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2.3 Analytical proceduresHistorically, colorimetric and gravimetric methods have been used for the determination of arsenic. However, these methods are either semi-quantitative or lack sensitivity. In recent years, atomic absorption spectrometry (AAS) has become the method of choice, as it offers the possibility of selectivity and sensitivity in the detection of a wide range of metals and non-metals including arsenic. Popular methods for generating atoms for AAS are flame and electrothermally heated graphite furnaces. However, a commonly used technique for the measurement of arsenic is the highly sensitive hydride generation atomic absorption spectrometric method (HGAAS).
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However, although it is suitable for total arsenic determination after appropriate digestion the technique is only routinely used to speciate a limited number of compounds – arsenite, arsenate, MMA, DMA, trimethylarsine oxide (TMAO).Hydride generation followed by cryogenic trapping and AAS detection is a relatively inexpensive technique for the speciation of inorganic arsenic and its methylated metabolites (Ng et al., 1998a), although more expensive hyphenated techniques may also be used.A number of other approaches have been reported for speciation of arsenic. Inductively coupled plasma-mass spectrometry (ICP-MS) offers very high sensitivity for the determination of arsenic, and coupled with HPLC enables equally sensitive estimation of a wide variety of arsenic species.
2.4 Sample preparation and treatment 2.4.1 Sampling and collectionCare must be taken to avoid contamination and prevent speciation changes during sample collection and storage. Plastic containers should be acid washed and traces of oxidizing and reducing agents avoided to preserve the oxidation state of arsenic compounds. Freezing samples to –80 °C has also been recommended (Crecelius, 1986). Concentrated hydrochloric acid (1 ml to 100 ml urine) has been added to urine to prevent bacterial growth (Concha et al., 1998a).For particulates in air and aerosols sampling, various types of filter have been employed including polytetrafluoroethylene (Rabano et al., 1989), cellulose ester (Yager et al., 1997), glass microfibre (Beceiro-Gonzalez et al.,1997) and filter paper (Tripathi et al., 1997). 2.4.2 Oxidative digestionAcid digestion (George & Roscoe, 1951) and dry ashing (George et al., 1973) are the two basic methods which have been widely employed for oxidative digestion of samples before analysis. In more recent years, microwave-assisted digestion has been used (Le et al., 1994b; Thomas et al., 1997). For analysis of biological soft tissues by ICP techniques, a simple partial digestion in a closed vessel at low temperature and pressure is often sufficient for the sample preparation and pretreatment step.
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2.4.3 ExtractionFor speciation of arsenic, solvent extraction is often required before analysis. For example, arsenite and arsenate in soil can be speciated after a hydrochloric acid and chloroform extraction procedure (Chappell et al., 1995; Ng et al., 1998b). Water has been used for the extraction of soluble arsenic compounds from soil with the aid of ultrasonic treatment (Hansen et al., 1992). Forms of arsenic compounds can also be separated by sequential extractions based on procedures described by Tessier et al.
Aqueous methanol has been widely used for the extraction of organic arsenic species (Edmonds & Francesconi, 1981a; Shiomi et al., 1988a; Shibata et al., 1996; Kuehnelt et al., 1997). Yu & Wai (1991) and Laintz et al.
(1992) described the use of sodium bis(trifluoroethyl) dithiocarbamate (NaFDDC) as a selective chelation reagent of arsenic followed by either a gas chromatograph (GC) detection or supercritical fluid chromatography (SFC) detection. The former gave a limit of detection of 10 µg As/litre in water and the latter gave similar sensitivity after 100–1000-fold preconcentration of the chelate complex in organic solvent. 2.4.4 Supercritical fluid extractionThere are very few publications on the use of supercritical fluid extraction (SFE) for the determination of arsenic. Wenclawiak & Krah (1995) reported a procedure for the measurement of arsenic species using SFE followed by GC or SFC detection. The authors described a rapid extraction of organic and inorganic arsenic species from spiked sand and soil samples by SFE with on-line derivatization using thioglycollic acid methylester (TGM) under supercritical conditions. The TGM derivatives are thermally stable, which makes them amenable to GC–SFC determination.
The extracts were chromatographed without further clean-up steps. The limits of detection were 1 ng As/µl and 3 ng As/µl injection for DMA-TGM and MMA-TGM respectively. 2.5 Macro-measurementMost procedures for the separation and determination of arsenic are based on distillation and hydrogen sulfide precipitation methods. Beard & Lyerly (1961) reported a gravimetric method for the measurement of arsenic following extraction of arsenic as AsCl 3 by benzene in strong hydrochloric acid. The recovery was close to 100% when 20 mg was spiked into an aqueous solution.Vogel (1954) described the historic Marsh test, a qualitative method based on the generation of arsine (AsH 3) by the addition of Zn granules to sulfuric acid. If the gas is mixed with hydrogen, and conducted through a heated glass tube, it decomposes into hydrogen and metallic arsenic which is deposited as a brownish-black 'mirror' just beyond the heated part of the tube.
2.6 Colorimetric methodsGeorge & Roscoe (1951) reported a spectroscopic emission measurement of the blue complex formed by the reaction of ammonium molybdate and hydrazine sulfate with arsenic in various biological materials. The sensitivity was about 0.01 µg.George et al. (1973) carried out a collaborative study for a colorimetric measurement of arsenic in poultry and swine tissues using silver diethyldithiocarbamate (AgDDTC) as the complexing agent. The sensitivity was 0.1 mg/kg in tissues.
(1997) reported a detection limit of 0.04 mg/litre with 95% confidence limit using AgDDTC in chloroform with hexamethylenetetramine.Gutzeit’s test (Vogel, 1954) is based on the generation of arsine from arsenic compounds by the addition of zinc granules to concentrated sulfuric acid. The arsine can be detected by means of a strip of filter paper moistened with silver nitrate or mercuric chloride.
The arsine reacts with silver nitrate to give a grey spot, and with mercuric chloride to give a yellow to reddish-brown spot. The sensitivity is about 1 µg. A modification of this method, using mercuric bromide, is found in a test kit currently being used in Bangladesh for groundwater testing which has a limit of detection of 50–100 µg/litre under field conditions. 2.7 Methods for total inorganic arsenicMethods for the analysis of inorganic arsenic based on its conversion to arsenic trichloride or arsenic tribromide by treatment with 6 mol/litre hydrochloric acid or hydrobromic acid have been described. The arsenic trihalide is separated from the remaining organic arsenic either by distillation (Maher, 1983) or by solvent extraction (Brooke & Evans, 1981). The methods have been applied routinely to the measurement of inorganic arsenic in a variety of foodstuffs, including those of marine origin where any inorganic arsenic is a small percentage of the total arsenic present (Flanjak, 1982; Shinagawa et al., 1983). 2.8 Atomic spectrometryCommon flame atomic absorption spectrometric methods are flame AAS (FAAS), electrothermal AAS (ETAAS) and hydride generation AAS (HGAAS).
FAAS is relatively less sensitive for the determination of arsenic than ETAAS and HGAAS. Its detection limit is usually in the range of sub-milligram quantities per litre, and therefore it has limited application, especially for biological samples.ETAAS, referred to also as graphite furnace-AAS (GFAAS), is generally one of the most sensitive atomic spectroscopic methods. Julshamn et al.
(1996) reported factors that are known to interfere with the GFAAS determination of arsenic. The study was carried out by four participating laboratories using five marine standard reference materials. A mixture of palladium and magnesium salts has been recommended as a chemical modifier to avoid nickel contamination of the graphite furnace. The use of a pyrolytically coated graphite furnace tube with the L’vov platform improves sensitivity. Larsen (1991) achieved characteristic masses of about 16 pg of arsenic for arsenate, monomethylarsonate, DMA, arsenobetaine, arsenocholine and tetramethylarsonium ion calculated from aqueous standard solutions.HGAAS is probably the most widely used method for the determination of arsenic in various matrices.
Most of the reported errors in the determination of arsenic by HGAAS with NaBH 4 can be attributed to variation in the production of the hydride and its transport into the atomizer. The reaction and atomization of arsine have been reviewed and discussed by Welz et al. The addition of a solution of l-cysteine to a sample before hydride generation eliminates interference by a number of transition metals in the generation of arsine from arsenite and arsenate (Boampong et al., 1988), and improves responses of arsine generated from MMA and DMA in the presence of arsenite and arsenate (Le et al., 1994a).Holak & Specchio (1991) described the determination of total arsenic, arsenite and arsenate in foods by HGAAS after a chloroform extraction procedure. The recovery was 80%. Similar methods (Chappell et al., 1995; Ng et al., 1998a) have been developed for arsenic speciation in soils. Ybanez et al. (1992) described a HGAAS determination of arsenic in dry ashed mussel products and reported a detection limit of 0.017 µg As/g with a precision of 3%.HGAAS has been used for arsenic speciation of inorganic arsenic and its urinary metabolites, MMA and DMA, since 1973, when Braman & Foreback (1973) introduced a cold-trapping step into a basic hydride generation system.
Since then a number of improvements have been made to this method (Crecelius, 1978; Buchet & Lauwerys, 1981; Van Cleuvenbergen et al., 1988). (1998b) described an optimized procedure for the speciation of arsenic metabolites in the urine of occupationally exposed workers and experimental animals with detection limits of 1, 1.3 and 3 ng per reaction of inorganic arsenic, MMA and DMA (equivalent to 0.25 µg/litre, 0.325 µg/litre, and 0.75 µg/litre respectively), using 4 ml of urine per reaction.HGAAS has also been widely employed for analysis of arsenic in water (Chen et al., 1994; Chatterjee et al., 1995; Mandal et al., 1996; Dhar et al., 1997; Biswas et al., 1998). Hasegawa et al. (1994) published the first report of trivalent methyl arsenicals, namely monomethylarsonous acid MMA(III) and dimethylarsinous acid DMA(III), being found and measured in natural waters. Arsenious acid, MMA(III) and DMA(III) were separated from the pentavalent species by solvent extraction using diethylammonium diethyldithiocarbamate (DDDC) and determined by HGAAS after cold trapping and chromatographic separations.
The detection limits were 13–17 pmol/litre and 110–180 pmol/litre for the trivalent and pentavalent species respectively.Atomic fluorescence spectrometry (AFS) has recently been used for the detection of arsenic hydrides in the ultraviolet spectral region because of the small background emission produced by the relatively cool hydrogen diffusion flame (Gomez-Ariza et al., 1998). The use of cold vapour or hydride generation, together with intense light sources, allows very low detection limits to be achieved. For example, arsenic species in seawater have been measured using hydride generation and cold trapping, coupled with AFS detection at 193.7 nm (Featherstone et al., 1998).
They found detection limits of 2.3, 0.9, 2.4 and 3.7 ng/litre for arsenite, arsenate, MMA and DMA respectively (in a 5 ml sample), with a precision of 3.5%. 2.9 ICP methodologiesThe main advantages of ICP-MS over ICP-AES are lower detection limits (sub-nanogram to sub-picogram) with wide linear range and isotope analysis capability of high precision. The detection limits of ICP-AES are typically in the range of sub-micrograms to sub-nanograms.ICP-MS is more susceptible to isobaric interferences arising from the plasma. For example, hydrochloric acid and perchloric acid are not desirable for sample preparation, because the chloride ions generated in the plasma combine with the argon gas to form argon chloride (ArCl). This has the same mass as arsenic (75) which could lead to error if not corrected. Therefore, whenever possible, only nitric acid should be used in sample preparation.
Careful sample preparation is as important as the final measurement, and special care should be taken to avoid contamination and losses by volatilization, adsorption and precipitation. 2.10 VoltammetryVoltammetric stripping methods are mostly based on the chemical reduction of As(V) to As(III) before the deposition step, because it has been generally assumed that As(V) is electrochemically inactive. Mercury and gold (or gold-plated) electrodes are most commonly used for the determination of arsenic.Sadana (1983) used differential pulse cathodic stripping voltammetry (DPCSV) coupled to a hanging mercury drop electrode (HMDE) to determine arsenic in drinking-water in the presence of Cu 2+ and reported a detection limit of 1 ng/ml and a relative standard deviation of 6.4%.
Zima & van den Berg (1994) reported a detection limit of 3 nmol/litre in seawater. DPCSV was employed by Higham & Tomkins (1993) to determine arsenic in canned tuna fish. They evaluated a number of digestion procedures and found the best procedure gave 93–96% recovery.
No detection limit was reported.A gold electrode affords better sensitivity than a mercury electrode. (1987) reported an automated determination of total arsenic in seawater by flow constant-current stripping analysis with a gold film fibre electrode, in which As(V) in the sample was reduced to As(III) with potassium iodide; the detection limit was 0.15 µg/litre. The reduction of As(V) to As(III) can also be achieved by reaction with sulfur dioxide or hydrazinium chloride for use with a gold electrode or HMDE respectively (Esteban et al., 1994).Huiliang et al. (1988) have shown that As(V) can be reduced to elemental arsenic provided that extremely low reduction potentials are used. They used this method to measure As(V) and total arsenic in seawater and urine. The detection limit was 0.1 µg/litre using constant-current stripping voltammetry on a gold-coated platinum-fibre electrode. Greulach & Henze (1995) developed a cathodic stripping voltammetric method for the determination of As(V) in water and stream sediment on the basis that As(V) can be reduced in perchloric acid solution containing d-mannitol, combined with the accumulation of arsenic by co-precipitation with copper on an HMDE.
The detection limit was 4.4 µg/litre.Pretty et al. (1993) developed an on-line anodic stripping voltammetry (ASV) flow cell coupled to ICP-MS for the determination of arsenic in spiked urine. The detection limit was 130 pg/ml and the recovery was 94–113%. 2.11 Radiochemical methodsOrvini et al. (1974) reported a combustion technique for sample preparation and determination of arsenic, selenium, zinc, cadmium and mercury by neutron activation analysis (NAA) in environmental matrices including a range of standard reference materials.
The recoveries were 98–100%. Sharif et al. (1993) described a NAA technique for the determination of arsenic in eight species of marine fishes caught in the bay of Bengal, Bangladesh.Haddad & Zikovsky (1985) measured several elements including arsenic in air from workroom welding fumes by NAA and reported a detection limit of 0.17 ± 0.07 µg/m 3. Landsberger & Wu (1995) reported the use of NAA to measure arsenic from environmental tobacco smoke in indoor air with a detection limit of 0.2 ng.Chutke et al. (1994) described a radiochemical solvent extraction procedure for the determination of arsenite using an arsenic-76 tracer. The procedure is based on the complexation of arsenite with toluene-3,4-dithiol (TDT) at pH 2 and subsequent extraction in benzene.
This isotopic dilution technique was employed to measure arsenic in a range of standard and certified reference materials. The detection limit was 250 ng with an accuracy of about 4% error and 170 ng with about 12% error. 2.12 X-ray spectroscopyParticle-induced X-ray emission spectrometry (PIXES) is an analytical technique that entails the bombardment of a sample (target) with charged particles, resulting in the emission of characteristic X-rays of the elements present. PIXES is a multi-elemental technique with a detection limit of approximately 0.1 µg As/g. It has the advantage of using small samples (1 mg or less) and being a non-destructive technique. Applications of PIXES in the environmental field have mostly focused on atmospheric particulate material (aerosol samples) (Maenhaut, 1987).Castilla et al. (1993) described the determination of arsenite and arsenate by X-ray fluorescence (XRF) spectroscopy in water with a detection limit of 3.1 ng/g.
The recovery was 97 ± 2.1% and 103 ± 1.4% for arsenite and arsenate respectively. In this method, the water sample was acidified to pH 2 and arsenite co-precipitated with sodium dibenzyldithiocarbamate (DBDTC). Arsenate in the filtrate was then reduced to arsenite with potassium iodide before the co-precipitation step for the XRF measurement.Although there are a variety of methods to determine the concentration and oxidation states of arsenic in coal and ash, there have been few attempts to determine the mineral forms of arsenic. Huffman et al.
(1994) described the use of X-ray absorption fine structure (XAFS) spectroscopy and its capability of providing speciation information at realistic concentrations of 10–100 mg/kg. They identified arsenic present as arsenopyrite in one coal sample and as aluminosilicate slag and calcium orthoarsenate in combustion ashes. 2.13 Hyphenated techniquesHyphenated techniques is a term referring to the coupling of more than two instrumental systems to form a single technique.The combination of chromatographic separation with element-specific spectrometric detection has been proved to be particularly useful for the speciation of arsenic compounds at trace levels in environmental samples. Woller et al. (1995) used AFS detection in combination with ultrasonically nebulized liquid chromatography (LC) for on-line speciation of arsenic, but found that the technique had limited sensitivity owing to matrix interferences. More recently, Slejkovec et al.
(1998) used LC and purge-and-trap GC interfaced with AFS to separate and quantify six arsenic species with detection limits of 0.5 ng/ml As (100 µl). Gomez-Ariza et al. (1998) coupled anion-exchange HPLC, hydride generation and AFS to achieve detection limits of 0.17, 0.45, 0.30 and 0.38 µg/litre for arsenite, DMA, MMA and arsenate respectively (using a 20 µl loop). Arsenobetaine was also determined by introducing an on-line photo-oxidation step after the chromatographic separation.Ebdon et al. (1988) described a number of coupled chromatograph–atomic spectrometry methods for arsenic speciation including GC or HPLC with detection by atomic spectrometry, namely FAAS, flame atomic fluorescence spectrometry (FAFS) and ICP-AES.
The FAAS system is capable of detection at less than 1 µg/kg (0.22–0.55 ng absolute for different species) when levels permit; HPLC–hydride generation–FAAS is probably the simplest routine method and HPLC–hydride generation–ICP-AES is preferred for multi-elemental analysis. HPLC–ICP-AES has been employed for the speciation of organic arsenic of aquatic origin (Francesconi et al., 1985; Gailer & Irgolic, 1996). Gjerde et al.
(1993) described the coupling of microbore columns with direct-injection nebulization to ICP-AES and reported a detection limit of 10 µg/litre (100 pg). Microbore HPLC has the advantage of analysing small sample size using low flow rates (80–100 µl/min) of mobile phases.Numerous methods (Shum et al., 1992; Larsen et al., 1993; Magnuson et al., 1996; Thomas et al., 1997; Le & Ma, 1997) have been developed for the speciation of arsenic using the separation power of chromatography coupled to the sensitivity of ICP-MS detection. Heitkemper et al. (1989) described an anion-exchange HPLC–ICP-MS method for the speciation of arsenite, arsenate, MMA and DMA in urine with absolute detection limits ranging from 36 to 96 pg (corresponding to 0.7–1.9 µg/litre in a 50 µl injection). Beauchemin et al.
(1989) reported detection limits for arsenic species in DORM-1 (a dogfish muscle certified reference material) ranging between 50 and 300 pg using ion pairing and ion exchange HPLC-ICP-MS. Anion exchange is more tolerant because of the higher buffering capacity of the mobile phase. Cation pairing is more suitable for the determination of DMAA and arsenobetaine in biological samples containing high concentrations of salts. Pergantis et al. (1997) analysed and speciated animal feed additives using microbore HPLC–ICP-MS with detection limits ranging from 0.1 to 0.26 pg. Hakala and Pyy (1992) described an ion-pairing HPLC-HGAAS method for speciation of arsenite, arsenate, MMA and DMA in urine with detection limits of 1.0, 1.6, 1.2 and 4.7 µg/litre respectively.Ding et al. (1995) described the coupling of micellar liquid chromatography (MLC) and ICP-MS for the speciation of arsenite, arsenate, MMA and DMA with detection limits of 90 pg for DMA and 300 pg for the other species.
MLC is a type of chromatography that uses surfactants in aqueous solutions, well above their critical micelle concentration, as alternative mobile phases for reversed-phase liquid chromatography (RPLC). MLC extends the analyte candidates to almost all hydrophobic and many hydrophilic compounds providing they can partition to the micelles. Other advantages of MLC over RPLC include simultaneous separation of both ionic and non-ionic compounds, faster analysis times and improved detection sensitivity and selectivity.Capillary electrophoresis (CE) is a versatile technique for the separation of a variety of analytes ranging from small inorganic ions to large biomolecules such as proteins and nucleic acids. CE-ICP-MS has been described for the speciation of arsenic by Liu et al. (1995) with detection limits of 100 pg arsenite/ml and 20 pg arsenate/ml and Olesik et al. (1995) with a detection limit of 8 µg/litre (1 pg injection).Although techniques such as HPLC–ICP-MS and MLC–ICP-MS offer the advantages of high sensitivity and selectivity as well as low detection limits, species identification is based on the comparison of chromatographic retention times to those of available standards. When structure information is required, as well as quantification, electrospray HPLC–MS (Siu et al., 1991) and ionspray MS (Corr, 1997) should be considered.
Corr & Larsen (1996) reported the use of LC–MS–MS for speciation of arsenic with a detection limit of 2 pg for the tetramethylarsonium cation.3. SOURCES AND OCCURRENCE OF ARSENIC IN THE ENVIRONMENT 3.1 Natural sourcesArsenic is the main constituent of more than 200 mineral species, of which about 60% are arsenate, 20% sulfide and sulfosalts and the remaining 20% include arsenides, arsenites, oxides and elemental arsenic (Onishi, 1969). The most common of the arsenic minerals is arsenopyrite, FeAsS, and arsenic is found associated with many types of mineral deposits, especially those including sulfide mineralization (Boyle & Jonasson, 1973). The ability of arsenic to bind to sulfur ligands means that it tends to be found associated with sulfide-bearing mineral deposits, either as separate As minerals or as a trace of a minor constituent of the other sulfide minerals. This leads to elevated levels in soils in many mineralized areas where the concentrations of associated arsenic can range from a few milligrams to 100 mg/kg.Concentrations of various types of igneous rocks range from 1 mg As/litre) in groundwater of geochemical origins have also been found in Taiwan (Chen et al., 1994), West Bengal, India (Chatterjee et al., 1995; Das et al., 1995, 1996; Mandal et al., 1996) and more recently in most districts of Bangladesh (Dhar et al., 1997; Biswas et al., 1998). Elevated arsenic concentrations were also found in the drinking-water in Chile (Borgono et al., 1977); North Mexico (Cebrian et al., 1983); and several areas of Argentina (Astolfi et al., 1981; Nicolli et al., 1989; De Sastre et al., 1992). Arsenic-contaminated groundwater was also found in parts of PR China (Xinjiang and Inner Mongolia) and the USA (California, Utah, Nevada, Washington and Alaska) (Valentine, 1994).
More recently, arsenic concentrations of.
To characterize the role of rare complete human knockouts in autism spectrum disorders (ASD), we identify genes with homozygous or compound heterozygous loss-of-function (LoF) variants (defined as nonsense and essential splice sites) from exome sequencing of 933 cases and 869 controls. We identify a two-fold increase in complete knockouts of autosomal genes with low rates of LoF variation (≤5% frequency) in cases and estimate a 3% contribution to ASD risk by these events, confirming this observation in an independent set of 563 probands and 4,605 controls. Outside the pseudo-autosomal regions on the X-chromosome, we similarly observe a significant 1.5-fold increase in rare hemizygous knockouts in males, contributing to another 2% of ASDs in males. Taken together these results provide compelling evidence that rare autosomal and X-chromosome complete gene knockouts are important inherited risk factors for ASD. INTRODUCTIONAutism spectrum disorder (ASD) is a highly heritable, common disorder that affects 1 in 88 individuals (2012). Previous studies have shown a reproducible contribution of de novo copy number variants (CNVs) (;;; ) and de novo single nucleotide variants (SNVs) (;;; ) to ASD risk - though these effects provide little explanation for the widely recognized high heritability.An early segregation analysis on 46 multiplex families (each with multiple affected children) suggested evidence for an autosomal recessive (or ‘2-hit’) model in ASD with a subsequent study showing that ASD is unlikely to fit a model with a major gene effect. Further to this point, the most recent results from de novo CNVs and SNVs point to a model in which hundreds of genes are likely to contribute to autism risk.
Building from these observations, as a means of providing insight into the heritable component of ASD risk, we sought to test the hypothesis that 2-hit etiologies exist in ASD and that these events, like the de novo CNVs and SNVs, are most likely to be distributed over many genes. Supporting this hypothesis are historical segregation analyses (; ), the successful use of homozygosity mapping in consanguineous populations , as well as recent studies showing that ASD probands had a significant excess of homozygous haplotype sharing, suggesting that there are recessive loci in these risk-conferring haplotypes (; ). Other studies have also implicated the role of a 2-hit or oligogenic model for rare CNVs in ASD.It has been shown that there are relatively few homozygous or compound heterozygous LoF variants (i.e., complete gene knockouts) in healthy individuals. Most of these complete knockouts found are common (MAF5%) and are distributed across a very small number (100–200) of genes, such as the olfactory receptors, that are apparently inessential and do not result in any obvious phenotype or severe medical consequence. We similarly observe in these ASD datasets that an average individual harbors 5 common complete knockouts (from nonsense and essential splice site variants) distributed across a small subset of genes on the autosomes. In striking contrast, if we consider only LoF variants with frequency ≤5%, fewer than 5% of individuals harbor even a single rare complete knockout.
While heterozygous LoF mutations are seen in thousands of genes, the very low frequency and paucity of observed complete knockouts suggests a broad pool of genes (including many Mendelian disorders) where 2-hit variants may give rise to severe and reproductively deleterious phenotypes. While genes with common complete knockouts are more likely to be benign (or unlikely to result in severe phenotypes with high penetrance), genes with rare complete knockouts are more likely to be disease-causing simply because selection prevents deleterious recessive-acting variants from reaching even moderate allele frequencies. The average number of rare (≤5%) and common (5%) homozygous LoF variants, as well as the average number of such variants calculated from the BI case-control dataset.If a subset of ASD cases were caused by rare 2-hit events with large effects (e.g.
Odds ratios of 5) distributed across many different genes, then family-based linkage or GWAS would have little power to detect such events, as each locus individually would explain a very small fraction of all cases given the commonness of the outcome and the large number of ASD genes. To evaluate evidence for such 2-hit etiologies in ASD, we studied the distribution and patterns of rare complete knockouts from whole-exome sequence data across two case-control studies comprised of 1,802 European subjects to identify events in which individuals carried 2 LoF autosomal variants in a single gene in trans. In this study, we show that rare complete knockouts on the autosomes (variant allele frequencies of ≤5%) are significantly enriched in cases, suggesting that these events contribute to the genetic etiology of ASD.A variant with a diploid allele frequency of 5% on the autosomes results in a complete knockout in 0.25% of the individuals. Outside the pseudo-autosomal regions on the X-chromosome in males, a single LoF variant with 0.25% allele frequency also results in a complete knockout in 0.25% of males. Similarly, we found that rare complete knockouts on the X-chromosome (variant allele frequencies of ≤0.25%) are also significantly enriched in male cases, further reinforcing the role of rare complete knockouts as risk factors for ASD. Exome Capture and SequencingTo assess the contribution of rare complete knockouts to ASD, we analyzed data from an ethnically-matched case-control population. We selected 933 cases and 869 controls sequenced in this study by matching them with multi-dimensional scaling (MDS) of common variants genotyped on Illumina 1M, Affymetrix 5.0, or 6.0 arrays to reduce potential confounding by population stratification.
The exomes were sequenced at two different sequencing centers – the Broad Institute (BI) and the Baylor College of Medicine (BCM). A total of 428 ASD cases selected from the Autism Genetic Resource Exchange (AGRE) and 378 NIMH controls (a total of 806 individuals) were sequenced at BI, and another 505 ASD cases selected from the Autism Simplex Collection (TASC) and 491 NIMH controls (a total of 996 individuals) were sequenced at BCM, resulting in 1,802 individuals across the two case-control datasets. All controls were selected from an NIMH control repository and were ascertained for not having schizophrenia or bipolar mood disorder.
Another 563 probands were added into the final analyses (388 trios/quartets from the Simons Simplex Collection (SSC) (; ), 175 trios from the Boston Autism Consortium sequenced at BI (104 from ) and together with 4,605 additional European controls from the NHLBI exome sequencing project and the 1000 Genomes Project, this resulted in a total of 6,000 exomes used in this study. The metrics for the case-control datasets are described in.
Enrichment of Rare Complete Knockouts in ASDGiven that rare complete knockouts consist of both compound heterozygous and homozygous variants on the autosomes, we adapted a statistical phasing approach similar to the four-haplotype test to eliminate instances in which multiple LoF variants may segregate in cis. There are a total of 91 such rare complete knockouts in the case-control datasets, with 62 of these found in the cases compared to 29 in the controls , representing a roughly 2-fold enrichment of these events in the cases (odds ratio (OR) = 2.0, 95% CI = 1.5, 2.5, one-sided permutation P = 0.0017). Based on the difference between cases and controls (6% of the cases versus 3.3% of the controls have a rare complete knockout), we estimate a 3% contribution by rare complete knockouts to ASD.
Similar Enrichment of Rare Complete Knockouts Observed on the X-chromosomeGiven the gender bias in ASD, with roughly 4 times as many affected males than females , we asked analogously whether rare gene knockouts outside the pseudo-autosomal regions on the X-chromosome (arising from hemizygous LoFs in males) were enriched in male cases versus male controls. To further increase the sample sizes, we included the male probands and their unaffected fathers from the trios and quartets.
The nucleotide diversity on the X-chromosome is estimated to be between half to three-quarters that of the autosomes and deleterious LoF variants on the X-chromosome are under stronger negative selection given the smaller effective population size and constant exposure in hemizygous males. To match the baseline knockout rate to the autosomes, where we examined variants with ≤5% minor allele frequency (MAF) and therefore ≤0.25% homozygosity, we examined LoF variants with population frequency (assessed in female control samples) of ≤0.25%.
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On average, we observed less than 1 such rare LoF variant on the X-chromosome in both males and females.Similar to the autosomes, we observed a significant enrichment of rare hemizygous LoFs in male cases , with 88 such events observed – 60 of them were found in male cases and 28 of them were found in male controls (OR = 1.5, 95% CI = 1.1, 2.0, one-sided hypergeometric test P = 0.034, ). No enrichment was seen in the internal controls of this comparison - rare hemizygous synonymous variants were not enriched in male cases compared to male controls (OR = 1.0, 95% CI = 0.9, 1.1), indicating the observed enrichment is specific to rare complete knockouts on the X-chromosome in male ASD cases.
Based on the difference between cases and controls, we further estimate another 1.7% contribution by rare complete knockouts on the X-chromosome in male cases. In addition, we found 2 of 170 female cases bearing a rare complete knockout on the X-chromosome and 0 of 452 female controls. As with the autosomes, we attempted validation for 44 of 50 rare X-chromosome LoF variants and all 44 validated. Rare hemizygous /heterozygous LoF variantsRare hemizygous / heterozygoussynonymous variantsHemizygous LoFs in males (N = 2,144)Cases (N = 1,245)60 events2,114 eventsControls (N = 899)28 events1,516 eventsOR 95% CI1.5 1.1, 2.01.0 0.9, 1.1Heterozygous LoFs in females (N = 622)Cases (N = 170)21 events641 eventsControls (N = 452)56 events1,256 eventsOR 95% CI1.0 0.5, 1.51.4 1.2, 1.6Rare homozygous LoFvariantsRare homozygous synonymousvariantsHomozygous LoFs in females (N = 622)Cases (N = 170)2 events5 eventsControls (N = 452)0 events0 eventsOR 95% CI. The number of rare hemizygous LoF and synonymous variants outside the pseudo-autosomal regions on the X-chromosome in males, as well as the number of rare heterozygous LoF and synonymous variants in females are shown, together with the respective odds ratios.We screened the list of rare complete knockouts observed on the autosomes and X-chromosome for instances where a knockout was observed only in cases and not in any of the controls and performed a screen for enrichment of pathways and microRNA targets using WebGestalt. The top pathway (“Complement and coagulation cascades”) was driven by 2 genes ( KNG1 and PLAT; corrected P = 0.0027). Scanning predicted targets of microRNAs, we found one ( mir-328) predicted to target 3 genes from the list ( HAP1, AFF2 and MECP2; corrected P = 0.0013; ).
Additional siblings (affected = 24, unaffected = 11) were available for 22 probands who were genotyped to examine segregation of a proposed recessive model. We observed 18 (expected 14) instances where segregation was consistent with a fully penetrant recessive model, including 4 genes with rare complete knockouts (PTH2R, MECP2, VSIG1 and ZCCHC16) observed in cases only and not in a single control in any wave of our study.
Gender and IQIt has been shown that the male gender bias is stronger in high-functioning ASD cases, and the gender bias is reduced for syndromic cases. We found that there was a higher rate of rare complete knockouts in females (5.4%) compared to males (4%). Although 16% of the cases sequenced were female, 25% of the cases harboring rare complete knockouts were female (OR = 1.7, 95% CI = 1.3, 2.1, one-sided Fisher’s P = 0.076). While not statistically significant, this trend is similar to previous observations that de novo CNVs and SNVs show a higher fraction of female cases with such events (;; ) and consistent with the model that females need a higher dose of genetic risk to manifest a diagnosis of ASD. We also observed a trend in IQ scores from 18 of these cases with rare complete knockouts to another 133 cases (mean Z-score = −0.26 in probands with rare complete knockouts versus 0.035 in other cases), but it was not statistically significant (one-sided Wilcox P = 0.11). DISCUSSIONAs shown previously, de novo copy number variants (CNVs) are extremely rare events in a control population and they occur at 1–2% in controls.
Given the rarity of such events, discovery of a global enrichment of these de novo CNVs at a much higher rate of 6–8% in ASD individuals suggested a 6% contribution to ASD by these de novo CNVs (;; ). This highlighted the significance of such events as risk factors for ASD and subsequent association and replication studies of such events with larger sample sizes pinpointed to specific de novo CNVs that have since been significantly associated with ASD, such as deletions and duplications on chromosome 16p11.2.Similar to the de novo CNV studies, as well as emerging de novo SNV studies, we observed that rare complete knockouts in the human exome are found in only 3% of a control population, but are present at a 2-fold enrichment in ASD cases. Given that these rare complete knockouts are not found in a single gene but, like the de novo CNVs and SNVs, are distributed across many different genes, these events would have been missed through previous association or linkage studies.
As with any genetic screen, population stratification can confound these results. However, the samples selected for sequencing were of European ancestry and individually matched in case-control pairs based on principal component analyses and selected from a much larger pool of potential samples. Owing to occasional sample failure, ultimately 88% of the final samples were matched one-to-one for ancestry and a similar 2-fold enrichment was observed in the subset of matched cases and controls for the rare complete knockouts (49 events in cases versus 25 events in controls, OR = 2, 95% CI = 1.5, 2.5).Interestingly, we observed a 1.5-fold enrichment of hemizygous LoF variants on the X-chromosome in male cases compared to male controls, but did not observe a significant global enrichment of heterozygous LoF variants on the X-chromosome in female cases compared to female controls.
There are genes on the X-chromosome that can cause ASD-related disorders like Rett Syndrome in an X-linked dominant mode of inheritance such as CDKL5 and MECP2. However, we found that while there is a significant 1.5-fold enrichment in hemizygous LoFs in male cases, we did not observe a significant enrichment in single-copy losses in female cases, consistent with the observation that we did not see an overall difference in single-copy (heterozygous) losses on the autosomes. Given that males have only a single copy of the X-chromosome and would be more susceptible to a complete knockout on the X-chromosome than females, these rare complete knockouts on the X-chromosome can also explain a small part of the male gender bias observed in ASD. Candidate genesAmong our list of consolidated genes with rare complete knockouts that were observed only in cases , we discovered a known autosomal recessive gene in one of the probands from the trios – Usher syndrome 2A protein ( USH2A), which has been reported to cause a known autosomal recessive disease Usher Syndrome Type II, characterized by mild to severe hearing loss and sometimes retinitis pigmentosa. We found and confirmed the bilinieal inheritance of two previously unreported compound heterozygous nonsense mutations (W2075X and Y4238X) in USH2A from both parents. Total Contribution to ASD From de novo and Inherited FactorsAs described previously in various studies, there is an estimated 6% contribution to ASD risk from de novo CNVs (;; ).
Recent studies have estimated another 10% contribution to ASD risk by de novo SNVs (;;; ). In this study, we estimate a 3% contribution to ASD risk by rare complete knockouts on the autosomes and another 2% contribution by rare complete knockouts on the X-chromosome, resulting in another 5% contribution to ASD risk. Because a comparably reliable and validated set of insertion and deletion variants are not yet available across our entire dataset, we have not fully evaluated the contribution of frameshifts. Given that there is likely a similar number of frameshift mutations as single nucleotide LoF variants (; ), the addition of frameshifts will likely increase this contribution further.The global enrichment of rare complete knockouts in cases highlights the significance of such events in the overall genetic etiology of ASD.
In addition, these events provide further insight into the heritable component of ASD, which have not yet been accounted for by de novo CNVs and SNVs. However, many of these rare complete knockouts are distributed across many different genes. This agrees with our current understanding of ASD genetics to date: that this complex disorder follows a multigenic model where hundreds of genes are involved and that each individual gene accounts for a small fraction of ASD. Together with the ongoing de novo CNV and SNV studies, our study and that of another study in this issue (Yu et al., 2013), demonstrate convincing evidence of a rare recessive contribution to the heritability of ASD. Data quality control and filteringBI data was processed with Picard , which utilizes base quality score recalibration and local realignment at known indels and BWA for mapping reads to hg19. SNPs were called using GATK. BCM data was processed with Picard and reads mapped to hg18 using Bfast.
The quality score recalibration and indel realignment was performed using GATK, followed by SNV identification using AtlasSNP 2 software. Genotyping data from Affymetrix 5.0 and 6.0 was filtered using an MAF threshold of ≥5% and missing genotypes with ≤2% using PLINK and concordance checks were performed on the variant calls from the sequencing and genotyping arrays.
3 samples with low concordance between the exome sequencing and genotyping arrays (≤90%) were detected in the BI case-control dataset and discarded from further analyses.The variants used in this study were restricted to sites that passed the standard GATK filters to eliminate SNPs with strand-bias, low quality for the depth of sequencing achieved, homopolymer runs, and SNPs near indels. And variants were required had an average read depth of ≥10× and a quality score of ≥30. Homozygous calls were required to have less than 10% of the alternate allele and heterozygous calls to have an allele balance of between 30% and 70%. A HWE threshold of ≥0.05 was used as well. A set of 160 rare variants was selected for Sequenom validation and the validation rate using these filters was 99.5%. Annotation and analysesFor the case-control datasets, we annotated each variant according to the longest transcript from the RefSeq database.
The trio and quartet datasets were annotated using a custom pipeline that was built on top of the Variant Effect Predictor to allow more stringent filtering of annotation artifacts from the 1000 Genomes Project. The cases and controls in the BI dataset was compared separately from the cases and controls in the BCM dataset before combining the results, to ensure that differences in sequencing technologies and platforms did not affect the results. Variants on the autosomes were filtered using MAF≤5% in the controls from each dataset.Variants on the X-chromosome were filtered using similar thresholds as the autosomal variants.
In addition, variants that were found to be heterozygous in males were removed from the analyses as such inconsistencies were most likely to have resulted from mis-alignment errors. To increase the number of observations for the X-chromosome analyses, male probands from the trios/quartets were added as additional cases to the overall counts from the case-control datasets and their fathers were added as additional controls, since male offspring do not inherit their X-chromosomes from their fathers and the X-chromosomes in their fathers would serve as perfect normal controls. In addition, the MAF for rare variants on the X-chromosome were calculated from a large set of control females from the NHLBI exome sequencing study. Linkage disequilibrium-based phasing of variant pairsWe adopted a linkage disequilibrium (LD) based method, similar to the four-haplotype test used to detect a recombination event, to phase pairs of variants within the same gene and applied this approach to predict compound heterozygous variants in the case-control datasets.
A pair of variants (A and B) was predicted to occur on different chromosomes if:.We observed at least 1 individual who is heterozygous for variant A; and,.we observed at least 1 individual who is heterozygous for variant B; and,.we did not observe any individual who is homozygous at 1 variant and has at least 1 copy of the second variant.In addition, since we cannot accurately phase singletons, we included all pairs of variants if at least one of them is a singleton. Statistical analyses for global enrichmentFor each variant, we calculated the MAF of the variant in the controls. The MAF of a variant pair is the maximum MAF of either variant in the pair. Multiple variant pairs within the same gene in the same individual were counted as a single complete knockout event. We calculated the normalized enrichment ratio as the (total number of events in cases/total number of events in controls)×(number of controls/number of cases) to handle the imbalance in the number of cases and controls that were sequenced. We assessed the statistical significance of the global enrichment by shuffling the case-control labels for 10,000 permutations. For the enrichment analyses on the X-chromosome, one-sided hypergeometric probabilities were calculated assuming that hemizygous synonymous variants in male cases and controls are largely neutral variants.
All the analyses were performed within each case-control dataset separately before combining the results, to ensure that the observations were not driven by a single dataset. We are most grateful to the families from all participating studies: Autism Genetic Resource Exchange (AGRE), the Autism Simplex Collection (TASC), National Database for Autism Research (NDAR), Boston Autism Consortium (AC) and Simons Simplex Collection (SSC). This work was directly supported by NIH grants R01MH089208 (MJD), R01 MH089025 (JDB), R01 MH089004 (GS), R01MH089175 (RG) and R01 MH089482 (JSS) and supported in part by NIH grants P50 HD055751 (EHC), RO1 MH057881 (BD), and R01 MH061009 (JSS). We thank Thomas Lehner (NIMH), Adam Felsenfeld (NHGRI), and Patrick Bender (NIMH) for their support and contribution to the project. EB, JDB, BD, MJD (communicating PI), RG, KR, AS, GS, JSS are lead investigators in the ARRA Autism Sequencing Consortium (ASC). We would also like to thank the NHLBI GO Exome Sequencing Project and its ongoing studies which produced and provided exome variant calls for comparison: the Lung GO Sequencing Project (HL-102923), the WHI Sequencing Project (HL-102924), the Broad GO Sequencing Project (HL-102925), the Seattle GO Sequencing Project (HL-102926) and the Heart GO Sequencing Project (HL-103010).
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