IBSrelate - Revision history

Albrecht: /* Examine the results */

2019-09-24T12:36:56Z

Examine the results

← Older revision		Revision as of 14:36, 24 September 2019
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	</pre>		</pre>

	=== Examine the results ===		=== Examine the IBS results ===
	<pre>		<pre>
	Rscript \		Rscript \
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	cat all.filelist		cat all.filelist
	</pre>		</pre>


	=Frequently asked Questions=		=Frequently asked Questions=

Albrecht: /* run IBS, this will analyse each pair of individuals */

2019-09-24T12:36:44Z

run IBS, this will analyse each pair of individuals

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	=== run IBS, this will analyse each pair of individuals ===		=== run IBS, this will analyse each pair of individuals ===
	Here we have specified "model 0", so the command will generate expected values for each of the 100 possible pairwise 2-allele genotypes for each pair of individuals (10 unique genotypes per individual).		Here we have specified "model 0", so the command will generate expected values for each of the 100 possible pairwise 2-allele genotypes for each pair of individuals (10 unique genotypes per individual).
	man page for misc/ibs [http://www.popgen.dk/angsd/index.php/Genotype_Distribution here].		The man page for misc/ibs [http://www.popgen.dk/angsd/index.php/Genotype_Distribution here].

	<pre>		<pre>

Albrecht: /* What data type is required for these analysis? */

2019-09-24T12:35:12Z

What data type is required for these analysis?

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	The best starting point are aligned sequencing reads (bam files). The above example shows how to process these into all of the intermediate and final outputs.		The best starting point are aligned sequencing reads (bam files). The above example shows how to process these into all of the intermediate and final outputs.

	The program [http://academic.oup.com/gigascience/article-abstract/8/5/giz034/5481763 ngsRelateV2] can also be used to estimate R0, R1, or KING-robust kinship. It can take multiple formats including ~~GLF,~~ BAM, and ~~VCF~~. Please notice that ngsRelateV2 has its own implementation of the something very close to the SFS-based method described in the paper, and so it assumes that you know one of the alleles that is present at each site.		The program [http://academic.oup.com/gigascience/article-abstract/8/5/giz034/5481763 ngsRelateV2] can also be used to estimate R0, R1, or KING-robust kinship. It can take multiple formats including BAM, VCF and GLF. Please notice that ngsRelateV2 has its own implementation of the something very close to the SFS-based method described in the paper, and so it assumes that you know one of the alleles that is present at each site.

	=Citation=		=Citation=

Albrecht: /* Frequently asked Questions */

2019-09-24T12:34:21Z

Frequently asked Questions

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	If only one of the individuals is inbred, the pair of individuals may appear less related than otherwise, as the pair of individuals may have an elevated number of alternate homozygous genotypes and a reduced number of shared heterozygous genotypes.		If only one of the individuals is inbred, the pair of individuals may appear less related than otherwise, as the pair of individuals may have an elevated number of alternate homozygous genotypes and a reduced number of shared heterozygous genotypes.

			When considering inbreeding, it can be useful to compare the heterozygosities of a pair of individuals. This is possible from the output of realSFS or IBS, and is facilitated by the parsing scripts included above.

	~~When considering inbreeding, it~~ can be ~~useful~~ to ~~compare the heterozygosities of a pair of individuals~~. ~~This is possible from~~ the ~~output of realSFS or IBS~~, and is ~~facilitated by the parsing scripts included above~~.		== What data type is required for these analysis? ==
			The best starting point are aligned sequencing reads (bam files). The above example shows how to process these into all of the intermediate and final outputs.

			The program [http://academic.oup.com/gigascience/article-abstract/8/5/giz034/5481763 ngsRelateV2] can also be used to estimate R0, R1, or KING-robust kinship. It can take multiple formats including GLF, BAM, and VCF. Please notice that ngsRelateV2 has its own implementation of the something very close to the SFS-based method described in the paper, and so it assumes that you know one of the alleles that is present at each site.

	=Citation=		=Citation=

Albrecht: /* How will inbreeding affects the estimates produces here? */

2019-09-24T12:27:41Z

How will inbreeding affects the estimates produces here?

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	== How will inbreeding affects the estimates produces here? ==		== How will inbreeding affects the estimates produces here? ==
			Inbreeding within one or both individuals of a pair can affect estimates of R0, R1, or KING-robust kinship, but there isn't a simple answer that covers all possibilities.

			If only one of the individuals is inbred, the pair of individuals may appear less related than otherwise, as the pair of individuals may have an elevated number of alternate homozygous genotypes and a reduced number of shared heterozygous genotypes.


			When considering inbreeding, it can be useful to compare the heterozygosities of a pair of individuals. This is possible from the output of realSFS or IBS, and is facilitated by the parsing scripts included above.

	=Citation=		=Citation=

Albrecht: /* How can the quality of the reference genome assembly affect R0, R1, or KING-robust kinship estimates? */

2019-09-24T12:03:59Z

How can the quality of the reference genome assembly affect R0, R1, or KING-robust kinship estimates?

← Older revision		Revision as of 14:03, 24 September 2019
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	One thing we found with the human data in the paper was that applying a "mappability" filter improved our estimates of R0, R1, or KING-robust kinship. This is essentially a scan of the reference genome that reports where in the genome it is possible to uniquely align reads, and needs to be calibrated to your read lengths. We used [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030377 GEM]		One thing we found with the human data in the paper was that applying a "mappability" filter improved our estimates of R0, R1, or KING-robust kinship. This is essentially a scan of the reference genome that reports where in the genome it is possible to uniquely align reads, and needs to be calibrated to your read lengths. We used [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030377 GEM]
	But there are also [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237805 new programs] that claim to do the same .		But there are also [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237805 new programs] that claim to do the same.

	== How will inbreeding affects the estimates produces here? ==		== How will inbreeding affects the estimates produces here? ==

Albrecht: /* How can reference genome assembly errors affect R0, R1, or KING-robust kinship estimates? */

2019-09-24T12:03:48Z

How can reference genome assembly errors affect R0, R1, or KING-robust kinship estimates?

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	Yes, point estimates of R0, R1, or KING-robust kinship do not require a reference genome that is assembled into chromosomes. If you have enough RAM, you can use all of your data at once, otherwise you will need to split your data into pieces, analyze each piece, and combine them afterwards.		Yes, point estimates of R0, R1, or KING-robust kinship do not require a reference genome that is assembled into chromosomes. If you have enough RAM, you can use all of your data at once, otherwise you will need to split your data into pieces, analyze each piece, and combine them afterwards.

	== How can reference genome assembly ~~errors~~ affect R0, R1, or KING-robust kinship estimates? ==		== How can the quality of the reference genome assembly affect R0, R1, or KING-robust kinship estimates? ==
	Probably in multiple, complicated ways!		Probably in multiple, complicated ways!

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	If possible these regions of the reference genome should be excluded from the analysis. With access to multiple individuals, it may be possible to identify them by estimating heterozygosity across all individuals, or with a HWE test (see [https://onlinelibrary.wiley.com/doi/full/10.1111/1755-0998.13019 here] for a method of testing HWE on low depth data that is robust to population structure). Without access to multiple individuals, it will be difficult to resolve these issues except through a better reference genome.		If possible these regions of the reference genome should be excluded from the analysis. With access to multiple individuals, it may be possible to identify them by estimating heterozygosity across all individuals, or with a HWE test (see [https://onlinelibrary.wiley.com/doi/full/10.1111/1755-0998.13019 here] for a method of testing HWE on low depth data that is robust to population structure). Without access to multiple individuals, it will be difficult to resolve these issues except through a better reference genome.

			One thing we found with the human data in the paper was that applying a "mappability" filter improved our estimates of R0, R1, or KING-robust kinship. This is essentially a scan of the reference genome that reports where in the genome it is possible to uniquely align reads, and needs to be calibrated to your read lengths. We used [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030377 GEM]
			But there are also [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237805 new programs] that claim to do the same .

	== How will inbreeding affects the estimates produces here? ==		== How will inbreeding affects the estimates produces here? ==

Albrecht: /* How can reference genome assembly errors affect R0, R1, or KING-robust kinship estimates? */

2019-09-24T11:54:07Z

How can reference genome assembly errors affect R0, R1, or KING-robust kinship estimates?

← Older revision		Revision as of 13:54, 24 September 2019
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	== How can reference genome assembly errors affect R0, R1, or KING-robust kinship estimates? ==		== How can reference genome assembly errors affect R0, R1, or KING-robust kinship estimates? ==
			Probably in multiple, complicated ways!

			But one to watch out for are regions of your reference genome that are attracting aligned reads originating from multiple, distinct parts of the genome. These regions can exhibit elevated heterozygosity across all individuals. This is particularly problematic for our analyses, as shared heterozygosity between a pair of individuals (sites where both individuals are heterozygotes) are the strongest signals we have that the individuals are related. This is because shared heterozygosity shows two important things about a site: 1) the site is variable (segregating) in the population, and 2) that the pair of individuals has the maximum possible allelic identity at the site. A upward bias in shared heterozygosity will result in KING-robust kinship estimates biased toward 0.5, elevated values of R1, and lower values of R0.

			If possible these regions of the reference genome should be excluded from the analysis. With access to multiple individuals, it may be possible to identify them by estimating heterozygosity across all individuals, or with a HWE test (see [https://onlinelibrary.wiley.com/doi/full/10.1111/1755-0998.13019 here] for a method of testing HWE on low depth data that is robust to population structure). Without access to multiple individuals, it will be difficult to resolve these issues except through a better reference genome.

	== How will inbreeding affects the estimates produces here? ==		== How will inbreeding affects the estimates produces here? ==

Albrecht: /* Can I estimate R0, R1, or KING-robust kinship even if my reference genome is not assembled into chromosomes? */

2019-09-24T11:32:38Z

Can I estimate R0, R1, or KING-robust kinship even if my reference genome is not assembled into chromosomes?

← Older revision		Revision as of 13:32, 24 September 2019
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	== Can I estimate R0, R1, or KING-robust kinship even if my reference genome is not assembled into chromosomes? ==		== Can I estimate R0, R1, or KING-robust kinship even if my reference genome is not assembled into chromosomes? ==
			Yes, point estimates of R0, R1, or KING-robust kinship do not require a reference genome that is assembled into chromosomes. If you have enough RAM, you can use all of your data at once, otherwise you will need to split your data into pieces, analyze each piece, and combine them afterwards.

	== How can reference genome assembly errors affect R0, R1, or KING-robust kinship estimates? ==		== How can reference genome assembly errors affect R0, R1, or KING-robust kinship estimates? ==

Albrecht: /* How can I estimate confidence intervals around my R0, R1, or KING-robust kinship estimates? */

2019-09-24T11:29:39Z

How can I estimate confidence intervals around my R0, R1, or KING-robust kinship estimates?

← Older revision		Revision as of 13:29, 24 September 2019
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	== How can I estimate confidence intervals around my R0, R1, or KING-robust kinship estimates? ==		== How can I estimate confidence intervals around my R0, R1, or KING-robust kinship estimates? ==
	Confidence intervals around R0, R1, and KING-robust kinship can be generated with a weighted block jackknife procedure. This involves dividing your data set into '''X''' blocks that can be of unequal sizes. Confidence intervals are then generated by leaving out each block and computing R0, R1, and KING-robust kinship on these '''X''' distinct sets [https://link.springer.com/article/10.1023/A:1008800423698 citation ]. This is a similar approach to generating Z-scores for D-statistics[https://www.genetics.org/content/192/3/1065.short] that are informative about admixture.		Confidence intervals around R0, R1, and KING-robust kinship can be generated with a weighted block jackknife procedure. This involves dividing your data set into '''X''' blocks that can be of unequal sizes. Confidence intervals are then generated by leaving out each block and computing R0, R1, and KING-robust kinship on these '''X''' distinct sets [https://link.springer.com/article/10.1023/A:1008800423698 method citation]. This is a similar approach to generating Z-scores for D-statistics[https://www.genetics.org/content/192/3/1065.short] that are informative about admixture.

	One important difference here is that this procedure assumes that the observations within each of the '''X''' blocks are independent from the observations in all the other pieces. For relatives, this implies that identity-by-descent (IBD) tracts should not extend into multiple blocks, as blocks within the same IBD tract will not be independent. One easy solution if your reference genome contains chromosomes is to assign each chromosome to a distinct block. However, if your reference genome only has smaller contigs, it will be difficult to interpret the confidence intervals produced by a block jackknife procedure like this as observations may not be fully independent. This issue is also discussed in the paper.		One important difference here is that this procedure assumes that the observations within each of the '''X''' blocks are independent from the observations in all the other pieces. For relatives, this implies that identity-by-descent (IBD) tracts should not extend into multiple blocks, as blocks within the same IBD tract will not be independent. One easy solution if your reference genome contains chromosomes is to assign each chromosome to a distinct block. However, if your reference genome only has smaller contigs, it will be difficult to interpret the confidence intervals produced by a block jackknife procedure like this as observations may not be fully independent. This issue is also discussed in the paper.