Human genome

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https://en.wikipedia.org/wiki/Human_genome

Genomic information

Schematic representation of the human diploid karyotype, showing the organization of the genome into chromosomes, as well as annotated bands and sub-bands as seen on G banding. This drawing shows both the female (XX) and male (XY) versions of the 23rd chromosome pair. Chromosomal changes during the cell cycle are displayed at top center. The mitochondrial genome is shown to scale at bottom left.
NCBI genome ID	51
Ploidy	diploid
Genome size	3,117,275,501 base pairs (bp)
Number of chromosomes	23 pairs

The human genome is a complete set of nucleic acid sequences for humans, encoded as DNA within the 23 chromosome pairs in cell nuclei and in a small DNA molecule found within individual mitochondria. These are usually treated separately as the nuclear genome and the mitochondrial genome. Human genomes include both protein-coding DNA sequences and various types of DNA that does not encode proteins. The latter is a diverse category that includes DNA coding for non-translated RNA, such as that for ribosomal RNA, transfer RNA, ribozymes, small nuclear RNAs, and several types of regulatory RNAs. It also includes promoters and their associated gene-regulatory elements, DNA playing structural and replicatory roles, such as scaffolding regions, telomeres, centromeres, and origins of replication, plus large numbers of transposable elements, inserted viral DNA, non-functional pseudogenes and simple, highly repetitive sequences. Introns make up a large percentage of non-coding DNA. Some of this non-coding DNA is non-functional junk DNA, such as pseudogenes, but there is no firm consensus on the total amount of junk DNA.

Haploid human genomes, which are contained in germ cells (the egg and sperm gamete cells created in the meiosis phase of sexual reproduction before fertilization) consist of 3,054,815,472 DNA base pairs (if X chromosome is used), while female diploid genomes (found in somatic cells) have twice the DNA content.

While there are significant differences among the genomes of human individuals (on the order of 0.1% due to single-nucleotide variants and 0.6% when considering indels), these are considerably smaller than the differences between humans and their closest living relatives, the bonobos and chimpanzees (~1.1% fixed single-nucleotide variants and 4% when including indels). Size in basepairs can vary too; the telomere length decreases after every round of DNA replication.

Although the sequence of the human genome has been completely determined by DNA sequencing in 2022 (including methylation), it is not yet fully understood. Most, but not all, genes have been identified by a combination of high throughput experimental and bioinformatics approaches, yet much work still needs to be done to further elucidate the biological functions of their protein and RNA products (in particular, annotation of the complete CHM13v2.0 sequence is still ongoing). And yet, overlapping genes are quite common, in some cases allowing two protein coding genes from each strand to reuse base pairs twice (for example, genes DCDC2 and KAAG1). Recent results suggest that most of the vast quantities of noncoding DNA within the genome have associated biochemical activities, including regulation of gene expression, organization of chromosome architecture, and signals controlling epigenetic inheritance. There are also a significant number of retroviruses in human DNA, at least 3 of which have been proven to possess an important function (i.e., HIV-like HERV-K, HERV-W, and HERV-FRD play a role in placenta formation by inducing cell-cell fusion).

In 2003, scientists reported the sequencing of 85% of the entire human genome, but as of 2020 at least 8% was still missing. In 2021, scientists reported sequencing the complete female genome (i.e., without the Y chromosome). This sequence identified 19,969 protein-coding sequences, accounting for approximately 1.5% of the genome, and 63,494 genes in total, most of them being non-coding RNA genes. The genome consists of regulatory DNA sequences, LINEs, SINEs, introns, and sequences for which as yet no function has been determined. The human Y chromosome, consisting of 62,460,029 base pairs from a different cell line and found in all males, was sequenced completely in January 2022.

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

Sequencing

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

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

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

In June 2016, scientists formally announced HGP-Write, a plan to synthesize the human genome.

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

Achieving completeness

Main article: Human Genome Project § State of completion

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

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

Molecular organization and gene content

See also: Lists of human genes

The total length of the human reference genome does not represent the sequence of any specific individual. The genome is organized into 22 paired chromosomes, termed autosomes, plus the 23rd pair of sex chromosomes (XX) in the female and (XY) in the male. The haploid genome is 3 054 815 472 base pairs, when the X chromosome is included, and 2 963 015 935 base pairs when the Y chromosome is substituted for the X chromosome. These chromosomes are all large linear DNA molecules contained within the cell nucleus. The genome also includes the mitochondrial DNA, a comparatively small circular molecule present in multiple copies in each mitochondrion.

Human reference data, by chromosome

Chromo- some	Length	Base pairs	Varia- tions	Protein- coding genes	Pseudo- genes	Total long ncRNA	Total small ncRNA	miRNA	rRNA	snRNA	snoRNA	Misc ncRNA	Links	Centromere position (Mbp)	Cumu- lative (%)
1	8.5 cm	248,387,328	12,151,146	2058	1220	1200	496	134	66	221	145	192	EBI	125	7.9
2	8.3 cm	242,696,752	12,945,965	1309	1023	1037	375	115	40	161	117	176	EBI	93.3	16.2
3	6.7 cm	201,105,948	10,638,715	1078	763	711	298	99	29	138	87	134	EBI	91	23
4	6.5 cm	193,574,945	10,165,685	752	727	657	228	92	24	120	56	104	EBI	50.4	29.6
5	6.2 cm	182,045,439	9,519,995	876	721	844	235	83	25	106	61	119	EBI	48.4	35.8
6	5.8 cm	172,126,628	9,130,476	1048	801	639	234	81	26	111	73	105	EBI	61	41.6
7	5.4 cm	160,567,428	8,613,298	989	885	605	208	90	24	90	76	143	EBI	59.9	47.1
8	5.0 cm	146,259,331	8,221,520	677	613	735	214	80	28	86	52	82	EBI	45.6	52
9	4.8 cm	150,617,247	6,590,811	786	661	491	190	69	19	66	51	96	EBI	49	56.3
10	4.6 cm	134,758,134	7,223,944	733	568	579	204	64	32	87	56	89	EBI	40.2	60.9
11	4.6 cm	135,127,769	7,535,370	1298	821	710	233	63	24	74	76	97	EBI	53.7	65.4
12	4.5 cm	133,324,548	7,228,129	1034	617	848	227	72	27	106	62	115	EBI	35.8	70
13	3.9 cm	113,566,686	5,082,574	327	372	397	104	42	16	45	34	75	EBI	17.9	73.4
14	3.6 cm	101,161,492	4,865,950	830	523	533	239	92	10	65	97	79	EBI	17.6	76.4
15	3.5 cm	99,753,195	4,515,076	613	510	639	250	78	13	63	136	93	EBI	19	79.3
16	3.1 cm	96,330,374	5,101,702	873	465	799	187	52	32	53	58	51	EBI	36.6	82
17	2.8 cm	84,276,897	4,614,972	1197	531	834	235	61	15	80	71	99	EBI	24	84.8
18	2.7 cm	80,542,538	4,035,966	270	247	453	109	32	13	51	36	41	EBI	17.2	87.4
19	2.0 cm	61,707,364	3,858,269	1472	512	628	179	110	13	29	31	61	EBI	26.5	89.3
20	2.1 cm	66,210,255	3,439,621	544	249	384	131	57	15	46	37	68	EBI	27.5	91.4
21	1.6 cm	45,090,682	2,049,697	234	185	305	71	16	5	21	19	24	EBI	13.2	92.6
22	1.7 cm	51,324,926	2,135,311	488	324	357	78	31	5	23	23	62	EBI	14.7	93.8
X	5.3 cm	154,259,566	5,753,881	842	874	271	258	128	22	85	64	100	EBI	60.6	99.1
Y	2.0 cm	62,460,029	211,643	71	388	71	30	15	7	17	3	8	EBI	10.4	100
mtDNA	5.4 μm	16,569	929	13	0	0	24	0	2	0	0	0	EBI	N/A	100
hapl 1-23 + X	104 cm	3,054,815,472		20328	14212	14656	4983	1741	523	1927	1518	2205
hapl 1-23 + Y	101 cm	2,963,015,935		19557	13726	14456	4755	1628	508	1859	1457	2113
dipl + mt♀	208.23 cm	6,109,647,513		40669	28424	29312	9990	3482	1048	3854	3036	4410
dipl + mt♂	205.00 cm	6,017,847,976		39898	27938	29112	9762	3369	1033	3786	2975	4318

Original analysis published in the Ensembl database at the European Bioinformatics Institute (EBI) and Wellcome Trust Sanger Institute. Chromosome lengths estimated by multiplying the number of base pairs (of older reference genome, not CHM13v2.0) by 0.34 nanometers (distance between base pairs in the most common structure of the DNA double helix; a recent estimate of human chromosome lengths based on updated data reports 205.00 cm for the diploid male genome and 208.23 cm for female, corresponding to weights of 6.41 and 6.51 picograms (pg), respectively). Number of proteins is based on the number of initial precursor mRNA transcripts, and does not include products of alternative pre-mRNA splicing, or modifications to protein structure that occur after translation.

Variations are unique DNA sequence differences that have been identified in the individual human genome sequences analyzed by Ensembl as of December 2016. The number of identified variations is expected to increase as further personal genomes are sequenced and analyzed. In addition to the gene content shown in this table, a large number of non-expressed functional sequences have been identified throughout the human genome (see below). Links open windows to the reference chromosome sequences in the EBI genome browser.

Small non-coding RNAs are RNAs of as many as 200 bases that do not have protein-coding potential. These include: microRNAs, or miRNAs (post-transcriptional regulators of gene expression), small nuclear RNAs, or snRNAs (the RNA components of spliceosomes), and small nucleolar RNAs, or snoRNA (involved in guiding chemical modifications to other RNA molecules). Long non-coding RNAs are RNA molecules longer than 200 bases that do not have protein-coding potential. These include: ribosomal RNAs, or rRNAs (the RNA components of ribosomes), and a variety of other long RNAs that are involved in regulation of gene expression, epigenetic modifications of DNA nucleotides and histone proteins, and regulation of the activity of protein-coding genes. Small discrepancies between total-small-ncRNA numbers and the numbers of specific types of small ncNRAs result from the former values being sourced from Ensembl release 87 and the latter from Ensembl release 68.

The number of genes in the human genome is not entirely clear because the function of numerous transcripts remains unclear. This is especially true for non-coding RNA. The number of protein-coding genes is better known but there are still on the order of 1,400 questionable genes which may or may not encode functional proteins, usually encoded by short open reading frames.

 

Discrepancies in human gene number estimates among different databases, as of July 2018

	Gencode	Ensembl	Refseq	CHESS
protein-coding genes	19,901	20,376	20,345	21,306
lncRNA genes	15,779	14,720	17,712	18,484
antisense RNA	5501		28	2694
miscellaneous RNA	2213	2222	13,899	4347
Pseudogenes	14,723	1740	15,952
total transcripts	203,835	203,903	154,484	328,827

Number of genes (orange) and base pairs (green, in millions) on each chromosome

Information content

The haploid human genome (23 chromosomes) is about 3 billion base pairs long and in 2018 was said to contain at least 46,831 genes. In 2022 the number increased again to 63,494 genes. The increase from the previously accepted number of around 20,000 is due to the difficulty of defining what a gene is. It is widely agreed that there are about 20,000 protein-coding genes, with some papers stating exact figures of 21,306. The higher figures include non-protein coding RNA-producing genes that perform other cell functions.

Since every base pair can be coded by 2 bits, this is about 750 megabytes of data. An individual somatic (diploid) cell contains twice this amount, that is, about 6 billion base pairs. Males have fewer than females because the Y chromosome is about 62 million base pairs whereas the X is about 154 million. Since individual genomes vary in sequence by less than 1% from each other, the variations of a given human's genome from a common reference can be losslessly compressed to roughly 4 megabytes.

The entropy rate of the genome differs significantly between coding and non-coding sequences. It is close to the maximum of 2 bits per base pair for the coding sequences (about 45 million base pairs), but less for the non-coding parts. It ranges between 1.5 and 1.9 bits per base pair for the individual chromosome, except for the Y chromosome, which has an entropy rate below 0.9 bits per base pair.

Coding vs. noncoding DNA

Main article: Junk DNA

The content of the human genome is commonly divided into coding and noncoding DNA sequences. Coding DNA is defined as those sequences that can be transcribed into mRNA and translated into proteins during the human life cycle; these sequences occupy only a small fraction of the genome (<2%). Noncoding DNA is made up of all of those sequences (approx. 98% of the genome) that are not used to encode proteins.

Some noncoding DNA contains genes for RNA molecules with important biological functions (noncoding RNA, for example ribosomal RNA and transfer RNA). The exploration of the function and evolutionary origin of noncoding DNA is an important goal of contemporary genome research, including the ENCODE (Encyclopedia of DNA Elements) project, which aims to survey the entire human genome, using a variety of experimental tools whose results are indicative of molecular activity. It is however disputed whether molecular activity (transcription of DNA into RNA) alone implies that the RNA produced has a meaningful biological function, since experiments have shown that random nonfunctional DNA will also reproducibly recruit transcription factors resulting in transcription into nonfunctional RNA.

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

Because non-coding DNA greatly outnumbers coding DNA, the concept of the sequenced genome has become a more focused analytical concept than the classical concept of the DNA-coding gene.

Coding sequences (protein-coding genes)

For a comprehensive list, see List of human protein-coding genes 1, List of human protein-coding genes 2, List of human protein-coding genes 3, and List of human protein-coding genes 4.

Human genes categorized by function of the transcribed proteins, given both as number of encoding genes and percentage of all genes

Protein-coding sequences represent the most widely studied and best understood component of the human genome. These sequences ultimately lead to the production of all human proteins, although several biological processes (e.g. DNA rearrangements and alternative pre-mRNA splicing) can lead to the production of many more unique proteins than the number of protein-coding genes. The complete modular protein-coding capacity of the genome is contained within the exome, and consists of DNA sequences encoded by exons that can be translated into proteins. Because of its biological importance, and the fact that it constitutes less than 2% of the genome, sequencing of the exome was the first major milepost of the Human Genome Project.

Number of protein-coding genes. About 20,000 human proteins have been annotated in databases such as Uniprot. Historically, estimates for the number of protein genes have varied widely, ranging up to 2,000,000 in the late 1960s, but several researchers pointed out in the early 1970s that the estimated mutational load from deleterious mutations placed an upper limit of approximately 40,000 for the total number of functional loci (this includes protein-coding and functional non-coding genes). The number of human protein-coding genes is not significantly larger than that of many less complex organisms, such as the roundworm and the fruit fly. This difference may result from the extensive use of alternative pre-mRNA splicing in humans, which provides the ability to build a very large number of modular proteins through the selective incorporation of exons.

Protein-coding capacity per chromosome. Protein-coding genes are distributed unevenly across the chromosomes, ranging from a few dozen to more than 2000, with an especially high gene density within chromosomes 1, 11, and 19. Each chromosome contains various gene-rich and gene-poor regions, which may be correlated with chromosome bands and GC-content. The significance of these nonrandom patterns of gene density is not well understood.

Size of protein-coding genes. The size of protein-coding genes within the human genome shows enormous variability. For example, the gene for histone H1a (HIST1HIA) is relatively small and simple, lacking introns and encoding an 781 nucleotide-long mRNA that produces a 215 amino acid protein from its 648 nucleotide open reading frame. Dystrophin (DMD) was the largest protein-coding gene in the 2001 human reference genome, spanning a total of 2.2 million nucleotides, while more recent systematic meta-analysis of updated human genome data identified an even larger protein-coding gene, RBFOX1 (RNA binding protein, fox-1 homolog 1), spanning a total of 2.47 million nucleotides. Titin (TTN) has the longest coding sequence (114,414 nucleotides), the largest number of exons (363), and the longest single exon (17,106 nucleotides). As estimated based on a curated set of protein-coding genes over the whole genome, the median size is 26,288 nucleotides (mean = 66,577), the median exon size, 133 nucleotides (mean = 309), the median number of exons, 8 (mean = 11), and the median encoded protein is 425 amino acids (mean = 553) in length.

Examples of human protein-coding genes

Protein	Chrom	Gene	Length	Exons	Exon length	Intron length	Alt splicing
Breast cancer type 2 susceptibility protein	13	BRCA2	83,736	27	11,386	72,350	yes
Cystic fibrosis transmembrane conductance regulator	7	CFTR	202,881	27	4,440	198,441	yes
Cytochrome b	MT	MTCYB	1,140	1	1,140	0	no
Dystrophin	X	DMD	2,220,381	79	10,500	2,209,881	yes
Glyceraldehyde-3-phosphate dehydrogenase	12	GAPDH	4,444	9	1,425	3,019	yes
Hemoglobin beta subunit	11	HBB	1,605	3	626	979	no
Histone H1A	6	HIST1H1A	781	1	781	0	no
Titin	2	TTN	281,434	364	104,301	177,133	yes

Noncoding DNA (ncDNA)

Main article: Noncoding DNA

Noncoding DNA is defined as all of the DNA sequences within a genome that are not found within protein-coding exons, and so are never represented within the amino acid sequence of expressed proteins. By this definition, more than 98% of the human genomes is composed of ncDNA.

Numerous classes of noncoding DNA have been identified, including genes for noncoding RNA (e.g. tRNA and rRNA), pseudogenes, introns, untranslated regions of mRNA, regulatory DNA sequences, repetitive DNA sequences, and sequences related to mobile genetic elements.

Numerous sequences that are included within genes are also defined as noncoding DNA. These include genes for noncoding RNA (e.g. tRNA, rRNA), and untranslated components of protein-coding genes (e.g. introns, and 5' and 3' untranslated regions of mRNA).

Protein-coding sequences (specifically, coding exons) constitute less than 1.5% of the human genome. In addition, about 26% of the human genome is introns. Aside from genes (exons and introns) and known regulatory sequences (8–20%), the human genome contains regions of noncoding DNA. The exact amount of noncoding DNA that plays a role in cell physiology has been hotly debated. An analysis by the ENCODE project indicates that 80% of the entire human genome is either transcribed, binds to regulatory proteins, or is associated with some other biochemical activity.

It however remains controversial whether all of this biochemical activity contributes to cell physiology, or whether a substantial portion of this is the result of transcriptional and biochemical noise, which must be actively filtered out by the organism. Excluding protein-coding sequences, introns, and regulatory regions, much of the non-coding DNA is composed of: Many DNA sequences that do not play a role in gene expression have important biological functions. Comparative genomics studies indicate that about 5% of the genome contains sequences of noncoding DNA that are highly conserved, sometimes on time-scales representing hundreds of millions of years, implying that these noncoding regions are under strong evolutionary pressure and purifying selection.

Many of these sequences regulate the structure of chromosomes by limiting the regions of heterochromatin formation and regulating structural features of the chromosomes, such as the telomeres and centromeres. Other noncoding regions serve as origins of DNA replication. Finally several regions are transcribed into functional noncoding RNA that regulate the expression of protein-coding genes (for example), mRNA translation and stability (see miRNA), chromatin structure (including histone modifications, for example), DNA methylation (for example), DNA recombination (for example), and cross-regulate other noncoding RNAs (for example). It is also likely that many transcribed noncoding regions do not serve any role and that this transcription is the product of non-specific RNA Polymerase activity.

Pseudogenes

Main article: Pseudogene

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

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

Genes for noncoding RNA (ncRNA)

Main article: Noncoding RNA

Noncoding RNA molecules play many essential roles in cells, especially in the many reactions of protein synthesis and RNA processing. Noncoding RNA include tRNA, ribosomal RNA, microRNA, snRNA and other non-coding RNA genes including about 60,000 long non-coding RNAs (lncRNAs). Although the number of reported lncRNA genes continues to rise and the exact number in the human genome is yet to be defined, many of them are argued to be non-functional.

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

Introns and untranslated regions of mRNA

In addition to the ncRNA molecules that are encoded by discrete genes, the initial transcripts of protein coding genes usually contain extensive noncoding sequences, in the form of introns, 5'-untranslated regions (5'-UTR), and 3'-untranslated regions (3'-UTR). Within most protein-coding genes of the human genome, the length of intron sequences is 10- to 100-times the length of exon sequences.

Regulatory DNA sequences

The human genome has many different regulatory sequences which are crucial to controlling gene expression. Conservative estimates indicate that these sequences make up 8% of the genome, however extrapolations from the ENCODE project give that 20-40% of the genome is gene regulatory sequence. Some types of non-coding DNA are genetic "switches" that do not encode proteins, but do regulate when and where genes are expressed (called enhancers).

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

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

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

Repetitive DNA sequences

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

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

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

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

Mobile genetic elements (transposons) and their relics

Transposable genetic elements, DNA sequences that can replicate and insert copies of themselves at other locations within a host genome, are an abundant component in the human genome. The most abundant transposon lineage, Alu, has about 50,000 active copies, and can be inserted into intragenic and intergenic regions. One other lineage, LINE-1, has about 100 active copies per genome (the number varies between people). Together with non-functional relics of old transposons, they account for over half of total human DNA. Sometimes called "jumping genes", transposons have played a major role in sculpting the human genome. Some of these sequences represent endogenous retroviruses, DNA copies of viral sequences that have become permanently integrated into the genome and are now passed on to succeeding generations.

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

Genomic variation in humans

Main article: Human genetic variation

Human reference genome

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

There are several important points concerning the human reference genome:

• The HRG is a haploid sequence. Each chromosome is represented once.
• The HRG is a composite sequence, and does not correspond to any actual human individual.
• The HRG is periodically updated to correct errors, ambiguities, and unknown "gaps".
• The HRG in no way represents an "ideal" or "perfect" human individual. It is simply a standardized representation or model that is used for comparative purposes.

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

Measuring human genetic variation

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

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

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

Mapping human genomic variation

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

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

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

Structural variation

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

SNP frequency across the human genome

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

TSC SNP distribution along the long arm of chromosome 22 (from https://web.archive.org/web/20130903043223/http://snp.cshl.org/ ). Each column represents a 1 Mb interval; the approximate cytogenetic position is given on the x-axis. Clear peaks and troughs of SNP density can be seen, possibly reflecting different rates of mutation, recombination and selection.

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

Personal genomes

See also: Personal genomics

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

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

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

Human knockouts

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

Populations with a high level of parental-relatedness result in a larger number of homozygous gene knockouts as compared to outbred populations.

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

A pedigree displaying a first-cousin mating (carriers both carrying heterozygous knockouts mating as marked by double line) leading to offspring possessing a homozygous gene knockout

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

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

Human genetic disorders

Further information: Genetic disorder

Most aspects of human biology involve both genetic (inherited) and non-genetic (environmental) factors. Some inherited variation influences aspects of our biology that are not medical in nature (height, eye color, ability to taste or smell certain compounds, etc.). Moreover, some genetic disorders only cause disease in combination with the appropriate environmental factors (such as diet). With these caveats, genetic disorders may be described as clinically defined diseases caused by genomic DNA sequence variation. In the most straightforward cases, the disorder can be associated with variation in a single gene. For example, cystic fibrosis is caused by mutations in the CFTR gene and is the most common recessive disorder in caucasian populations with over 1,300 different mutations known.

Disease-causing mutations in specific genes are usually severe in terms of gene function and are fortunately rare, thus genetic disorders are similarly individually rare. However, since there are many genes that can vary to cause genetic disorders, in aggregate they constitute a significant component of known medical conditions, especially in pediatric medicine. Molecularly characterized genetic disorders are those for which the underlying causal gene has been identified. Currently there are approximately 2,200 such disorders annotated in the OMIM database.

Studies of genetic disorders are often performed by means of family-based studies. In some instances, population based approaches are employed, particularly in the case of so-called founder populations such as those in Finland, French-Canada, Utah, Sardinia, etc. Diagnosis and treatment of genetic disorders are usually performed by a geneticist-physician trained in clinical/medical genetics. The results of the Human Genome Project are likely to provide increased availability of genetic testing for gene-related disorders, and eventually improved treatment. Parents can be screened for hereditary conditions and counselled on the consequences, the probability of inheritance, and how to avoid or ameliorate it in their offspring.

There are many different kinds of DNA sequence variation, ranging from complete extra or missing chromosomes down to single nucleotide changes. It is generally presumed that much naturally occurring genetic variation in human populations is phenotypically neutral, i.e., has little or no detectable effect on the physiology of the individual (although there may be fractional differences in fitness defined over evolutionary time frames). Genetic disorders can be caused by any or all known types of sequence variation. To molecularly characterize a new genetic disorder, it is necessary to establish a causal link between a particular genomic sequence variant and the clinical disease under investigation. Such studies constitute the realm of human molecular genetics.

With the advent of the Human Genome and International HapMap Project, it has become feasible to explore subtle genetic influences on many common disease conditions such as diabetes, asthma, migraine, schizophrenia, etc. Although some causal links have been made between genomic sequence variants in particular genes and some of these diseases, often with much publicity in the general media, these are usually not considered to be genetic disorders per se as their causes are complex, involving many different genetic and environmental factors. Thus there may be disagreement in particular cases whether a specific medical condition should be termed a genetic disorder.

Additional genetic disorders of mention are Kallman syndrome and Pfeiffer syndrome (gene FGFR1), Fuchs corneal dystrophy (gene TCF4), Hirschsprung's disease (genes RET and FECH), Bardet-Biedl syndrome 1 (genes CCDC28B and BBS1), Bardet-Biedl syndrome 10 (gene BBS10), and facioscapulohumeral muscular dystrophy type 2 (genes D4Z4 and SMCHD1).

Genome sequencing is now able to narrow the genome down to specific locations to more accurately find mutations that will result in a genetic disorder. Copy number variants (CNVs) and single nucleotide variants (SNVs) are also able to be detected at the same time as genome sequencing with newer sequencing procedures available, called Next Generation Sequencing (NGS). This only analyzes a small portion of the genome, around 1–2%. The results of this sequencing can be used for clinical diagnosis of a genetic condition, including Usher syndrome, retinal disease, hearing impairments, diabetes, epilepsy, Leigh disease, hereditary cancers, neuromuscular diseases, primary immunodeficiencies, severe combined immunodeficiency (SCID), and diseases of the mitochondria. NGS can also be used to identify carriers of diseases before conception. The diseases that can be detected in this sequencing include Tay-Sachs disease, Bloom syndrome, Gaucher disease, Canavan disease, familial dysautonomia, cystic fibrosis, spinal muscular atrophy, and fragile-X syndrome. The Next Genome Sequencing can be narrowed down to specifically look for diseases more prevalent in certain ethnic populations.

Prevalence and associated gene/chromosome for some human genetic disorders

Disorder	Prevalence	Chromosome or gene involved
Chromosomal conditions
Down syndrome	1:600	Chromosome 21
Klinefelter syndrome	1:500–1000 males	Additional X chromosome
Turner syndrome	1:2000 females	Loss of X chromosome
Sickle cell anemia	1 in 50 births in parts of Africa; rarer elsewhere	β-globin (on chromosome 11)
Bloom syndrome	1:48000 Ashkenazi Jews	BLM
Cancers
Breast/Ovarian cancer (susceptibility)	~5% of cases of these cancer types	BRCA1, BRCA2
FAP (hereditary nonpolyposis coli)	1:3500	APC
Lynch syndrome	5–10% of all cases of bowel cancer	MLH1, MSH2, MSH6, PMS2
Fanconi anemia	1:130000 births	FANCC
Neurological conditions
Huntington disease	1:20000	Huntingtin
Alzheimer disease - early onset	1:2500	PS1, PS2, APP
Tay-Sachs	1:3600 births in Ashkenazi Jews	HEXA gene (on chromosome 15)
Canavan disease	2.5% Eastern European Jewish ancestry	ASPA gene (on chromosome 17)
Familial dysautonomia	600 known cases worldwide since discovery	IKBKAP gene (on chromosome 9)
Fragile X syndrome	1.4:10000 in males, 0.9:10000 in females	FMR1 gene (on X chromosome)
Mucolipidosis type IV	1:90 to 1:100 in Ashkenazi Jews	MCOLN1
Other conditions
Cystic fibrosis	1:2500	CFTR
Duchenne muscular dystrophy	1:3500 boys	Dystrophin
Becker muscular dystrophy	1.5–6:100000 males	DMD
Beta thalassemia	1:100000	HBB
Congenital adrenal hyperplasia	1:280 in Native Americans and Yupik Eskimos 1:15000 in American Caucasians	CYP21A2
Glycogen storage disease type I	1:100000 births in America	G6PC
Maple syrup urine disease	1:180000 in the U.S. 1:176 in Mennonite/Amish communities 1:250000 in Austria	BCKDHA, BCKDHB, DBT, DLD
Niemann–Pick disease, SMPD1-associated	1,200 cases worldwide	SMPD1
Usher syndrome	1:23000 in the U.S. 1:28000 in Norway 1:12500 in Germany	CDH23, CLRN1, DFNB31, GPR98, MYO7A, PCDH15, USH1C, USH1G, USH2A

Evolution

Hominin timeline
−10 — – −9 — – −8 — – −7 — – −6 — – −5 — – −4 — – −3 — – −2 — – −1 — – 0 —	Miocene Pliocene Pleistocene Hominini Nakalipithecus Samburupithecus Ouranopithecus (Ou. turkae) (Ou. macedoniensis) Chororapithecus Oreopithecus Sivapithecus Sahelanthropus Graecopithecus Orrorin (O. praegens) (O. tugenensis) Ardipithecus (Ar. kadabba) (Ar. ramidus) Australopithecus (Au. africanus) (Au. afarensis) (Au. anamensis) H. habilis (H. rudolfensis) (Au. garhi) H. erectus (H. antecessor) (H. ergaster) (Au. sediba) H. heidelbergensis Homo sapiens Neanderthals Denisovans	← Earlier apes ← Gorilla split ← Chimpanzee split ← Earliest bipedal ← Earliest sign of Ardipithecus ← Earliest sign of Australopithecus ← Earliest stone tools ← Earliest sign of Homo ← Dispersal beyond Africa ← Earliest fire / cooking ← Earliest rock art ← Earliest clothes ← Modern humans H o m i n i d s P a r a n t h r o p u s
(million years ago)

See also: Human evolution and Chimpanzee Genome Project

Comparative genomics studies of mammalian genomes suggest that approximately 5% of the human genome has been conserved by evolution since the divergence of extant lineages approximately 200 million years ago, containing the vast majority of genes. The published chimpanzee genome differs from that of the human genome by 1.23% in direct sequence comparisons. Around 20% of this figure is accounted for by variation within each species, leaving only ~1.06% consistent sequence divergence between humans and chimps at shared genes. This nucleotide by nucleotide difference is dwarfed, however, by the portion of each genome that is not shared, including around 6% of functional genes that are unique to either humans or chimps.

In other words, the considerable observable differences between humans and chimps may be due as much or more to genome level variation in the number, function and expression of genes rather than DNA sequence changes in shared genes. Indeed, even within humans, there has been found to be a previously unappreciated amount of copy number variation (CNV) which can make up as much as 5–15% of the human genome. In other words, between humans, there could be +/- 500,000,000 base pairs of DNA, some being active genes, others inactivated, or active at different levels. The full significance of this finding remains to be seen. On average, a typical human protein-coding gene differs from its chimpanzee ortholog by only two amino acid substitutions; nearly one third of human genes have exactly the same protein translation as their chimpanzee orthologs. A major difference between the two genomes is human chromosome 2, which is equivalent to a fusion product of chimpanzee chromosomes 12 and 13. (later renamed to chromosomes 2A and 2B, respectively).

Humans have undergone an extraordinary loss of olfactory receptor genes during our recent evolution, which explains our relatively crude sense of smell compared to most other mammals. Evolutionary evidence suggests that the emergence of color vision in humans and several other primate species has diminished the need for the sense of smell.

In September 2016, scientists reported that, based on human DNA genetic studies, all non-Africans in the world today can be traced to a single population that exited Africa between 50,000 and 80,000 years ago.

Mitochondrial DNA

The human mitochondrial DNA is of tremendous interest to geneticists, since it undoubtedly plays a role in mitochondrial disease. It also sheds light on human evolution; for example, analysis of variation in the human mitochondrial genome has led to the postulation of a recent common ancestor for all humans on the maternal line of descent (see Mitochondrial Eve).

Due to the lack of a system for checking for copying errors, mitochondrial DNA (mtDNA) has a more rapid rate of variation than nuclear DNA. This 20-fold higher mutation rate allows mtDNA to be used for more accurate tracing of maternal ancestry. Studies of mtDNA in populations have allowed ancient migration paths to be traced, such as the migration of Native Americans from Siberia or Polynesians from southeastern Asia. It has also been used to show that there is no trace of Neanderthal DNA in the European gene mixture inherited through purely maternal lineage. Due to the restrictive all or none manner of mtDNA inheritance, this result (no trace of Neanderthal mtDNA) would be likely unless there were a large percentage of Neanderthal ancestry, or there was strong positive selection for that mtDNA. For example, going back 5 generations, only 1 of a person's 32 ancestors contributed to that person's mtDNA, so if one of these 32 was pure Neanderthal an expected ~3% of that person's autosomal DNA would be of Neanderthal origin, yet they would have a ~97% chance of having no trace of Neanderthal mtDNA.

Epigenome

See also: Epigenetics

Epigenetics describes a variety of features of the human genome that transcend its primary DNA sequence, such as chromatin packaging, histone modifications and DNA methylation, and which are important in regulating gene expression, genome replication and other cellular processes. Epigenetic markers strengthen and weaken transcription of certain genes but do not affect the actual sequence of DNA nucleotides. DNA methylation is a major form of epigenetic control over gene expression and one of the most highly studied topics in epigenetics. During development, the human DNA methylation profile experiences dramatic changes. In early germ line cells, the genome has very low methylation levels. These low levels generally describe active genes. As development progresses, parental imprinting tags lead to increased methylation activity.

Epigenetic patterns can be identified between tissues within an individual as well as between individuals themselves. Identical genes that have differences only in their epigenetic state are called epialleles. Epialleles can be placed into three categories: those directly determined by an individual's genotype, those influenced by genotype, and those entirely independent of genotype. The epigenome is also influenced significantly by environmental factors. Diet, toxins, and hormones impact the epigenetic state. Studies in dietary manipulation have demonstrated that methyl-deficient diets are associated with hypomethylation of the epigenome. Such studies establish epigenetics as an important interface between the environment and the genome.

Statistical model

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Statistical_model

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, the data-generating process. When referring specifically to probabilities, the corresponding term is probabilistic model.

A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables. As such, a statistical model is "a formal representation of a theory" (Herman Adèr quoting Kenneth Bollen).

All statistical hypothesis tests and all statistical estimators are derived via statistical models. More generally, statistical models are part of the foundation of statistical inference.

Introduction

Informally, a statistical model can be thought of as a statistical assumption (or set of statistical assumptions) with a certain property: that the assumption allows us to calculate the probability of any event. As an example, consider a pair of ordinary six-sided dice. We will study two different statistical assumptions about the dice.

The first statistical assumption is this: for each of the dice, the probability of each face (1, 2, 3, 4, 5, and 6) coming up is 1/6. From that assumption, we can calculate the probability of both dice coming up 5:  1/6 × 1/6 = 1/36.  More generally, we can calculate the probability of any event: e.g. (1 and 2) or (3 and 3) or (5 and 6).

The alternative statistical assumption is this: for each of the dice, the probability of the face 5 coming up is 1/8 (because the dice are weighted). From that assumption, we can calculate the probability of both dice coming up 5:  1/8 × 1/8 = 1/64.  We cannot, however, calculate the probability of any other nontrivial event, as the probabilities of the other faces are unknown.

The first statistical assumption constitutes a statistical model: because with the assumption alone, we can calculate the probability of any event. The alternative statistical assumption does not constitute a statistical model: because with the assumption alone, we cannot calculate the probability of every event.

In the example above, with the first assumption, calculating the probability of an event is easy. With some other examples, though, the calculation can be difficult, or even impractical (e.g. it might require millions of years of computation). For an assumption to constitute a statistical model, such difficulty is acceptable: doing the calculation does not need to be practicable, just theoretically possible.

Formal definition

In mathematical terms, a statistical model is usually thought of as a pair (${\displaystyle S,\mathcal{P}}$), where ${\displaystyle S}$ is the set of possible observations, i.e. the sample space, and ${\displaystyle \mathcal{P}}$ is a set of probability distributions on ${\displaystyle S}$.

The intuition behind this definition is as follows. It is assumed that there is a "true" probability distribution induced by the process that generates the observed data. We choose ${\displaystyle \mathcal{P}}$ to represent a set (of distributions) which contains a distribution that adequately approximates the true distribution.

Note that we do not require that ${\displaystyle \mathcal{P}}$ contains the true distribution, and in practice that is rarely the case. Indeed, as Burnham & Anderson state, "A model is a simplification or approximation of reality and hence will not reflect all of reality"—hence the saying "all models are wrong".

The set ${\displaystyle \mathcal{P}}$ is almost always parameterized: ${\displaystyle \mathcal{P}=\{F_{\theta }:\theta \in \mathrm{\Theta }\}}$. The set of distributions ${\displaystyle \mathrm{\Theta }}$ defines the parameters of the model. A parameterization is generally required to have distinct parameter values give rise to distinct distributions, i.e. ${\displaystyle F_{{\theta }_1}=F_{{\theta }_2}\Rightarrow {\theta }_1={\theta }_2}$ must hold (in other words, it must be injective). A parameterization that meets the requirement is said to be identifiable.

An example

Suppose that we have a population of children, with the ages of the children distributed uniformly, in the population. The height of a child will be stochastically related to the age: e.g. when we know that a child is of age 7, this influences the chance of the child being 1.5 meters tall. We could formalize that relationship in a linear regression model, like this: height_i = b₀ + b₁age_i + ε_i, where b₀ is the intercept, b₁ is a parameter that age is multiplied by to obtain a prediction of height, ε_i is the error term, and i identifies the child. This implies that height is predicted by age, with some error.

An admissible model must be consistent with all the data points. Thus, a straight line (height_i = b₀ + b₁age_i) cannot be the equation for a model of the data—unless it exactly fits all the data points, i.e. all the data points lie perfectly on the line. The error term, ε_i, must be included in the equation, so that the model is consistent with all the data points.

To do statistical inference, we would first need to assume some probability distributions for the ε_i. For instance, we might assume that the ε_i distributions are i.i.d. Gaussian, with zero mean. In this instance, the model would have 3 parameters: b₀, b₁, and the variance of the Gaussian distribution.

We can formally specify the model in the form (${\displaystyle S,\mathcal{P}}$) as follows. The sample space, ${\displaystyle S}$, of our model comprises the set of all possible pairs (age, height). Each possible value of ${\displaystyle \theta }$ = (b₀, b₁, σ²) determines a distribution on ${\displaystyle S}$; denote that distribution by ${\displaystyle F_{\theta }}$. If ${\displaystyle \mathrm{\Theta }}$ is the set of all possible values of ${\displaystyle \theta }$, then ${\displaystyle \mathcal{P}=\{F_{\theta }:\theta \in \mathrm{\Theta }\}}$. (The parameterization is identifiable, and this is easy to check.)

In this example, the model is determined by (1) specifying ${\displaystyle S}$ and (2) making some assumptions relevant to ${\displaystyle \mathcal{P}}$. There are two assumptions: that height can be approximated by a linear function of age; that errors in the approximation are distributed as i.i.d. Gaussian. The assumptions are sufficient to specify ${\displaystyle \mathcal{P}}$—as they are required to do.

General remarks

A statistical model is a special class of mathematical model. What distinguishes a statistical model from other mathematical models is that a statistical model is non-deterministic. Thus, in a statistical model specified via mathematical equations, some of the variables do not have specific values, but instead have probability distributions; i.e. some of the variables are stochastic. In the above example with children's heights, ε is a stochastic variable; without that stochastic variable, the model would be deterministic.

Statistical models are often used even when the data-generating process being modeled is deterministic. For instance, coin tossing is, in principle, a deterministic process; yet it is commonly modeled as stochastic (via a Bernoulli process).

Choosing an appropriate statistical model to represent a given data-generating process is sometimes extremely difficult, and may require knowledge of both the process and relevant statistical analyses. Relatedly, the statistician Sir David Cox has said, "How [the] translation from subject-matter problem to statistical model is done is often the most critical part of an analysis".

There are three purposes for a statistical model, according to Konishi & Kitagawa.

• Predictions
• Extraction of information
• Description of stochastic structures

Those three purposes are essentially the same as the three purposes indicated by Friendly & Meyer: prediction, estimation, description. The three purposes correspond with the three kinds of logical reasoning: deductive reasoning, inductive reasoning, abductive reasoning.

Dimension of a model

Suppose that we have a statistical model (${\displaystyle S,\mathcal{P}}$) with ${\displaystyle \mathcal{P}=\{F_{\theta }:\theta \in \mathrm{\Theta }\}}$. In notation, we write that ${\displaystyle \mathrm{\Theta }\subseteq {\mathbb{R}}^k}$ where k is a positive integer (${\displaystyle \mathbb{R}}$ denotes the real numbers; other sets can be used, in principle). Here, k is called the dimension of the model. The model is said to be parametric if ${\displaystyle \mathrm{\Theta }}$ has finite dimension.

As an example, if we assume that data arise from a univariate Gaussian distribution, then we are assuming that

${\displaystyle \mathcal{P}=\left\{F_{\mu ,\sigma }(x)\equiv \frac{1}{\sqrt{2\pi }\sigma }\exp \left(-\frac{(x-\mu {)}^2}{2{\sigma }^2}\right):\mu \in \mathbb{R},\sigma >0\right\}}$.

In this example, the dimension, k, equals 2.

As another example, suppose that the data consists of points (x, y) that we assume are distributed according to a straight line with i.i.d. Gaussian residuals (with zero mean): this leads to the same statistical model as was used in the example with children's heights. The dimension of the statistical model is 3: the intercept of the line, the slope of the line, and the variance of the distribution of the residuals. (Note the set of all possible lines has dimension 2, even though geometrically, a line has dimension 1.)

Although formally ${\displaystyle \theta \in \mathrm{\Theta }}$ is a single parameter that has dimension k, it is sometimes regarded as comprising k separate parameters. For example, with the univariate Gaussian distribution, ${\displaystyle \theta }$ is formally a single parameter with dimension 2, but it is often regarded as comprising 2 separate parameters—the mean and the standard deviation.

A statistical model is nonparametric if the parameter set ${\displaystyle \mathrm{\Theta }}$ is infinite dimensional. A statistical model is semiparametric if it has both finite-dimensional and infinite-dimensional parameters. Formally, if k is the dimension of ${\displaystyle \mathrm{\Theta }}$ and n is the number of samples, both semiparametric and nonparametric models have ${\displaystyle k\to \mathrm{\infty }}$ as ${\displaystyle n\to \mathrm{\infty }}$. If ${\displaystyle k/n\to 0}$ as ${\displaystyle n\to \mathrm{\infty }}$, then the model is semiparametric; otherwise, the model is nonparametric.

Parametric models are by far the most commonly used statistical models. Regarding semiparametric and nonparametric models, Sir David Cox has said, "These typically involve fewer assumptions of structure and distributional form but usually contain strong assumptions about independencies".

Nested models

Not to be confused with Multilevel models.

Two statistical models are nested if the first model can be transformed into the second model by imposing constraints on the parameters of the first model. As an example, the set of all Gaussian distributions has, nested within it, the set of zero-mean Gaussian distributions: we constrain the mean in the set of all Gaussian distributions to get the zero-mean distributions. As a second example, the quadratic model

y = b₀ + b₁x + b₂x² + ε,    ε ~ 𝒩(0, σ²)

has, nested within it, the linear model

y = b₀ + b₁x + ε,    ε ~ 𝒩(0, σ²)

—we constrain the parameter b₂ to equal 0.

In both those examples, the first model has a higher dimension than the second model (for the first example, the zero-mean model has dimension 1). Such is often, but not always, the case. As a example where they have the same dimension, the set of positive-mean Gaussian distributions is nested within the set of all Gaussian distributions; they both have dimension 2.

Comparing models

See also: Statistical model selection

Comparing statistical models is fundamental for much of statistical inference. Indeed, Konishi & Kitagawa (2008, p. 75) state this: "The majority of the problems in statistical inference can be considered to be problems related to statistical modeling. They are typically formulated as comparisons of several statistical models."

Common criteria for comparing models include the following: R², Bayes factor, Akaike information criterion, and the likelihood-ratio test together with its generalization, the relative likelihood.

Volatile organic compound

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Volatile_organic_compound

Volatile organic compounds (VOCs) are organic compounds that have a high vapor pressure at room temperature. High vapor pressure correlates with a low boiling point, which relates to the number of the sample's molecules in the surrounding air, a trait known as volatility.

VOCs are responsible for the odor of scents and perfumes as well as pollutants. VOCs play an important role in communication between animals and plants, e.g. attractants for pollinators, protection from predation, and even inter-plant interactions. Some VOCs are dangerous to human health or cause harm to the environment. Anthropogenic VOCs are regulated by law, especially indoors, where concentrations are the highest. Most VOCs are not acutely toxic, but may have long-term chronic health effects. Some VOCs have been used in pharmacy, while others are target of administrative controls because of their recreational use.

Definitions

Diverse definitions of the term VOC are in use. Some examples are presented below.

Canada

Health Canada classifies VOCs as organic compounds that have boiling points roughly in the range of 50 to 250 °C (122 to 482 °F). The emphasis is placed on commonly encountered VOCs that would have an effect on air quality.

European Union

The European Union defines a VOC as "any organic compound as well as the fraction of creosote, having at 293.15 K a vapour pressure of 0,01 kPa or more, or having a corresponding volatility under the particular conditions of use". The VOC Solvents Emissions Directive was the main policy instrument for the reduction of industrial emissions of volatile organic compounds (VOCs) in the European Union. It covers a wide range of solvent-using activities, e.g. printing, surface cleaning, vehicle coating, dry cleaning and manufacture of footwear and pharmaceutical products. The VOC Solvents Emissions Directive requires installations in which such activities are applied to comply either with the emission limit values set out in the Directive or with the requirements of the so-called reduction scheme. Article 13 of The Paints Directive, approved in 2004, amended the original VOC Solvents Emissions Directive and limits the use of organic solvents in decorative paints and varnishes and in vehicle finishing products. The Paints Directive sets out maximum VOC content limit values for paints and varnishes in certain applications. The Solvents Emissions Directive was replaced by the Industrial Emissions Directive from 2013.

China

The People's Republic of China defines a VOC as those compounds that have "originated from automobiles, industrial production and civilian use, burning of all types of fuels, storage and transportation of oils, fitment finish, coating for furniture and machines, cooking oil fume and fine particles (PM 2.5)", and similar sources. The Three-Year Action Plan for Winning the Blue Sky Defence War released by the State Council in July 2018 creates an action plan to reduce 2015 VOC emissions 10% by 2020.

India

The Central Pollution Control Board of India released the Air (Prevention and Control of Pollution) Act in 1981, amended in 1987, to address concerns about air pollution in India. While the document does not differentiate between VOCs and other air pollutants, the CPCB monitors "oxides of nitrogen (NO_x), sulphur dioxide (SO₂), fine particulate matter (PM10) and suspended particulate matter (SPM)".

United States

Thermal oxidizers provide an air pollution abatement option for VOCs from industrial air flows. A thermal oxidizer is an EPA-approved device to treat VOCs.

The definitions of VOCs used for control of precursors of photochemical smog used by the U.S. Environmental Protection Agency (EPA) and state agencies in the US with independent outdoor air pollution regulations include exemptions for VOCs that are determined to be non-reactive, or of low-reactivity in the smog formation process. Prominent is the VOC regulation issued by the South Coast Air Quality Management District in California and by the California Air Resources Board (CARB). However, this specific use of the term VOCs can be misleading, especially when applied to indoor air quality because many chemicals that are not regulated as outdoor air pollution can still be important for indoor air pollution.

Following a public hearing in September 1995, California's ARB uses the term "reactive organic gases" (ROG) to measure organic gases. The CARB revised the definition of "Volatile Organic Compounds" used in their consumer products regulations, based on the committee's findings.

In addition to drinking water, VOCs are regulated in pollutant discharges to surface waters (both directly and via sewage treatment plants) as hazardous waste, but not in non-industrial indoor air. The Occupational Safety and Health Administration (OSHA) regulates VOC exposure in the workplace. Volatile organic compounds that are classified as hazardous materials are regulated by the Pipeline and Hazardous Materials Safety Administration while being transported.

Biologically generated VOCs

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  Limonene, a common biogenic VOC, is emitted into the atmosphere primarily by trees which grow in coniferous forests.

Most VOCs in Earth's atmosphere are biogenic, largely emitted by plants.

Major biogenic VOCs

compound	relative contribution	amount emitted (Tg/y)
isoprene	62.2%	594±34
terpenes	10.9%	95±3
pinene isomers	5.6%	48.7±0.8
sesquiterpenes	2.4%	20±1
methanol	6.4%	130±4

Biogenic volatile organic compounds (BVOCs) encompass VOCs emitted by plants, animals, or microorganisms, and while extremely diverse, are most commonly terpenoids, alcohols, and carbonyls (methane and carbon monoxide are generally not considered). Not counting methane, biological sources emit an estimated 760 teragrams of carbon per year in the form of VOCs. The majority of VOCs are produced by plants, the main compound being isoprene. Small amounts of VOCs are produced by animals and microbes. Many VOCs are considered secondary metabolites, which often help organisms in defense, such as plant defense against herbivory. The strong odor emitted by many plants consists of green leaf volatiles, a subset of VOCs. Although intended for nearby organisms to detect and respond to, these volatiles can be detected and communicated through wireless electronic transmission, by embedding nanosensors and infrared transmitters into the plant materials themselves.

Emissions are affected by a variety of factors, such as temperature, which determines rates of volatilization and growth, and sunlight, which determines rates of biosynthesis. Emission occurs almost exclusively from the leaves, the stomata in particular. VOCs emitted by terrestrial forests are often oxidized by hydroxyl radicals in the atmosphere; in the absence of NO_x pollutants, VOC photochemistry recycles hydroxyl radicals to create a sustainable biosphere-atmosphere balance. Due to recent climate change developments, such as warming and greater UV radiation, BVOC emissions from plants are generally predicted to increase, thus upsetting the biosphere-atmosphere interaction and damaging major ecosystems. A major class of VOCs is the terpene class of compounds, such as myrcene.

Providing a sense of scale, a forest 62,000 square kilometres (24,000 sq mi) in area, the size of the US state of Pennsylvania, is estimated to emit 3,400,000 kilograms (7,500,000 lb) of terpenes on a typical August day during the growing season. Researchers investigating mechanisms of induction of genes producing volatile organic compounds, and the subsequent increase in volatile terpenes, has been achieved in maize using (Z)-3-hexen-1-ol and other plant hormones.

Anthropogenic sources

The handling of petroleum-based fuels is a major source of VOCs.

Anthropogenic sources emit about 142 teragrams (1.42 × 10¹¹ kg) of carbon per year in the form of VOCs.

The major source of man-made VOCs are:

• Fossil fuel use and production, e.g. incompletely combusted fossil fuels or unintended evaporation of fuels. The most prevalent VOC is ethane, a relatively inert compound.
• Solvents used in coatings, paints, and inks. Approximately 12 billion litres of paint are produced annually. Typical solvents include aliphatic hydrocarbons, ethyl acetate, glycol ethers, and acetone. Motivated by cost, environmental concerns, and regulation, the paint and coating industries are increasingly shifting toward aqueous solvents.
• Compressed aerosol products, mainly butane and propane, estimated to contribute 1.3 billion tonnes of VOC emissions per year globally.
• Biofuel use, e.g., cooking oils in Asia and bioethanol in Brazil.
• Biomass combustion, especially from rain forests. Although combustion principally releases carbon dioxide and water, incomplete combustion affords a variety of VOCs.

Indoor VOCs

Concentrations of VOCs in indoor air may be 2 to 5 times greater than in outdoor air, sometimes far greater. During certain activities, indoor levels of VOCs may reach 1,000 times that of the outside air. Studies have shown that emissions of individual VOC species are not that high in an indoor environment, but the total concentration of all VOCs (TVOC) indoors can be up to five times higher than that of outdoor levels.

New buildings experience particularly high levels of VOC off-gassing indoors because of the abundant new materials (building materials, fittings, surface coverings and treatments such as glues, paints and sealants) exposed to the indoor air, emitting multiple VOC gases. This off-gassing has a multi-exponential decay trend that is discernible over at least two years, with the most volatile compounds decaying with a time-constant of a few days, and the least volatile compounds decaying with a time-constant of a few years.

New buildings may require intensive ventilation for the first few months, or a bake-out treatment. Existing buildings may be replenished with new VOC sources, such as new furniture, consumer products, and redecoration of indoor surfaces, all of which lead to a continuous background emission of TVOCs, and requiring improved ventilation.

Numerous studies show strong seasonal variations in indoors VOC emissions, with emission rates increasing in summer. This is largely due to the rate of diffusion of VOC species through materials to the surface, increasing with temperature. Most studies have shown that this leads to generally higher concentrations of TVOCs indoors in summer.

Indoor air quality measurements

Measurement of VOCs from the indoor air is done with sorption tubes e. g. Tenax (for VOCs and SVOCs) or DNPH-cartridges (for carbonyl-compounds) or air detector. The VOCs adsorb on these materials and are afterwards desorbed either thermally (Tenax) or by elution (DNPH) and then analyzed by GC-MS/FID or HPLC. Reference gas mixtures are required for quality control of these VOC-measurements. Furthermore, VOC emitting products used indoors, e.g. building products and furniture, are investigated in emission test chambers under controlled climatic conditions. For quality control of these measurements round robin tests are carried out, therefore reproducibly emitting reference materials are ideally required. Other methods have used proprietary Silcosteel-coated canisters with constant flow inlets to collect samples over several days. These methods are not limited by the adsorbing properties of materials like Tenax.

Regulation of indoor VOC emissions

In most countries, a separate definition of VOCs is used with regard to indoor air quality that comprises each organic chemical compound that can be measured as follows: adsorption from air on Tenax TA, thermal desorption, gas chromatographic separation over a 100% nonpolar column (dimethylpolysiloxane). VOC (volatile organic compounds) are all compounds that appear in the gas chromatogram between and including n-hexane and n-hexadecane. Compounds appearing earlier are called VVOC (very volatile organic compounds); compounds appearing later are called SVOC (semi-volatile organic compounds).

France, Germany (AgBB/DIBt), Belgium, Norway (TEK regulation), and Italy (CAM Edilizia) have enacted regulations to limit VOC emissions from commercial products. European industry has developed numerous voluntary ecolabels and rating systems, such as EMICODE, M1, Blue Angel, GuT (textile floor coverings), Nordic Swan Ecolabel, EU Ecolabel, and Indoor Air Comfort. In the United States, several standards exist; California Standard CDPH Section 01350 is the most common one. These regulations and standards changed the marketplace, leading to an increasing number of low-emitting products.

Health risks

See also: Chronic solvent-induced encephalopathy and Substance-induced psychosis

Respiratory, allergic, or immune effects in infants or children are associated with man-made VOCs and other indoor or outdoor air pollutants.

Some VOCs, such as styrene and limonene, can react with nitrogen oxides or with ozone to produce new oxidation products and secondary aerosols, which can cause sensory irritation symptoms. VOCs contribute to the formation of tropospheric ozone and smog.

Health effects include eye, nose, and throat irritation; headaches, loss of coordination, nausea; and damage to the liver, kidney, and central nervous system. Some organics can cause cancer in animals; some are suspected or known to cause cancer in humans. Key signs or symptoms associated with exposure to VOCs include conjunctival irritation, nose and throat discomfort, headache, allergic skin reaction, dyspnea, declines in serum cholinesterase levels, nausea, vomiting, nose bleeding, fatigue, dizziness.

The ability of organic chemicals to cause health effects varies greatly from those that are highly toxic to those with no known health effects. As with other pollutants, the extent and nature of the health effect will depend on many factors including level of exposure and length of time exposed. Eye and respiratory tract irritation, headaches, dizziness, visual disorders, and memory impairment are among the immediate symptoms that some people have experienced soon after exposure to some organics. At present, not much is known about what health effects occur from the levels of organics usually found in homes.

Ingestion

While null in comparison to the concentrations found in indoor air, benzene, toluene, and methyl tert-butyl ether (MTBE) were found in samples of human milk and increase the concentrations of VOCs that we are exposed to throughout the day. A study notes the difference between VOCs in alveolar breath and inspired air suggesting that VOCs are ingested, metabolized, and excreted via the extra-pulmonary pathway. VOCs are also ingested by drinking water in varying concentrations. Some VOC concentrations were over the EPA’s National Primary Drinking Water Regulations and China’s National Drinking Water Standards set by the Ministry of Ecology and Environment.

Dermal absorption

The presence of VOCs in the air and in groundwater has prompted more studies. Several studies have been performed to measure the effects of dermal absorption of specific VOCs. Dermal exposure to VOCs like formaldehyde and toluene downregulate antimicrobial peptides on the skin like cathelicidin LL-37, human β-defensin 2 and 3. Xylene and formaldehyde worsen allergic inflammation in animal models. Toluene also increases the dysregulation of filaggrin: a key protein in dermal regulation. this was confirmed by immunofluorescence to confirm protein loss and western blotting to confirm mRNA loss. These experiments were done on human skin samples. Toluene exposure also decreased the water in the trans-epidermal layer allowing for vulnerability in the skin’s layers.

Limit values for VOC emissions

Limit values for VOC emissions into indoor air are published by AgBB, AFSSET, California Department of Public Health, and others. These regulations have prompted several companies in the paint and adhesive industries to adapt with VOC level reductions their products. VOC labels and certification programs may not properly assess all of the VOCs emitted from the product, including some chemical compounds that may be relevant for indoor air quality. Each ounce of colorant added to tint paint may contain between 5 and 20 grams of VOCs. A dark color, however, could require 5–15 ounces of colorant, adding up to 300 or more grams of VOCs per gallon of paint.

VOCs in healthcare settings

VOCs are also found in hospital and health care environments. In these settings, these chemicals are widely used for cleaning, disinfection, and hygiene of the different areas. Thus, health professionals such as nurses, doctors, sanitation staff, etc., may present with adverse health effects such as asthma; however, further evaluation is required to determine the exact levels and determinants that influence the exposure to these compounds.

Studies have shown that the concentration levels of different VOCs such as halogenated and aromatic hydrocarbons differ substantially between areas of the same hospital. However, one of these studies reported that ethanol, isopropanol, ether, and acetone were the main compounds in the interior of the site. Following the same line, in a study conducted in the United States, it was established that nursing assistants are the most exposed to compounds such as ethanol, while medical equipment preparers are most exposed to 2-propanol.

In relation to exposure to VOCs by cleaning and hygiene personnel, a study conducted in 4 hospitals in the United States established that sterilization and disinfection workers are linked to exposures to d-limonene and 2-propanol, while those responsible for cleaning with chlorine-containing products are more likely to have higher levels of exposure to α-pinene and chloroform. Those who perform floor and other surface cleaning tasks (e.g., floor waxing) and who use quaternary ammonium, alcohol, and chlorine-based products are associated with a higher VOC exposure than the two previous groups, that is, they are particularly linked to exposure to acetone, chloroform, α-pinene, 2-propanol or d-limonene.

Other healthcare environments such as nursing and age care homes have been rarely a subject of study, even though the elderly and vulnerable populations may spend considerable time in these indoor settings where they might be exposed to VOCs, derived from the common use of cleaning agents, sprays and fresheners. In a study conducted in France, a team of researchers developed an online questionnaire for different social and age care facilities, asking about cleaning practices, products used, and the frequency of these activities. As a result, more than 200 chemicals were identified, of which 41 are known to have adverse health effects, 37 of them being VOCs. The health effects include skin sensitization, reproductive and organ-specific toxicity, carcinogenicity, mutagenicity, and endocrine-disrupting properties. Furthermore, in another study carried out in the same European country, it was found that there is a significant association between breathlessness in the elderly population and elevated exposure to VOCs such as toluene and o-xylene, unlike the remainder of the population.

Analytical methods

Sampling

Obtaining samples for analysis is challenging. VOCs, even when at dangerous levels, are dilute, so preconcentration is typically required. Many components of the atmosphere are mutually incompatible, e.g. ozone and organic compounds, peroxyacyl nitrates and many organic compounds. Furthermore, collection of VOCs by condensation in cold traps also accumulates a large amount of water, which generally must be removed selectively, depending on the analytical techniques to be employed. Solid-phase microextraction (SPME) techniques are used to collect VOCs at low concentrations for analysis. As applied to breath analysis, the following modalities are employed for sampling: gas sampling bags, syringes, evacuated steel and glass containers.

Principle and measurement methods

In the U.S., standard methods have been established by the National Institute for Occupational Safety and Health (NIOSH) and another by U.S. OSHA. Each method uses a single component solvent; butanol and hexane cannot be sampled, however, on the same sample matrix using the NIOSH or OSHA method.

VOCs are quantified and identified by two broad techniques. The major technique is gas chromatography (GC). GC instruments allow the separation of gaseous components. When coupled to a flame ionization detector (FID) GCs can detect hydrocarbons at the parts per trillion levels. Using electron capture detectors, GCs are also effective for organohalide such as chlorocarbons.

The second major technique associated with VOC analysis is mass spectrometry, which is usually coupled with GC, giving the hyphenated technique of GC-MS.

Direct injection mass spectrometry techniques are frequently utilized for the rapid detection and accurate quantification of VOCs. PTR-MS is among the methods that have been used most extensively for the on-line analysis of biogenic and anthropogenic VOCs. PTR-MS instruments based on time-of-flight mass spectrometry have been reported to reach detection limits of 20 pptv after 100 ms and 750 ppqv after 1 min. measurement (signal integration) time. The mass resolution of these devices is between 7000 and 10,500 m/Δm, thus it is possible to separate most common isobaric VOCs and quantify them independently.

Chemical fingerprinting and breath analysis

The exhaled human breath contains a few thousand volatile organic compounds and is used in breath biopsy to serve as a VOC biomarker to test for diseases, such as lung cancer. One study has shown that "volatile organic compounds ... are mainly blood borne and therefore enable monitoring of different processes in the body." And it appears that VOC compounds in the body "may be either produced by metabolic processes or inhaled/absorbed from exogenous sources" such as environmental tobacco smoke. Chemical fingerprinting and breath analysis of volatile organic compounds has also been demonstrated with chemical sensor arrays, which utilize pattern recognition for detection of component volatile organics in complex mixtures such as breath gas.

Metrology for VOC measurements

To achieve comparability of VOC measurements, reference standards traceable to SI-units are required. For a number of VOCs gaseous reference standards are available from specialty gas suppliers or national metrology institutes, either in the form of cylinders or dynamic generation methods. However, for many VOCs, such as oxygenated VOCs, monoterpenes, or formaldehyde, no standards are available at the appropriate amount of fraction due to the chemical reactivity or adsorption of these molecules. Currently, several national metrology institutes are working on the lacking standard gas mixtures at trace level concentration, minimising adsorption processes, and improving the zero gas. The final scopes are for the traceability and the long-term stability of the standard gases to be in accordance with the data quality objectives (DQO, maximum uncertainty of 20% in this case) required by the WMO/GAW program.