What are multiallelic genes?

Is there a clear detailed description for multiallelic genes? Are they simply genes that are more than two alleles?

More than two alternative forms (alleles)of a gene in a population occupying the same locus on a chromosome or its homologue is known as multiple alleles.

Multiple alleles arise due to mutations of gene.A gene can mutate several times by producing a series of alternative expressions.Different alleles in a series show dominant-recessive relation or may show co-dominance or incomplete dominance among themselves.

In Drosophila,a large number of multiple alleles are known.One of them is the series of wing abnormality ranging in size from normal wings to no wings.The normal wing is wild type.The extreme expression with no wings I.e is just stumps is due to one allele 'vg',in homozygous condition .

For reference.

Multiallelic disruption of the rictor gene in mice reveals that mTOR complex 2 is essential for fetal growth and viability

The rapamycin-insensitive mTOR complex 2 (mTORC2) has been suggested to play an important role in growth factor-dependent signaling. To explore this possibility further in a mammalian model system, we disrupted the expression of rictor, a specific component of mTORC2, in mice by using a multiallelic gene targeting strategy. Embryos that lack rictor develop normally until E9.5, and then exhibit growth arrest and die by E11.5. Although placental defects occur in null embryos, an epiblast-specific knockout of rictor only delayed lethality by a few days, thereby suggesting other important roles for this complex in the embryo proper. Analyses of rictor null embryos and fibroblasts indicate that mTORC2 is a primary kinase for Ser473 of Akt/PKB. Rictor null fibroblasts exhibit low proliferation rates, impaired Akt/PKB activity, and diminished metabolic activity. Taken together, these findings indicate that both rictor and mTORC2 are essential for the development of both embryonic and extraembryonic tissues.

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3. Results

3.1. Type I Error and Power Evaluation for Single-Variant Association Test

3.2. Type I Error and Power Evaluation for Gene-Level Association Test

20% decrease in power, particularly when the effects of causal alleles are in the same directions. For example, when the MAF cutoff of 0.01 and 20% of the variants were causal, power for the burden/SKAT/VT tests were respectively 50%/39%/68%, which were substantially higher than the power for the three tests analyzing only bi-allelic variants (42%/35%/61%, respectively). This is consistent with the benchmark of rare-variant association methods [23,27], where the erroneous exclusion of causal variants drastically reduces power.

3.3. Analysis of Cigarettes-Per-Day Phenotype

Multiallelic caller in bcftools variant caller

I was wondering what does the multiallelic caller actually do? The documentation and the paper explaining the formulas behind the algorithm are not clear. What particular advantage does it offer? And I have noticed that some papers have used it to call variants of covid-19 sequenced samples however the covid genome is single stranded and consists of a single RNA molecule (not a pair) so there's only 1 allele of a given gene. Why would the multiallelic caller be used in this case?

what does the multiallelic caller actually do?

however the covid genome is single stranded and consists of a single RNA molecule (not a pair) so there's only 1 allele of a given gene. Why would the multiallelic caller be used in this case?

Possible heterogeneity in your population? Assuming the reads aren't generated from a single genome molecule, you're looking at a consensus that's representing a possible quasispecies when you do sequencing.

Please correct me if I'm wrong, but I think it refers to recognizing multiple different alleles at the same locus. While you will mostly see reports of biallelic SNPs, for example an A to T variant, in a multiallelic it could be A to T and A to C variants. Multiple alleles could also arise from more complex events like insertion/deletion (indels) at the same coordinate, adding to the multiallelic possible variations. I assume you would need new algorithms to calculate the likelihood of overlapping multiallelic variants, as opposed to just one simple variant.

DNA multiallelic systems reveal gene/longevity associations not detected by diallelic systems. The APOB locus

To identify possible genetic factors affecting human longevity we compared allele pools at two candidate loci for longevity between a sample of 143 centenarians (S) and a control sample of 158 individuals (C). The candidate loci were APOB and TPO, which code for apolipoprotein B and thyroid peroxidase, respectively. Both restriction fragment length (RFL) (XbaI2488 and EcoRI4154) and variable number of tandem repeat (VNTR) (3′APOB-VNTR) polymorphisms were analysed at the APOB locus the TPO-VNTR polymorphism (intron 10) was analysed at the TPO locus. The main result of the investigation was that there is an association between the APOB locus and longevity that is revealed only when multiallelic polymorphisms are considered. In particular: (i) the frequency of 3′APOB-VNTR alleles with fewer than 35 repeats is significantly lower in cases than in controls (ii) the linkage disequilibrium between the XbaI-RFLP and the EcoRI-RFLP is significantly different from 0 in cases but not in controls (iii) the EcoRI-RFLP and XbaI-RFLP allele frequencies do not discriminate between cases and controls. The differences observed between case and control allele pools are specific to the APOB locus, since no significant difference was observed at the TPO locus.

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Multiple loci

Once the multiallelic S matrix has been obtained, the decomposition of the genetic variance through expressions (14, 15) naturally holds for multiple loci. The multilocus operator H can be built from the single-locus ones as ( mathop otimes limits_^ <1>left( <>_^ <(rk)>> ight) ) , to account for the additional (due to interactions among loci) variance components. Applying expressions (14, 15) to the system of loci A and B leads to a vector of variance components ( >_^ <(3,2)>) in which the order of the variance components is the same as the one of the genetic effects in the vector E for two alleles (Álvarez-Castro and Carlborg 2007).

For the multilocus genetic systems, it is possible to test for orthogonality in the same way as shown for the one-locus case at the end of “Appendix A”. By doing so at the system of loci A and B considered just above, we have obtained a matrix with nine independent non-zero blocks for the mean, additive effect of locus A, additive effects of locus B, dominance effect of locus A, dominance effects of locus B, and the additive-by-additive, additive-by-dominance, dominance-by-additive and dominance-by-dominance interactions—i.e. an analogous matrix to (26), although larger and having more independent blocks (not shown). These separate blocks reflect the orthogonality of all the variance components. Thus, expressions (14, 15) comprise a straightforward routine to perform orthogonal decomposition of variance from the NOIA model for genetic systems of arbitrary numbers of alleles at multiple epistatic loci under LE.


We aligned fastq raw sequence read files for 2455 samples from 26 populations to a concatenated reference sequence made of all chromosome 8 exons plus 300 bp flanking sequence. We grouped the samples from the 26 populations into four continental groups. After removing samples where the reads per kilobase per million mapped reads (RPKM) mean value was less than 50, (Fig. 1, Table 1) we calculated single variant decomposition scores batch wise, with each batch representing a distinct continental group/sequencing centre combination. Raw SVD-ZRPKM mean of all exons of the beta-defensin genes were retrieved and a Gaussian mixture model fitted for each set of data using CNVtools. Observing clear clustering of SVD-ZRPKM mean values about integer copy number values leads us to have high confidence in the Gaussian mixture model fit and therefore in the final copy number calls. Importantly, batches needed to be defined by both sequencing centre and continental group, otherwise poor clustering was observed. For example, Fig. 2 shows histograms of raw SVD-ZRPKM mean values from the BGI sequencing centre, for East Asians (Fig. 2a), South Asians (Fig. 2b) and East and South Asians together (Fig. 2c). Although a Gaussian mixture model can be fitted for all three histograms, the clustering of the SVD-ZRPKM values of the combined batches (Fig. 2c) is visibly less distinct than when each batch is analysed separately. For most batches, clear clustering of raw SVD-ZRPKM values was observed, increasing confidence that the correct copy number was being called (Fig. 3a). However some showed SVD-ZRPKM values that did not cluster well (e.g. African samples from BCM sequencing centre, Fig. 3b), and these were removed from subsequent analyses.

Distribution of reads-per-kilobase-per-million-reads (RPKM) values of different samples stratified by sequencing centre. The kernel density plot shows density of RPKM values from mrsFAST alignments for four different sequencing centres, distinguished by the different colours. The vertical dotted line indicates the cutoff value at RPKM = 50, with samples above that threshold taken on for copy number calling

Effects of continental group batch of origin on copy number clustering. The histograms show normalised sequence depth coverage data for the beta-defensin region generated by the BGI sequencing centre. X-axis values represent raw mean SVD-ZRPKM values, and the y-axis represents number of samples. Curved lines indicating the Gaussian curves used to call integer copy number. a) samples from East Asian populations (n = 269), b) samples from South Asian populations (n = 165), c) samples from South Asian and East Asian populations analysed as one batch (n = 434)

Effects of sequencing centre and batch size on copy number clustering. The histograms show normalised sequence depth coverage data for the beta-defensin region for sub-Saharan African samples. X-axis values represent raw mean SVD-ZRPKM values a) BCM sequencing centre, n = 81 (15 YRI, 57 LWK, 9 ASW). b) BGI sequencing centre, n = 172 (26 YRI, 3 LWK, 25 GWD, 43 MSL, 47 ESN, 5 ASW, 23 ACB), with curved lines indicating the Gaussian mixture model used to call integer copy number

Gaussian mixture modelling generated a copy number call for each sample with an associated posterior probability of that call. The proportion of calls with a posterior probability greater than 0.95 varied between sequencing centres (Table 1), but overall was 87 %. The distribution of copy number reflected previous results, with 4 being the modal copy number in all continental groups apart from sub-Saharan Africans, and the range of common variation extending from 2 copies to 8 copies per diploid genome (Table 2).

We validated our copy number calls by comparing calls on a subset of samples with copy number estimates made previously by Triplex PRT [13, 26], by Nanostring [28] (Fig. 4a), and also with copy number calls of the region made by whole genome sequencing [4] (Fig. 4b). It is clear that copy number calls made using exome SRD agree well with both PRT and Nanostring consensus copy number. Exome SRD calls also agree well with whole genome SRD data, although there is a significant discrepancy rate of 11.8 %. Most discrepancies are at the higher copy numbers, and seem to be due to exome SRD underestimating copy number. Of the 7 samples that are discrepant between exome SRD and both PRT and Nanostring, three are also discrepant with whole genome SRD copy number calls, all are spread across the different sequencing centres (Table 3), suggesting the discrepancies are due to random assay noise rather than a systematic bias.

Validated of beta-defensin copy number calling. The plots show comparisons between two methods of calling integer beta-defensin copy number. a) comparison with triplex paralogue ratio test and Nanostring nCounter. b) comparison with integer calls from phase 1 low coverage whole genome data [4]. The figures in red indicate the numbers of samples concordant for that particular copy number. The numbers in blue indicate the numbers of discordant samples

We used our exome SRD data to investigate the extent of contiguous copy number variation at this locus, gene by gene. Individual SVD-ZRPKM mean values of each gene were correlated with the individual SVD-ZRPKM mean values of genes within and surrounding the beta-defensin CNV region, both at distal 8p23.1 (a region called REPD [31]), and proximal 8p23.1 (REPP), across the 171 European samples sequenced at the BGI. We would expect genes on the CNV block to show highly correlated SVD-ZRPKM scores across these individuals, reflecting the CNV. Indeed, the core defensin genes (DEFB4 to DEFB107) showed a very high correlation (Fig. 5) indicating that these are on a contiguous block that shows CNV. This block of highly correlated genes extends distally as far as FAM90A13 and includes DEFB109, albeit with lower correlation coefficients, which is likely to be due to mapping of sequence reads derived from known segmental duplications involving these genes on chromosome 4 and chromosome 12. This confirms the observation made previously using arrayCGH that these genes are involved in the beta-defensin CNV [21], and shows that analysis of exome SRD can be a powerful approach to identify CNV boundaries. Interestingly, a moderate correlation coefficient is observed for some genes at REPP, including DEFB130 but not DEFB134, DEFB135 nor DEFB136. The beta-defensin repeat region (involving DEFB4 to FAM90A13) is not assembled here, but it is known from genetic data that the repeat region can be polymorphically present here at this location [20], and this signal we observe is likely to be due to CNV of the beta-defensin repeat region at REPP.

Correlation of SVD-ZRPKM values between genes at 8p23.1. Plot of pairwise correlation between SVD-ZRPKM values among genes at chromosome region 8p23.1. The SVD-ZRPKM mean for all exons belonging to each gene was calculated and the pairwise correlation for each pair of genes was evaluated by the r 2 metric (the correlation is increasing with gray shading). Gene presence and location is based on the annotation of the hg19 human genome assembly. Complex repeat-rich regions REPP and REPD are indicated, and several genes between REPP and REPD are omitted to save space, as indicated by the red dashed line

We used our exome alignment files to call sequence variation across the beta-defensin genes within the CNV. Using FreeBayes, a sequence caller that uses diploid copy number as an extra parameter and therefore can make sequence variant calls from non-diploid regions, we called 436 single nucleotide variants spanning 8811 bp of sequence representing the combined length of the beta-defensin genes. 299 are intronic or intergenic, with 137 within the untranslated regions or coding regions. The majority of variants called are rare or very rare, and are specific to particular continental groups, suggesting that they have arisen very recently in human evolutionary history. 355 variants (81 % of total) are novel and have been submitted to dbSNP.

Sixty-seven variants (64 non-synonymous substitutions and 3 stop codon gains) were called that were predicted to affect amino acids within the beta-defensin genes (Fig. 6). We validated two frequent non-synonymous variants, rs140952426 in DEFB104 that changes arginine to a glutamine at position 38, and rs200757797 in DEFB105 that changes a cysteine to a tyrosine at position 73. It was important not only to validate the presence of the variant but also the correct number of copies of that variant. We did this by amplifying across the variant using genomic DNA, cloning the resulting PCR product, and then counting the number of clones (each derived from a single amplified DNA molecule from the PCR) that had each allele of the variant using colony PCR followed by restriction enzyme digestion (Table 4). This gave an estimate of the proportion of each allele at each variant for each sample, which could be then compared with that predicted by exome sequencing – for example a GGGA genotype (where three copies have a G and one copy has an A at the same paralogous nucleotide site) would be regarded as 0.25 A allele. For both variants, samples homozygous for the variant that is cut by the restriction enzyme was included to provide a background rate of cut failure either due to experimental error or mutation of restriction site during amplification and cloning. The proportion of each allele measured using this approach is consistent with the genotype called from exome SRD data for all samples, except one. The exception is NA12763, where the molecular cloning method generates an estimate which agrees with a copy number of 6 called by PRT rather than a copy number of 4 called by exome SRD, and therefore reflects an error in copy number calling by exome SRD.

Summary of predicted amino acid changes inferred from sequence variation. The six beta-defensin proteins encoded by the genes analysed in this study are shown. The prepro region, which is cleaved during processing, is shown under the blue bar with the mature peptide sequence is shown under the red bar. The canonical six cysteines are highlighted in red, with sequence variants identified in this study shown in green. X represents a stop codon, and hbd2, hbd3, hbd4, hbd5, hbd6, and hbd7 are the proteins encoded by DEFB4, DEFB103, DEFB104, DEFB105, DEFB106 and DEFB107 respectively

We considered whether a signature of selection at these beta-defensin genes could be inferred from the frequency of sequence variants. By comparing the sequence variant frequency distribution of non-synonymous and synonymous SNPs within coding regions, it is possible to detect the effect of negative selection or balancing selection across the region. Assuming that selection does not act on synonymous variants and therefore their variant frequency distribution represents the neutral null model, we would expect to see an enrichment of non-synonymous variants at low frequency under negative selection, and an enrichment of non-synonymous variants at high frequency (0.4-0.5) under balancing selection.

Given the small exon size and therefore small number of polymorphisms in the coding region of each gene, we compared the sequence variant frequency distribution for each continental group separately, combining data across all beta-defensin genes measured in the CNV. We did not find a statistically significant difference between any non-synonymous and synonymous sequence variant frequency distributions, for any of the continental groups. This suggests that selection is not acting on these genes, and that the sequence variants observed are essentially neutral.


The genomes of incipient species diverge at heterogeneous rates, and recently diverged model species are key systems to investigate the causes of this heterogeneity [1]–[3]. Hybridization followed by introgression between recently diverged plant and animal species with incomplete reproductive barriers is one of the main processes generating the genomic heterogeneity in species divergence [4]. Indeed, some regions appear to be crossing the species barriers more readily than the genomic background (in Helianthus [5], Anopheles [6], Quercus [7], Mytilus [8], Mus [9] and Drosophila [10]). Although much of this heterogeneity may be accounted for by stochasticity of the genetic drift process, natural selection may also play an important role. In particular, because introgressive hybridization brings genetic material from one species into the co-adapted background of another species, some chromosomal fragments are expected to be selected against and resist introgression [11].

On the other hand, selection can also promote introgression when a transferred chromosome fragment is advantageous in the recipient species. In such a situation, introgression can potentially mediate the transfer of adaptations. Examples of adaptive introgression involving the transfer of transgenes conferring adaptations such as herbicide or insect resistance via hybridization with close relatives of crop species [12] have been documented, but other examples in natural populations are strikingly rare [13]. In the Louisiana Iris species complex for instance, detailed experimental studies provided support for the transfer of adaptations (flood and shade tolerance) between Iris fulva and I. hexagona [14]. In Helianthus, a recent experimental study reported that herbivore resistance traits have introgressed from Heliantus debilis to H. annuus, thereby increasing adaptation of their naturally occurring hybrid H. annuus taxanus [15]. All these documented examples are thus associated with strong directional selection for adaptive traits recently evolved in one of the species and then transmitted horizontally. Theory predicts that adaptive introgression should also be a general property of alleles at genes evolving under multi-allelic balancing selection, such as the vertebrate MHC system, plant disease resistance or self-incompatibility (SI) genes [16]. In these systems, rare alleles enjoy a strong selective advantage [17]. Assuming that a given allele is absent from one of two related species, introgression of this allele would then be as strongly favored as a new allele arising by mutation, unless this is impeded by linked genes that are not well adapted to the recipient species. Thus, in multi-allelic systems evolving under balancing selection, repeated exchanges of alleles promoted by adaptive introgression may be expected between closely related species, as long as fertile hybrids can be formed. Therefore, in the course of evolution of strong reproductive isolation between incipient species, such genomic regions should be among the last to stop introgressing.

In this study, we test whether multi-allelic balancing selection mediates introgression between closely related species. We do this by contrasting divergence of a portion of the gene controlling self-incompatibility specificity (SRK) with the background level of genomic divergence in two closely related plant species. The study system consists of two closely related Arabidopsis species, A. lyrata and A. halleri, whose genomes diverged approximately 2 million years ago [18]. The two species have overlapping distributions in Northern Europe [19] and relatively recent introgression has been demonstrated for a small fraction of nuclear genes [20]. SI prevents self-fertilization and some matings among relatives through recognition and rejection of pollen expressing identical specificity. Molecular and genetic analyses of the self-incompatibility locus (S-locus) in A. lyrata and A. halleri identified many specificities, and the SRK sequences often form monophyletic pairs of high sequence similarity, each of which probably represent the same SI specificity in the two species derived from one specificity in their common ancestor. We refer to these pairs as trans-specifically shared pairs of S-alleles. We use divergence at fourfold degenerate sites between alleles within trans-specifically shared pairs to estimate the divergence corresponding to the time of the last introgression event for S-alleles between the two species, and we find that introgression has occurred at a higher rate or continued over more extended periods of time at the S-locus than at the rest of the nuclear genome.

Next-Generation Sequencing and Droplet Digital™ PCR Accurately Determine Copy Number States for Multiallelic Copy Number Variations

Hercules, CA &mdash March 3, 2015 &mdash Using next-generation sequencing (NGS) and Bio-Rad&rsquos Droplet Digital PCR (ddPCR&trade) technology, researchers at Harvard Medical School and Bio-Rad&rsquos Digital Biology Center have solved the technical challenge of accurately counting the diverse copy number states of multiallelic copy number variations (mCNVs).

&ldquoAfter a long period of not being able to do precise genetic analysis of mCNVs in human genetics, these tools &mdash both whole-genome sequencing and nimble Droplet Digital PCR assays &mdash will finally enable careful genetic analysis of mCNVs in human cohorts. Reassuringly, these approaches appear to yield results that agree strongly with each other,&rdquo said Steven McCarroll, professor of genetics at Harvard Medical School, director of genetics for the Stanley Center for Psychiatric Research at the Broad Institute, and senior author of the Nature Genetics paper in which these findings were published.

McCarroll&rsquos team found that mCNVs are responsible for nearly 90% of the observed differences in gene copy number, or gene dosage, between humans.

&ldquomCNVs are much more extensive and have a larger impact on gene-dosage variation than previously thought,&rdquo said McCarroll. &ldquoWe also determined that mCNVs contribute substantially to gene expression variation, suggesting that they have the potential to contribute to variation in phenotypes.&rdquo

Because some genes affected by multicopy regions, such as HPR1 and ORM1, have disease associations, further investigation of mCNVs could enable studies of how copy number changes in these regions impact human phenotypes and disease, said Jennifer Berman, one of the paper&rsquos coauthors and staff scientist at Bio-Rad&rsquos Digital Biology Center.

Droplet Digital PCR Validates NGS Analysis
mCNVs have been notoriously difficult to study, due to a specific technical challenge: the inability to discriminate between higher order, consecutive copy number states (for example, six vs. seven) using existing low-precision techniques such as microarrays or standard qPCR.

The advent of NGS in the last decade, and ddPCR in the last few years, has broadened and deepened the ability of researchers to perform genetic analysis.

"The only two methods available to robustly call consecutive high copy number states are NGS with the new algorithms McCarroll&rsquos lab developed as part of the study and Droplet Digital PCR,&rdquo said Berman.

Droplet Digital PCR, a technology developed by Bio-Rad that has been referenced in nearly 200 papers since it came to market in late 2011, is an ultraprecise and sensitive form of PCR that enables discrimination of small fold differences in target DNA copy numbers.

McCarroll&rsquos research team used advanced computational methods in conjunction with existing NGS data to identify and catalog more than 8,500 CNVs in the human genome. In the analysis of 849 human genome sequences from the 1000 Genomes Project, approximately 3,900 duplication CNVs were discovered, of which roughly one-third were found to be mCNVs.

To validate their NGS computational approach, which uncovered diploid copy numbers ranging from one to 15, Dr. McCarroll and his team used Bio-Rad Laboratories&rsquo award-winning QX200&trade Droplet Digital PCR (ddPCR) System. Results obtained using the two techniques were highly consistent: per individual genome, the number of copies of mCNVs quantified by ddPCR had a 99% match rate with those of NGS. This type of orthogonal validation reinforces each technique&rsquos approach and strengthens the conclusions of the study.

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