Whole exome sequencing (WES) has recently become a popular strategy for discovering potential causal variants in individuals with inherited Mendelian disorders, providing a cost- effective, fast-track approach to variant discovery. However, a typical human genome differs from the reference genome at over 10,000 potentially functional sites 1; identifying the disease-causing mutation among this plethora of variants can be a significant challenge. For this reason, exome sequencing is often preceded by genetic linkage analysis, which allows variants outside of linkage peaks to be excluded. The linkage peaks delineate tracts of identity by descent sharing that match the proposed genetic model. This combination strategy has been successfully used to identify variants causing autosomal dominant 234 and recessive 567891011 diseases, as well as those affecting quantitative traits 121314. Linkage analysis has also been used in conjunction with whole genome sequencing (WGS) 15.

Other WES studies have not performed formal linkage analysis, but have nonetheless considered inheritance information, such as searching for large regions of homozygosity shared by affected family members using genotypes obtained from genotyping arrays 161718 or exome data 1920. This method does not incorporate genetic map or allele frequency information, which could help to eliminate regions from consideration, and is applicable only to recessive diseases resulting from consanguinity. Recently, it has been suggested that identity by descent regions be identified from exome data using a non-homogeneous hidden Markov model (HMM), allowing variants outside these regions to be eliminated 2122. This method incorporates genetic map information but not allele frequency information and requires a strict genetic model (recessive and fully penetrant) and sampling scheme (exomes of two or more affected siblings must be sequenced). It would be suboptimal for use with diseases resulting from consanguinity, for which filtering by homozygosity by descent would be more effective than filtering by identity by descent. Finally, several WES studies have been published that make no use of inheritance information whatsoever, despite the fact that DNA from other informative family members was available 232425262728293031.

Classical linkage analysis using the multipoint Lander-Green algorithm 32, which is a HMM, incorporates genetic map and allele frequency information and allows for great flexibility in the disease model. Unlike the methods just mentioned, linkage analysis allows dominant, recessive or X-linked inheritance models, as well as permitting variable penetrances, non-parametric analysis and formal haplotype inference. There are few constraints upon the sampling design, with unaffected individuals able to contribute information to parametric linkage analyses. The Lander-Green algorithm has produced many important linkage results, which have facilitated the identification of the underlying disease-causing mutations.

We investigated whether linkage analysis using the Lander-Green algorithm could be performed using genotypes inferred from WES data, removing the need for the array-based genotyping step 33. We inferred genotypes at the location of HapMap Phase II SNPs, 34 as this resource provides comprehensive annotation, including the population allele frequencies and genetic map positions required for linkage analysis. We adapted our existing software 35 to extract HapMap Phase II SNP genotypes from WES data and format them for linkage analysis.

We anticipated two potential disadvantages to this approach. Firstly, exome capture only targets exonic SNPs, resulting in gaps in marker coverage outside of exons. Secondly, genotypes obtained using massively parallel sequencing (MPS) technologies such as WES tend to have a higher error rate than those obtained from genotyping arrays 36. The use of erroneous genotypes in linkage analyses may reduce power to detect linkage peaks or result in false positive linkage peaks 37.

We compared the results of linkage analysis using array-based and exome genotypes for three families with different neurological disorders showing Mendelian inheritance (Figure 1). We sequenced the exomes of two affected siblings from family M, an Anglo-Saxon ancestry family showing autosomal dominant inheritance. The exome of a single affected individual, the offspring of first cousins, from Iranian family A was sequenced, as was the exome of a single affected individual, the offspring of parents thought to be first cousins once removed, from the Pakistani family T. Families A and T showed recessive inheritance. Due to the consanguinity present in these families, we can perform linkage analysis using genotypes from a single affected individual, a method known as homozygosity mapping 33.

Linkage analysis is of great potential benefit to WES studies that aim to discover genetic variants resulting in Mendelian disorders. As variants outside of linkage peaks can be eliminated, it reduces the number of identified variants that need to be investigated further. Linkage analysis of WES genotypes provides information regarding the location of the disease locus to be extracted from WES data even if the causal variant is not captured, suggesting regions of interest that may be targeted in follow-up studies. However, many such studies are being published that employ less sophisticated substitutes for linkage analysis or do not consider inheritance information at all. Anecdotal evidence suggests that a substantial proportion of MPS studies of individuals with Mendelian disorders fail to identify a causal variant, though an exact number is not known due to publication bias.

We describe how to extract HapMap Phase II SNP genotypes from massively parallel sequencing data, providing software to facilitate this process and generate files ready to be analyzed by popular linkage programs. Our method allows linkage analysis to be performed without requiring genotyping arrays. The flexibility of linkage analysis means that our method can be applied to any disease model and a variety of sampling schemes, unlike existing methods of considering inheritance information for WES data. Linkage analysis incorporates population allele frequencies and genetic map positions, which allows superior identification of statistically unusual sharing of haplotypes between affected individuals in a family.

We demonstrate linkage using WES genotypes for three small nuclear families - a dominant family from which two exomes were sequenced and two consanguineous families from which a single exome was sequenced. As these families are not very powerful for linkage analysis, multiple linkage peaks with relatively low LOD scores were identified. Nonetheless, discarding variants outside of the linkage peaks eliminated between 81.2% and 99.43% of all nonsynonymous exonic variants detected in these families. The number of variants remaining could be reduced further by applying standard strategies, such as discarding known SNPs with minor allele frequencies above a certain threshold. Our work demonstrates the value of considering inheritance information, even in very small families that may consist, at the extreme, of a single inbred individual. As the price of exome sequencing falls, it will become feasible to sequence more individuals from each family, resulting in fewer linkage peaks with higher LOD scores.

Exome capture using current technologies yields large numbers of useful SNPs for linkage mapping. Over half of all SNPs covered by five or more reads were not targeted by the exome capture platform. Approximately 78% of these captured untargeted SNPs lay within 200 bp of a targeted feature. This reflects the fact that fragment lengths typically exceed probe lengths, resulting in flanking sequences at both ends of a probe or bait being captured and sequenced. The serendipitous result is that a substantial number of non-exonic SNPs become available, which can and should be used for linkage analysis.

We found that setting the prior probability of heterozygosity to 0.5 during genotype inference resulted in the best concordance between WES and array genotypes. The authors of the MAQ SNP model recommend using t = 0.2 for inferring genotypes at known SNPs 38, while the default value used to detect variants is t = 0.001. Our results highlight the need to tailor this parameter to the specific application, either genotyping or rare variant detection. Although we anticipated WES genotypes being less accurate than array genotypes, all four samples achieved a high concordance of 99.7% for SNPs covered by five or more reads at t = 0.5

We found that LOD scores obtained from WES genotypes agreed well with those obtained from array genotypes from the same individual(s) at the location of linkage peaks, with the median difference in LOD score zero to two or three decimal places for all three families. This was despite the fact that the array-based genotype sets used for analysis contained more markers and had higher average heterozygosities than the corresponding WES genotype sets, reflecting the fact that genotyping arrays are designed to interrogate SNPs with relatively high minor allele frequencies that are relatively evenly spaced throughout the genome. By contrast, genotypes extracted from WES data tend to be clustered around exons, resulting in fewer and less heterozygous markers after pruning to achieve linkage equilibrium. We conclude that if available, array-based genotypes from a high resolution SNP array are preferable to WES genotypes; but if not, linkage analysis of WES genotypes produces acceptable results.

Once WGS is more economical, we will be able to perform linkage analysis using genotypes extracted from WGS data, which will obviate the problem of gaps in SNP coverage outside of exons. The software tools we provide can accommodate WGS genotypes without requiring modification. In the future, initiatives such as the 1000 Genomes Project 1 may provide population-specific allele frequencies for SNPs not currently included in HapMap, further increasing the number of SNPs available for analyses, as well as the number of populations studied.

The classic Lander-Green algorithm requires markers to be in linkage equilibrium 40. Modeling linkage disequilibrium would allow incorporation of all markers without the need to select a subset of markers in linkage equilibrium. This would allow linkage mapping using distant relationships, such as distantly inbred individuals who would share a sub-linkage (< 1 cM) tract of DNA homozygous by descent. Methods that incorporate linkage disequilibrium have already been proposed, including a variable length HMM that can be applied to detect distantly related individuals 41. Further work is being targeted towards approximations of distant relationships to connect sets of related pedigrees 42. These methods will extract the maximum information from MPS data from individuals with inherited diseases.

We have integrated the relatively new field of MPS in families with classical linkage analysis. Where feasible, we strongly advocate the use of linkage mapping in combination with MPS studies that aim to discover variants causing Mendelian disorders. This approach does not require purpose-built HMMs, but can utilize existing software implementations of the Lander-Green algorithm. Where genotyping array genotypes are not available, we recommend utilizing MPS data to their full capacity by using MPS genotypes to perform linkage analysis. This will reduce the number of candidate disease-causing variants that need to be evaluated further. Should the causal variant not be identified by a WES study, linkage analysis will highlight regions of the genome where targeted resequencing is most likely to identify this variant.

bp: base pair; HMM: hidden Markov model; MPS: massively parallel sequencing; SNP: single nucleotide polymorphism; VCF: variant call format; WES: whole exome sequencing; WGS: whole genome sequencing.

KRS conceived of the study and performed all analyses described in the article. MB provided guidance and ideas. CJB wrote software tools. MSH, AES, and RJHS performed whole exome sequencing. MSH performed array-based SNP genotyping. RJHS, RJL, HN, GM and DJA collected families and clinical data. PJL contributed reagents and materials. KRS and MB drafted the initial article. All authors discussed the results and commented on the manuscript.