The most commonly studied human haplogroups are  Y-chromosome (Y-DNA) haplogroups  and  mitochondrial DNA (mtDNA) haplogroups , both of which can be used to define genetic populations. Y-DNA is passed solely along the  patrilineal  line, from father to son, while mtDNA is passed down the  matrilineal  line, from mother to both daughter or son. The Y-DNA and mtDNA may change by chance mutation at each generation.


A variable number tandem repeat (VNTR) is the variation of length of a  tandem repeat . A tandem repeat is the adjacent repetition of a short  nucleotide sequence . Tandem repeats exist on many  chromosomes , and their length varies between individuals. Each variant acts as an  inherited   allele , so they are used for personal or parental identification. Their analysis is useful in genetics and biology research,  forensics , and  DNA fingerprinting .


Short tandem repeats (about 5 base pairs) are called  microsatellites , while longer ones are called  minisatellites .


The  recent African origin of modern humans  paradigm assumes the  dispersal  of non-African populations of  anatomically modern humans  after 70,000 years ago. Dispersal within Africa occurred significantly earlier, at least 130,000 years ago. The "out of Africa" theory originates in the 19th century, as a tentative suggestion in Charles Darwin's  Descent of Man , [ 47 ]  but remained speculative until the 1980s when it was supported by the study of present-day mitochondrial DNA, combined with evidence from  physical anthropology  of archaic  specimens .


According to a 2000 study of Y-chromosome sequence variation, [ 48 ]  human Y-chromosomes trace ancestry to Africa, and the descendants of the derived lineage left Africa and eventually were replaced by archaic human Y-chromosomes in Eurasia. The study also shows that a minority of contemporary populations in East Africa and the  Khoisan  are the descendants of the most ancestral patrilineages of anatomically modern humans that left Africa 35,000 to 89,000 years ago. [ 48 ]  Other evidence supporting the theory is that variations in skull measurements decrease with distance from Africa at the same rate as the decrease in genetic diversity. Human genetic diversity decreases in native populations with migratory distance from Africa, and this is thought to be due to  bottlenecks  during human migration, which are events that temporarily reduce population size. [ 49 ] [ 50 ]


A 2009 genetic clustering study, which genotyped 1327 polymorphic markers in various African populations, identified six ancestral clusters. The clustering corresponded closely with ethnicity, culture and language. [ 51 ]  A 2018  whole genome sequencing  study of the world's populations observed similar clusters among the populations in Africa. At K=9, distinct ancestral components defined the  Afroasiatic -speaking populations inhabiting  North Africa  and  Northeast Africa ; the  Nilo-Saharan -speaking populations in Northeast Africa and  East Africa ; the  Ari  populations in Northeast Africa; the  Niger-Congo -speaking populations in West-Central Africa,  West Africa , East Africa and  Southern Africa ; the  Pygmy  populations in  Central Africa ; and the  Khoisan  populations in Southern Africa. [ 52 ]


In May 2023, scientists reported, based on genetic studies, a more complicated pathway of human evolution than previously understood. According to the studies, humans evolved from different places and times in Africa, instead of from a single location and period of time. [ 53 ] [ 54 ]


Because of the common ancestry of all humans, only a small number of variants have large differences in frequency between populations. However, some rare variants in the world's human population are much more frequent in at least one population (more than 5%). [ 55 ]


It is commonly assumed that early humans left Africa, and thus must have passed through a population bottleneck before their African-Eurasian divergence around 100,000 years ago (ca. 3,000 generations). The rapid expansion of a previously  small population  has two important effects on the distribution of genetic variation. First, the so-called  founder effect  occurs when founder populations bring only a subset of the genetic variation from their ancestral population. Second, as founders become more geographically separated, the probability that two individuals from different founder populations will mate becomes smaller. The effect of this  assortative mating  is to reduce gene flow between geographical groups and to increase the genetic distance between groups. [ citation needed ]


The expansion of humans from Africa affected the distribution of genetic variation in two other ways. First, smaller (founder) populations experience greater  genetic drift  because of increased fluctuations in neutral polymorphisms. Second, new polymorphisms that arose in one group were less likely to be transmitted to other groups as gene flow was restricted. [ citation needed ]


Populations in Africa tend to have lower amounts of  linkage disequilibrium  than do populations outside Africa, partly because of the larger size of human populations in Africa over the course of human history and partly because the number of modern humans who left Africa to colonize the rest of the world appears to have been relatively low. [ 57 ]  In contrast, populations that have undergone dramatic size reductions or rapid expansions in the past and populations formed by the mixture of previously separate ancestral groups can have unusually high levels of linkage disequilibrium [ 57 ]


The distribution of genetic variants within and among human populations are impossible to describe succinctly because of the difficulty of defining a "population," the clinal nature of variation, and heterogeneity across the genome (Long and Kittles 2003). In general, however, an average of 85% of genetic variation exists within local populations, ~7% is between local populations within the same continent, and ~8% of variation occurs between large groups living on different continents. [ 58 ] [ 59 ]  The  recent African origin  theory for humans would predict that in Africa there exists a great deal more diversity than elsewhere and that diversity should decrease the further from Africa a population is sampled.


Sub-Saharan Africa  has the most human genetic diversity and the same has been shown to hold true for phenotypic variation in skull form. [ 49 ] [ 60 ]  Phenotype is connected to genotype through  gene expression . Genetic diversity decreases smoothly with migratory distance from that region, which many scientists believe to be the origin of modern humans, and that decrease is mirrored by a decrease in phenotypic variation. Skull measurements are an example of a physical attribute whose within-population variation decreases with distance from Africa.


The distribution of many physical traits resembles the distribution of genetic variation within and between human populations ( American Association of Physical Anthropologists  1996; Keita and Kittles 1997). For example, ~90% of the variation in human head shapes occurs within continental groups, and ~10% separates groups, with a greater variability of head shape among individuals with recent African ancestors (Relethford 2002).


A prominent exception to the common distribution of physical characteristics within and among groups is  skin color . Approximately 10% of the variance in skin color occurs within groups, and ~90% occurs between groups (Relethford 2002). This distribution of skin color and its geographic patterning – with people whose ancestors lived predominantly near the equator having darker skin than those with ancestors who lived predominantly in higher latitudes – indicate that this attribute has been under strong  selective pressure . Darker skin appears to be strongly selected for in equatorial regions to prevent sunburn, skin cancer, the  photolysis  of  folate , and damage to sweat glands. [ 61 ]


Understanding how genetic diversity in the human population impacts various levels of gene expression is an active area of research. While earlier studies focused on the relationship between DNA variation and RNA expression, more recent efforts are characterizing the genetic control of various aspects of gene expression including chromatin states, [ 62 ]  translation, [ 63 ]  and protein levels. [ 64 ]  A study published in 2007 found that 25% of genes showed different levels of gene expression between populations of European and Asian descent. [ 65 ] [ 66 ] [ 67 ] [ 68 ] [ 69 ]  The primary cause of this difference in gene expression was thought to be SNPs in gene regulatory regions of DNA. Another study published in 2007 found that approximately 83% of genes were expressed at different levels among individuals and about 17% between populations of European and African descent. [ 70 ] [ 71 ]


The population geneticist  Sewall Wright  developed the  fixation index  (often abbreviated to  F ST ) as a way of measuring genetic differences between populations. This statistic is often used in taxonomy to compare differences between any two given populations by measuring the genetic differences among and between populations for individual genes, or for many genes simultaneously. [ 72 ]  It is often stated that the fixation index for humans is about 0.15. This translates to an estimated 85% of the variation measured in the overall human population is found within individuals of the same population, and about 15% of the variation occurs between populations.  These estimates imply that any two individuals from different populations may be more similar to each other than either is to a member of their own group. [ 73 ] [ 74 ]
"The shared evolutionary history of living humans has resulted in a high relatedness among all living people, as indicated for example by the very low fixation index (F ST ) among living human populations."  Richard Lewontin , who affirmed these ratios, thus concluded neither "race" nor "subspecies" were appropriate or useful ways to describe human populations. [ 58 ]


Wright himself believed that values >0.25 represent very great genetic variation and that an  F ST  of 0.15–0.25 represented great variation. However, about 5% of human variation occurs between populations within continents, therefore  F ST  values between continental groups of humans (or races) of as low as 0.1 (or possibly lower) have been found in some studies, suggesting more moderate levels of genetic variation. [ 72 ]  Graves (1996) has countered that  F ST  should not be used as a marker of subspecies status, as the statistic is used to measure the degree of differentiation between populations, [ 72 ]  although see also Wright (1978). [ 75 ]


Jeffrey Long and Rick Kittles give a long critique of the application of  F ST  to human populations in their 2003 paper "Human Genetic Diversity and the Nonexistence of Biological Races". They find that the figure of 85% is misleading because it implies that all human populations contain on average 85% of all genetic diversity. They argue the underlying statistical model incorrectly assumes equal and independent histories of variation for each large human population. A more realistic approach is to understand that some human groups are parental to other groups and that these groups represent  paraphyletic  groups to their descent groups. For example, under the  recent African origin  theory the human population in Africa is paraphyletic to all other human groups because it represents the ancestral group from which all non-African populations derive, but more than that, non-African groups only derive from a small non-representative sample of this African population. This means that all non-African groups are more closely related to each other and to some African groups (probably east Africans) than they are to others, and further that the migration out of Africa represented a  genetic bottleneck , with much of the diversity that existed in Africa not being carried out of Africa by the emigrating groups. Under this scenario, human populations do not have equal amounts of local variability, but rather diminished amounts of diversity the further from Africa any population lives. Long and Kittles find that rather than 85% of human genetic diversity existing in all human populations, about 100% of human diversity exists in a single African population, whereas only about 70% of human genetic diversity exists in a population derived from New Guinea. Long and Kittles argued that this still produces a global human population that is genetically homogeneous compared to other mammalian populations. [ 76 ]


Anatomically modern humans interbred with Neanderthals during the  Middle Paleolithic . In May 2010, the  Neanderthal Genome Project  presented genetic evidence that  interbreeding  took place and that a small but significant portion, around 2–4%, of Neanderthal admixture is present in the DNA of modern Eurasians and Oceanians, and nearly absent in sub-Saharan African populations. [ 77 ] [ 78 ]


Between 4% and 6% of the genome of  Melanesians  (represented by the Papua New Guinean and Bougainville Islander) appears to derive from  Denisovans  – a previously unknown hominin which is more closely related to Neanderthals than to Sapiens. It was possibly introduced during the early migration of the ancestors of Melanesians into Southeast Asia. This history of interaction suggests that Denisovans once ranged widely over eastern Asia. [ 79 ]


Thus, Melanesians emerge as one of the most archaic-admixed populations, having Denisovan/Neanderthal-related admixture of ~8%. [ 79 ]


In a study published in 2013, Jeffrey Wall from University of California studied whole sequence-genome data and found higher rates of introgression in Asians compared to Europeans. [ 80 ]  Hammer et al. tested the hypothesis that contemporary African genomes have signatures of gene flow with archaic human ancestors and found evidence of archaic admixture in the genomes of some African groups, suggesting that modest amounts of gene flow were widespread throughout time and space during the evolution of anatomically modern humans. [ 81 ]


A study published in 2020 found that the  Yoruba  and  Mende  populations of West Africa derive between 2% and 19% of their genome from an as-yet unidentified archaic hominin population that likely diverged before the split of modern humans and the ancestors of Neanderthals and Denisovans, [ 82 ]  potentially making these groups the most archaic-admixed human populations identified yet.


New data on human genetic variation has reignited the debate about a possible biological basis for categorization of humans into races. Most of the controversy surrounds the question of how to interpret the genetic data and whether conclusions based on it are sound. Some researchers argue that self-identified race can be used as an indicator of geographic ancestry for certain  health risks and medications .


Although the genetic differences among human groups are relatively small, these differences in certain genes such as  duffy ,  ABCC11 ,  SLC24A5 , called  ancestry-informative markers  (AIMs) nevertheless can be used to reliably situate many individuals within broad, geographically based groupings. For example, computer analyses of hundreds of polymorphic loci sampled in globally distributed populations have revealed the existence of genetic clustering that roughly is associated with groups that historically have occupied large continental and subcontinental regions (Rosenberg  et al.  2002; Bamshad  et al.  2003).


Some commentators have argued that these patterns of variation provide a biological justification for the use of traditional racial categories. They argue that the continental clusterings correspond roughly with the division of human beings into  sub-Saharan Africans ;  Europeans ,  Western Asians ,  Central Asians ,  Southern Asians  and  Northern Africans ;  Eastern Asians ,  Southeast Asians ,  Polynesians  and  Native Americans ; and other inhabitants of  Oceania  (Melanesians,  Micronesians  & Australian Aborigines) (Risch  et al.  2002). Other observers disagree, saying that the same data undercut traditional notions of racial groups (King and Motulsky 2002; Calafell 2003; Tishkoff and Kidd 2004 [ 24 ] ). They point out, for example, that major populations considered races or subgroups within races do not necessarily form their own clusters.


Racial categories are also undermined by findings that genetic variants which are limited to one region tend to be rare within that region, variants that are common within a region tend to be shared across the globe, and most differences between individuals, whether they come from the same region or different regions, are due to global variants. [ 85 ]   No genetic variants have been found which are  fixed  within a continent or major region and found nowhere else. [ 86 ]


Furthermore, because human genetic variation is clinal, many individuals affiliate with two or more continental groups. Thus, the genetically based "biogeographical ancestry" assigned to any given person generally will be broadly distributed and will be accompanied by sizable uncertainties (Pfaff  et al.  2004).


In many parts of the world, groups have mixed in such a way that many individuals have relatively recent ancestors from widely separated regions. Although genetic analyses of large numbers of loci can produce estimates of the percentage of a person's ancestors coming from various continental populations (Shriver  et al.  2003; Bamshad  et al.  2004), these estimates may assume a false distinctiveness of the parental populations, since human groups have exchanged mates from local to continental scales throughout history (Cavalli-Sforza  et al.  1994; Hoerder 2002). Even with large numbers of markers, information for estimating admixture proportions of individuals or groups is limited, and estimates typically will have wide  confidence intervals  (Pfaff  et al.  2004).


Genetic data can be used to infer population structure and assign individuals to groups that often correspond with their self-identified geographical ancestry. Jorde and Wooding (2004) argued that "Analysis of many loci now yields reasonably accurate estimates of genetic similarity among individuals, rather than populations. Clustering of individuals is correlated with geographic origin or ancestry." [ 23 ]  However, identification by geographic origin may quickly break down when considering historical ancestry shared between individuals back in time. [ 87 ]


An analysis of  autosomal   SNP  data from the  International HapMap Project  (Phase II) and  CEPH  Human Genome Diversity Panel samples was published in 2009.
The study of 53 populations taken from the HapMap and CEPH data (1138 unrelated individuals) suggested that  natural selection  may shape the human genome much more slowly than previously thought, with factors such as migration within and among continents more heavily influencing the distribution of genetic variations. [ 88 ]
A similar study published in 2010 found strong genome-wide evidence for selection due to changes in ecoregion, diet, and subsistence
particularly in connection with polar ecoregions, with foraging, and with a diet rich in roots and tubers. [ 89 ]  In a 2016 study,  principal component analysis  of genome-wide data was capable of recovering previously-known targets for positive selection (without prior definition of populations) as well as a number of new candidate genes. [ 90 ]


Forensic anthropologists  can assess the ancestry of skeletal remains by analyzing skeletal morphology as well as using genetic and chemical markers, when possible. [ 91 ]  While these assessments are never certain, the accuracy of skeletal morphology analyses in determining true ancestry has been estimated at 90%. [ 92 ]


Gene flow between two populations reduces the average genetic distance between the populations, only totally isolated human populations experience no gene flow and most populations have continuous gene flow with other neighboring populations which create the clinal distribution observed for most genetic variation. When gene flow takes place between well-differentiated genetic populations the result is referred to as "genetic admixture".


Admixture mapping is a technique used to study how genetic variants cause differences in disease rates between population. [ 93 ]  Recent admixture populations that trace their ancestry to multiple continents are well suited for identifying genes for traits and diseases that differ in prevalence between parental populations. African-American populations have been the focus of numerous population genetic and admixture mapping studies, including studies of complex genetic traits such as white cell count, body-mass index, prostate cancer and renal disease. [ 94 ]


An analysis of phenotypic and genetic variation including skin color and socio-economic status was carried out in the population of Cape Verde which has a well documented history of contact between Europeans and Africans. The studies showed that pattern of admixture in this population has been sex-biased (involving mostly matings between European men and African women) and there is a significant interaction between socioeconomic status and skin color, independent of ancestry. [ 95 ]  Another study shows an increased risk of  graft-versus-host disease  complications after transplantation due to genetic variants in human leukocyte antigen (HLA) and non-HLA proteins. [ 96 ]


Differences in  allele frequencies  contribute to group differences in the incidence of some  monogenic diseases , and they may contribute to differences in the incidence of some common diseases. [ 97 ]  For the monogenic diseases, the frequency of causative alleles usually correlates best with ancestry, whether familial (for example,  Ellis–Van Creveld syndrome  among the Pennsylvania  Amish ), ethnic ( Tay–Sachs disease  among  Ashkenazi Jewish  populations), or geographical (hemoglobinopathies among people with ancestors who lived in malarial regions). To the extent that ancestry corresponds with racial or ethnic groups or subgroups, the incidence of monogenic diseases can differ between groups categorized by race or ethnicity, and health-care professionals typically take these patterns into account in making diagnoses. [ 98 ]


Even with common diseases involving numerous genetic variants and environmental factors, investigators point to evidence suggesting the involvement of differentially distributed alleles with small to moderate effects. Frequently cited examples include  hypertension  (Douglas  et al.  1996),  diabetes  (Gower  et al.  2003),  obesity  (Fernandez  et al.  2003), and  prostate cancer  (Platz  et al.  2000). However, in none of these cases has allelic variation in a susceptibility gene been shown to account for a significant fraction of the difference in disease prevalence among groups, and the role of genetic factors in generating these differences remains uncertain (Mountain and Risch 2004).


Some other variations on the other hand are beneficial to human, as they prevent certain diseases and increase the chance to adapt to the environment. For example, mutation in  CCR5  gene that protects against  AIDS . CCR5 gene is absent on the surface of cell due to mutation. Without CCR5 gene on the surface, there is nothing for  HIV  viruses to grab on and bind into. Therefore, the mutation on CCR5 gene decreases the chance of an individual's risk with AIDS. The mutation in CCR5 is also quite common in certain areas, with more than 14% of the population carry the mutation in  Europe  and about 6–10% in  Asia  and  North Africa . [ 99 ]


Apart from mutations, many genes that may have aided humans in ancient times plague humans today. For example, it is suspected that genes that allow humans to more efficiently process food are those that make people susceptible to obesity and diabetes today. [ 100 ]


Neil Risch  of  Stanford University  has proposed that self-identified race/ethnic group could be a valid means of categorization in the US for public health and policy considerations. [ 101 ] [ 97 ]  A 2002 paper by  Noah Rosenberg 's group makes a similar claim: "The structure of human populations is relevant in various epidemiological contexts. As a result of variation in frequencies of both genetic and nongenetic risk factors, rates of disease and of such phenotypes as adverse drug response vary across populations. Further, information about a patient's population of origin might provide health care practitioners with information about risk when direct causes of disease are unknown." [ 102 ]  However, in 2018  Noah Rosenberg  released a study arguing against genetically essentialist ideas of health disparities between populations stating environmental variants are a more likely cause [ 103 ]


Human  genome projects  are  scientific  endeavors that determine or study the structure of the  human genome . The  Human Genome Project  was a landmark genome project.


There are numerous related projects that deal with genetic variation (or variation in the encoded proteins), e.g. organized by the following organizations:


In  genetics  and  bioinformatics , a  single-nucleotide polymorphism  ( SNP   / s n ɪ p / ; plural  SNPs   / s n ɪ p s / ) is a  germline  substitution of a single  nucleotide  at a specific position in the  genome . Although certain definitions require the substitution to be present in a sufficiently large fraction of the population (e.g. 1% or more), [ 1 ]  many publications [ 2 ] [ 3 ] [ 4 ]  do not apply such a frequency threshold.


For example, a  G nucleotide  present at a specific location in a  reference genome  may be replaced by an  A  in a minority of individuals. The two possible nucleotide variations of this SNP – G or A – are called  alleles . [ 5 ]


SNPs can help explain differences in susceptibility to a wide range of  diseases  across a population. For example, a common SNP in the  CFH  gene is associated with increased risk of age-related  macular degeneration . [ 6 ]  Differences in the severity of an illness or response to treatments may also be manifestations of genetic variations caused by SNPs. For example, two common SNPs in the  APOE  gene,  rs429358  and rs7412, lead to three major  APO-E alleles  with different associated risks for development of  Alzheimer's disease  and age at onset of the disease. [ 7 ]


Single nucleotide substitutions with an allele frequency of less than 1% are sometimes called  single-nucleotide variants  ( SNVs ). [ 8 ]  "Variant" may also be used as a general term for any single nucleotide change in a DNA sequence, [ 9 ]  encompassing both common SNPs and rare  mutations , whether  germline  or  somatic . [ 10 ] [ 11 ]  The term SNV has therefore been used to refer to  point mutations  found in cancer cells. [ 12 ]  DNA variants must also commonly be taken into consideration in molecular diagnostics applications such as designing  PCR  primers to detect viruses, in which the viral RNA or DNA sample may contain SNVs. [ citation needed ]  However, this nomenclature uses arbitrary distinctions (such as an allele frequency of 1%) and is not used consistently across all fields; the resulting disagreement has prompted calls for a more consistent framework for naming differences in DNA sequences between two samples. [ 13 ] [ 14 ]


Single-nucleotide  polymorphisms  may fall within coding sequences of  genes ,  non-coding regions of genes , or in the  intergenic regions  (regions between genes). SNPs within a coding sequence do not necessarily change the  amino acid  sequence of the  protein  that is produced, due to  degeneracy of the genetic code . [ 15 ]


SNPs in the coding region are of two types: synonymous SNPs and nonsynonymous SNPs. Synonymous SNPs do not affect the protein sequence, while nonsynonymous SNPs change the amino acid sequence of protein. [ 16 ]


SNPs that are not in protein-coding regions may still affect  gene splicing ,  transcription factor  binding,  messenger RNA  degradation, or the sequence of noncoding RNA. Gene expression affected by this type of SNP is referred to as an eSNP (expression SNP) and may be upstream or downstream from the gene.


More than 600 million SNPs have been identified across the human genome in the world's population. [ 22 ]  A typical genome differs from the reference human genome at 4–5 million sites, most of which (more than 99.9%) consist of SNPs and short  indels . [ 23 ]


The genomic distribution of SNPs is not homogenous; SNPs occur in  non-coding regions  more frequently than in  coding regions  or, in general, where natural selection is acting and "fixing" the  allele  (eliminating other variants) of the SNP that constitutes the most favorable genetic adaptation. [ 24 ]  Other factors, like  genetic recombination  and mutation rate, can also determine SNP density. [ 25 ]


SNP density can be predicted by the presence of  microsatellites : AT microsatellites in particular are potent predictors of SNP density, with long (AT)(n) repeat tracts tending to be found in regions of significantly reduced SNP density and low  GC content . [ 26 ]


Since there are variations between human populations, a SNP allele that is common in one geographical or ethnic group may be rarer in another. However, this pattern of variation is relatively rare; in a global sample of 67.3 million SNPs, the  Human Genome Diversity Project  "found no such private variants that are  fixed  in a given continent or major region. The highest frequencies are reached by a few tens of variants present at >70% (and a few thousands at >50%) in Africa, the Americas, and Oceania. By contrast, the highest frequency variants private to Europe, East Asia, the Middle East, or Central and South Asia reach just 10 to 30%." [ 27 ]


Within a population, SNPs can be assigned a  minor allele frequency  (MAF)—the lowest allele frequency at a  locus  that is observed in a particular population. [ 28 ]  This is simply the lesser of the two allele frequencies for single-nucleotide polymorphisms.


With this knowledge, scientists have developed new methods in analyzing population structures in less studied species. [ 29 ] [ 30 ] [ 31 ]  By using pooling techniques, the cost of the analysis is significantly lowered. [ citation needed ]  These techniques are based on sequencing a population in a pooled sample instead of sequencing every individual within the population by itself. With new bioinformatics tools, there is a possibility of investigating population structure, gene flow, and gene migration by observing the allele frequencies within the entire population. With these protocols there is a possibility for combining the advantages of SNPs with micro satellite markers. [ 32 ] [ 33 ]  However, there is information lost in the process, such as linkage disequilibrium and zygosity information.


Variations in the DNA sequences of humans can affect how humans develop  diseases  and respond to  pathogens ,  chemicals ,  drugs ,  vaccines , and other agents. SNPs are also critical for  personalized medicine . [ 37 ]  Examples include biomedical research, forensics, pharmacogenetics, and disease causation, as outlined below.


One of main contributions of SNPs in clinical research is genome-wide association study (GWAS). [ 38 ]  Genome-wide genetic data can be generated by multiple technologies, including SNP array and whole genome sequencing. GWAS has been commonly used in identifying SNPs associated with diseases or clinical phenotypes or traits. Since GWAS is a genome-wide assessment, a large sample site is required to obtain sufficient statistical power to detect all possible associations. Some SNPs have relatively small effect on diseases or clinical phenotypes or traits. To estimate study power, the genetic model for disease needs to be considered, such as dominant, recessive, or additive effects. Due to genetic heterogeneity, GWAS analysis must be adjusted for race.


Candidate gene association study is commonly used in genetic study before the invention of high throughput genotyping or sequencing technologies. [ 39 ]  Candidate gene association study is to investigate limited number of pre-specified SNPs for association with diseases or clinical phenotypes or traits. So this is a hypothesis driven approach. Since only a limited number of SNPs are tested, a relatively small sample size is sufficient to detect the association. Candidate gene association approach is also commonly used to confirm findings from GWAS in independent samples.


Genome-wide SNP data can be used for homozygosity mapping. [ 40 ]  Homozygosity mapping is a method used to identify homozygous autosomal recessive loci, which can be a powerful tool to map genomic regions or genes that are involved in disease pathogenesis.


Recently, preliminary results reported SNPs as important components of the epigenetic program in organisms. [ 41 ] [ 42 ]  Moreover, cosmopolitan studies in European and South Asiatic populations have revealed the influence of SNPs in the methylation of specific CpG sites. [ 43 ]  In addition, meQTL enrichment analysis using GWAS database, demonstrated that those associations are important toward the prediction of biological traits. [ 43 ] [ 44 ] [ 45 ]


SNPs have historically been used to match a forensic DNA sample to a suspect but has been made obsolete due to advancing  STR -based  DNA fingerprinting  techniques. However, the development of  next-generation-sequencing  (NGS) technology may allow for more opportunities for the use of SNPs in phenotypic clues such as  ethnicity ,  hair color , and  eye color  with a good probability of a match. This can additionally be applied to increase the accuracy of facial reconstructions by providing information that may otherwise be unknown, and this information can be used to help identify suspects even without a STR  DNA profile  match.


Some cons to using SNPs versus STRs is that SNPs yield less information than STRs, and therefore more SNPs are needed for analysis before a profile of a suspect is able to be created. Additionally, SNPs heavily rely on the presence of a database for comparative analysis of samples. However, in instances with degraded or small volume samples, SNP techniques are an excellent alternative to STR methods. SNPs (as opposed to STRs) have an abundance of potential markers, can be fully automated, and a possible reduction of required fragment length to less than 100 bp. [ 26 ]


Pharmacogenetics focuses on identifying genetic variations including SNPs associated with differential responses to treatment. [ 46 ]  Many drug metabolizing enzymes, drug targets, or target pathways can be influenced by SNPs. The SNPs involved in drug metabolizing enzyme activities can change drug pharmacokinetics, while the SNPs involved in drug target or its pathway can change drug  pharmacodynamics . Therefore, SNPs are potential genetic markers that can be used to predict drug exposure or effectiveness of the treatment. Genome-wide pharmacogenetic study is called  pharmacogenomics . Pharmacogenetics and pharmacogenomics are important in the development of precision medicine, especially for life-threatening diseases such as cancers.


Only small amount of SNPs in the human genome may have impact on human diseases. Large scale GWAS has been done for the most important human diseases, including  heart diseases ,  metabolic diseases ,  autoimmune diseases , and  neurodegenerative  and  psychiatric disorders . [ 38 ]  Most of the SNPs with relatively large effects on these diseases have been identified. These findings have significantly improved understanding of disease pathogenesis and molecular pathways, and facilitated development of better treatment. Further GWAS with larger samples size will reveal the SNPs with relatively small effect on diseases. For common and complex diseases, such as  type-2 diabetes ,  rheumatoid arthritis , and  Alzheimer's disease , multiple genetic factors are involved in disease etiology. In addition, gene-gene interaction and gene-environment interaction also play an important role in disease initiation and progression. [ 47 ]


As there are for genes,  bioinformatics  databases exist for SNPs.


The International SNP Map working group mapped the sequence flanking each SNP by alignment to the genomic sequence of large-insert clones in Genebank. These alignments were converted to chromosomal coordinates that is shown in Table 1. [ 60 ]  This list has greatly increased since, with, for instance, the Kaviar database now listing 162 million single nucleotide variants (SNVs).


The nomenclature for SNPs include several variations for an individual SNP, while lacking a common consensus.


The rs### standard is that which has been adopted by  dbSNP  and uses the prefix "rs", for "reference SNP", followed by a unique and arbitrary number. [ 61 ]  SNPs are frequently referred to by their dbSNP rs number, as in the examples above.


The Human Genome Variation Society (HGVS) uses a standard which conveys more information about the SNP. Examples are:


SNPs can be easily assayed due to only containing two possible  alleles  and three possible  genotypes  involving the two alleles:  homozygous  A, homozygous B and  heterozygous  AB, leading to many possible techniques for analysis. Some include:  DNA sequencing ;  capillary electrophoresis ;  mass spectrometry ;  single-strand conformation polymorphism  (SSCP);  single base extension ; electrochemical analysis; denaturating HPLC and  gel electrophoresis ;  restriction fragment length polymorphism ; and  hybridization  analysis.


An important group of SNPs are those that corresponds to  missense mutations  causing amino acid change on protein level.  Point mutation  of particular residue can have different effect on protein function (from no effect to complete disruption its function). Usually, change in amino acids with similar size and physico-chemical properties (e.g. substitution from leucine to valine) has mild effect, and opposite. Similarly, if SNP disrupts  secondary structure  elements (e.g. substitution to proline in  alpha helix  region) such mutation usually may affect whole protein structure and function. Using those simple and many other  machine learning  derived rules a group of programs for the prediction of SNP effect was developed: [ 66 ]


The  1000 Genomes Project  ( 1KGP ), taken place from January 2008 to 2015, was an international research effort to establish the most detailed catalogue of  human genetic variation  at the time. Scientists planned to  sequence  the  genomes  of at least one thousand anonymous healthy participants from a number of different ethnic groups within the following three years, using advancements in  newly developed technologies . In 2010, the project finished its pilot phase, which was described in detail in a publication in the journal  Nature . [ 1 ]  In 2012, the sequencing of 1092 genomes was announced in a  Nature  publication. [ 2 ]   In 2015, two papers in  Nature  reported results and the completion of the project and opportunities for future research. [ 3 ] [ 4 ]


Many rare variations, restricted to closely related groups, were identified, and eight structural-variation classes were analyzed. [ 5 ]


The project united multidisciplinary research teams from institutes around the world, including  China ,  Italy ,  Japan ,  Kenya ,  Nigeria ,  Peru , the  United Kingdom , and the  United States  contributing to the sequence dataset and to a refined  human genome map  freely accessible through public databases to the scientific community and the general public alike. [ 2 ]


The  International Genome Sample Resource  was created to host and expand on the data set after the project's end. [ 6 ]


Since the completion of the  Human Genome Project  advances in human  population genetics  and  comparative genomics  enabled further insight into genetic diversity. [ 7 ]  The understanding about  structural variations  (insertions/deletions ( indels ),  copy number variations  (CNV),  retroelements ),  single-nucleotide polymorphisms  (SNPs), and  natural selection  were being solidified. [ 8 ] [ 9 ] [ 10 ] [ 11 ]


The diversity of  Human genetic variation  such as that  Indels  were being uncovered and investigating human genomic variations [ citation needed ]


It also aimed to provide evidence that can be used to explore the impact of  natural selection  on population differences. Patterns of  DNA polymorphisms  can be used to reliably detect signatures of selection and may help to identify genes that might underlie variation in disease resistance or drug metabolism. [ 12 ] [ 13 ]  Such insights could improve understanding of  phenotypic variations ,  genetic disorders  and  Mendelian inheritance  and their effects on survival and/or reproduction of different human populations.


The 1000 Genomes Project was designed to bridge the gap of knowledge between rare genetic variants that have a severe effect predominantly on simple traits (e.g.  cystic fibrosis ,  Huntington disease ) and common genetic variants have a mild effect and are implicated in complex traits (e.g.  cognition ,  diabetes ,  heart disease ). [ 14 ]


The primary goal of this project was to create a complete and detailed catalogue of  human genetic variations , which can be used for  association studies  relating genetic variation to disease. The consortium aimed to discover >95 % of the variants (e.g. SNPs, CNVs, indels) with  minor allele frequencies  as low as 1% across the genome and 0.1-0.5% in gene regions, as well as to estimate the population frequencies,  haplotype  backgrounds and  linkage disequilibrium  patterns of variant alleles. [ 15 ]


Secondary goals included the support of better SNP and probe selection for  genotyping  platforms in future studies and the improvement of the  human reference sequence . The completed database was expected be a useful tool for studying regions under selection, variation in multiple populations and understanding the underlying processes of mutation and  recombination . [ 15 ]


The  human genome  consists of approximately 3 billion DNA base pairs and is estimated to carry around 20,000  protein  coding  genes . In designing the study the consortium needed to address several critical issues regarding the project metrics such as technology challenges, data quality standards and sequence coverage. [ 15 ]


Over the course of the next three years, [ clarification needed ]  scientists at the  Sanger Institute ,  BGI Shenzhen  and the  National Human Genome Research Institute ’s Large-Scale Sequencing Network planned to sequence a minimum of 1,000 human genomes. Due to the large amount of sequence data that was required, recruiting additional participants was maintained. [ 14 ]


Almost 10 billion bases were to be sequenced per day over a period of the two year production phase, equating to more than two human genomes every 24 hours. The intended sequence dataset was to comprise 6 trillion DNA bases, 60-fold more sequence data than what has been published in  DNA  databases at the time. [ 14 ]


To determine the final design of the full project three pilot studies were to be carried out within the first year of the project. The first pilot intends to genotype 180 people of 3  major geographic groups  at low coverage (2×). For the second pilot study, the genomes of two nuclear families (both parents and an adult child) are going to be sequenced with deep coverage (20× per genome). The third pilot study involves sequencing the coding regions ( exons ) of 1,000 genes in 1,000 people with deep coverage (20×). [ 14 ] [ 15 ]


It was estimated that the project would likely cost more than $500 million if standard DNA sequencing technologies were used. Several newer technologies (e.g.  Solexa ,  454 ,  SOLiD ) were to be applied, lowering the expected costs to between $30 million and $50 million. The major support was provided by the  Wellcome Trust Sanger Institute  in Hinxton, England; the  Beijing Genomics Institute , Shenzhen (BGI Shenzhen), China; and the  NHGRI , part of the National Institutes of Health (NIH). [ 14 ]


In keeping with  Fort Lauderdale principles [ 16 ]  all genome sequence data (including variant calls) is freely available as the project progresses and can be downloaded via ftp from the 1000 genomes project webpage. [ 17 ]


Based on the overall goals for the project, the samples will be chosen to provide power in populations where  association studies  for common diseases are being carried out. Furthermore, the samples do not need to have medical or phenotype information since the proposed catalogue will be a basic resource on human variation. [ 15 ]


For the pilot studies human genome samples from the  HapMap  collection will be sequenced. It will be useful to focus on samples that have additional data available (such as  ENCODE  sequence, genome-wide genotypes,  fosmid -end sequence, structural variation assays, and  gene expression ) to be able to compare the results with those from other projects. [ 15 ]


Complying with extensive ethical procedures, the 1000 Genomes Project will then use samples from volunteer donors. The following populations will be included in the study:  Yoruba  in  Ibadan  (YRI),  Nigeria ;  Japanese  in  Tokyo  (JPT);  Chinese  in  Beijing  (CHB);  Utah  residents with ancestry from northern and western  Europe  (CEU);  Luhya  in  Webuye ,  Kenya  (LWK);  Maasai  in  Kinyawa , Kenya (MKK); Toscani in  Italy  (TSI); Peruvians in  Lima ,  Peru  (PEL); Gujarati Indians in  Houston  (GIH); Chinese in metropolitan  Denver  (CHD); people of  Mexican  ancestry in  Los Angeles  (MXL); and people of  African  ancestry in the southwestern  United States  (ASW). [ 14 ]


* Population that was collected in diaspora


Data generated by the 1000 Genomes Project is widely used by the genetics community, making the first 1000 Genomes Project one of the most cited papers in biology. [ 19 ]  To support this user community, the project held a community analysis meeting in July 2012 that included talks highlighting key project discoveries, their impact on population genetics and human disease studies, and summaries of other large-scale sequencing studies. [ 20 ]


The pilot phase consisted of three projects:


It was found that on average, each person carries around 250–300 loss-of-function variants in annotated genes and 50-100 variants previously implicated in inherited disorders. Based on the two trios, it is estimated that the rate of de novo germline mutation is approximately 10 −8  per base per generation. [ 1 ]


Genome-wide complex trait analysis  ( GCTA )  Genome-based  restricted maximum likelihood  ( GREML ) is a statistical method for  heritability  estimation in genetics, which quantifies the total additive contribution of a set of genetic variants to a trait. GCTA is typically applied to common single nucleotide polymorphisms ( SNPs ) on a genotyping array (or "chip") and thus termed "chip" or "SNP" heritability.


GCTA operates by directly quantifying the chance genetic similarity of unrelated individuals and comparing it to their measured similarity on a trait; if two unrelated individuals are relatively similar genetically and also have similar trait measurements, then the measured genetics are likely to causally influence that trait, and the correlation can to some degree tell how much. This can be illustrated by plotting the squared pairwise trait differences between individuals against their estimated degree of relatedness. [ 1 ]  GCTA makes a number of modeling assumptions and whether/when these assumptions are satisfied continues to be debated.


The GCTA framework has also been extended in a number of ways: quantifying the contribution from multiple SNP categories (i.e. functional partitioning); quantifying the contribution of Gene-Environment interactions; quantifying the contribution of non-additive/non-linear effects of SNPs; and bivariate analyses of multiple phenotypes to quantify their genetic covariance (co-heritability or  genetic correlation ).


GCTA estimates have implications for the potential for discovery from  Genome-wide Association Studies (GWAS)  as well as the design and accuracy of  polygenic scores . GCTA estimates from common variants are typically substantially lower than other estimates of total or narrow-sense heritability (such as from twin or kinship studies), which has contributed to the debate over the  Missing heritability problem .


Estimation in biology/animal breeding using standard  ANOVA / REML  methods of variance components such as heritability, shared-environment, maternal effects etc. typically requires individuals of known relatedness such as parent/child; this is often unavailable or the pedigree data unreliable, leading to inability to apply the methods or requiring strict laboratory control of all breeding (which threatens the  external validity  of all estimates), and several authors have noted that relatedness could be measured directly from genetic markers (and if individuals were reasonably related, economically few markers would have to be obtained for statistical power), leading Kermit Ritland to propose in 1996 that directly measured pairwise relatedness could be compared to pairwise phenotype measurements (Ritland 1996,  "A Marker-based Method for Inferences About Quantitative Inheritance in Natural Populations"   Archived  2009-06-11 at the  Wayback Machine [ 2 ] ).


As genome sequencing costs dropped steeply over the 2000s, acquiring enough markers on enough subjects for reliable estimates using very distantly related individuals became possible. An early application of the method to humans came with Visscher et al. 2006 [ 3 ] /2007, [ 4 ]  which used SNP markers to estimate the actual relatedness of siblings and estimate heritability from the direct genetics. In humans, unlike the original animal/plant applications, relatedness is usually known with high confidence in the 'wild population', and the benefit of GCTA is connected more to avoiding assumptions of classic behavioral genetics designs and verifying their results, and partitioning heritability by SNP class and chromosomes. The first use of GCTA proper in humans was published in 2010, finding 45% of variance in human height can be explained by the included SNPs. [ 5 ] [ 6 ]  (Large GWASes on height have since confirmed the estimate. [ 7 ] ) The GCTA algorithm was then described and a software implementation published in 2011. [ 8 ]  It has since been used to study a wide variety of biological, medical, psychiatric, and psychological traits in humans, and inspired many variant approaches.


Twin and family studies have long been used to estimate variance explained by particular categories of genetic and environmental causes. Across a wide variety of human traits studied, there is typically minimal shared-environment influence, considerable non-shared environment influence, and a large genetic component (mostly additive), which is on average ~50% and sometimes much higher for some traits such as height or intelligence. [ 9 ]  However, the twin and family studies have been criticized for their reliance on a number of assumptions that are difficult or impossible to verify, such as the equal environments assumption (that the environments of  monozygotic  and  dizygotic  twins are equally similar), that there is no misclassification of zygosity (mistaking identical for fraternal & vice versa), that twins are unrepresentative of the general population, and that there is no  assortative mating . Violations of these assumptions can result in both upwards and downwards bias of the parameter estimates. [ 10 ]  (This debate & criticism have particularly focused on the  heritability of IQ .)


The use of SNP or whole-genome data from unrelated subject participants (with participants too related, typically >0.025 or ~fourth cousins levels of similarity, being removed, and several  principal components  included in the regression to avoid & control for  population stratification ) bypasses many heritability criticisms: twins are often entirely uninvolved, there are no questions of equal treatment, relatedness is estimated precisely, and the samples are drawn from a broad variety of subjects.


In addition to being more robust to violations of the twin study assumptions, SNP data can be easier to collect since it does not require rare twins and thus also heritability for rare traits can be estimated (with due correction for  ascertainment bias ).


GCTA estimates can be used to resolve the  missing heritability problem  and  design  GWASes which will yield genome-wide statistically-significant hits. This is done by comparing the GCTA estimate with the results of smaller GWASes. If a GWAS of n=10k using SNP data fails to turn up any hits, but the GCTA indicates a high heritability accounted for by SNPs, then that implies that a large number of variants are involved ( polygenicity ) and thus that much larger GWASes will be required to accurately estimate each SNP's effect and directly account for a fraction of the GCTA heritability.


GCTA provides an unbiased estimate of the total variance in phenotype explained by all variants included in the relatedness matrix (and any variation correlated with those SNPs). This estimate can also be interpreted as the maximum prediction accuracy (R^2) that could be achieved from a linear predictor using all SNPs in the relatedness matrix. The latter interpretation is particularly relevant to the development of Polygenic Risk Scores, as it defines their maximum accuracy. GCTA estimates are sometimes misinterpreted as estimates of total (or narrow-sense, i.e. additive) heritability, but this is not a guarantee of the method. GCTA estimates are likewise sometimes misinterpreted as "lower bounds" on the narrow-sense heritability but this is also incorrect: first because GCTA estimates can be biased (including biased upwards) if the model assumptions are violated, and second because, by definition (and when model assumptions are met), GCTA can provide an unbiased estimate of the narrow-sense heritability if all causal variants are included in the relatedness matrix. The interpretation of the GCTA estimate in relation to the narrow-sense heritability thus depends on the variants used to construct the relatedness matrix.


Most frequently, GCTA is run with a single relatedness matrix constructed from common SNPs and will not capture (or not fully capture) the contribution of the following factors:


GCTA makes several model assumptions and may produce biased estimates under the following conditions:


The original "GCTA" software package is the most widely used; its primary functionality covers the GREML estimation of SNP heritability, but includes other functionality:


Other implementations and variant algorithms include:


The  International HapMap Project  was an organization that aimed to develop a  haplotype   map  ( HapMap ) of the  human genome , to describe the common patterns of human  genetic variation . HapMap is used to find genetic variants affecting health, disease and responses to drugs and environmental factors. The information produced by the project is made freely available for research.


The International HapMap Project is a collaboration among researchers at academic centers, non-profit biomedical research groups and private companies in  Canada ,  China  (including  Hong Kong ),  Japan ,  Nigeria , the  United Kingdom , and the  United States . It officially started with a meeting on October 27 to 29, 2002, and was expected to take about three years. It comprises three phases; the complete data obtained in Phase I were published on 27 October 2005. [ 1 ]  The analysis of the Phase II dataset was published in October 2007. [ 2 ]   The Phase III dataset was released in spring 2009 and the publication presenting the final results published in September 2010. [ 3 ]


Unlike with the  rarer   Mendelian  diseases, combinations of different  genes  and the environment play a role in the development and progression of common diseases (such as  diabetes ,  cancer ,  heart disease ,  stroke ,  depression , and  asthma ), or in the individual response to  pharmacological  agents. [ 4 ]  To find the genetic factors involved in these diseases, one could in principle do a  genome-wide association study : obtain the complete genetic sequence of several individuals, some with the disease and some without, and then search for differences between the two sets of genomes. At the time, this approach was not feasible because of the cost of  full genome sequencing . The HapMap project proposed a shortcut.


Although any two unrelated people share about 99.5% of their  DNA  sequence, their  genomes  differ at specific  nucleotide  locations. Such sites are known as  single nucleotide polymorphisms  (SNPs), and each of the possible resulting gene forms is called an  allele . [ 5 ]  The HapMap project focuses only on common SNPs, those where each allele occurs in at least 1% of the population.


Each person has two copies of all  chromosomes , except the  sex chromosomes  in  males . For each SNP, the combination of alleles a person has is called a  genotype .  Genotyping  refers to uncovering what genotype a person has at a particular site. The HapMap project chose a sample of 269 individuals and selected several million well-defined SNPs, genotyped the individuals for these SNPs, and published the results. [ 6 ]


The alleles of nearby SNPs on a single chromosome are correlated. Specifically, if the allele of one SNP for a given individual is known, the alleles of nearby SNPs can often be predicted, a process known as  genotype imputation . [ 7 ]  This is because each SNP arose in evolutionary history as a single point  mutation , and was then passed down on the chromosome surrounded by other, earlier, point mutations. SNPs that are separated by a large distance on the chromosome are typically not very well correlated, because  recombination  occurs in each generation and mixes the allele sequences of the two chromosomes. A sequence of consecutive alleles on a particular chromosome is known as a  haplotype . [ 8 ]


To find the genetic factors involved in a particular disease, one can proceed as follows. First a certain region of interest in the genome is identified, possibly from earlier inheritance studies. In this region one locates a set of  tag SNPs  from the HapMap data; these are SNPs that are very well correlated with all the other SNPs in the region. Using these, genotype imputation can be used to determine (impute) the other SNPs and thus the entire haplotype with high confidence. Next, one determines the genotype for these tag SNPs in several individuals, some with the disease and some without. By comparing the two groups, one determines the likely locations and haplotypes that are involved in the disease.


Haplotypes  are generally shared between populations, but their frequency can differ widely. Four populations were selected for inclusion in the HapMap: 30 adult-and-both-parents  Yoruba  trios from  Ibadan ,  Nigeria  (YRI), 30 trios of Utah residents of northern and western  European  ancestry (CEU), 44 unrelated Japanese individuals from  Tokyo ,  Japan  (JPT) and 45 unrelated  Han Chinese  individuals from  Beijing ,  China  (CHB). Although the haplotypes revealed from these populations should be useful for studying many other populations, parallel studies are currently examining the usefulness of including additional populations in the project.


All samples were collected through a community engagement process with appropriate informed consent. The community engagement process was designed to identify and attempt to respond to culturally specific concerns and give participating communities input into the informed consent and sample collection processes. [ 9 ]


In phase III, 11 global ancestry groups have been assembled: ASW (African ancestry in Southwest USA); CEU (Utah residents with Northern and Western European ancestry from the CEPH collection); CHB (Han Chinese in Beijing, China); CHD (Chinese in Metropolitan Denver, Colorado); GIH (Gujarati Indians in Houston, Texas); JPT (Japanese in Tokyo, Japan); LWK (Luhya in Webuye, Kenya); MEX (Mexican ancestry in Los Angeles, California); MKK (Maasai in Kinyawa, Kenya); TSI (Tuscans in Italy); YRI (Yoruba in Ibadan, Nigeria). [ 10 ]


Three combined panels have also been created, which allow better identification of SNPs in groups outside the nine homogenous samples: CEU+TSI (Combined panel of Utah residents with Northern and Western European ancestry from the CEPH collection and Tuscans in Italy); JPT+CHB (Combined panel of Japanese in Tokyo, Japan and Han Chinese in Beijing, China) and JPT+CHB+CHD (Combined panel of Japanese in Tokyo, Japan, Han Chinese in Beijing, China and Chinese in Metropolitan Denver, Colorado). CEU+TSI, for instance, is a better model of UK British individuals than is CEU alone. [ 10 ]


It was expensive in the 1990s to sequence patients’ whole genomes. So the  National Institutes of Health  embraced the idea for a "shortcut", which was to look just at sites on the genome where many people have a variant DNA unit. The theory behind the shortcut was that, since the major diseases are common, so too would be the genetic variants that caused them.  Natural selection  keeps the human genome free of variants that damage health before children are grown, the theory held, but fails against variants that strike later in life, allowing them to become quite common (In 2002 the  National Institutes of Health  started a $138 million project called the  HapMap  to catalog the common variants in European, East Asian and African genomes). [ 11 ]


For the Phase I, one common SNP was genotyped every 5,000 bases. Overall, more than one million SNPs were genotyped. The genotyping was carried out by 10 centres using five different genotyping technologies. Genotyping quality was assessed by using duplicate or related samples and by having periodic quality checks where centres had to genotype common sets of SNPs.


The Canadian team was led by  Thomas J. Hudson  at  McGill University  in  Montreal  and focused on chromosomes 2 and 4p. The Chinese team was led by  Huanming Yang  in  Beijing  and  Shanghai , and  Lap-Chee Tsui  in  Hong Kong  and focused on chromosomes 3, 8p and 21. The Japanese team was led by  Yusuke Nakamura  at the  University of Tokyo  and focused on chromosomes 5, 11, 14, 15, 16, 17 and 19. The British team was led by  David R. Bentley  at the  Sanger Institute  and focused on chromosomes 1, 6, 10, 13 and 20. There were four United States' genotyping centres: a team led by  Mark Chee  and  Arnold Oliphant  at  Illumina Inc.  in  San Diego  (studying chromosomes 8q, 9, 18q, 22 and X), a team led by  David Altshuler  and  Mark Daly  at the  Broad Institute  in  Cambridge, USA  (chromosomes 4q, 7q, 18p, Y and  mitochondrion ), a team led by  Richard Gibbs  at the  Baylor College of Medicine  in  Houston  (chromosome 12), and a team led by  Pui-Yan Kwok  at the  University of California, San Francisco  (chromosome 7p).


To obtain enough SNPs to create the Map, the Consortium funded a large re-sequencing project to discover millions of additional SNPs. These were submitted to the public  dbSNP  database. As a result, by August 2006, the database included more than ten million SNPs, and more than 40% of them were known to be  polymorphic . By comparison, at the start of the project, fewer than 3 million SNPs were identified, and no more than 10% of them were known to be polymorphic.


During Phase II, more than two million additional SNPs were genotyped throughout the genome by David R. Cox,  Kelly A. Frazer  and others at  Perlegen Sciences  and 500,000 by the company  Affymetrix .


All of the data generated by the project, including SNP frequencies,  genotypes  and  haplotypes , were placed in the public domain and are available for download. [ 12 ]  This website also contains a genome browser which allows to find SNPs in any region of interest, their allele frequencies and their association to nearby SNPs. A tool that can determine tag SNPs for a given region of interest is also provided. These data can also be directly accessed from the widely used  Haploview  program.


A  Manhattan plot  is a type of  plot , usually used to display data with a large number of data-points, many of non-zero amplitude, and with a distribution of higher-magnitude values. The plot is commonly used in  genome-wide association studies  (GWAS) to display significant  SNPs . [ 1 ]


It gains its name from the similarity of such a plot to the  Manhattan   skyline : a profile of  skyscrapers  towering above the lower level "buildings" which vary around a lower height.


In GWAS Manhattan plots, genomic coordinates are displayed along the  x -axis, with the negative  logarithm  of the association  p -value  for each  single nucleotide polymorphism  (SNP) displayed on the  y -axis, meaning that each dot on the Manhattan plot signifies an SNP. Because the strongest associations have the smallest  p -values (e.g., 10 −15 ), their negative logarithms will be the greatest (e.g., 15). The different colors of each block usually show the extent of each chromosome.


This  statistics -related article is a  stub . You can help Wikipedia by  expanding it .


mt-SNP  is a  single nucleotide polymorphism  on the  mitochondrial   chromosome . mt-SNPs are often used in maternal  genealogical DNA testing . [ 1 ]


A single nucleotide polymorphism (SNP) is a change to a single  nucleotide  in a  DNA sequence . The relative  mutation rate  for an SNP is extremely low. This makes them ideal for marking the history of the human genetic tree. SNPs are named with a letter code and a number. The letter indicates the lab or research team that discovered the SNP. The number indicates the order in which it was discovered. For example M173 is the 173rd SNP, documented by the Human Population Genetics Laboratory at  Stanford University , which uses the letter M.


SNPedia  (pronounced "snipedia") is a  wiki -based  bioinformatics  web site that serves as a database of  single nucleotide polymorphisms  (SNPs). Each article on a SNP provides a short description, links to scientific articles and  personal genomics  web sites, as well as microarray information about that SNP. Thus SNPedia may support the interpretation of results of personal genotyping from, e.g.,  23andMe  and similar companies. [ 1 ]


SNPedia is a  semantic wiki , powered by  MediaWiki  and the  Semantic MediaWiki  extension.


SNPedia was created, and is run by, geneticist Greg Lennon [ 2 ]  and programmer Mike Cariaso, [ 3 ]  who at the time of the site's founding were both located in  Bethesda, Maryland . [ 4 ]


As of 27 June 2019 [update] , the website has 537 medical conditions [ 5 ]  and 109,729 SNPs in its database. [ 6 ]  The number of SNPs in SNPedia has doubled roughly once every 14 months since August 2007. [ 7 ]


On 7 September 2019,  MyHeritage  announced that they acquired both SNPedia and Promethease. All non-European raw genetic data files previously uploaded to Promethease, and not deleted by users by 1 Nov 2019, are to be copied to MyHeritage and the users will receive a free MyHeritage account with paid level of services, including Cousin Matching and Ethnicities. [ 8 ]


An associated computer program called Promethease, also developed by the SNPedia team, allows users to compare personal genetics results against the SNPedia database, generating a report with information about a person's attributes, such as propensity to diseases, based on the presence of specific SNPs within their genome. [ 3 ]


In May 2008 Cariaso, using Promethease, won an online contest sponsored by 23andMe to determine as much information as possible about an anonymous woman based only on her genome. Cariaso won in all three categories of "accuracy, creativity and cleverness". [ 9 ]  In 2009, the anonymous woman ("Lilly Mendel") was revealed to be 23andMe co-founder  Linda Avey , allowing a direct comparison between her actual traits and those predicted by Promethease a year earlier. [ 10 ]


In a June 2008 article on personal genomics, a doctor from the  Southern Illinois University School of Medicine  said:


The availability of online tools such as SNPedia means we are now in the position where the patient often knows more about their risk implications than their doctor [...] [ 11 ]


In January 2011, technology journalist  Ronald Bailey  posted the full result of his Promethease report online. Writing about his decision in  Reason  magazine, he stated:


We are fast approaching an era in which genetic information is no longer exclusive or medicalized. Instead, as screening costs plummet and our knowledge about genetics expands, virtually everyone will soon be able to have their genotypes at their fingertips. Knowing and sharing that information will enhance, not jeopardize, our sense of ourselves, change the way we consume medicine and plan for the future, and influence how we relate to each other. [ 12 ]


Members of the medical community have criticised Promethease for technical complexity and a poorly defined "magnitude" scale that causes misconceptions, confusion and panic among its users. [ 13 ] [ 14 ] [ 15 ]


Tiger eye  or  goat eye  is a gene causing diluted eye color in horses. There are two variants, Tiger-eye 1 (TE1) and Tiger-eye 2 (TE2), which are both  recessive . [ 1 ]  Horses displaying tiger eye typically have a yellow, orange, or amber  iris . Tiger eye has only been found in  Puerto Rican Paso Fino  horses. Horses of related breeds were tested (90  Colombian Pasos , 20  Mangalargas , 44  Lusitanos , and 42  Andalusian horses ), and none were found to have either tiger eye allele. No obvious link between eye shade and coat color was seen, making this the first studied gene in horses to affect eye color but not coat color. Tiger eye does not appear to affect vision, and there were no signs of reduced pigment on the  retina  or  retinal pigment epithelium . [ 2 ]


The  gene  involved codes for  SLC24A5 , a solute carrier known to be involved in pigmentation in other species.  SLC24A5  is found on equine chromosome 1 base pairs 141,657,837–141,678,329 and the  protein  is a potassium-dependent sodium–calcium ion exchanger involved in  melanocyte  maturation. The protein is believed to be located in the  trans -golgi network  of melanocytes. Tiger-eye 1 is a missense mutation (c.272A>T and p.Phe91Tyr) in which a single  adenine  is replaced with a  thymine  in  exon  2, changing a  phenylalanine  to a  tyrosine  in the resulting protein. Tiger-eye 2 is a deletion (c.875-340_1081+82del) in which the entirety of exon 7, and a bit of the  introns  on either side, are removed, resulting in a protein that is 69 amino acids shorter. Both mutations are predicted to be deleterious to protein function. [ 2 ] [ 3 ]


Another mutation at this same gene is associated with the shade of black horses. One variant is mainly found in jet black horses, while the other variant is mainly found in fading black horses. However, the association is not perfect and there are probably other mutations that can also cause the fading black appearance. [ 4 ]


SLC24A5  is involved in pigmentation in humans, mice, and zebrafish. In mice a targeted mutation was found which diluted the eye color without visibly affecting coat color, though  a closer examination found that the  melanosomes  were smaller and paler than the  wild type . [ 5 ]  Unlike tiger eye in horses, the mice showed reduced pigment in the retinal pigment epithelium. In humans a widespread mutation to the homologous gene plays a large role in the light skin color of European humans, [ 6 ]  and another mutation can cause oculocutaneous albinism (OCA) type 6 (OCA6), which impairs vision. No vision impairment is seen in horses, and the coat color is not visibly affected. [ 2 ]


A  Y-SNP  is a  single-nucleotide polymorphism  on the  Y chromosome . Y-SNPs are often used in paternal  genealogical DNA testing . [ 1 ]


A single nucleotide polymorphism (SNP) is a change to a single  nucleotide  in a  DNA sequence . [ 2 ]  The relative  mutation rate  for an SNP is extremely low. [ 3 ]  This makes them ideal for marking the history of the human genetic tree. SNPs are named with a letter code and a number. The letter indicates the lab or research team that discovered the SNP. The number indicates the order in which it was discovered. For example, M173 is the 173rd SNP documented by the Human Population Genetics Laboratory at  Stanford University , which uses the letter M.


In  biology , a  mutation  is an alteration in the  nucleic acid sequence  of the  genome  of an  organism ,  virus , or  extrachromosomal DNA . [ 1 ]  Viral genomes contain either  DNA  or  RNA . Mutations result from errors during  DNA  or  viral replication ,  mitosis , or  meiosis  or other types of  damage  to DNA (such as  pyrimidine dimers  caused by exposure to  ultraviolet  radiation), which then may undergo error-prone repair (especially  microhomology-mediated end joining ), [ 2 ]  cause an error during other forms of repair, [ 3 ] [ 4 ]  or cause an error during replication ( translesion synthesis ). Mutations may also result from  substitution ,  insertion  or  deletion  of segments of DNA due to  mobile genetic elements . [ 5 ] [ 6 ] [ 7 ]


Mutations may or may not produce detectable changes in the observable characteristics ( phenotype ) of an organism. Mutations play a part in both normal and abnormal biological processes including:  evolution ,  cancer , and the development of the  immune system , including  junctional diversity . Mutation is the ultimate source of all  genetic variation , providing the raw material on which evolutionary forces such as  natural selection  can act.


Mutation can result in many different types of change in sequences. Mutations in  genes  can have no effect, alter the  product of a gene , or prevent the gene from functioning properly or completely. Mutations can also occur in  non-genic regions . A 2007 study on  genetic variations  between different  species  of  Drosophila  suggested that, if a mutation changes a  protein  produced by a gene, the result is likely to be harmful, with an estimated 70% of  amino acid   polymorphisms  that have damaging effects, and the remainder being either neutral or marginally beneficial. [ 8 ]


Mutation and  DNA damage  are the two major types of errors that occur in DNA, but they are fundamentally different.  DNA damage is a physical alteration in the DNA structure, such as a single or double strand break, a modified guanosine residue in DNA such as  8-hydroxydeoxyguanosine , or a  polycyclic aromatic hydrocarbon  adduct.  DNA damages can be recognized by enzymes, and therefore can be correctly repaired using the complementary undamaged strand in DNA as a template or an undamaged sequence in a homologous chromosome if it is available.  If DNA damage remains in a cell,  transcription  of a gene may be prevented and thus translation into a protein may also be blocked.   DNA replication  may also be blocked and/or the cell may die.  In contrast to a DNA damage, a mutation is an alteration of the base sequence of the DNA.  Ordinarily, a mutation cannot be recognized by enzymes once the base change is present in both DNA strands, and thus a mutation is not ordinarily repaired.  At the cellular level, mutations can alter protein function and regulation.  Unlike DNA damages, mutations are replicated when the cell replicates.  At the level of cell populations, cells with mutations will increase or decrease in frequency according to the effects of the mutations on the ability of the cell to survive and reproduce.  Although distinctly different from each other, DNA damages and mutations are related because DNA damages often cause errors of DNA synthesis during replication or repair and these errors are a major source of mutation. [ 9 ]


Mutations can involve the  duplication  of large sections of DNA, usually through  genetic recombination . [ 10 ]  These duplications are a major source of raw material for evolving new genes, with tens to hundreds of genes duplicated in animal genomes every million years. [ 11 ]  Most genes belong to larger  gene families  of shared ancestry, detectable by their  sequence homology . [ 12 ]  Novel genes are produced by several methods, commonly through the duplication and mutation of an ancestral gene, or by recombining parts of different genes to form new combinations with new functions. [ 13 ] [ 14 ]


Here,  protein domains  act as modules, each with a particular and independent function, that can be mixed together to produce genes encoding new proteins with novel properties. [ 15 ]  For example, the  human eye  uses four genes to make structures that sense light: three for  cone cell  or  colour vision  and one for  rod cell  or night vision; all four arose from a single ancestral gene. [ 16 ]  Another advantage of duplicating a gene (or even an entire genome) is that this increases  engineering redundancy ; this allows one gene in the pair to acquire a new function while the other copy performs the original function. [ 17 ] [ 18 ]  Other types of mutation occasionally create new genes from previously  noncoding DNA . [ 19 ] [ 20 ]


Changes in  chromosome  number may involve even larger mutations, where segments of the DNA within chromosomes break and then rearrange. For example, in the  Homininae , two chromosomes fused to produce human  chromosome 2 ; this fusion did not occur in the  lineage  of the other  apes , and they retain these separate chromosomes. [ 21 ]  In evolution, the most important role of such chromosomal rearrangements may be to accelerate the divergence of a population into  new species  by making populations less likely to interbreed, thereby preserving genetic differences between these populations. [ 22 ]


Sequences of DNA that can move about the genome, such as  transposons , make up a major fraction of the genetic material of plants and animals, and may have been important in the evolution of genomes. [ 23 ]  For example, more than a million copies of the  Alu sequence  are present in the  human genome , and these sequences have now been recruited to perform functions such as regulating  gene expression . [ 24 ]  Another effect of these mobile DNA sequences is that when they move within a genome, they can mutate or delete existing genes and thereby produce genetic diversity. [ 6 ]


Nonlethal mutations accumulate within the  gene pool  and increase the amount of genetic variation. [ 25 ]  The abundance of some genetic changes within the gene pool can be reduced by  natural selection , while other "more favorable" mutations may accumulate and result in adaptive changes.


For example, a  butterfly  may produce  offspring  with new mutations. The majority of these mutations will have no effect; but one might change the  colour  of one of the butterfly's offspring, making it harder (or easier) for predators to see. If this color change is advantageous, the chances of this butterfly's surviving and producing its own offspring are a little better, and over time the number of butterflies with this mutation may form a larger percentage of the population. [ citation needed ]


Neutral mutations  are defined as mutations whose effects do not influence the  fitness  of an individual. These can increase in frequency over time due to  genetic drift . It is believed that the overwhelming majority of mutations have no significant effect on an organism's fitness. [ 26 ] [ 27 ]  Also, DNA repair mechanisms are able to mend most changes before they become permanent mutations, and many organisms have mechanisms, such as  apoptotic pathways , for eliminating otherwise-permanently mutated  somatic cells . [ 28 ]


Beneficial mutations can improve reproductive success. [ 29 ] [ 30 ]


Four classes of mutations are (1)  spontaneous  mutations (molecular decay), (2) mutations due to error-prone replication bypass of  naturally occurring DNA damage  (also called error-prone translesion synthesis), (3) errors introduced during DNA repair, and (4) induced mutations caused by  mutagens . Scientists may sometimes deliberately introduce mutations into cells or research organisms for the sake of scientific experimentation. [ 31 ]


One 2017 study claimed that 66% of cancer-causing mutations are random, 29% are due to the environment (the studied population spanned 69 countries), and 5% are inherited. [ 32 ]


Humans on average pass 60 new mutations to their children but fathers pass more mutations depending on their age with every year adding two new mutations to a child. [ 33 ]


Spontaneous mutations  occur with non-zero probability even given a healthy, uncontaminated cell. Naturally occurring oxidative DNA damage is estimated to occur 10,000 times per cell per day in humans and 100,000 times per cell per day in  rats . [ 34 ]  Spontaneous mutations can be characterized by the specific change: [ 35 ]


There is increasing evidence that the majority of spontaneously arising mutations are due to error-prone replication ( translesion synthesis ) past DNA damage in the template strand. In  mice , the majority of mutations are caused by translesion synthesis. [ 38 ]  Likewise, in  yeast , Kunz et al. [ 39 ]  found that more than 60% of the spontaneous single base pair substitutions and deletions were caused by translesion synthesis.


Although naturally occurring double-strand breaks occur at a relatively low frequency in DNA, their repair often causes mutation.  Non-homologous end joining  (NHEJ) is a major pathway for repairing double-strand breaks. NHEJ involves removal of a few  nucleotides  to allow somewhat inaccurate alignment of the two ends for rejoining followed by addition of nucleotides to fill in gaps. As a consequence, NHEJ often introduces mutations. [ 40 ]


Induced mutations are alterations in the gene after it has come in contact with mutagens and environmental causes.


Induced mutations  on the molecular level can be caused by:


Whereas in former times mutations were assumed to occur by chance, or induced by mutagens, molecular mechanisms of mutation have been discovered in bacteria and across the tree of life. As S. Rosenberg states, "These mechanisms reveal a picture of highly regulated mutagenesis, up-regulated temporally by stress responses and activated when cells/organisms are maladapted to their environments—when stressed—potentially accelerating adaptation." [ 44 ]  Since they are self-induced mutagenic mechanisms that increase the adaptation rate of organisms, they have some times been named as adaptive mutagenesis mechanisms, and include the SOS response in bacteria, [ 45 ]  ectopic intrachromosomal recombination [ 46 ]  and other chromosomal events such as duplications. [ 44 ]


The sequence of a gene can be altered in a number of ways. [ 47 ]  Gene mutations have varying effects on health depending on where they occur and whether they alter the function of essential proteins.
Mutations in the structure of genes can be classified into several types. [ citation needed ]


Large-scale mutations in  chromosomal  structure include:


Small-scale mutations affect a gene in one or a few nucleotides. (If only a single nucleotide is affected, they are called  point mutations .) Small-scale mutations include:


The effect of a mutation on protein sequence depends in part on where in the genome it occurs, especially whether it is in a  coding  or  non-coding region . Mutations in the non-coding  regulatory sequences  of a gene, such as promoters, enhancers, and silencers, can alter levels of gene expression, but are less likely to alter the protein sequence. Mutations within  introns  and in regions with no known biological function (e.g.  pseudogenes ,  retrotransposons ) are generally  neutral , having no effect on phenotype – though intron mutations could alter the protein product if they affect mRNA splicing.


Mutations that occur in coding regions of the genome are more likely to alter the protein product, and can be categorized by their effect on amino acid sequence:


A mutation becomes an effect on function mutation when the exactitude of functions between a mutated protein and its direct interactor undergoes change. The interactors can be other proteins, molecules, nucleic acids, etc. There are many mutations that fall under the category of by effect on function, but depending on the specificity of the change the mutations listed below will occur. [ 53 ]


In  genetics , it is sometimes useful to classify mutations as either  harmful  or beneficial  (or  neutral ):


Large-scale quantitative mutagenesis screens , in which thousands of millions of mutations are tested, invariably find that a larger fraction of mutations has harmful effects but always returns a number of beneficial mutations as well. For instance, in a screen of all gene deletions in  E. coli , 80% of mutations were negative, but 20% were positive, even though many had a very small effect on growth (depending on condition). [ 59 ]  Gene  deletions  involve removal of whole genes, so that point mutations almost always have a much smaller effect. In a similar screen in  Streptococcus pneumoniae , but this time with  transposon  insertions, 76% of insertion mutants were classified as neutral, 16% had a significantly reduced fitness, but 6% were advantageous. [ 60 ]


This classification is obviously relative and somewhat artificial: a harmful mutation can quickly turn into a beneficial mutations when conditions change. Also, there is a gradient from harmful/beneficial to neutral, as many mutations may have small and mostly neglectable effects but under certain conditions will become relevant. Also, many traits are determined by hundreds of genes (or loci), so that each locus has only a minor effect. For instance, human height is determined by hundreds of genetic variants ("mutations") but each of them has a very minor effect on height, [ 61 ]  apart from the impact of  nutrition . Height (or size) itself may be more or less beneficial as the huge range of sizes in animal or plant groups shows.


Attempts have been made to infer the distribution of fitness effects (DFE) using  mutagenesis  experiments and theoretical models applied to molecular sequence data. DFE, as used to determine the relative abundance of different types of mutations (i.e., strongly deleterious, nearly neutral or advantageous), is relevant to many evolutionary questions, such as the maintenance of  genetic variation , [ 62 ]  the rate of  genomic decay , [ 63 ]  the maintenance of  outcrossing   sexual reproduction  as opposed to  inbreeding [ 64 ]  and the evolution of  sex  and  genetic recombination . [ 65 ]  DFE can also be tracked by tracking the skewness of the distribution of mutations with putatively severe effects as compared to the distribution of mutations with putatively mild or absent effect. [ 66 ]  In summary, the DFE plays an important role in predicting  evolutionary dynamics . [ 67 ] [ 68 ]  A variety of approaches have been used to study the DFE, including theoretical, experimental and analytical methods.


One of the earliest theoretical studies of the distribution of fitness effects was done by  Motoo Kimura , an influential theoretical population  geneticist . His neutral theory of  molecular evolution  proposes that most novel mutations will be highly deleterious, with a small fraction being neutral. [ 26 ] [ 80 ]  A later proposal by Hiroshi Akashi proposed a  bimodal  model for the DFE, with modes centered around highly deleterious and neutral mutations. [ 81 ]  Both theories agree that the vast majority of novel mutations are neutral or deleterious and that advantageous mutations are rare, which has been supported by experimental results. One example is a study done on the DFE of random mutations in  vesicular stomatitis virus . [ 69 ]  Out of all mutations, 39.6% were lethal, 31.2% were non-lethal deleterious, and 27.1% were neutral. Another example comes from a high throughput mutagenesis experiment with yeast. [ 74 ]  In this experiment it was shown that the overall DFE is bimodal, with a cluster of neutral mutations, and a broad distribution of deleterious mutations.


Though relatively few mutations are advantageous, those that are play an important role in evolutionary changes. [ 82 ]  Like neutral mutations, weakly selected advantageous mutations can be lost due to random genetic drift, but strongly selected advantageous mutations are more likely to be fixed. Knowing the DFE of advantageous mutations may lead to increased ability to predict the evolutionary dynamics. Theoretical work on the DFE for advantageous mutations has been done by  John H. Gillespie [ 83 ]  and  H. Allen Orr . [ 84 ]  They proposed that the distribution for advantageous mutations should be  exponential  under a wide range of conditions, which, in general, has been supported by experimental studies, at least for strongly selected advantageous mutations. [ 85 ] [ 86 ] [ 87 ]


In general, it is accepted that the majority of mutations are neutral or deleterious, with advantageous mutations being rare; however, the proportion of types of mutations varies between species. This indicates two important points: first, the proportion of effectively neutral mutations is likely to vary between species, resulting from dependence on  effective population size ; second, the average effect of deleterious mutations varies dramatically between species. [ 25 ]  In addition, the DFE also differs between coding regions and  noncoding regions , with the DFE of noncoding DNA containing more weakly selected mutations. [ 25 ]


In  multicellular organisms  with dedicated  reproductive cells , mutations can be subdivided into  germline mutations , which can be passed on to descendants through their reproductive cells, and  somatic  mutations (also called acquired mutations), [ 88 ]  which involve cells outside the dedicated reproductive group and which are not usually transmitted to descendants.


Diploid organisms (e.g., humans) contain two copies of each gene—a paternal and a maternal allele. Based on the occurrence of mutation on each chromosome, we may classify mutations into three types. A  wild type  or homozygous non-mutated organism is one in which neither allele is mutated.


A germline mutation in the reproductive cells of an individual gives rise to a  constitutional mutation  in the offspring, that is, a mutation that is present in every cell. A constitutional mutation can also occur very soon after  fertilization , or continue from a previous constitutional mutation in a parent. [ 90 ]  A germline mutation can be passed down through subsequent generations of organisms.


The distinction between germline and somatic mutations is important in animals that have a dedicated germline to produce reproductive cells. However, it is of little value in understanding the effects of mutations in plants, which lack a dedicated germline. The distinction is also blurred in those animals that  reproduce asexually  through mechanisms such as  budding , because the cells that give rise to the daughter organisms also give rise to that organism's germline.


A new germline mutation not inherited from either parent is called a  de novo  mutation .


A change in the genetic structure that is not inherited from a parent, and also not passed to offspring, is called a  somatic  mutation . [ 88 ]  Somatic mutations are not inherited by an organism's offspring because they do not affect the  germline . However, they are passed down to all the progeny of a mutated cell within the same organism during mitosis. A major section of an organism therefore might carry the same mutation. These types of mutations are usually prompted by environmental causes, such as ultraviolet radiation or any exposure to certain harmful chemicals, and can cause diseases including cancer. [ 91 ]


With plants, some somatic mutations can be propagated without the need for seed production, for example, by  grafting  and stem cuttings. These type of mutation have led to new types of fruits, such as the "Delicious"  apple  and the "Washington" navel  orange . [ 92 ]


Human and mouse  somatic cells  have a mutation rate more than ten times higher than the  germline  mutation rate for both species; mice have a higher rate of both somatic and germline mutations per  cell division  than humans. The disparity in mutation rate between the germline and somatic tissues likely reflects the greater importance of  genome  maintenance in the germline than in the soma. [ 93 ]


In order to categorize a mutation as such, the "normal" sequence must be obtained from the DNA of a "normal" or "healthy" organism (as opposed to a "mutant" or "sick" one), it should be identified and reported; ideally, it should be made publicly available for a straightforward nucleotide-by-nucleotide comparison, and agreed upon by the scientific community or by a group of expert geneticists and  biologists , who have the responsibility of establishing the  standard  or so-called "consensus" sequence. This step requires a tremendous scientific effort. Once the consensus sequence is known, the mutations in a genome can be pinpointed, described, and classified. The committee of the Human Genome Variation Society (HGVS) has developed the standard human sequence variant nomenclature, [ 100 ]  which should be used by researchers and  DNA diagnostic  centers to generate unambiguous mutation descriptions. In principle, this nomenclature can also be used to describe mutations in other organisms. The nomenclature specifies the type of mutation and base or amino acid changes.


Mutation rates  vary substantially across species, and the evolutionary forces that generally determine mutation are the subject of ongoing investigation.


In  humans , the  mutation rate  is about 50–90  de novo  mutations per genome per generation, that is, each human accumulates about 50–90 novel mutations that were not present in his or her parents. This number has been established by  sequencing  thousands of human trios, that is, two parents and at least one child. [ 101 ]


The genomes of  RNA viruses  are based on  RNA  rather than DNA. The RNA viral genome can be double-stranded (as in DNA) or single-stranded. In some of these viruses (such as the single-stranded  human immunodeficiency virus ), replication occurs quickly, and there are no mechanisms to check the genome for accuracy. This error-prone process often results in mutations.


The rate of de novo mutations, whether germline or somatic, vary among organisms. [ 102 ]  Individuals within the same species can even express varying rates of mutation. [ 103 ]  Overall, rates of de novo mutations are low compared to those of inherited mutations, which categorizes them as rare forms of  genetic variation . [ 104 ]  Many observations of de novo mutation rates have associated higher rates of mutation correlated to paternal age. In sexually reproducing organisms, the comparatively higher frequency of cell divisions in the parental sperm donor germline drive conclusions that rates of de novo mutation can be tracked along a common basis. The frequency of error during the DNA replication process of  gametogenesis , especially amplified in the rapid production of sperm cells, can promote more opportunities for de novo mutations to replicate unregulated by DNA repair machinery. [ 105 ]  This claim combines the observed effects of increased probability for mutation in rapid  spermatogenesis  with short periods of time between cellular divisions that limit the efficiency of repair machinery. [ 106 ]  Rates of de novo mutations that affect an organism during its development can also increase with certain environmental factors. For example, certain intensities of exposure to radioactive elements can inflict damage to an organism's genome, heightening rates of mutation. In humans, the appearance of  skin cancer  during one's lifetime is induced by overexposure to  UV radiation  that causes mutations in the cellular and skin genome. [ 107 ]


There is a widespread assumption that mutations are (entirely) "random" with respect to their consequences (in terms of probability). This was shown to be wrong as mutation frequency can vary across regions of the genome, with such  DNA repair - and mutation-biases being associated with various factors. For instance, Monroe and colleagues demonstrated that—in the studied plant ( Arabidopsis thaliana )—more important genes mutate less frequently than less important ones. They demonstrated that mutation is "non-random in a way that benefits the plant". [ 108 ] [ 109 ]  Additionally, previous experiments typically used to demonstrate mutations being random with respect to fitness (such as the  Fluctuation Test  and  Replica plating ) have been shown to only support the weaker claim that those mutations are random with respect to external selective constraints, not fitness as a whole. [ 110 ]


Changes in DNA caused by mutation in a coding region of DNA can cause errors in protein sequence that may result in partially or completely non-functional proteins. Each cell, in order to function correctly, depends on thousands of proteins to function in the right places at the right times. When a mutation alters a protein that plays a critical role in the body, a medical condition can result. One study on the comparison of genes between different species of  Drosophila  suggests that if a mutation does change a protein, the mutation will most likely be harmful, with an estimated 70 per cent of amino acid polymorphisms having damaging effects, and the remainder being either neutral or weakly beneficial. [ 8 ]  Some mutations alter a gene's DNA base sequence but do not change the protein made by the gene.  Studies have shown that only 7% of point mutations in noncoding DNA of yeast are deleterious and 12% in coding DNA are deleterious. The rest of the mutations are either neutral or slightly beneficial. [ 111 ]


If a mutation is present in a  germ cell , it can give rise to offspring that carries the mutation in all of its cells. This is the case in hereditary diseases. In particular, if there is a mutation in a DNA repair gene within a germ cell, humans carrying such germline mutations may have an increased risk of cancer. A list of 34 such germline mutations is given in the article  DNA repair-deficiency disorder . An example of one is  albinism , a mutation that occurs in the  OCA1  or  OCA2  gene. Individuals with this disorder are more prone to many types of cancers, other disorders and have impaired vision.