Detection, imputation, and association analysis of small deletions and null alleles on oligonucleotide arrays

Chromosome X-Wide Common Variant Association Study (XWAS) in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) displays a notable male bias in prevalence. Research into rare (<0.1) genetic variants on the X chromosome has implicated over 20 genes in ASD pathogenesis, such as MECP2, DDX3X, and DMD. The "female protective effect" in ASD suggests that females may require a higher genetic burden to manifest similar symptoms as males, yet the mechanisms remain unclear. Despite technological advances in genomics, the complexity of the biological nature of sex chromosomes leave them underrepresented in genome-wide studies. Here, we conducted an X chromosome-wide association study (XWAS) using whole-genome sequencing data from 6,873 individuals with ASD (82% males) across Autism Speaks MSSNG, Simons Simplex Cohort SSC, and Simons Foundation Powering Autism Research SPARK, alongside 8,981 population controls (43% males). We analyzed 418,652 X-chromosome variants, identifying 59 associated with ASD (p-values 7.9×10-6 to 1.51×10-5), surpassing Bonferroni-corrected thresholds. Key findings include significant regions on chrXp22.2 (lead SNP=rs12687599, p=3.57×10-7) harboring ASB9/ASB11, and another encompassing DDX53/PTCHD1-AS long non-coding RNA (lead SNP=rs5926125, p=9.47×10-6). When mapping genes within 10kb of the 59 most significantly associated SNPs, 91 genes were found, 17 of which yielded association with ASD (GRPR, AP1S2, DDX53, HDAC8, PCDH19, PTCHD1, PCDH11X, PTCHD1-AS, DMD, SYAP1, CNKSR2, GLRA2, OFD1, CDKL5, GPRASP2, NXF5, SH3KBP1). FGF13 emerged as a novel X-linked ASD candidate gene, highlighted by sex-specific differences in minor allele frequencies. These results reveal significant new insights into X chromosome biology in ASD, confirming and nominating genes and pathways for further investigation.

X-Chromosome Association Study in Latin American Cohorts Identifies New Loci in Parkinson's Disease

BACKGROUND

Sex differences in Parkinson's disease (PD) risk are well-known. However, the role of sex chromosomes in the development and progression of PD is still unclear.

OBJECTIVE

The objective of this study was to perform the first X-chromosome-wide association study for PD risk in a Latin American cohort.

METHODS

We used data from three admixed cohorts: (1) Latin American Research consortium on the Genetics of Parkinson's Disease (n = 1504) as discover cohort, and (2) Latino cohort from International Parkinson Disease Genomics Consortium (n = 155) and (3) Bambui Aging cohort (n = 1442) as replication cohorts. We also developed an X-chromosome framework specifically designed for admixed populations.

RESULTS

We identified eight linkage disequilibrium regions associated with PD. We replicated one of these regions (top variant rs525496; discovery odds ratio [95% confidence interval]: 0.60 [0.478-0.77], P = 3.13 × 10-5 replication odds ratio: 0.60 [0.37-0.98], P = 0.04). rs5525496 is associated with multiple expression quantitative trait loci in brain and non-brain tissues, including RAB9B, H2BFM, TSMB15B, and GLRA4, but colocalization analysis suggests that rs5525496 may not mediate risk by expression of these genes. We also replicated a previous X-chromosome-wide association study finding (rs28602900), showing that this variant is associated with PD in non-European populations.

CONCLUSIONS

Our results reinforce the importance of including X-chromosome and diverse populations in genetic studies. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

X-Chromosome Association Study in Latin American Cohorts Identifies New Loci in Parkinson Disease

Sex differences in Parkinson Disease (PD) risk are well-known. However, it is still unclear the role of sex chromosomes in the development and progression of PD. We performed the first X-chromosome Wide Association Study (XWAS) for PD risk in Latin American individuals. We used data from three admixed cohorts: (i) Latin American Research consortium on the GEnetics of Parkinson's Disease (n=1,504) as discover cohort and (ii) Latino cohort from International Parkinson Disease Genomics Consortium (n = 155) and (iii) Bambui Aging cohort (n= 1,442) as replication cohorts. After developing a X-chromosome framework specifically designed for admixed populations, we identified eight linkage disequilibrium regions associated with PD. We fully replicated one of these regions (top variant rs525496; discovery OR [95%CI]: 0.60 [0.478 - 0.77], p = 3.13 × 10 ^-5 ; replication OR: 0.60 [0.37-0.98], p = 0.04). rs525496 is an expression quantitative trait loci for several genes expressed in brain tissues, including RAB9B, H2BFM, TSMB15B and GLRA4 . We also replicated a previous XWAS finding (rs28602900), showing that this variant is associated with PD in non-European populations. Our results reinforce the importance of including X-chromosome and diverse populations in genetic studies.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

Comparing BeadChip and WGS Genotyping: Non-Technical Failed Calling Is Attributable to Additional Variation within the Probe Target Sequence

Microarray-based genomic selection is a central tool to increase the genetic gain of economically significant traits in dairy cattle. Yet, the effectivity of this tool is slightly limited, as estimates based on genotype data only partially explain the observed heritability. In the analysis of the genomes of 17 Israeli Holstein bulls, we compared genotyping accuracy between whole-genome sequencing (WGS) and microarray-based techniques. Using the standard GATK pipeline, the short-variant discovery within sequence reads mapped to the reference genome (ARS-UCD1.2) was compared to the genotypes from Illumina BovineSNP50 BeadChip and to an alternative method, which computationally mimics the hybridization procedure by mapping reads to 50 bp spanning the BeadChip source sequences. The number of mismatches between the BeadChip and WGS genotypes was low (0.2%). However, 17,197 (40% of the informative SNPs) had extra variation within 50 bp of the targeted SNP site, which might interfere with hybridization-based genotyping. Consequently, with respect to genotyping errors, BeadChip varied significantly and systematically from WGS genotyping, introducing null allele-like effects and Mendelian errors (<0.5%), whereas the GATK algorithm of local de novo assembly of haplotypes successfully resolved the genotypes in the extra-variable regions. These findings suggest that the microarray design should avoid polymorphic genomic regions that are prone to extra variation and that WGS data may be used to resolve erroneous genotyping, which may partially explain missing heritability.

Common X-Chromosome Variants Are Associated with Parkinson Disease Risk

OBJECTIVE

The objective of this study was to identify genetic variants on the X-chromosome associated with Parkinson disease (PD) risk.

METHODS

We performed an X-chromosome-wide association study (XWAS) of PD risk by meta-analyzing results from sex-stratified analyses. To avoid spurious associations, we designed a specific harmonization pipeline for the X-chromosome and focused on a European ancestry sample. We included 11,142 cases, 280,164 controls, and 5,379 proxy cases, based on parental history of PD. Additionally, we tested the association of significant variants with (1) PD risk in an independent replication with 1,561 cases and 2,465 controls and (2) putamen volume in 33,360 individuals from the UK Biobank.

RESULTS

In the discovery meta-analysis, we identified rs7066890 (odds ratio [OR] = 1.10, 95% confidence interval [CI] = 1.06-1.14, p = 2.2 × 10-9 ), intron of GPM6B, and rs28602900 (OR = 1.10, 95% CI = 1.07-1.14, p = 1.6 × 10-8 ) in a high gene density region including RPL10, ATP6A1, FAM50A, and PLXNA3. The rs28602900 association with PD was replicated (OR = 1.16, 95% CI = 1.03-1.30, p = 0.016) and shown to colocalize with a significant expression quantitative locus (eQTL) regulating RPL10 expression in the putamen and other brain tissues in the Genotype-Tissue Expression Project. Additionally, the rs28602900 locus was found to be associated with reduced brain putamen volume. No results reached genome-wide significance in the sex-stratified analyses.

INTERPRETATION

We report the first XWAS of PD and identify 2 genome-wide significant loci. The rs28602900 association was replicated in an independent PD dataset and showed concordant effects in its association with putamen volume. Critically, rs26802900 is a significant eQTL of RPL10. These results support a role for ribosomal proteins in PD pathogenesis and show that the X-chromosome contributes to PD genetic risk. ANN NEUROL 2021;90:22-34.

Integration of Infinium and Axiom SNP array data in the outcrossing species Malus × domestica and causes for seemingly incompatible calls

Background

Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. As new arrays are developed to take advantage of new technology and of improved probe design using new genome sequence and panel data, a need to integrate data from different arrays and array platforms has arisen. This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (Malus × domestica). In this study, we qualify and quantify the compatibility of SNP calling, defined as SNP calls that are both accurate and concordant, across both arrays by two approaches. First, the concordance of SNP calls was evaluated using a set of 417 duplicate individuals genotyped on both arrays starting from a set of 10,295 robust SNPs on the Infinium array. Next, the accuracy of the SNP calls was evaluated on additional germplasm (n = 3141) from both arrays using Mendelian inconsistent and consistent errors across thousands of pedigree links. While performing this work, we took the opportunity to evaluate reasons for probe failure and observed discordant SNP calls.

Results

Concordance among the duplicate individuals was on average of 97.1% across 10,295 SNPs. Of these SNPs, 35% had discordant call(s) that were further curated, leading to a final set of 8412 (81.7%) SNPs that were deemed compatible. Compatibility was highly influenced by the presence of alternate probe binding locations and secondary polymorphisms. The impact of the latter was highly influenced by their number and proximity to the 3′ end of the probe.

Conclusions

The Infinium and Axiom SNP array data were mostly compatible. However, data integration required intense data filtering and curation. This work resulted in a workflow and information that may be of use in other data integration efforts. Such an in-depth analysis of array concordance and accuracy as ours has not been previously described in the literature and will be useful in future work on SNP array data integration and interpretation, and in probe/platform development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07565-7.

Association analysis of copy numbers of FC-gamma receptor genes for rheumatoid arthritis and other immune-mediated phenotypes

Segmental duplications (SDs) comprise about 5% of the human genome and are enriched for immune genes. SD loci often show copy numbers variations (CNV), which are difficult to tag with genotyping methods. CNV in the Fcγ receptor region (FCGR) has been suggested to be associated with rheumatic diseases. The objective of this study was to delineate association of FCGR-CNV with rheumatoid arthritis (RA), coeliac disease and Inflammatory bowel disease incidence. We developed a method to accurately quantify CNV in SD loci based on the intensity values from the Immunochip platform and applied it to the FCGR locus. We determined the method's validity using three independent assays: segregation analysis in families, arrayCGH, and whole genome sequencing. Our data showed the presence of two separate CNVs in the FCGR locus. The first region encodes FCGR2A, FCGR3A and part of FCGR2C gene, the second encodes another part of FCGR2C, FCGR3B and FCGR2B. Analysis of CNV status in 4578 individuals with RA and 5457 controls indicated association of duplications in the FCGR3B gene in antibody-negative RA (P=0.002, OR=1.43). Deletion in FCGR3B was associated with increased risk of antibody-positive RA, consistently with previous reports (P=0.023, OR=1.23). A clear genotype-phenotype relationship was observed: CNV polymorphisms of the FCGR3A gene correlated to CD16A expression (encoded by FCGR3A) on CD8 T-cells. In conclusion, our method allows determining the CNV status of the FCGR locus, we identified association of CNV in FCGR3B to RA and showed a functional relationship between CNV in the FCGR3A gene and CD16A expression.

Efficient Identification of Null-Allele Single Nucleotide Polymorphism Markers

OBJECTIVES

At the beginning of a genome-wide association study, many markers are discarded because they fail to meet standard quality control criteria. Some of these markers are out of Hardy-Weinberg equilibrium (HWE) because they have 'null alleles' (which may be deletions or third alleles that do not hybridize to standard probes). It may be useful to identify null-allele markers so that they can be analyzed under different models or in order to explore regions of copy number variation.

METHODS

We present a model for the chip-based genotype data that are produced when a null-allele single nucleotide polymorphism (SNP) is genotyped under standard (2-allele) assumptions. We show that this model can be combined with the standard HWE model to develop classification procedures based on the supervised learning algorithms Support Vector Machines (SVM), Classification and Regression Trees (CART) or Random Forests for identifying null-allele SNPs.

RESULTS

We report a list of null-allele SNPs we identified on the Illumina 660W-Quad chip and provide suggestions for applying our CART model to other SNP sets.

CONCLUSIONS

Properly identified null-allele SNPs can be used to test for genotype-phenotype associations or to identify regions which may contain copy number variants.

A method for calling copy number polymorphism using haplotypes

Single nucleotide polymorphism (SNP) and copy number variation (CNV) are both widespread characteristic of the human genome, but are often called separately on common genotyping platforms. To capture integrated SNP and CNV information, methods have been developed for calling allelic specific copy numbers or so called copy number polymorphism (CNP), using limited inter-marker correlation. In this paper, we proposed a haplotype-based maximum likelihood method to call CNP, which takes advantage of the valuable multi-locus linkage disequilibrium (LD) information in the population. We also developed a computationally efficient algorithm to estimate haplotype frequencies and optimize individual CNP calls iteratively, even at presence of missing data. Through simulations, we demonstrated our model is more sensitive and accurate in detecting various CNV regions, compared with commonly-used CNV calling methods including PennCNV, another hidden Markov model (HMM) using CNP, a scan statistic, segCNV, and cnvHap. Our method often performs better in the regions with higher LD, in longer CNV regions, and in common CNV than the opposite. We implemented our method on the genotypes of 90 HapMap CEU samples and 23 patients with acute lung injury (ALI). For each ALI patient the genotyping was performed twice. The CNPs from our method show good consistency and accuracy comparable to others.

Comparing the van Oosterhout and Chybicki-Burczyk methods of estimating null allele frequencies for inbred populations

In spite of the usefulness of codominant markers in population genetics, the existence of null alleles raises challenging estimation issues in natural populations that are characterized by positive inbreeding coefficients (F > 0). Disregarding the possibility of F > 0 in a population will generally lead to overestimates of null allele frequencies. Conversely, estimates of inbreeding coefficients (F) may be strongly biased upwards (excess homozygotes), in the presence of nontrivial frequencies of null alleles. An algorithm has been presented for the estimation of null allele frequencies in inbred populations (van Oosterhout method), using external estimates of the F-statistics. The goal of this study is to introduce a modification of this method and to provide a formal comparison with an alternative likelihood-based method (Chybicki-Burczyk). Using simulated data, we illustrate the strengths and limitations of these competing methods. Under most circumstances, the likelihood method is preferable, but for highly inbred organisms, a modified van Oosterhout method offers some advantages.

PlatinumCNV: a Bayesian Gaussian mixture model for genotyping copy number polymorphisms using SNP array signal intensity data

We present a statistical model for allele-specific patterns of copy number polymorphisms (CNPs) in commercial single nucleotide polymorphism (SNP) array data. This model is based on the observation that fluorescent signal intensities tend to cluster into clouds of similar allele-specific copy number (ASCN) genotypes at each SNP locus. To capture the tendency of this clustering to be made vague by instrumental errors, our model allows for cluster memberships to overlap each other, according to a Bayesian Gaussian mixture model (GMM). This approach is flexible, allowing for both absolute scale differences and X/Y scale imbalances of fluorescent signal intensities. The resulting model is also robust toward unobserved ASCN genotypes, which can be problematic for ordinary GMMs. We illustrated the utility of the model by applying it to commercial SNP array intensity data obtained from the Illumina HumanHap 610K platform. We retrieved more than 4,000 allele-specific CNPs, though 99% of them showed rather simple allele-specific CNP patterns with only a single aneuploid haplotype among the normal haplotypes. The genotyping accuracy was assessed by two approaches, quantitative PCR and replicated subjects. The results of both of these approaches demonstrated mean genotyping error rates of 1%. We demonstrated a preliminary genome-wide association study of three hematological traits. The result exhibited that it could form the foundation for new, more effective statistical methods for the mapping of both disease genes and quantitative trait loci with genome-wide CNPs. The methods described in this work are implemented in a software package, PlatinumCNV, available on the Internet.

Development of a dense SNP-based linkage map of an apple rootstock progeny using the Malus Infinium whole genome genotyping array

Background

A whole-genome genotyping array has previously been developed for Malus using SNP data from 28 Malus genotypes. This array offers the prospect of high throughput genotyping and linkage map development for any given Malus progeny. To test the applicability of the array for mapping in diverse Malus genotypes, we applied the array to the construction of a SNP-based linkage map of an apple rootstock progeny.

Results

Of the 7,867 Malus SNP markers on the array, 1,823 (23.2%) were heterozygous in one of the two parents of the progeny, 1,007 (12.8%) were heterozygous in both parental genotypes, whilst just 2.8% of the 921 Pyrus SNPs were heterozygous. A linkage map spanning 1,282.2 cM was produced comprising 2,272 SNP markers, 306 SSR markers and the S-locus. The length of the M432 linkage map was increased by 52.7 cM with the addition of the SNP markers, whilst marker density increased from 3.8 cM/marker to 0.5 cM/marker. Just three regions in excess of 10 cM remain where no markers were mapped. We compared the positions of the mapped SNP markers on the M432 map with their predicted positions on the ‘Golden Delicious’ genome sequence. A total of 311 markers (13.7% of all mapped markers) mapped to positions that conflicted with their predicted positions on the ‘Golden Delicious’ pseudo-chromosomes, indicating the presence of paralogous genomic regions or mis-assignments of genome sequence contigs during the assembly and anchoring of the genome sequence.

Conclusions

We incorporated data for the 2,272 SNP markers onto the map of the M432 progeny and have presented the most complete and saturated map of the full 17 linkage groups of M. pumila to date. The data were generated rapidly in a high-throughput semi-automated pipeline, permitting significant savings in time and cost over linkage map construction using microsatellites. The application of the array will permit linkage maps to be developed for QTL analyses in a cost-effective manner, and the identification of SNPs that have been assigned erroneous positions on the ‘Golden Delicious’ reference sequence will assist in the continued improvement of the genome sequence assembly for that variety.

Algorithm implementation for CNV discovery using Affymetrix and Illumina SNP array data

SNP array data can be analysed for the purpose of calling SNP alleles but also for determining the absolute copy number of a certain genomic segment. Here, the method for detecting copy number (CN) change using intensity data from SNP arrays is focused on. Methods incorporating data from the two main genotyping platforms, Affymetrix and Illumina, are described and possible options and problems that may be faced are examined. We discuss the importance of the quality control when using this analysis method and present some guidelines for implementation, both prior and post to algorithm use. A discussion of algorithms available for CN detection is included as well as ideas for further analysis protocols.

Copy number variation across European populations

Genome analysis provides a powerful approach to test for evidence of genetic variation within and between geographical regions and local populations. Copy number variants which comprise insertions, deletions and duplications of genomic sequence provide one such convenient and informative source. Here, we investigate copy number variants from genome wide scans of single nucleotide polymorphisms in three European population isolates, the island of Vis in Croatia, the islands of Orkney in Scotland and the South Tyrol in Italy. We show that whereas the overall copy number variant frequencies are similar between populations, their distribution is highly specific to the population of origin, a finding which is supported by evidence for increased kinship correlation for specific copy number variants within populations.

Copy number variation

Recent genetic epidemiology studies have been dominated by genome-wide association (GWA) studies using single nucleotide polymorphisms (SNPs). However, a form of structural genomic variation, termed copy number variation (CNV), is also widespread throughout the human genome, and can be highly polymorphic between individuals. Such variation has long been shown, through candidate gene studies using low-throughput molecular biology techniques, to have direct consequences on human health and variation. Many studies have now sought to extensively characterise this variation on a genome-wide scale and, increasingly, attempts are being made to identify associations between CNV and human disease. Although many of the study design issues that have been described for SNP GWA studies are also relevant for CNV GWA studies, CNV studies also present their own unique set of challenges and considerations. New microarray-based technologies are enabling more accurate mapping of CNVs, and CNV maps of the human genome are being regularly refined with increasing resolution. The study of CNV and its effects on human health and disease therefore present a dynamic and exciting challenge for researchers in the field of genetic epidemiology.

Assessment of copy number variation using the Illumina Infinium 1M SNP-array: a comparison of methodological approaches in the Spanish Bladder Cancer/EPICURO study

High-throughput single nucleotide polymorphism (SNP)-array technologies allow to investigate copy number variants (CNVs) in genome-wide scans and specific calling algorithms have been developed to determine CNV location and copy number. We report the results of a reliability analysis comparing data from 96 pairs of samples processed with CNVpartition, PennCNV, and QuantiSNP for Infinium Illumina Human 1Million probe chip data. We also performed a validity assessment with multiplex ligation-dependent probe amplification (MLPA) as a reference standard. The number of CNVs per individual varied according to the calling algorithm. Higher numbers of CNVs were detected in saliva than in blood DNA samples regardless of the algorithm used. All algorithms presented low agreement with mean Kappa Index (KI) <66. PennCNV was the most reliable algorithm (KI(w=) 98.96) when assessing the number of copies. The agreement observed in detecting CNV was higher in blood than in saliva samples. When comparing to MLPA, all algorithms identified poorly known copy aberrations (sensitivity = 0.19-0.28). In contrast, specificity was very high (0.97-0.99). Once a CNV was detected, the number of copies was truly assessed (sensitivity >0.62). Our results indicate that the current calling algorithms should be improved for high performance CNV analysis in genome-wide scans. Further refinement is required to assess CNVs as risk factors in complex diseases.

On the genome-wide analysis of copy number variants in family-based designs: methods for combining family-based and population-based information for testing dichotomous or quantitative traits, or completely ascertained samples

We propose a new approach for the analysis of copy number variants (CNVs)for genome-wide association studies in family-based designs. Our new overall association test combines the between-family component and the within-family component of the family-based data so that the new test statistic is fully efficient and, at the same time, maintains robustness against population-admixture and stratification, like classical family-based association tests that are based only on the within-family component. Although all data are incorporated into the test statistic, an adjustment for genetic confounding is not needed, even for the between-family component. The new test statistic is valid for testing either quantitative or dichotomous phenotypes. If external CNV data are available, the approach can also be applied to completely ascertained samples. Similar to the approach by Ionita-Laza et al. ([2008]. Genet Epidemiol 32:273-284), the proposed test statistic does not require a CNV-calling algorithm and is based directly on the CNV probe intensities. We show, via simulation studies, that our methodology increases the power of the FBAT statistic to levels comparable to those of population-based designs. The advantages of the approach in practice are demonstrated by an application to a genome-wide association study for body mass index.

A genome-wide screen for depression in two independent Dutch populations

BACKGROUND

Depression has a strong genetic component but candidate gene studies conducted to date have not shown consistent associations.

METHODS

We conducted a genome-wide parametric and nonparametric linkage analysis in a large-scale family-based study including 115 individuals with depression who were identified based on the Hospital Anxiety Depression Scale, Center for Epidemiologic Studies Depression Rating Scale, or use of antidepressive medication. Further, we investigated the most promising chromosomal regions found in the genome-wide linkage analysis with an association analysis in 734 individuals in the family-based study and 2373 individuals in the population-based study.

RESULTS

Our study demonstrated evidence for significant linkage of depression to chromosome 2p16.1-15 (logarithm of odds [LOD] = 5.13; parametric analysis) and suggestive evidence for linkage in nonparametric analysis to chromosome 5p15.33 (LOD = 2.14), 11q25 (LOD = 2.27), and 19p13.3 (LOD = 2.66). The subsequent association analysis in the family-based study showed region-wide significant association in intron 1 of the OPCML gene on chromosome 11q25 (empirical p value = .04). The association analysis in the population-based study did not show any region-wide significant association, yet showed suggestive association in intron 1 of the APLP2 gene on chromosome 11q25.

CONCLUSIONS

Our linkage and association studies suggest a locus for depression on chromosomes 2p16.1-15 and 11q25. The linkage to chromosome 11q25 may be, in part, explained by the OPCML or the APLP2 gene. Further, there is evidence for a role of the GNG7 gene (chromosome 19p13.3).

Multiple common variants for celiac disease influencing immune gene expression

We performed a second-generation genome-wide association study of 4,533 individuals with celiac disease (cases) and 10,750 control subjects. We genotyped 113 selected SNPs with P(GWAS) < 10(-4) and 18 SNPs from 14 known loci in a further 4,918 cases and 5,684 controls. Variants from 13 new regions reached genome-wide significance (P(combined) < 5 x 10(-8)); most contain genes with immune functions (BACH2, CCR4, CD80, CIITA-SOCS1-CLEC16A, ICOSLG and ZMIZ1), with ETS1, RUNX3, THEMIS and TNFRSF14 having key roles in thymic T-cell selection. There was evidence to suggest associations for a further 13 regions. In an expression quantitative trait meta-analysis of 1,469 whole blood samples, 20 of 38 (52.6%) tested loci had celiac risk variants correlated (P < 0.0028, FDR 5%) with cis gene expression.

Positional effects of polymorphisms in probe-target sequences on genoplot images of oligonucleotide microarrays

Single nucleotide polymorphisms (SNPs) present in probe-target sequences (SPTS) have been shown to be associated with abnormal genoplot images. We explored the effects of SPTS positions on genoplot images using a data set from a genome-wide association study typed on an Illumina Human Hap300 platform. We screened the physical genomic positions of 308,330 autosomal probes to identify SPTS candidates deposited in dbSNP. The genoplot images across 293 individuals were inspected further in SNPs bearing an SPTS candidate. We identified 35,185 SNPs bearing a single SPTS candidate, including 264 SNPs showing abnormal genoplot images. The frequencies of SPTS at distances within 10 bases from the target SNP were significantly higher in the 264 SNPs showing abnormal genoplot images, than in the remaining 34,921 SNPs (49.62 vs 12.87%; Fisher exact test; P = 2.2 x 10(-16)). Of these 264 SNPs, we randomly selected 20 SNPs and resequenced them in 97 individuals. An SPTS within 10 bases of the target SNP was confirmed in all 20 SNPs, except for one SNP with a small deletion (7 bases) in the probe-target sequence. Taken together, these results suggest an association of a proximal SPTS with an abnormal genoplot image, which could result in spurious genotype detections, highlighting the importance of minimizing systematic errors in microarray experiments.

An extended IUPAC nomenclature code for polymorphic nucleic acids

The International Union of Pure and Applied Chemistry (IUPAC) code specified nearly 25 years ago provides a nomenclature for incompletely specified nucleic acids. However, no system currently exists that allows for the informatics representation of the relative abundance at polymorphic nucleic acids (e.g. single nucleotide polymorphisms) in a single specified character, or a string of characters. Here, I propose such an information code as a natural extension to the IUPAC nomenclature code, and present some potential uses and limitations to such a code. The primary anticipated use of this extended nomenclature code is to assist in the representation of the rapidly growing space of information in human genetic variation.

CONTACT

johnsonad2@nhlbi.nih.gov

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genotyping technologies for genetic research

The past few years have seen enormous advances in genotyping technology, including chips that accommodate in excess of 1 million SNP assays. In addition, the cost per genotype has been driven down to levels unimagined only a few years ago. These developments have resulted in an explosion of positive whole-genome association studies and the identification of many new genes for common diseases. Here I review high-throughput genotyping platforms as well as other approaches for lower numbers of assays but high sample throughput, which play an important role in genotype validation and study replication. Further, the utility of SNP arrays for detecting structural variation through the development of genotyping algorithms is reviewed and methods for long-range haplotyping are presented. It is anticipated that in the future, sample throughput and cost savings will be increased further through the combination of automation, microfluidics, and nanotechnologies.

Comparing CNV detection methods for SNP arrays

Data from whole genome association studies can now be used for dual purposes, genotyping and copy number detection. In this review we discuss some of the methods for using SNP data to detect copy number events. We examine a number of algorithms designed to detect copy number changes through the use of signal-intensity data and consider methods to evaluate the changes found. We describe the use of several statistical models in copy number detection in germline samples. We also present a comparison of data using these methods to assess accuracy of prediction and detection of changes in copy number.

High, clustered, nucleotide diversity in the genome of Anopheles gambiae revealed through pooled-template sequencing: implications for high-throughput genotyping protocols

Background

Association mapping approaches are dependent upon discovery and validation of single nucleotide polymorphisms (SNPs). To further association studies in Anopheles gambiae we conducted a major resequencing programme, primarily targeting regions within or close to candidate genes for insecticide resistance.

Results

Using two pools of mosquito template DNA we sequenced over 300 kbp across 660 distinct amplicons of the An. gambiae genome. Comparison of SNPs identified from pooled templates with those from individual sequences revealed a very low false positive rate. False negative rates were much higher and mostly resulted from SNPs with a low minor allele frequency. Pooled-template sequencing also provided good estimates of SNP allele frequencies. Allele frequency estimation success, along with false positive and negative call rates, improved significantly when using a qualitative measure of SNP call quality. We identified a total of 7062 polymorphic features comprising 6995 SNPs and 67 indels, with, on average, a SNP every 34 bp; a high rate of polymorphism that is comparable to other studies of mosquitoes. SNPs were significantly more frequent in members of the cytochrome p450 mono-oxygenases and carboxy/cholinesterase gene-families than in glutathione-S-transferases, other detoxification genes, and control genomic regions. Polymorphic sites showed a significantly clustered distribution, but the degree of SNP clustering (independent of SNP frequency) did not vary among gene families, suggesting that clustering of polymorphisms is a general property of the An. gambiae genome.

Conclusion

The high frequency and clustering of SNPs has important ramifications for the design of high-throughput genotyping assays based on allele specific primer extension or probe hybridisation. We illustrate these issues in the context of the design of Illumina GoldenGate assays.

Detection of disease-associated deletions in case-control studies using SNP genotypes with application to rheumatoid arthritis

Genomic deletions have long been known to play a causative role in microdeletion syndromes. Recent whole-genome genetic studies have shown that deletions can increase the risk for several psychiatric disorders, suggesting that genomic deletions play an important role in the genetic basis of complex traits. However, the association between genomic deletions and common, complex diseases has not yet been systematically investigated in gene mapping studies. Likelihood-based statistical methods for identifying disease-associated deletions have recently been developed for familial studies of parent-offspring trios. The purpose of this study is to develop statistical approaches for detecting genomic deletions associated with complex disease in case-control studies. Our methods are designed to be used with dense single nucleotide polymorphism (SNP) genotypes to detect deletions in large-scale or whole-genome genetic studies. As more and more SNP genotype data for genome-wide association studies become available, development of sophisticated statistical approaches will be needed that use these data. Our proposed statistical methods are designed to be used in SNP-by-SNP analyses and in cluster analyses based on combined evidence from multiple SNPs. We found that these methods are useful for detecting disease-associated deletions and are robust in the presence of linkage disequilibrium using simulated SNP data sets. Furthermore, we applied the proposed statistical methods to SNP genotype data of chromosome 6p for 868 rheumatoid arthritis patients and 1,197 controls from the North American Rheumatoid Arthritis Consortium. We detected disease-associated deletions within the region of human leukocyte antigen in which genomic deletions were previously discovered in rheumatoid arthritis patients.

Molecular karyotyping in 17 patients and mutation screening in 41 patients with Kabuki syndrome

The Kabuki syndrome (KS, OMIM 147920), also known as the Niikawa-Kuroki syndrome, is a multiple congenital anomaly/mental retardation syndrome characterized by a distinct facial appearance. The cause of KS has been unidentified, even by whole-genome scan with array comparative genomic hybridization (CGH). In recent years, high-resolution oligonucleotide array technologies have enabled us to detect fine copy number alterations. In 17 patients with KS, molecular karyotyping was carried out with GeneChip 250K NspI array (Affymetrix) and Copy Number Analyser for GeneChip (CNAG). It showed seven copy number alterations, three deleted regions and four duplicated regions among the patients, with the exception of registered copy number variants (CNVs). Among the seven loci, only the region of 9q21.11-q21.12 (approximately 1.27 Mb) involved coding genes, namely, transient receptor potential cation channel, subfamily M, member 3 (TRPM3), Kruppel-like factor 9 (KLF9), structural maintenance of chromosomes protein 5 (SMC5) and MAM domain containing 2 (MAMDC2). Mutation screening for the genes detected 10 base substitutions consisting of seven single-nucleotide polymorphisms (SNPs) and three silent mutations in 41 patients with KS. Our study could not show the causative genes for KS, but the locus of 9q21.11-q21.12, in association with a cleft palate, may contribute to the manifestation of KS in the patient. As various platforms on oligonucleotide arrays have been developed, higher resolution platforms will need to be applied to search tiny genomic rearrangements in patients with KS.

Cost-effective HLA typing with tagging SNPs predicts celiac disease risk haplotypes in the Finnish, Hungarian, and Italian populations

Human leukocyte antigen (HLA) genes, located on chromosome 6p21.3, have a crucial role in susceptibility to various autoimmune and inflammatory diseases, such as celiac disease and type 1 diabetes. Certain HLA heterodimers, namely DQ2 (encoded by the DQA1*05 and DQB1*02 alleles) and DQ8 (DQA1*03 and DQB1*0302), are necessary for the development of celiac disease. Traditional genotyping of HLA genes is laborious, time-consuming, and expensive. A novel HLA-genotyping method, using six HLA-tagging single-nucleotide polymorphisms (SNPs) and suitable for high-throughput approaches, was described recently. Our aim was to validate this method in the Finnish, Hungarian, and Italian populations. The six previously reported HLA-tagging SNPs were genotyped in patients with celiac disease and in healthy individuals from Finland, Hungary, and two distinct regions of Italy. The potential of this method was evaluated in analyzing how well the tag SNP results correlate with the HLA genotypes previously determined using traditional HLA-typing methods. Using the tagging SNP method, it is possible to determine the celiac disease risk haplotypes accurately in Finnish, Hungarian, and Italian populations, with specificity and sensitivity ranging from 95% to 100%. In addition, it predicts homozygosity and heterozygosity for a risk haplotype, allowing studies on genotypic risk effects. The method is transferable between populations and therefore suited for large-scale research studies and screening of celiac disease among high-risk individuals or at the population level.

Genotype imputation

Genotype imputation is now an essential tool in the analysis of genome-wide association scans. This technique allows geneticists to accurately evaluate the evidence for association at genetic markers that are not directly genotyped. Genotype imputation is particularly useful for combining results across studies that rely on different genotyping platforms but also increases the power of individual scans. Here, we review the history and theoretical underpinnings of the technique. To illustrate performance of the approach, we summarize results from several gene mapping studies. Finally, we preview the role of genotype imputation in an era when whole genome resequencing is becoming increasingly common.