi-stakeholder, interdisciplinary collaborative that is striving to bring out the value of observational health data through large-scale analytics. The main objective of OHDSI is to establish a research community for observational health data sciences that enables active engagement across multiple disciplines spanning multiple stakeholder groups. [ 3 ]


OpenClinica : It is the world's most widely used,  open-source software  for clinical research. First released in 2005, OpenClinica is designed to meet the diverse needs of modern research environments. It is built as a lightweight, extensible, and modular application. The software is web based and users can access it with a standard web browser and internet connection. [ 4 ]


OpenEHR : It is an open standard specification in  health informatics  that describes the management and storage, retrieval and exchange of health data in electronic health records. In OpenEHR, all health data for a person is stored in a "one lifetime", vendor-independent, person-centered EHR. [ 5 ]


Clinical3PO:  clinical3po is an open source big data environment for the Veteran Affairs informatics and computing infrastructure, enables scalable markup of electronic health record events to be used for predictive analysis. [ 6 ]


Beacon Project : Beacon Project is an open web service that tests the willingness of international sites to share genetic data. It is being implemented on the websites of the world's top genomic research organizations. [ 7 ]


Matchmaker Exchange : Matchmaker Exchange is a federated network of databases whose goal is to find genetic causes of rare diseases by matching similar  phenotypic  and  genotypic  profiles.


BRCA Challenge : The BRCA Challenge aims to advance understanding of the genetic basis of breast and other cancers using data from around the world.


More and more people are getting their DNA sequenced, but the use of genetic data to inform medical decisions is lagging. More than a decade since the  Human Genome Project  was declared complete, fewer than 60 genetic variants are deemed worthy for use in clinical care, most for severe conditions in very young children. These genetic variants can guide medical decisions. Genomic data alone is not very useful, but needs to be examined in the right context in research and healthcare


Some health-care organizations are working out ways to collect evidence from ongoing care to improve patient outcomes. Such efforts systematically analyze clinical data from peer-reviewed literature and introduce selected findings into clinical care. Then they track and evaluate outcomes.


One of the best examples of such a 'learning health-care system' is the  Geisinger Health System , which serves 2.6 million patients in Pennsylvania. Once diagnosed with chronic conditions such as diabetes, heart failure or depression, patients' care and outcomes are monitored. Metrics such as hospitalization rates are used to recommend that certain individuals receive coordinated attention from a team of specialists including mental-health practitioners and nutritionists. Over three years, this reduced hospital admissions by 18% and seemed to reduce spending by 7%. [ 8 ]


Genomic counseling  is the process by which a person gets informed about his or her  genome  often in the setting of  elective genetic and genomic testing . In contrast to  genetic counseling , which focuses on  Mendelian diseases  and typically involves person-to-person communication with a genetic counselor or other medical genetics expert, genomic counseling is not limited to currently clinically relevant information.  It is often based on genomic information that is of interest for the informed person, such as increased risk for common complex disease that has actionable components (for example  diabetes  or  obesity ), genetically determined non-disease related traits (for example  baldness ), or recreational forms of information and  genetic genealogy  data. An individual's response to certain medications/drugs based on their pharmacogenomic profile may be provided.


The need for genomic counseling is currently driven by  personal genomics  companies, including  23andMe , Helix, and  Pathway Genomics . Given the wealth and complexity of information obtained by personal genomics tests, genomic counseling can require expertise from a wide range of experts, including physicians, geneticists, molecular biologists, evolutionary biologists, population geneticists, statisticians, or bioinformaticians. Access of consumers to complete personal genomes through cheap  full genome sequencing  is likely to exacerbate this problem in the near future. [ 1 ]  There are currently not enough genetic counselors and clinical geneticists to help patients interpret whole-genome sequencing results regarding health-relevant information, and research shows that primary-care physicians lack the knowledge and expertise to help patients understand even single-gene test results. [ 2 ] [ 3 ]


Typically, a family history is taken to more fully allow interpretation and incorporation of genomic data. Such an assessment is used to "identify and quantify" risk for inherited hereditary diseases. Whole genome sequencing and whole exome sequencing  [WES]  "may not provide full coverage of critical genes" and performing a risk assessment "allows the genetic team to generate a differential diagnosis and order more sensitive  genetic testing  if necessary". [ 4 ]


"Whole genomes have gone from becoming that discovery technique to being present in the clinic on a regular basis". [ 5 ]  Genomic counseling sessions may include briefings on "general genetic principles, modes of inheritance, family/individual specific risk assessment, an in-depth discussion of the diagnosis and natural history, potential testing options, and case management for the condition occurring… or for which they are at risk". The delivery of such information should be aimed to the patient's level of comprehension and take into account his or her culture and other personal context. [ 4 ]


Challenges in genetic counseling exists in both the decision and discussion of which incidental findings will be assessed and returned to patients. Patients may not be familiar with the condition and may be unable to make an informed opinion regarding the next steps in treatment. Genetic counselors and geneticists "already sit on interpretation panels for determining what warrants disclosure". This role will become "increasingly relevant for all genetic counselors" in order to ensure that all in the profession are "proficient in variant interpretation and understand the laboratory and bioinformatics processes" of such tests. [ 4 ]


Qualifications for who can practice genomic counseling and what the practice will entail is up for debate as genetic counseling begins to incorporate genome counseling. A few changes that may see changes include the roles of risk assessments, education, and informed consent.


Telegenetics is "videoconferencing for clinical genetics services" and is becoming an "increasingly utilized method of delivering genetic counseling to rural areas". [ 6 ]


Direct to consumer genetic testing (DTC GT) frequently utilizes telegenetics regardless of the client's "geographical location or ability to attend face-to-face sessions". [ 7 ]


There has been very limited study of patients receiving potentially actionable genomic based results or the utilization of genetic counselors in the online result delivery process. A randomized controlled trial on 199 patients with chronic disease each receiving eight personalized and actionable complex disease reports online. Primary study aims were to assess the impact of in-person genomic counseling on 1) causal attribution of disease risk, 2) personal awareness of disease risk, and 3) perceived risk of developing a particular disease. Of 98 intervention arm participants (mean age = 57.8; 39% female) randomized for in-person genomic counseling, 76 (78%) were seen. In contrast, control arm participants (n = 101; mean age = 58.5; 54% female) were initially not offered genomic counseling as part of the study protocol but were able to access in-person genomic counseling, if they requested it, 3-months post viewing of at least one test report and post-completion of the study-specific follow-up survey. A total of 64 intervention arm and 59 control arm participants completed follow-up survey measures. We found that participants receiving in-person genomic counseling had enhanced objective understanding of the genetic variant risk contribution for multiple complex diseases. Genomic counseling was associated with lowered participant causal beliefs in genetic influence across all eight diseases, compared to control participants. Our findings also illustrate that for the majority of diseases under study, intervention arm participants believed they knew their genetic risk status better than control arm subjects. Disease risk was modified for the majority during genomic counseling, due to the assessment of more comprehensive family history. In conclusion, for patients receiving personalized and actionable genomic results through a web portal, genomic counseling enhanced their objective understanding of the genetic variant risk contribution to multiple common diseases. These results support the development of additional genomic counseling interventions to ensure a high level of patient comprehension and improve patient-centered health outcomes. [ 8 ]


Licensure poses a conflict for the provision of virtual genomic counseling. Medical practitioners are typically licensed to practice in the state in which they reside. In order to practice across state borders, the practitioner must apply for licensure in that state as well. Obtaining multiple licenses is costly and time-consuming for practitioners who often don't want to deal with the hoops required to become duly licensed. Providers that do offer telemedicine or telegenetics services to out-of-state clients face "civil and criminal sanctions for practicing medicine without a license". Due to the limitation that licensure poses, clients who require the service of medical geneticists but live in a state with no licensed practitioners are required to travel to nearby states or go without the service. [ 9 ]


The  genomic evolution of birds  has come under scrutiny since the advent of rapid  DNA sequencing , as birds have the smallest  genomes  of the  amniotes  despite acquiring  highly derived   phenotypic  traits. Whereas  mammalian  and  reptilian  genomes range between 1.0 and 8.2  giga base pairs  (Gb), avian genomes have sizes between 0.91 Gb (black-chinned hummingbird,  Archilochus alexandri ) and 1.3 Gb (common ostrich,  Struthio camelus ). [ 1 ]  Avian genomes reflect the action of  natural selection  and are the basis of their phenotypes, reflected in their  morphology  and  behaviour , which have  evolved significantly  since their divergence from other  archosaurian ,  diapsid , and amniotic lineages.


Compared to other  tetrapod  lineages, birds have fewer repeated elements in their genomes, comprising only 4–10% of its extent, compared to 34–52% in mammals. The total size of avian  short interspersed nuclear elements (SINEs)  has been drastically reduced, averaging only 1.3 mega base pairs (Mb), compared to animals in similar lineages: The American alligator ( Alligator mississippiensis ), a fellow archosaur, averages 12.6 Mb SINEs, and the green sea turtle ( Chelonia mydas ), a non-archosaur diapsid, averages 34.9 Mb. These data suggest that the  last common ancestor  of modern birds already had a reduced number of SINEs. [ 1 ]


The mean size of  introns ,  intergenic sequences , and even  exons  is significantly reduced.  Mammalian  and reptilian introns have an average size of 4.3 kb and 3.1 kb respectively, whereas those of birds are only 2.1 kb long. Likewise, gene spacing averages 91 kb for mammals and 61 kb for reptiles, but only 49 kb in birds. Similar reductions have occurred in bats, suggesting that genome-size reduction is advantageous to  flying animals , allowing, for example, the rapid  regulation of gene expression  required for powered  flight . To rule out the possibility of genomic expansion in mammals and reptiles, Zhang et al. (2014) reconstructed successive deletion events in the avian ancestral genome compared to the reptilian. [ 1 ]


Birds have experienced the most genomic reductions of any vertebrate group. The early chromosomic fragmentation event that led to the appearance of  microchromosomes  in birds possibly contributed to this gene loss. [ 1 ]  These fragmentation events must have taken place in a  common ancestor  of most birds, since approximately every two out of three species studied have at least 30 pairs of  microchromosomes ,  2n  = 80 being the size of the average  karyotype  of birds (with the only exception the family  Falconidae , which are 2n = 6–12). [ 2 ]


Macrosynteny  studies have determined that, in  vocal learner birds , genes have undergone a deeper rearrangement along their corresponding chromosomes than those of  non-vocal learner birds . In addition to that,  microsynteny  studies revealed that birds possess a higher number of  orthologous genes  that maintain  synteny . This proves that gene order along chromosomes is more conserved in birds than in other animal groups. A clear example are genes coding for  haemoglobin   subunits . These genes are easily duplicated and lost. As a consequence, there are huge differences regarding the number and relative position of the genes of alpha-hemoglobins and beta-hemoglobins in mammals. In birds, that is not the case. Both position and number of these genes are highly conserved among them.


Birds'  point mutation  rate (1.9 × 10 −3  mutations per site per Ma) is smaller than that of mammals (2.7 × 10 −3  mutations per site per Ma). This rate is also smaller among  aequornithes  (water birds) than that of  telluraves  (land birds). In this last group,  birds of prey  have the smallest mutation rate, and  songbirds  have the highest. These rates are consistent with the broad distribution of birds in different  environments  and the phenotypic changes in response to  selective pressures  exerted by the  ecological niches  they occupy. [ 1 ]


The presence of functional restrictions in genome self-regulation can be studied by comparing the genomes of species whose  last common ancestor  is more ancient. It is known that, approximately, 7.5% of bird genome is comprised by  highly conserved elements  (HCEs). Of those HCEs, 12.6% are directly involved in protein coding genes functionality. Non-coding HCEs that are bird specific (not found in mammals) happen to be related with the regulation of the activity of  transcription factors  related to  metabolism . In comparison, mammal HCEs are related to controlling  cell signalling , development, and  response to stimuli .


The rate of genetic variation is not homogeneous across the genome. This can be assayed using  Ka/Ks ratio  studies (also known as dN/dS) to estimate the balance between neutral mutations, purifying mutations, and beneficial mutations.


In birds,  Z-chromosome  genes have the highest variability, perhaps due to the low gene density in the Z-chromosome. Genetic variability is higher in  macrochromosomes  than in  microchromosomes , perhaps related to the lower  recombination  frequency of the latter.


While genes that mediate the development of the  central nervous system  vary most rapidly in mammals, the genes that mediate morphological  development  undergoes the most rapid change in avian genomes.


The avian ability to learn songs has appeared independently at least twice: once in the ancestor of  hummingbirds  and another in the common ancestor of  songbirds  and  parrots . These lineages possess a number of neuronal circuits not found in lineages unable to learn songs. A dN/dS analysis showed conserved evolution in 227 genes, most of which are highly expressed in the regions of the brain that control singing. Furthermore, 20% of them seemed to be regulated by singing.


To  fly , bird ancestors had to undergo a series of changes at the molecular level that translate into changes at  morphological level . Approximately half of the genes involved in  ossification  are known to have been positively selected. Some relevant examples are  AHSG , that controls bone mineralization density, and  P2RX7 , which is associated to bone  homeostasis . Their action would be responsible for the differences observed between mammal and bird  bones .


Something similar occurs with the  respiratory system . In mammals, the total inner volume of  lungs  changes during  ventilation . However, this does not happen in birds. They make the air circulate through their lungs by contracting and expanding their  air sacs . Five genes are involved in this process in mammals and birds.


Feathers  are one of the most characteristic features of birds, along with the  beak . Feathers are formed of α- and β- keratins . Compared to reptiles and mammals,  α-keratin  protein family has been reduced in birds, whereas  β-keratins  has expanded enormously. Since every major bird lineage possess at least one protein of each of the six β-keratin groups, it can be said that their last common ancestor already possessed a large diversity of this kind of protein. 56% of β-keratins are feather-specific and can only be found in birds, whereas those that make up  scales  and  claws  can also be found in reptiles. The variety and number of copies of these genes seems to correlate with the bird's lifestyle, land birds having a larger variety, and the variety being larger still in  domestic birds .


Birds are also known for being toothless. This feature seems to be a consequence of several modifications and deletions which occurred in the exons of the genes implicated in the formation of  enamel  and  dentine . It is thought that the common ancestor of birds already lacked mineralized  teeth , and that later genome changes pushed the situation to the current status.


Also, birds have the best  vision system  known in vertebrates. They have a higher number of  photoreceptors , and most birds are  tetrachromats . [ 3 ]  The only exception are penguins, which have only three functional  opsin  genes (and hence are  trichromats ). This exception could be related to the aquatic lifestyle, since marine mammals have also lost either one or two cone opsin genes.


In many birds the right  ovary  has become non-functional. [ 4 ]  There are two ovary development-related genes,  MMP19  and  AKR1C3 , that have disappeared in birds. The fact that a high number of genes related to  spermatogenesis  are evolving fast (which does not happen in those related to ovogenensis) suggest that males undergo a stronger  selective pressure .


Rapid genomic sequencing data enabled research into the early evolution and divergence of bird groups and produced a more detailed  phylogenetic tree . Earlier phylogenetic reconstructions based on single genes proved inadequate due to  incomplete lineage sorting .


The low resolution of single-gene phylogenies, scarcity of coding DNA data, and  convergent evolution  confused early attempts to reconstruct avian phylogeny. For example, when base pairing errors occur,  DNA repair  mechanisms  favor GC pairs .


Genomic analysis suggests 36 bird lineages arose in a period of 10–15 My, relatively quickly on an evolutionary timescale. That period includes the massive  K-Pg extinction event  that freed many niches, allowing for the  adaptive radiation  and diversification of surviving species. The genomic analysis accords with  fossil record  data and with mammal evolution estimates. [ 5 ]


The cladogram from this analysis recovers  Accipitriformes  as a separate clade from  Falconiformes , which traditionally subsumed it.


Zebra finch


Medium ground finch


American crow


Golden-collared manakin


Rifleman


Budgerigar


Kea


Peregrine falcon


Red-legged seriema


Carmine Bee-eater


Downy Woodpecker


Rhinoceros hornbill


Bar-tailed trogon


Cuckoo roller


Speckled mousebird


Barn Owl


Bald eagle


White-tailed eagle


Turkey vulture


Dalmatian pelican


Little egret


Ibis


Great cormorant


Northern fulmar


Adelie penguin


Emperor penguin


Red-throated loon


White-tailed tropicbird


Sunbittern


Killdeer


Grey-crowned crane


Hoatzin


Hummingbirds


Common swift


Chuck-will's-widow


Bustard


Red-crested turaco


Common cuckoo


Brown mesite


Yellow-throated sandgrouse


Pigeon


American flamingo


Great crested grebe


Mallard


Tufted duck


Turkey


Chicken


White-throated tinamou


Common ostrich


Phylogenetic tree of birds according to  Jarvis et al.  2014 [ 5 ]  In the present representation, due to format restrictions, evolutive distances between different taxa are not directly presented.


The  hereditary material  i.e.  DNA  (deoxyribonucleic acid) of an organism is composed of a sequence of four  nucleotides  in a specific pattern, which encodes information as a function of their order. Genomic organization refers to the linear order of DNA elements and their division into  chromosomes . "Genome organization" can also refer to the 3D  structure of chromosomes  and the positioning of DNA sequences within the nucleus.


Organisms  have a vast array of ways in which their respective  genomes  are organized.  A comparison of the genomic organization of six major model organisms shows size expansion with the increase of  complexity  of the organism. There is a more than the 300-fold difference between the genome sizes of  yeast  and  mammals , but only a modest 4- to 5-fold increase in overall  gene  number (see the figure on the right). However, the ratio of coding to noncoding and repetitive sequences is indicative of the complexity of the genome: The largely "open" genomes of  unicellular   fungi  have relatively little noncoding  DNA  compared with the highly  heterochromatic  genomes of  multicellular  organisms.  [ citation needed ]


In particular, mammals have accumulated considerable repetitive elements and noncoding regions, which account for the majority of their DNA sequences (52% non-coding and 44%  repetitive DNA ). [ 1 ] [ 2 ]  Only 1.2% of the mammalian genome thus encodes for  protein  function. This massive expansion of repetitive and noncoding sequences in multicellular organisms is most likely due to the incorporation of invasive elements, such as DNA  transposons ,  retrotransposons , and other repetitive elements. [ 3 ]  The expansion of repetitive elements (such as  Alu sequences ) has even infiltrated the  transcriptional  units of the mammalian genome. This results in transcription units that are frequently much larger (30–200 kb), commonly containing multiple promoters and DNA repeats within untranslated  introns . [ citation needed ]


The vast expansion of the genome with noncoding and repetitive DNA in higher  eukaryotes  implies more extensive  epigenetic  silencing mechanisms. Studies of the genomic organization are thought to be the future of genomic  medicine , which will provide the opportunity for personalized  prognoses  in clinics. [ 4 ]


Genomic phylostratigraphy  is a novel  genetic   statistical  method developed in order to date the origin of specific genes by looking at its  homologs  across  species . It was first developed by  Ruđer Bošković Institute  in  Zagreb ,  Croatia . [ 1 ]  The system links genes to their  founder gene , allowing us to then determine their age. This could help us better understand many evolutionary processes such as patterns of gene birth throughout evolution, or the relationship between the age of a transcriptome throughout embryonic development. Bioinformatic tools like  GenEra  have been developed to calculate relative gene ages based on genomic phylostratigraphy. [ 2 ]


This technique relies on the assumption that the diversity of the  genome  is not only due to  gene duplications  but also to continuous frequent de novo gene births. These genes (called "founder genes") would form from  non-genic   DNA sequences , as well as from changes in reading frame (or other ways of arising from within existing genes), or even from very rapid evolution of the  protein  that would modify the sequence beyond recognition. [ 3 ]  These new genes would at first have high evolutionary rates that would then slow down with time, allowing us to recognise their lineage in their descendants. [ 1 ]  The founder genes can then be put in a specific  phylostratum . The  phylostratum  is represented as the clade that includes all the genes that derive from the same founder gene, signifying that this gene was formed in the common ancestor of this clade (e.g. Arthropoda, Mammalia, Metazoa, etc.). Positioning these founder genes and their descendants on different phylostrata can allow us to age them. This can then be used to analyse the origin of certain functions of proteins and developmental processes on a macroevolutionary scale, by observing connections between certain genes as well.


The original method for genomic phylostratigraphy involves the use of a  BLAST   sequence similarity search  with a 10 −3  E-value cut off. The genes deemed similar enough in sequence are gathered and the  clade  englobing all the  taxa  represented by those genes is determined. This clade then becomes the phylostratum of these genes. Modern implementations replace BLAST for DIAMOND since it is orders of magnitude faster  [ 4 ]  and have refined this process to account for sequence contamination, horizontal gene transfer and event for homology detection failure. [ 2 ]  By determining the  common ancestor  of this clade, we can hence give an age to the founder gene and all its descendants. Applying the process on a genome-wide scale can then allow us to detect patterns of founder genes births and infer the role of certain genes involved in clade-specific developmental processes and physiological pathways, and the origin of those traits. The developers of the method gave in the original paper [ 1 ]  an example how to exploit this system in practice using  Drosophila . They gathered 13,000 genes for which they determined the founder genes, regrouping them in their respective phylostrata. They also segregated the families of genes depending on whether they were mainly expressed in either of the three  germ layers  ( endoderm ,  mesoderm ,  ectoderm ). By studying the frequencies of expression of genes in those different phylostrata, they were able to hypothetically pinpoint the possible original formation of those germ layers to specific periods and ancestral organisms in evolutionary history.


Other studies have found that bursts of gene founder events are linked to important evolutionary innovations such as the emergence of  bilateral symmetry  in animals, the emergence of  multicellularity  in streptophyte algae, the  colonization of land by plants  or the emergence of  flowering plants . [ 2 ]


Since its invention, genomic phylostratigraphy has been regularly used by this research team [ 5 ]  as well as others, [ 6 ]  notably in an attempt to determine the origin of  cancer genes , seemingly showing a strong link between a peak in the formation of cancer genes and the transition to  multicellular organisms , a connection which had been previously hypothesised and is hence further supported by phylostratigraphy. As its use has grown, the method has been assessed and enhanced on multiple occasions, and programs that run it automatically and more efficiently have been developed. [ 2 ] [ 3 ]


One of the most prominent uses of genomic phylostratigraphy has been in inferring the correlation between phylogeny and developmental processes (often called the phylogeny-ontogeny correlation). Using genomic phylostratigraphy, to this day scientists have found a significant phylogeny-ontogeny correlation in animals, [ 7 ]  plants, [ 8 ]  fungi, [ 9 ] [ 10 ]  and even bacterial biofilms. [ 11 ]


Albeit it being now used frequently by the scientific community, genomic phylostratigraphy has also received some criticism for being too inaccurate for its measurements to be trustworthy. First of all, according to some authors precision lacks in the assumptions. [ 12 ] [ 13 ]  It is erroneous to assume for example that all species beyond the organism of focus share the same protein evolutionary rate, which isn't true as it varies depending on  cell cycle  speeds, leading to problems in setting the limits of BLAST error to englobe all proteins originated from the same founder gene. Another point is that the BLAST search assumes that protein evolutionary rates are constant at all its sites, which is also false. Lastly, it could be said that the model does not account correctly for gene duplications, as well as gene losses: the changes in evolutionary rates caused by gene duplications due to new functional changes would increase BLAST error rates, and gene loss in taxa distant to the one studied could lead to great underestimations in the calculated gene age and phylostratum of founder genes compared to their true values. However, rather than demanding to simply abandon the method, critics have been trying to work at refining it from its original state, by introducing other potential mathematical formulas or sequence searching tools, [ 14 ]  although the Ruđer Bošković Institute has replied to such criticism claiming their original approach was valid and did not need to be extensively revised. [ 15 ]  This debate is also included as part of the wider discussion on the importance of  de novo  gene births in creating genetic diversity, in which genomic phylostratigraphy supports that they do hold a strong effect. Gene founder events have also been proposed to not only represent  de novo  gene birth events but also significant changes in the protein sequence that change them beyond recognition with other related genes and thus acquire novel biological activities. [ 16 ]  Nonetheless, genomic phylostratigraphy assumes that the detection of such "founder events" entails evolutionary novelty. However, recent studies suggest that the detection of genes that are restricted to certain taxonomic groups (called taxonomically restricted genes) can be explained by homology detection failure, that is the inability of bioinformatic tools (BLAST or any other tool to detect sequence homology) to trace back homology when protein sequences are small and evolve at a fast rate, leading to gene untraceability. [ 17 ]  Thus, much of the proposed "founder events" that were originally detected by genomic phylostratigraphy could be reinterpreted as a methodological artifact due to the technological limitations of detecting more distantly related homologs. Luckily, homology detection failure can be explicitly tested. [ 17 ]  The recently developed tool  GenEra  is able to disentangle homology detection failure from potential gene founder events by explicitly testing for gene untraceability. [ 2 ]


Personality traits  are patterns of thoughts, feelings and behaviors that reflect the tendency to respond in certain ways under certain circumstances. [ 1 ]


Personality is influenced by genetic and environmental factors and associated with  mental health . [ 2 ]  Beside the environment factor, genetic variants can be detected for personality traits. These traits are  polygenic . Significant genetic variants are present for most of the behavioral traits. There is a consistency in detection of genetic variants and genomic association for traits derived from  pedigree . [ 3 ]


Personality trait research has been conducted both for humans and non-human animals like dogs.


For humans, the  Big Five personality traits , also known as the five-factor model (FFM) or the OCEAN model, is the prevailing model for  personality  traits. When  factor analysis  (a statistical technique) is applied to  personality survey  data, some words or questionnaire items used to describe aspects of personality are often applied to the same person. For example, someone described as  conscientious  is more likely to be described as "always prepared" rather than "messy". This theory uses descriptors of common language and therefore suggests five broad dimensions commonly used to describe the human  personality  and  psyche .


The five factors are:


The methods mostly used in  genomics  of personality traits' studies are two: analytic methods and non-analytic ones (such as questionnaires).


Analytical techniques that can be used to measure genomics of personality include:


Non-analytic methods mainly use approach is that of questionnaire, here discuss:


Questionnaires:


As  previously mentioned,   questionnaires  were often used as another tool to analyze the association of a behaviour to genetic variances.


In some studies, questionnaires were indirectly given at the owners of the animals involved in the experiment [ 3 ]  and in other studies they were directly given to the patients involved. [ 1 ] [ 4 ]  These questionnaires were:


Some examples of questions are: 'Do you often feel lonely?', to which individuals answered 'yes' (recorded as cases) or 'no' (controls); other questions are based on the quality of social interactions as: 'How often are you able to confide in someone close to you?' (cases were defined as those who answered 'Never or almost never', controls were defined as those who answered 'Almost daily'). [ 1 ]


Scientists demonstrated that most of personality traits cluster together and they also cluster with most  neuropsychiatric disorders  and are therefore related. In research, scientists used  linkage disequilibrium regression score  to investigate the correlation between personality traits and psychiatric disorder. According to LDSC, there is a positive correlation between  major depression disorder  and neuroticism and a small correlation between schizophrenia and  neuroticism ; these correlation have also been confirmed in twins studies. Also, neuroticism and openness show strong genetic correlation. Besides, scientists found that there is a positive correlation between first principal component and all psychiatric disorders, but first principal component showed negative correlation with conscientiousness and agreeableness. [ 1 ]


Personality features are highly linked with mental, social and physical outcome. For example, scientists realized that schizophrenia and bipolar disorders cluster with openness. Moreover, they demonstrated that  ADHD  shows the highest correlation with personality trait especially  extraversion . Recently, the negative correlation between neuroticism and loneliness was identified as well as a strong correlation between anxiety and neuroticism. Plus,  narcissism , psychopathy and  Machiavellianism  have association with low agreeableness. In general, neuroticism and other personality traits show negative correlation, while openness, extraversion, agreeableness and conscientiousness shows positive correlation. [ 2 ]


Example for the genes that they find to be correlative are:


Within 8p23.1, MTMR9 has  intronic  variant which has association with extraversion and also with neuroticism shows inverse association. Another one is 12q23.3, WSCD2 which is found for extraversion, by using GWAS, it had been shown that this locus has association with  bipolar disorder . In addition, L3MBTL2 is associated with both  schizophrenia  and  neuroticism . [ 2 ]  Another gene is DRD4 which has association with both ADHD and novelty seeking behavior. [ 10 ]


Parental (paternal and maternal) haplarithms are the outputs of  haplarithmisis  process. For instance, paternal haplarithm represents  chromosome  specific profile illuminating paternal  haplotype  of that chromosome (including  homologous recombination  between the two paternal homologous chromosomes) and the amount of those haplotypes. Importantly, the haplarithm signatures allow tracing back the genomic aberration to  meiosis  and/or  mitosis . [ 1 ]


Haplarithmisis  (Greek for  haplotype  numbering) is a conceptual process in  Genetics  that enables simultaneous haplotyping and  copy-number  profiling of DNA samples derived from cells. Haplarithmisis also reveals parental, segregation, and mechanistic origins of genomic anomalies. [ 1 ] [ 2 ]  The resulting profiles of haplarithmisis are called parental  haplarithms  (i.e. paternal  haplarithm  and maternal  haplarithm ).


Haplarithmisis enabled a new form of  preimplantation genetic diagnosis , by which segmental and full chromosome anomalies could not only be detected but also traced back to  meiosis  or  mitosis . [ 3 ] [ 4 ]


In its first application in basic genome research, haplarithmisis led to discovery of parental genome segregation, a phenomenon that causes the segregation of entire parental genomes in distinct blastomere lineages causing cleavage-stage chimerism and mixoploidy.  [ 5 ] [ 6 ]


In  genetics , a  haplotype block  is a region of an organism's  genome  in which there is little evidence of a history of  genetic recombination , and which contain only a small number of distinct  haplotypes . [ 1 ]  According to the haplotype-block model, such blocks should show high levels of  linkage disequilibrium  and be separated from one another by numerous recombination events. [ 2 ]  The boundaries of haplotype blocks cannot be directly observed; they must instead be inferred indirectly through the use of  algorithms . However, some evidence suggests that different algorithms for identifying haplotype blocks give very different results when used on the same data, [ 3 ]  though another study suggests that their results are generally consistent. [ 4 ]  The  National Institutes of Health  funded the  HapMap  project to catalog haplotype blocks throughout the  human genome . [ 5 ]


There are two main ways that the term "haplotype block" is defined: one based on whether a given genomic sequence displays higher linkage disequilibrium than a predetermined threshold, and one based on whether the sequence consists of a minimum number of  single nucleotide polymorphisms  (SNPs) that explain a majority of the common haplotypes in the sequence (or a lower-than-usual number of unique haplotypes). [ 6 ]  In 2001, Patil et al. [ 7 ]  proposed the following definition of the term: "Suppose we have a number of haplotypes consisting of a set of consecutive SNPs. A segment of consecutive SNPs is a block if at least α percent of haplotypes are represented more than once". [ 8 ]


In  genetics ,  HAPPY Mapping , first proposed by Paul H. Dear and Peter R. Cook in 1989, is a method used to study the  linkage  between two or more  DNA sequences . [ 1 ]  According to the  Single Molecule Genomics Group , it is  "Mapping based on the analysis of approximately HAPloid DNA samples using the PolYmerase chain reaction" . In  genomics , HAPPY mapping can be applied to assess the  synteny  and orientation of various DNA sequences across a particular genome - the generation of a "genomic" map.


As with  linkage mapping , HAPPY mapping relies on the differential probability of two or more DNA sequences being separated. In genetic mapping, the probability of a  recombination  event between two genetic  loci  on the same  chromosome  is directly proportional to the distance between them. HAPPY mapping replaces recombination with fragmentation - instead of relying on recombination to separate genetic loci, the entire genome is fragmented, for example, by radiation or mechanical shearing. If the DNA is broken on a random basis, the longer the distance between two DNA sequences, the higher the chances of it to break between the two, and vice versa.


HAPPY mapping retains the benefits of genetic mapping while removing some of the problems associated with recombination. I.e., the need for  polymorphism , and breeding. Also, recombination can be locale specific whereas breakage of genomic DNA by radiation or mechanical shearing seems to be more random.  It has been used to genetically map several organisms. [ 2 ] [ 3 ]


HAPPY mapping has also been adapted to allow the precise  analysis of copy-number variation , and in particular the analysis of copy-number changes in cancer. [ 4 ]


The  gene  known as  HCP5  ( HLA  Complex P5 ) is a human  endogenous retrovirus , meaning that it is a  fossil of an ancient virus  that at one time infected people, but has now become an integral part of the  human genome . [ 1 ]


One variation of HCP5 appears to provide some delay or resistance to the development of  AIDS  when a person is infected with  HIV . This variation of HCP5 frequently occurs in conjunction with a particular version of an  immune system  gene called  HLA-B . [ 1 ]


HCP5 has been reported to become  upregulated  after  human papillomavirus infection  and may promote the development of  cervical cancer . [ 2 ] [ 3 ]


Hologenomics  is the  omics  study of  hologenomes . A hologenome is the whole set of  genomes  of a  holobiont , an organism together with all co-habitating  microbes , other life forms, and  viruses . [ 1 ]  While the term hologenome originated from the  hologenome theory of evolution , which postulates that  natural selection  occurs on the holobiont level, [ 2 ]  hologenomics uses an integrative framework to investigate interactions between the  host  and its associated  species . Examples include  gut microbe [ 3 ]  or viral [ 4 ]  genomes linked to human or animal genomes for host-microbe interaction research. [ 5 ]  Hologenomics approaches have also been used to explain genetic diversity in the microbial communities of  marine sponges . [ 6 ]


The origins of hologenomics revolves around the hologenome theory of evolution, which describes individual  multicellular organisms , microbes, and viruses establishing  symbiotic relationships  and undergoing  coevolution  together. [ 2 ] [ 7 ]   Richard Jefferson  introduced the term 'hologenome' to describe the host-symbiont genome as an evolutionary unit. [ 8 ]  Prior to this,  Lynn Margulis  used the term 'holobiont' to describe hosts and their associated species as an ecological unit. [ 9 ]


Earliest evidence of multicellular-unicellular interactions are seen in sponges, which are a well studied hologenomic system.  Porifera  are often described as holobionts because they harbor a wide range of  bacteria ,  archaea  and  algae . Microbial communities present have been observed in facilitating  metabolic  functions and  immune  responses. [ 10 ]  Offspring inherit these microbial colonies via  vertical  and/or  horizontal transmission . [ 10 ]  Symbiont  colonies  are transferred through parental  gametes  in vertical transmission, whereas  offspring  acquire same colonies from their environment in horizontal transmission. Vertical transmission is also seen in  terrestrial  organisms like  C. ocellatus , where  gammaproteobacteria  in the parental gut is vertically transferred through egg contamination. [ 11 ]


The hologenome theory evolution is not fully accepted, and research in  microbial-host phylogenetics  is ongoing. Rather than the selection of corals with certain symbiotic microbial communities,  coral bleaching  may simply be a result of  environmental stressors , and bacterial presence in bleached coral may be explained simply as opportunistic  colonization . [ 12 ]  Ubiquity testing also revealed many different  bacterial  and  algal  symbionts that are not associated with a single species of coral, [ 13 ]  suggesting that hologenomics just identifies and validates mechanistic interactions between  pathogens , microbes, and their hosts.  [ 14 ]


It's hypothesized the continued incidence  non-infectious diseases  is a result of  modernization  reducing the diversity of symbiotic microbes. [ 14 ]  The human microbiome has also been correlated to numerous  etiologies  of  non-communicable disease , such as brain disorders, [ 18 ]  cancer, [ 19 ] [ 20 ]  and heart disease. [ 21 ]  Interactions between human microbiome and human health are complex and suggest a hologenomic approach.


Disease  biomarkers  can be found by investigating lifestyle, genomic differences, and  mRNA / protein / metabolite  profiles of the patient and their microbiota. [ 14 ]  For investigating microbiomes and specifically microbiota subcommunities that may contribute to a disease  phenotype ,  longitudinal studies  are recommended as everyone has a personalized microbiome with small differences in microbiome  phylotypes . [ 14 ]  A personalized plan managing a person’s microbiome can then be developed, with  prebiotics  nurturing beneficial endogenous microbes, and  probiotics  manipulating a person’s hologenome. [ 22 ]


Conditional  mutualism , where  parasites  have mutualistic effects under certain environmental/ecological conditions, have been found with holobiont-holobiont interactions. [ 23 ]  Maturation of mammalian host immune systems has been known to involve  gastrointestinal   flora . [ 24 ]  Understanding microorganism recognition of foreign pathogenic invasion and how host immunity favors the most ideal symbiont may aid in discovering novel therapeutic treatments to combat evolving diseases.


The  Honey Bee Genome Sequencing Consortium  is an international collaborative group of  genomics  scientists, scientific organisations and universities trying to decipher the  genome sequences  of the honey bee ( Apis mellifera ).  It was formed in 2001 by American scientists. In the US, the project is funded by the  National Human Genome Research Institute  (a division of the  National Institutes of Health (NIH) ), the  United States Department of Agriculture (USDA) , the Texas Agricultural Experiment Station, the  University of Illinois   Sociogenomics  Initiative, and various  beekeepers  association and the bee industry.


First scientific findings show that the  honey bee genome  may have evolved more slowly than the genomes of the  fruit fly  and  malaria mosquito . [ 1 ]   The bee genome  contains versions of some important  mammalian genes .


The complete genome of  Apis mellifera  has been sequenced and consists of 10,000 genes with approximately 236 million  base pairs .   The size of the genome is a tenth of the  human genome . [ 2 ]
The Western honey bee gene sequence showed 163 chemical receptors for smell but only 10 for taste.    Besides the discovery of new genes for the use of pollen and nectar, researchers found that, in comparison with other insects,  Apis mellifera  has fewer genes for immunity, detoxification and the development of the  cuticula . [ 3 ]
The population genetic analysis showed Africa as the origin and hypothesized that the spread into Europe happened in at least two independent waves. [ 4 ]


Data from the scientific collaboration was made available on  BeeBase  led by  Texas A&M University . [ 5 ]


BeeSpace led by the University of Illinois [ 6 ]  is an effort to complete a web navigable catalog of related information.


The  human genome  is a complete set of nucleic acid sequences for humans, encoded as the  DNA  within each of the 24 distinct chromosomes in the cell nucleus. A small DNA molecule is found within individual  mitochondria . These are usually treated separately as the nuclear genome and the  mitochondrial genome . [ 1 ]  Human  genomes  include both protein-coding DNA sequences and various types of  DNA that does not encode proteins . The latter is a diverse category that includes DNA coding for non-translated RNA, such as that for  ribosomal RNA ,  transfer RNA ,  ribozymes ,  small nuclear RNAs , and several types of  regulatory RNAs . It also includes  promoters  and their associated  gene-regulatory elements , DNA playing structural and replicatory roles, such as  scaffolding regions ,  telomeres ,  centromeres , and  origins of replication , plus large numbers of  transposable elements , inserted  viral  DNA, non-functional  pseudogenes  and simple,  highly repetitive sequences .  Introns  make up a large percentage of  non-coding DNA . Some of this non-coding DNA is non-functional  junk DNA , such as pseudogenes, but there is no firm consensus on the total amount of junk DNA.


Although the sequence of the human genome has been completely determined by DNA sequencing in 2022 (including  methylome ), it is not yet fully understood. Most, but not all,  genes  have been identified by a combination of high throughput experimental and  bioinformatics  approaches, yet much work still needs to be done to further elucidate the biological functions of their protein and  RNA  products.


In 2000, scientists reported the sequencing of 88% of human genome, [ 2 ]  but as of 2020, at least 8% was still missing. [ 3 ]  In 2021, scientists reported sequencing a complete, female genome (i.e., without the Y chromosome). [ 4 ] [ 3 ]  The  human Y chromosome , consisting of 62,460,029 base pairs from a different cell line and found in all males, was sequenced completely in January 2022. [ 5 ]


The current version of the standard reference genome is called GRCh38.p14 (July 2023). It consists of 22 autosomes plus one copy of the X chromosome and one copy of the Y chromosome. It contains approximately 3.1 billion base pairs (3.1 Gb or 3.1 x 10 9  bp). [ 6 ]  This represents the size of a composite genome based on data from multiple individuals but it is a good indication of the typical amount of DNA in a haploid set of chromosomes because the Y chromosome is quite small. [ 7 ]  Most human cells are diploid so they contain twice as much DNA (~6.2 billion base pairs).


In 2023, a draft human  pangenome  reference was published. [ 8 ]  It is based on 47 genomes from persons of varied ethnicity. [ 8 ]  Plans are underway for an improved reference capturing still more biodiversity from a still wider sample. [ 8 ]


While there are significant differences among the genomes of human individuals (on the order of 0.1% due to  single-nucleotide variants [ 9 ]  and 0.6% when considering  indels ), [ 10 ]  these are considerably smaller than the differences between humans and their closest living relatives, the  bonobos  and  chimpanzees  (~1.1%  fixed  single-nucleotide variants [ 11 ]  and 4% when including indels). [ 12 ]


The total length of the human  reference genome  does not represent the sequence of any specific individual, nor does it represent the sequence of all of the DNA found within a cell. The human reference genome only includes one copy of each of the paired, homologous autosomes plus one copy of each of the two sex chromosomes (X and Y). The total amount of DNA in this reference genome is 3.1 billion base pairs (3.1 Gb). [ 13 ]


Protein-coding sequences represent the most widely studied and best understood component of the human genome. These sequences ultimately lead to the production of all human  proteins , although several biological processes (e.g.  DNA rearrangements  and  alternative pre-mRNA splicing ) can lead to the production of many more unique proteins than the number of protein-coding genes.


The human reference genome contains somewhere between 19,000 and 20,000 protein-coding genes. [ 14 ] [ 15 ]  These genes contain an average of 10 introns and the average size of an intron is about 6 kb (6,000 bp). [ 16 ]  This means that the average size of a protein-coding gene is about 62 kb and these genes take up about 40% of the genome. [ 17 ]


Exon sequences consist of coding DNA and untranslated regions (UTRs) at either end of the mature mRNA. The total amount of coding DNA is about 1-2% of the genome. [ 18 ] [ 16 ]


Many people divide the genome into coding and non-coding DNA based on the idea that coding DNA is the most important functional component of the genome. About 98-99% of the human genome is non-coding DNA.


Noncoding RNA molecules play many essential roles in cells, especially in the many reactions of  protein synthesis  and  RNA processing . Noncoding genes include those for  tRNAs ,  ribosomal  RNAs,  microRNAs ,  snRNAs  and  long non-coding RNAs  (lncRNAs). [ 19 ] [ 20 ] [ 21 ] [ 22 ]  The number of reported non-coding genes continues to rise slowly but the exact number in the human genome is yet to be determined. Many RNAs are thought to be non-functional. [ 23 ]


Many ncRNAs are critical elements in gene regulation and expression. Noncoding RNA also contributes to epigenetics, transcription, RNA splicing, and the translational machinery. The role of RNA in genetic regulation and disease offers a new potential level of unexplored genomic complexity. [ 24 ]


Pseudogenes are inactive copies of protein-coding genes, often generated by  gene duplication , that have become nonfunctional through the accumulation of inactivating mutations. The number of pseudogenes in the human genome is on the order of 13,000, [ 25 ]  and in some chromosomes is nearly the same as the number of functional protein-coding genes. Gene duplication is a major mechanism through which new genetic material is generated during  molecular evolution .


For example, the  olfactory receptor  gene family is one of the best-documented examples of pseudogenes in the human genome. More than 60 percent of the genes in this family are non-functional pseudogenes in humans. By comparison, only 20 percent of genes in the mouse olfactory receptor gene family are pseudogenes. Research suggests that this is a species-specific characteristic, as the most closely related primates all have proportionally fewer pseudogenes. This genetic discovery helps to explain the less acute sense of smell in humans relative to other mammals. [ 26 ]


The human genome has many different  regulatory sequences  which are crucial to controlling  gene expression . Conservative estimates indicate that these sequences make up 8% of the genome, [ 27 ]  however extrapolations from the  ENCODE  project give that 20 [ 28 ]  or more [ 29 ]  of the genome is gene regulatory sequence. Some types of non-coding DNA are genetic "switches" that do not encode proteins, but do regulate when and where genes are expressed (called  enhancers ). [ 30 ]


Regulatory sequences have been known since the late 1960s. [ 31 ]  The first identification of regulatory sequences in the human genome relied on recombinant DNA technology. [ 32 ]  Later with the advent of genomic sequencing, the identification of these sequences could be inferred by evolutionary conservation. The evolutionary branch between the  primates  and  mouse , for example, occurred 70–90 million years ago. [ 33 ]  So computer comparisons of gene sequences that identify  conserved non-coding sequences  will be an indication of their importance in duties such as gene regulation. [ 34 ]


Other genomes have been sequenced with the same intention of aiding conservation-guided methods, for exampled the  pufferfish  genome. [ 35 ]  However, regulatory sequences disappear and re-evolve during evolution at a high rate. [ 36 ] [ 37 ] [ 38 ]


As of 2012, the efforts have shifted toward finding interactions between DNA and regulatory proteins by the technique  ChIP-Seq , or gaps where the DNA is not packaged by  histones  ( DNase hypersensitive sites ), both of which tell where there are active regulatory sequences in the investigated cell type. [ 27 ]


Repetitive DNA sequences  comprise approximately 50% of the human genome. [ 39 ]


About 8% of the human genome consists of tandem DNA arrays or tandem repeats, low complexity repeat sequences that have multiple adjacent copies (e.g. "CAGCAGCAG..."). [ 40 ]  The tandem sequences may be of variable lengths, from two nucleotides to tens of nucleotides. These sequences are highly variable, even among closely related individuals, and so are used for  genealogical DNA testing  and  forensic DNA analysis . [ 41 ]


Repeated sequences of fewer than ten nucleotides (e.g. the dinucleotide repeat (AC) n ) are termed microsatellite sequences. Among the microsatellite sequences, trinucleotide repeats are of particular importance, as sometimes occur within  coding regions  of genes for proteins and may lead to genetic disorders. For example, Huntington's disease results from an expansion of the trinucleotide repeat (CAG) n  within the  Huntingtin  gene on human chromosome 4.  Telomeres  (the ends of linear chromosomes) end with a microsatellite hexanucleotide repeat of the sequence (TTAGGG) n . [ citation needed ]


Tandem repeats of longer sequences (arrays of repeated sequences 10–60 nucleotides long) are termed  minisatellites . [ 42 ]


Transposable genetic elements , DNA sequences that can replicate and insert copies of themselves at other locations within a host genome, are an abundant component in the human genome. The most abundant transposon lineage,  Alu , has about 50,000 active copies, [ 43 ]  and can be inserted into intragenic and intergenic regions. [ 44 ]  One other lineage, LINE-1, has about 100 active copies per genome (the number varies between people). [ 45 ]  Together with non-functional relics of old transposons, they account for over half of total human DNA. [ 46 ]  Sometimes called "jumping genes", transposons have played a major role in sculpting the human genome. Some of these sequences represent  endogenous retroviruses , DNA copies of viral sequences that have become permanently integrated into the genome and are now passed on to succeeding generations. There are also a significant number of  retroviruses in human DNA , at least 3 of which have been proven to possess an important function (i.e.,  HIV -like functional HERV-K; envelope genes of non-functional viruses HERV-W and HERV-FRD play a role in placenta formation by inducing cell-cell fusion).


Mobile elements within the human genome can be classified into  LTR retrotransposons  (8.3% of total genome),  SINEs  (13.1% of total genome) including  Alu elements ,  LINEs  (20.4% of total genome), SVAs (SINE- VNTR -Alu) and  Class II DNA transposons  (2.9% of total genome).


There is no consensus on what constitutes a "functional" element in the genome since geneticists, evolutionary biologists, and molecular biologists employ different definitions and methods. [ 47 ] [ 48 ]  Due to the ambiguity in the terminology, different schools of thought have emerged. [ 49 ]  In evolutionary definitions, "functional" DNA, whether it is coding or non-coding, contributes to the fitness of the organism, and therefore is maintained by negative  evolutionary pressure  whereas "non-functional" DNA has no benefit to the organism and therefore is under neutral selective pressure. This type of DNA has been described as  junk DNA . [ 50 ] [ 51 ]  In genetic definitions, "functional" DNA is related to how DNA segments manifest by phenotype and "nonfunctional" is related to loss-of-function effects on the organism. [ 47 ]  In biochemical definitions, "functional" DNA relates to DNA sequences that specify molecular products (e.g. noncoding RNAs) and biochemical activities with mechanistic roles in gene or genome regulation (i.e. DNA sequences that impact cellular level activity such as cell type, condition, and molecular processes). [ 52 ] [ 47 ]  There is no consensus in the literature on the amount of functional DNA since, depending on how "function" is understood, ranges have been estimated from up to 90% of the human genome is likely nonfunctional DNA (junk DNA) [ 53 ]  to up to 80% of the genome is likely functional. [ 54 ]  It is also possible that junk DNA may acquire a function in the future and therefore may play a role in evolution, [ 55 ]  but this is likely to occur only very rarely. [ 50 ]  Finally DNA that is deliterious to the organism and is under negative selective pressure is called garbage DNA. [ 51 ]


The first human  genome sequences  were published in nearly complete draft form in February 2001 by the  Human Genome Project [ 56 ]  and  Celera Corporation . [ 57 ]  Completion of the Human Genome Project's sequencing effort was announced in 2004 with the publication of a draft genome sequence, leaving just 341 gaps in the sequence, representing highly repetitive and other DNA that could not be sequenced with the technology available at the time. [ 58 ]  The human genome was the first of all vertebrates to be sequenced to such near-completion, and as of 2018, the diploid genomes of over a million individual humans had been determined using  next-generation sequencing . [ 59 ]


These data are used worldwide in  biomedical science ,  anthropology ,  forensics  and other branches of science. Such genomic studies have led to advances in the diagnosis and treatment of diseases, and to new insights in many fields of biology, including  human evolution . [ citation needed ]


By 2018, the total number of genes had been raised to at least 46,831, [ 60 ]  plus another 2300  micro-RNA  genes. [ 61 ]  A 2018 population survey found another 300 million bases of human genome that was not in the reference sequence. [ 62 ]  Prior to the acquisition of the full genome sequence, estimates of the number of human genes ranged from 50,000 to 140,000 (with occasional vagueness about whether these estimates included non-protein coding genes). [ 63 ]  As genome sequence quality and the methods for identifying protein-coding genes improved, [ 58 ]  the count of recognized protein-coding genes dropped to 19,000–20,000. [ 64 ]


In 2022, the Telomere-to-Telomere (T2T) consortium reported the complete sequence of a human female genome, [ 3 ]  filling all the gaps in the  X chromosome  (2020) and the 22 autosomes (May 2021). [ 3 ] [ 65 ]  The previously unsequenced parts contain  immune response  genes that help to adapt to and survive infections, as well as genes that are important for predicting  drug response . [ 66 ]  The completed human genome sequence will also provide better understanding of human formation as an individual organism and how humans vary both between each other and other species. [ 66 ]


Although the 'completion' of the human genome project was announced in 2001, [ 2 ]  there remained hundreds of gaps, with about 5–10% of the total sequence remaining undetermined. The missing genetic information was mostly in repetitive  heterochromatic  regions and near the  centromeres  and  telomeres , but also some gene-encoding  euchromatic  regions. [ 67 ]  There remained 160 euchromatic gaps in 2015 when the sequences spanning another 50 formerly unsequenced regions were determined. [ 68 ]  Only in 2020 was the first truly complete telomere-to-telomere sequence of a human chromosome determined, namely of the  X chromosome . [ 69 ]  The first complete telomere-to-telomere sequence of a human autosomal chromosome,  chromosome 8 , followed a year later. [ 70 ]  The complete human genome (without Y chromosome) was published in 2021, while with Y chromosome in January 2022. [ 3 ] [ 4 ] [ 71 ]


With the exception of identical twins, all humans show significant variation in genomic DNA sequences. The human  reference genome  (HRG) is used as a standard sequence reference.


There are several important points concerning the human reference genome:


The  Genome Reference Consortium  is responsible for updating the HRG. Version 38 was released in December 2013. [ 72 ]


Most studies of human genetic variation have focused on  single-nucleotide polymorphisms  (SNPs), which are substitutions in individual bases along a chromosome. Most analyses estimate that SNPs occur 1 in 1000 base pairs, on average, in the  euchromatic  human genome, although they do not occur at a uniform density. Thus follows the popular statement that "we are all, regardless of  race , genetically 99.9% the same", [ 73 ]  although this would be somewhat qualified by most geneticists. For example, a much larger fraction of the genome is now thought to be involved in  copy number variation . [ 74 ]  A large-scale collaborative effort to catalog SNP variations in the human genome is being undertaken by the  International HapMap Project . [ citation needed ]


The genomic loci and length of certain types of small  repetitive sequences  are highly variable from person to person, which is the basis of  DNA fingerprinting  and  DNA paternity testing  technologies. The  heterochromatic  portions of the human genome, which total several hundred million base pairs, are also thought to be quite variable within the human population (they are so repetitive and so long that they cannot be accurately sequenced with current technology). These regions contain few genes, and it is unclear whether any significant  phenotypic  effect results from typical variation in repeats or heterochromatin.


Most gross genomic mutations in  gamete  germ cells probably result in inviable embryos; however, a number of human diseases are related to large-scale genomic abnormalities.  Down syndrome ,  Turner Syndrome , and a number of other diseases result from  nondisjunction  of entire chromosomes.  Cancer  cells frequently have  aneuploidy  of chromosomes and chromosome arms, although a  cause and effect  relationship between aneuploidy and cancer has not been established.


Whereas a genome sequence lists the order of every DNA base in a genome, a genome map identifies the landmarks. A genome map is less detailed than a genome sequence and aids in navigating around the genome. [ 75 ] [ 76 ]


An example of a variation map is the HapMap being developed by the  International HapMap Project . The HapMap is a  haplotype  map of the human genome, "which will describe the common patterns of human DNA sequence variation." [ 77 ]  It catalogs the patterns of small-scale variations in the genome that involve single DNA letters, or bases.


Researchers published the first sequence-based map of large-scale structural variation across the human genome in the journal  Nature  in May 2008. [ 78 ] [ 79 ]  Large-scale structural variations are differences in the genome among people that range from a few thousand to a few million DNA bases; some are gains or losses of stretches of genome sequence and others appear as re-arrangements of stretches of sequence. These variations include  differences in the number of copies  individuals have of a particular gene, deletions, translocations and inversions.


Structural variation refers to genetic variants that affect larger segments of the human genome, as opposed to point  mutations . Often, structural variants (SVs) are defined as variants of 50 base pairs (bp) or greater, such as deletions, duplications, insertions, inversions and other rearrangements. About 90% of structural variants are noncoding deletions but most individuals have more than a thousand such deletions; the size of deletions ranges from dozens of base pairs to tens of thousands of bp. [ 80 ]  On average, individuals carry ~3 rare structural variants that alter coding regions, e.g. delete  exons . About 2% of individuals carry ultra-rare megabase-scale structural variants, especially rearrangements. That is, millions of base pairs may be inverted within a chromosome; ultra-rare means that they are only found in individuals or their family members and thus have arisen very recently. [ 80 ]


Single-nucleotide polymorphisms (SNPs) do not occur homogeneously across the human genome. In fact, there is enormous diversity in  SNP  frequency between genes, reflecting different selective pressures on each gene as well as different mutation and recombination rates across the genome. However, studies on SNPs are biased towards coding regions, the data generated from them are unlikely to reflect the overall distribution of SNPs throughout the genome. Therefore, the  SNP Consortium  protocol was designed to identify SNPs with no bias towards coding regions and the Consortium's 100,000 SNPs generally reflect sequence diversity across the human chromosomes. The  SNP Consortium  aims to expand the number of SNPs identified across the genome to 300 000 by the end of the first quarter of 2001. [ 81 ]


Changes in  non-coding sequence  and synonymous changes in  coding sequence  are generally more common than non-synonymous changes, reflecting greater selective pressure reducing diversity at positions dictating amino acid identity. Transitional changes are more common than transversions, with CpG dinucleotides showing the highest mutation rate, presumably due to deamination. [ citation needed ]


A personal genome sequence is a (nearly) complete  sequence  of the chemical base pairs that make up the  DNA  of a single person. Because medical treatments have different effects on different people due to genetic variations such as  single-nucleotide polymorphisms  (SNPs), the analysis of personal genomes may lead to personalized medical treatment based on individual genotypes. [ 82 ]


The first personal genome sequence to be determined was that of  Craig Venter  in 2007. Personal genomes had not been sequenced in the public Human Genome Project to protect the identity of volunteers who provided DNA samples. That sequence was derived from the DNA of several volunteers from a diverse population. [ 83 ]  However, early in the Venter-led  Celera Genomics  genome sequencing effort the decision was made to switch from sequencing a composite sample to using DNA from a single individual, later revealed to have been Venter himself. Thus the Celera human genome sequence released in 2000 was largely that of one man. Subsequent replacement of the early composite-derived data and determination of the diploid sequence, representing both sets of  chromosomes , rather than a haploid sequence originally reported, allowed the release of the first personal genome. [ 84 ]  In April 2008, that of  James Watson  was also completed. In 2009, Stephen Quake published his own genome sequence derived from a sequencer of his own design, the Heliscope. [ 85 ]  A Stanford team led by  Euan Ashley  published a framework for the medical interpretation of human genomes implemented on Quake's genome and made whole genome-informed medical decisions for the first time. [ 86 ]  That team further extended the approach to the West family, the first family sequenced as part of Illumina's Personal Genome Sequencing program. [ 87 ]  Since then hundreds of personal genome sequences have been released, [ 88 ]  including those of  Desmond Tutu , [ 89 ] [ 90 ]  and of a  Paleo-Eskimo . [ 91 ]  In 2012, the whole genome sequences of two family trios among 1092 genomes was made public. [ 9 ]  In November 2013, a Spanish family made four personal exome datasets (about 1% of the genome) publicly available under a  Creative Commons public domain license . [ 92 ] [ 93 ]  The  Personal Genome Project  (started in 2005) is among the few to make both genome sequences and corresponding medical phenotypes publicly available. [ 94 ] [ 95 ]


The sequencing of individual genomes further unveiled levels of genetic complexity that had not been appreciated before. Personal genomics helped reveal the significant level of diversity in the human genome attributed not only to SNPs but structural variations as well. However, the application of such knowledge to the treatment of disease and in the medical field is only in its very beginnings. [ 96 ]   Exome sequencing  has become increasingly popular as a tool to aid in diagnosis of genetic disease because the exome contributes only 1% of the genomic sequence but accounts for roughly 85% of mutations that contribute significantly to disease. [ 97 ]


In humans,  gene knockouts  naturally occur as  heterozygous  or  homozygous   loss-of-function  gene knockouts. These knockouts are often difficult to distinguish, especially within  heterogeneous  genetic backgrounds. They are also difficult to find as they occur in low frequencies.


Populations with high rates of  consanguinity , such as countries with high rates of first-cousin marriages, display the highest frequencies of homozygous gene knockouts. Such populations include Pakistan, Iceland, and Amish populations. These populations with a high level of parental-relatedness have been subjects of human knock out research which has helped to determine the function of specific genes in humans. By distinguishing specific knockouts, researchers are able to use phenotypic analyses of these individuals to help characterize the gene that has been knocked out.


Knockouts in specific genes can cause genetic diseases, potentially have beneficial effects, or even result in no phenotypic effect at all. However, determining a knockout's phenotypic effect and in humans can be challenging. Challenges to characterizing and clinically interpreting knockouts include difficulty calling of DNA variants, determining disruption of protein function (annotation), and considering the amount of influence  mosaicism  has on the phenotype. [ 98 ]


One major study that investigated human knockouts is the Pakistan Risk of Myocardial Infarction study. It was found that individuals possessing a heterozygous loss-of-function gene knockout for the  APOC3  gene had lower triglycerides in the blood after consuming a high fat meal as compared to individuals without the mutation. However, individuals possessing homozygous loss-of-function gene knockouts of the APOC3 gene displayed the lowest level of triglycerides in the blood after the fat load test, as they produce no functional APOC3 protein. [ 99 ]